Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications
Abstract
:1. Introduction
1.1. International Recognition and Cooperation towards Documenting Planet Earth
1.2. The Rationale for More Sophisticated Space-Based Surveillance of Our Earth System
1.3. Materials and Methods
2. Background: EO Satellites
2.1. Primary Types of Orbiting Instruments
2.1.1. Passive Imagers
2.1.2. Active Instruments
LiDAR (Light Detection and Ranging) Imagers
Radar and Microwave Imagers
2.2. Types of Satellite Orbits
2.3. Statistics on Current Satellites in Orbit
3. High-Impact Pioneering Satellites from the 1970s to the Early 2000s
3.1. The AVHRR on the NOAA POES and EUMETSAT METOP Satellites
3.2. The METOP-A Satellite (2006–2021)
3.3. The European Remote-Sensing Satellites: ERS-1 and ERS-2
3.4. The Project for On-Board Autonomy-1 (PROBA-1)
3.5. Envisat: ESA’s Pioneering Atmospheric and Land Platform
3.5.1. Medium-Resolution Imaging Spectrometer (MERIS)
3.5.2. Scanning Imaging Absorption Spectrometer for Atmospheric CartograpHY (SCIAMACHY)
3.6. The JAXA Greenhouse Gas Observatory Satellites (GOSAT’s 1 and 2)
3.7. NASA/USGS Landsat-7, the Last of the Thematic Mapper Series
4. Pioneering NASA Missions, EOS-1 and the EOS Flagship Satellites from Early 2000s
4.1. NASA’s Demonstration Satellite, Earth Observing 1 (EO-1)
4.1.1. Hyperion on EO-1
4.1.2. Advanced Line Imager on EO-1
4.2. EOS Flagships: Missions Expected to End in 2025–2027
4.2.1. Terra
4.2.2. Aqua Flagship
4.2.3. Aura Flagship
4.3. Some Important Instruments Carried on the NASA Flagships
4.3.1. MODIS (MODerate-Resolution Imaging Spectroradiometer)
4.3.2. CERES (Clouds and the Earth’s Radiant Energy System)
4.3.3. Spaceborne Thermal Emission and Reflection Radiometer (ASTER)
4.3.4. Multiangle Imaging Spectroradiometer (MISR)
4.3.5. AIRS (Atmospheric Infrared Sounder)
4.3.6. TES (Tropospheric Emission Spectrometer)
4.4. The International A-Train
5. Important Research Satellites Launched since 2010
5.1. Gravity Recovery and Climate Experiment (GRACE)
5.2. GRACE FOLLOW-ON (GRACE-FO)
5.3. Aquarius/SAC-D Oceanography Pathfinder
5.4. NASA’s Soil Moisture Active Passive (SMAP)
5.5. Surface Water and Ocean Topography (SWOT)
5.6. Global Precipitation Mission
5.7. NASA’s First LiDAR Mission: ICESat
5.8. Ice, Cloud and Land Elevation Satellite (ICESAT-2)
5.9. NASA’s Orbiting Carbon Observatory-2 (OCO-2)
5.10. Second-Generation Atmospheric Chemistry: Sentinel-5P
6. ESA’s Modern Research Satellites: The Earth Explorer (EE) Program
6.1. Earth Explorer-5 Atmospheric Dynamics Mission—Aeolus
6.2. Earth Explorer-2 (EE-2) Soil Moisture and Ocean Salinity (SMOS)
6.3. Earth Explorer-4 SWARM
7. The Operational Satellites
7.1. Operational USA Polar-Orbiting Satellites
7.1.1. Landsat’s Operational Multispectral Imagers
7.1.2. VIIRS (Visible Infrared Imaging Radiometer Suite)
7.1.3. VIIRS on NOAA 20 and 21
7.2. The Operational E.U. Polar Orbiting Copernicus Sentinel Satellites
7.2.1. The Sentinel-1 Satellites
7.2.2. The Sentinel-2 Multispectral Imagers (MSI)
7.2.3. Harmonized Landsat-8 and L-9 and Sentinel-2 (A, B) Data
7.3. Sentinel-3 Hyperspectral Imager Constellation
7.3.1. The Ocean and Land Color Instrument (OLCI) Imager
7.3.2. The Sea and Land Surface Temperature Radiometer (SLSTR)
7.3.3. Three Instruments That Provide High Precision Onboard Orbit Tracking on Sentinel-3
7.4. Sentinel-4, -5P, and -5 Satellites
7.5. Sentinel-6 Michael Freilich
8. A New Generation of Imaging Spectrometers: Europe’s Pioneers
8.1. PRecursore IperSpettraie Della Missione Applicativa (PRISMA)
8.2. Environmental Mapping and Analysis Program (EnMAP)
9. The International Space Station (ISS) Hosts Experimental Instruments from a Non-Polar Orbit
9.1. Experimental Missions from a Non-Plar Orbit for Advancing Earth Remote Sensing from the ISS
9.2. DLR’s Earth Sensing Imaging Spectrometer (DESIS)
9.3. The Hyperspectral Imager Suite (HISUI)
9.4. The Earth Surface Mineral Dust Source Investigation (EMIT)
9.5. The Orbiting Carbon Observatory (OCO-3)
9.6. ECOsystem Spaceborne Thermal Radiometer Experiment on the Space Station (ECOTRESS)
9.7. The Global Ecosystem Dynamics Investigation (GEDI)
10. Commercial Sector: High-Spatial-Resolution Multispectral Imagers and Imaging Spectrometers
10.1. The Satellite Pour L’Observation de la Terre (SPOT) 6/7
10.2. Pléiades 1A and 1B Satellites
10.3. Pléiades NEO
10.4. TanDEM-X
10.5. Planet Scope Constellations
11. Second and Third-Generation Geostationary Satellites
11.1. The Advanced Himawari Imager (AHI) and the Advanced Baseline Imager (ABI)
11.2. The Lightning Imagers on the GOES 16, 18 and the MTG-I1
11.3. EUMETSAT METOP Third-Generation Satellite-Imager (MTG-I)
11.4. EUMETSAT MetOp Third-Generation Satellite Sounder (MTG-S)
11.5. Sentinel-4 on the MTG-S-I
12. There Are 90 EO Satellites Still in Orbit at the Beginning of 2024 for Science and Applications
12.1. Satellites Whose Primary Instrument Is a VSWIR Imaging Spectrometer
12.2. Satellites Whose Primary Instrument Is a Moderate-Spatial-Resolution Multispectral Imager
12.3. Satellites Whose Primary Instrument Is a Coarse Spatial Resolution Multispectral Imager
12.4. Satellites Whose Primary Instrument Is a Non-Imaging Spectrometer
12.5. Satellites Whose Primary Instrument Is an Active System
12.6. Passive Coarse Spatial Resolution Multispectral Observations from GEO
12.7. Experimental Instruments on the International Space Station
12.8. High-Spatial-Resolution Commercial Satellites That Are Used for Earth Science
13. Starting in 2024, the Next Imaging Spectrometers
13.1. The Transition to New Satellites in 2024
13.2. The Plankton, Aerosol, Cloud, Ocean, Ecosystem (PACE)
13.3. The Carbon Mapper
13.4. The MethaneSat Satellite
13.5. Thermal Infrared Imaging Satellite for High-Resolution Natural Resource Assessment (TRISHNA)
13.6. NASA-Indian Space Research Organization SAR (NISAR)
14. Second-Generation EO Polar Weather Satellites: METOP SG-1A and SG-1B
14.1. METOP SG-1A
14.2. MetOp-SG-1B
14.3. Sentintel-5
15. New ESA Earth Explorer Satellites Expected between 2024 and 2026
15.1. ESA’s 6th Earth Explorer Mission: Earth Clouds, Aerosols and Radiation (EarthCARE)
15.2. ESA’s 7th Earth Explorer Mission: Biomass
15.3. ESA’s 8th Earth Explorer Mission: FLuorescence EXplorer (FLEX)
15.4. Earth Explorer-10 Harmony
16. Recommended Science Priorities to NASA in the 2017 Decadal Survey
16.1. Surface Biology and Geology (SBG) Mission
16.1.1. Surface Biology and Geology: SBG-VSWIR
16.1.2. Surface Biology and Geology: SBG-TIR
16.2. Mass Change (MC) and Geosciences International Constellation (MAGIC)
16.3. Surface Deformation and Change (SDC)
16.4. The Atmosphere-Observing System (AOS)
17. The Copernicus Expansion Missions
17.1. The Copernicus Carbon Dioxide Monitoring Mission (CO2M)
17.2. The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME)
17.3. The Copernicus Land Surface Temperature Monitoring Mission (LSTM)
17.4. The Copernicus Observation System for Europe, ROSE-L
17.5. The Copernicus Imaging Microwave Radiometer (CIMR)
17.6. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL)
18. The Satellites Coming in 2025 and into the 2030s
18.1. Japanese GOSAT-GW
18.2. WildFireSat (Canada/Planning Phase)
18.3. The Fourth-Generation HIMAWARI-10 Geostationary Satellite
18.4. The Fourth-Generation NOAA GeoXO Imager (GXI)
18.5. The Next Landsat: “Landsat Next” (the Future Landsat-10)
19. Summary
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Ustin, S.L.; Middleton, E.M. Current and Near-Term Advances in Earth Observation for Ecological Applications. Ecol. Process 2021, 10, 1. [Google Scholar] [CrossRef] [PubMed]
- Elsen, P.R.; Saxon, E.C.; Simmons, B.A.; Ward, M.; Williams, B.A.; Grantham, H.S.; Kark, S.; Levin, N.; Perez-Hammerle, K.V.; Reside, A.E.; et al. Accelerated Shifts in Terrestrial Life Zones under Rapid Climate Change. Glob. Chang. Biol. 2022, 28, 918–935. [Google Scholar] [CrossRef] [PubMed]
- Schuur, E.A.G.; Abbott, B.W.; Commane, R.; Ernakovich, J.; Euskirchen, E.; Hugelius, G.; Grosse, G.; Jones, M.; Koven, C.; Leshyk, V.; et al. Permafrost and Climate Change: Carbon Cycle Feedbacks From The Warming Arctic. Ann. Rev. Environ. Res. 2022, 47, 343–371. [Google Scholar] [CrossRef]
- Hughes, D.J.; Alderdice, R.; Cooney, C.; Kühl, M.; Pernice, M.; Voolstra, C.R.; Suggett, D.J. Coral Reef Survival under Accelerating Ocean Deoxygenation. Nat. Clim. Chang. 2020, 10, 296–307. [Google Scholar] [CrossRef]
- Cardinale, B.J.; Duffy, J.E.; Gonzalez, A.; Hooper, D.U.; Perrings, C.; Venail, P.; Narwani, A.; MacE, G.M.; Tilman, D.; Wardle, D.A.; et al. Biodiversity Loss and Its Impact on Humanity. Nature 2012, 486, 59–67. [Google Scholar] [CrossRef] [PubMed]
- Ceballos, G.; Ehrlich, P.R.; Barnosky, A.D.; García, A.; Pringle, R.M.; Palmer, T.M. Accelerated Modern Human-Induced Species Losses: Entering the Sixth Mass Extinction. Sci. Adv. 2015, 1, e1400253. [Google Scholar] [CrossRef] [PubMed]
- Collins, J.P. Amphibian Decline and Extinction: What We Know and What We Need to Learn. Dis. Aquat. Organ. 2010, 92, 93–99. [Google Scholar] [CrossRef] [PubMed]
- Santamaria, C.; Bodin, B. Implementation of the Strategic Plan for Biodiversity 2011–2020 and Forest-Related Aichi Biodiversity Targets. For. Mediterr. 2017, 38, 419–420. [Google Scholar]
- Lenton, T.M.; Rockstrom, J.; Gaffney, O.; Rahmstorf, S.; Richardson, K.; Steffen, W.; Schellnhuber, H.J. Climate Tipping Points—Too Risky to Bet Against. Nature 2019, 575, 592–595. [Google Scholar] [CrossRef] [PubMed]
- McKay, D.I.A.; Staal, A.; Abrams, J.F.; Winkelmann, R.; Sakschewski, B.; Loriani, S.; Fetzer, I.; Cornell, S.E.; Rockström, J.; Lenton, T.M. Exceeding 1.5°C Global Warming Could Trigger Multiple Climate Tipping Points. Science 2022, 377, 1171. [Google Scholar] [CrossRef]
- Cole, M.B.; Augustin, M.A.; Robertson, M.J.; Manners, J.M. The Science of Food Security. NPJ Sci. Food 2018, 2, 14. [Google Scholar] [CrossRef]
- Cottrell, R.S.; Nash, K.L.; Halpern, B.S.; Remenyi, T.A.; Corney, S.P.; Fleming, A.; Fulton, E.A.; Hornborg, S.; Johne, A.; Watson, R.A.; et al. Food Production Shocks across Land and Sea. Nat. Sustain. 2019, 2, 130–137. [Google Scholar] [CrossRef]
- Vågsholm, I.; Arzoomand, N.S.; Boqvist, S. Food Security, Safety, and Sustainability—Getting the Trade-Offs Right. Front. Sustain. Food Syst. 2020, 4, 00016. [Google Scholar] [CrossRef]
- Mittermeier, R.A.; Turner, W.R.; Larsen, F.W.; Brooks, T.M.; Gascon, C. Global Biodiversity Conservation: The Critical Role of Hotspots. In Biodiversity Hotspots; Springer: Berlin/Heidelberg, Germany, 2011; pp. 3–22. [Google Scholar]
- Martin, D.A.; Andrianisaina, F.; Fulgence, T.R.; Osen, K.; Rakotomalala, A.A.N.A.; Raveloaritiana, E.; Soazafy, M.R.; Wurz, A.; Andriafanomezantsoa, R.; Andriamaniraka, H.; et al. Land-Use Trajectories for Sustainable Land System Transformations: Identifying Leverage Points in a Global Biodiversity Hotspot. Proc. Natl. Acad. Sci. USA 2022, 119, e2107747119. [Google Scholar] [CrossRef] [PubMed]
- Lee, C.M.; Cable, M.L.; Hook, S.J.; Green, R.O.; Ustin, S.L.; Mandl, D.J.; Middleton, E.M. An Introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) Mission and Preparatory Activities. Remote Sens. Environ. 2015, 167, 6–19. [Google Scholar] [CrossRef]
- Stavros, E.N.; Chrone, J.; Cawse-Nicholson, K.; Freeman, A.; Glenn, N.F.; Guild, L.; Kokaly, R.; Lee, C.; Luvall, J.; Pavlick, R.; et al. Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s. J. Geophys. Res. Biogeosci. 2023, 128, e2021JG006471. [Google Scholar] [CrossRef]
- Laguë, M.M.; Bonan, G.B.; Swann, A.L.S. Separating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in Both the Coupled and Uncoupled Land-Atmosphere System. Am. Meteor. Soc. 2019, 32, 5725–5744. [Google Scholar] [CrossRef]
- Tyagi, K.; Kumar, M.; Drews, M. Application of Dynamic Vegetation Models for Climate Change Impact Studies. In Forest Dynamics and Conservation: Science, Innovations and Policies; Springer: Singapore, 2022; pp. 311–329. [Google Scholar] [CrossRef]
- Garaba, S.P.; Aitken, J.; Slat, B.; Dierssen, H.M.; Lebreton, L.; Zielinski, O.; Reisser, J. Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager. Environ. Sci. Technol. 2018, 52, 11699–11707. [Google Scholar] [CrossRef]
- Dierssen, H.M.; Ackleson, S.G.; Joyce, K.E.; Hestir, E.L.; Castagna, A.; Lavender, S.; McManus, M.A. Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook. Front. Environ. Sci. 2021, 9, 649528. [Google Scholar] [CrossRef]
- Gao, K.; Beardall, J.; Häder, D.P.; Hall-Spencer, J.M.; Gao, G.; Hutchins, D.A. Effects of Ocean Acidification on Marine Photosynthetic Organisms under the Concurrent Influences of Warming, UV Radiation, and Deoxygenation. Front. Mar. Sci. 2019, 6, 322. [Google Scholar] [CrossRef]
- Hall-Spencer, J.M.; Harvey, B.P. Ocean Acidification Impacts on Coastal Ecosystem Services Due to Habitat Degradation. Emerg. Top. Life Sci. 2019, 3, 197–206. [Google Scholar] [PubMed]
- Sellers, P.J.; Schimel, D.S.; Moore, B.; Liu, J.; Eldering, A. Observing Carbon Cycle–Climate Feedbacks from Space. Proc. Natl. Acad. Sci. USA 2018, 115, 7860–7868. [Google Scholar] [CrossRef] [PubMed]
- Schimel, D.; Schneider, F.D. Flux Towers in the Sky: Global Ecology from Space. New Phytol. 2019, 224, 570–584. [Google Scholar] [CrossRef] [PubMed]
- Steemers, K. Energy and the City: Density, Buildings and Transport. Energy Build. 2003, 35, 3–14. [Google Scholar] [CrossRef]
- Tsoka, S.; Tsikaloudaki, K.; Theodosiou, T.; Bikas, D. Urban Warming and Cities’ Microclimates: Investigation Methods and Mitigation Strategies—A Review. Energies 2020, 13, 1414. [Google Scholar] [CrossRef]
- Farrell, S.L.; Duncan, K.; Buckley, E.M.; Richter-Menge, J.; Li, R. Mapping Sea Ice Surface Topography in High Fidelity With ICESat-2. Geophys. Res. Lett. 2020, 47, e2020GL090708. [Google Scholar] [CrossRef]
- Swart, S.; Gille, S.T.; Delille, B.; Josey, S.; Mazloff, M.; Newman, L.; Thompson, A.F.; Thomson, J.; Ward, B.; Du Plessis, M.D.; et al. Constraining Southern Ocean Air-Sea-Ice Fluxes through Enhanced Observations. Front. Mar. Sci. 2019, 6, 1–10. [Google Scholar] [CrossRef]
- Lee, S.M.; Shi, H.; Sohn, B.J.; Gasiewski, A.J.; Meier, W.N.; Dybkjær, G. Winter Snow Depth on Arctic Sea Ice From Satellite Radiometer Measurements (2003–2020): Regional Patterns and Trends. Geophys. Res. Lett. 2021, 48, e2021GL094541. [Google Scholar] [CrossRef]
- Land, P.E.; Shutler, J.D.; Findlay, H.S.; Girard-Ardhuin, F.; Sabia, R.; Reul, N.; Piolle, J.F.; Chapron, B.; Quilfen, Y.; Salisbury, J.; et al. Salinity from Space Unlocks Satellite-Based Assessment of Ocean Acidification. Environ. Sci. Technol. 2015, 49, 1987–1994. [Google Scholar] [CrossRef]
- Boutin, J.; Reul, N.; Koehler, J.; Martin, A.; Catany, R.; Guimbard, S.; Rouffi, F.; Vergely, J.L.; Arias, M.; Chakroun, M.; et al. Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies. J. Geophys. Res. Ocean. 2021, 126, e2021JC017676. [Google Scholar] [CrossRef]
- Wasowski, J.; Bovenga, F. Investigating Landslides and Unstable Slopes with Satellite Multi Temporal Interferometry: Current Issues and Future Perspectives. Eng. Geol. 2014, 174, 103–138. [Google Scholar] [CrossRef]
- West, H.; Quinn, N.; Horswell, M. Remote Sensing for Drought Monitoring & Impact Assessment: Progress, Past Challenges and Future Opportunities. Remote Sens. Environ. 2019, 232, 11291. [Google Scholar] [CrossRef]
- Khan, A.; Gupta, S.; Gupta, S.K. Multi-Hazard Disaster Studies: Monitoring, Detection, Recovery, and Management, Based on Emerging Technologies and Optimal Techniques. Int. J. Disaster. Risk Reduct. 2020, 47, 101642. [Google Scholar] [CrossRef]
- Poland, M.P.; Lopez, T.; Wright, R.; Pavolonis, M.J. Forecasting, Detecting, and Tracking Volcanic Eruptions from Space. Remote Sens. Earth Sci. 2020, 3, 55–94. [Google Scholar] [CrossRef]
- Green, R.O.; Schaepman, M.E.; Mouroulis, P.; Geier, S.; Shaw, L.; Hueini, A.; Bernas, M.; McKinley, I.; Smith, C.; Wehbe, R.; et al. Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3). In Proceedings of the 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 5–12 March 2022; pp. 1–10. [Google Scholar]
- Casagli, N.; Intrieri, E.; Tofani, V.; Gigli, G.; Raspini, F. Landslide Detection, Monitoring and Prediction with Remote-Sensing Techniques. Nat. Rev. Earth Environ. 2023, 4, 51–64. [Google Scholar] [CrossRef]
- Roy, D.P.; Wulder, M.A.; Loveland, T.R.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Helder, D.; Irons, J.R.; Johnson, D.M.; Kennedy, R.; et al. Landsat-8: Science and Product Vision for Terrestrial Global Change Research. Remote Sens. Environ. 2014, 145, 154–172. [Google Scholar] [CrossRef]
- Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sens. 2015, 7, 8830–8857. [Google Scholar] [CrossRef]
- Green, R.O.; Eastwood, M.L.; Sarture, C.M.; Chrien, T.G.; Aronsson, M.; Chippendale, B.J.; Faust, J.A.; Pavri, B.E.; Chovit, C.J.; Solis, M.; et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 1998, 65, 227–248. [Google Scholar] [CrossRef]
- Chapman, J.W.; Thompson, D.R.; Helmlinger, M.C.; Bue, B.D.; Green, R.O.; Eastwood, M.L.; Geier, S.; Olson-Duvall, W.; Lundeen, S.R. Spectral and Radiometric Calibration of the Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). Remote Sens. 2019, 11, 2129. [Google Scholar] [CrossRef]
- Green, R.O. Spectral Calibration Requirement for Earth-Looking Imaging Spectrometers in the Solar-Reflected Spectrum. Appl. Opt. 1998, 37, 683–690. [Google Scholar] [CrossRef]
- Levelt, P.F.; Van Den Oord, G.H.J.; Dobber, M.R.; Mälkki, A.; Visser, H.; De Vries, J.; Stammes, P.; Lundell, J.O.V.; Saari, H. The Ozone Monitoring Instrument. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1093–1100. [Google Scholar] [CrossRef]
- Bradley, C.L.; Thingvold, E.; Moore, L.B.; Haag, J.M.; Raouf, N.A.; Mouroulis, P.; Green, R.O. Optical Design of the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectrometer. In Imaging Spectrometry XXIV: Applications, Sensors, and Processing; SPIE: Washington, DC, USA, 2020; p. 150402. [Google Scholar] [CrossRef]
- Vane, G.; Goetz, A.F.H. Wellman, Airborne Imaging Spectrometer: A New Tool for Remote Sensing. IEEE Trans. Geosci. Remote Sens. 1984, GE-22, 546–549. [Google Scholar] [CrossRef]
- Schimel, D.; Pavlick, R.; Fisher, J.B.; Asner, G.P.; Saatchi, S.; Townsend, P.; Miller, C.; Frankenberg, C.; Hibbard, K.; Cox, P. Observing Terrestrial Ecosystems and the Carbon Cycle from Space. Glob. Chang. Biol. 2015, 21, 1762–1776. [Google Scholar] [CrossRef]
- Jetz, W.; Cavender-Bares, J.; Pavlick, R.; Schimel, D.; Davis, F.W.; Asner, G.P.; Guralnick, R.; Kattge, J.; Latimer, A.M.; Moorcroft, P.; et al. Monitoring Plant Functional Diversity from Space. Nat. Plants 2016, 2, 16024. [Google Scholar] [CrossRef] [PubMed]
- Berger, K.; Verrelst, J.; Féret, J.B.; Wang, Z.; Wocher, M.; Strathmann, M.; Danner, M.; Mauser, W.; Hank, T. Crop Nitrogen Monitoring: Recent Progress and Principal Developments in the Context of Imaging Spectroscopy Missions. Remote Sens. Environ. 2020, 242, 111758. [Google Scholar] [CrossRef]
- Fisher, J.B.; Melton, F.; Middleton, E.; Hain, C.; Anderson, M.; Allen, R.; McCabe, M.F.; Hook, S.; Baldocchi, D.; Townsend, P.A.; et al. The Future of Evapotranspiration: Global Requirements for Ecosystem Functioning, Carbon and Climate Feedbacks, Agricultural Management, and Water Resources. Water Resour. Res. 2017, 53, 2618–2626. [Google Scholar] [CrossRef]
- Lu, B.; Dao, P.D.; Liu, J.; He, Y.; Shang, J. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens. 2020, 12, 2659. [Google Scholar] [CrossRef]
- Meireles, J.E.; Cavender-Bares, J.; Townsend, P.A.; Ustin, S.; Gamon, J.A.; Schweiger, A.K.; Schaepman, M.E.; Asner, G.P.; Martin, R.E.; Singh, A.; et al. Leaf Reflectance Spectra Capture the Evolutionary History of Seed Plants. New Phytol. 2020, 228, 485–493. [Google Scholar] [CrossRef]
- Carmon, N.; Berk, A.; Bohn, N.; Brodrick, P.G.; Dozier, J.; Johnson, M.; Miller, C.E.; Thompson, D.R.; Turmon, M.; Bachmann, C.M.; et al. Shape from Spectra. Remote Sens. Environ. 2023, 288, 113497. [Google Scholar] [CrossRef]
- Cavender-Bares, J.; Schneider, F.D.; Santos, M.J.; Armstrong, A.; Carnaval, A.; Dahlin, K.M.; Fatoyinbo, L.; Hurtt, G.C.; Schimel, D.; Townsend, P.A.; et al. Integrating Remote Sensing with Ecology and Evolution to Advance Biodiversity Conservation. Nat. Ecol. Evol. 2022, 6, 506–519. [Google Scholar] [CrossRef]
- Eitel, J.U.H.; Höfle, B.; Vierling, L.A.; Abellán, A.; Asner, G.P.; Deems, J.S.; Glennie, C.L.; Joerg, P.C.; LeWinter, A.L.; Magney, T.S.; et al. Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences. Remote Sens. Environ. 2016, 186, 372–392. [Google Scholar] [CrossRef]
- Treuhaft, R.; Lei, Y.; Gonçalves, F.; Keller, M.; dos Santos, J.R.; Neumann, M.; Almeida, A. Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry. Forests 2017, 8, 277. [Google Scholar] [CrossRef]
- Camarretta, N.; Harrison, P.A.; Bailey, T.; Potts, B.; Lucieer, A.; Davidson, N.; Hunt, M. Monitoring Forest Structure to Guide Adaptive Management of Forest Restoration: A Review of Remote Sensing Approaches. New 2020, 51, 573–596. [Google Scholar] [CrossRef]
- Hudak, A.T.; Fekety, P.A.; Kane, V.R.; Kennedy, R.E.; Filippelli, S.K.; Falkowski, M.J.; Tinkham, W.T.; Smith, A.M.S.; Crookston, N.L.; Domke, G.M.; et al. A Carbon Monitoring System for Mapping Regional, Annual Aboveground Biomass across the Northwestern USA. Environ. Res. Lett. 2020, 15, 095003. [Google Scholar] [CrossRef]
- Cawse-Nicholson, K.; Raiho, A.M.; Thompson, D.R.; Hulley, G.C.; Miller, C.E.; Miner, K.R.; Poulter, B.; Schimel, D.; Schneider, F.D.; Townsend, P.A.; et al. Surface Biology and Geology Imaging Spectrometer: A Case Study to Optimize the Mission Design Using Intrinsic Dimensionality. Remote Sens. Environ. 2023, 290, 113534. [Google Scholar] [CrossRef]
- Pinzon, J.E.; Tucker, C.J. A Non-Stationary 1981–2012 AVHRR NDVI-3g Time Series. Remote Sens. 2014, 6, 6929–6960. [Google Scholar] [CrossRef]
- Tucker, C.J.D.A.; Slaybac, J.E.; Pinzon, S.O.; Los, R.B.; Myneni, M.G. Taylor, Higher Northern Latitude Normalized Difference Vegetation Index and Growing Season Trends from 1982–1999. Int. J. Meteorol. 2001, 45, 184–190. [Google Scholar]
- Anyamba, A.; Tucker, C.J. Analysis of Sahelian Vegetation Dynamics Using NOAA-AVHRR NDVI Data from 1981–2003. Proc J. Arid. Environ. 2005, 63, 596–614. [Google Scholar] [CrossRef]
- Loveland, T.R.; Merchant, J.W.; Brown, J.F.; Ohlen, D.O.; Reed, B.C.; Olson, P.; Hutchinson, J. Seasonal Land-Cover Regions of the United States. Ann. Assoc. Am. Geogr. 1995, 85, 339–355. [Google Scholar] [CrossRef]
- Martin, R.V.; Parrish, D.D.; Ryerson, T.B.; Nicks, J.K.; Chance, K.; Kurosu, T.P.; Jacob, D.J.; Sturges, E.D.; Fried, A.; Wert, B.P. Evaluation of GOME Satellite Measurements of Tropospheric NO2 and HCHO Using Regional Data from Aircraft Campaigns in the Southeastern United States. J. Geophys. Res. D Atmos. 2004, 109, 1–11. [Google Scholar] [CrossRef]
- Ma, J.; Richter, A.; Burrows, J.P.; Nüß, H.; Van Aardenne, J.A. Comparison of Model-Simulated Tropospheric NO2 over China with GOME-Satellite Data. Atmos Environ. 2006, 40, 593–604. [Google Scholar] [CrossRef]
- Theys, N.; Van Roozendael, M.; Hendrick, F.; Yang, X.; De Smedt, I.; Richter, A.; Begoin, M.; Errera, Q.; Johnston, P.V.; Kreher, K.; et al. Global Observations of Tropospheric BrO Columns Using GOME-2 Satellite Data. Atmos Chem. Phys. 2011, 11, 1791–1811. [Google Scholar] [CrossRef]
- Munro, R.; Lang, R.; Klaes, D.; Poli, G.; Retscher, C.; Lindstrot, R.; Huckle, R.; Lacan, A.; Grzegorski, M.; Holdak, A.; et al. The GOME-2 Instrument on the METOP Series of Satellites: Instrument Design, Calibration, and Level 1 Data Processing—An Overview. Atmos Meas. Tech. 2016, 9, 1279–1301. [Google Scholar] [CrossRef]
- Kalluri, S.; Cao, C.; Heidinger, A.; Ignatov, A.; Key, J.; Smith, T. The Advanced Very High Resolution Radiometer Contributing to Earth Observations for over 40 Years. Bull. Am. Meteorol. Soc. 2021, 102, E351–E366. [Google Scholar] [CrossRef]
- Barnsley, M.J.; Settle, J.J.; Cutter, M.A.; Lobb, D.R.; Teston, F. The PROBA/CHRIS Mission: A Low-Cost Smallsat for Hyperspectral Multiangle Observations of the Earth Surface and Atmosphere. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1512–1520. [Google Scholar] [CrossRef]
- Bovensmann, H.; Burrows, J.P.; Buchwitz, M.; Frerick, J.; Noe, S.; Rozanov, V.V.; Chance, K.V.; Goede, A.P.H. SCIAMACHY: Mission Objectives and Measurement Modes. Am. Meteorol. Soc. 1999, 56, 127–150. [Google Scholar] [CrossRef]
- Reuter, M.; Bovensmann, H.; Buchwitz, M.; Burrows, J.P.; Connor, B.J.; Deutscher, N.M.; Griffith, D.W.T.; Heymann, J.; Keppel-Aleks, G.; Messerschmidt, J.; et al. Retrieval of Atmospheric CO2 with Enhanced Accuracy and Precision from SCIAMACHY: Validation with FTS Measurements and Comparison with Model Results. J. Geophys. Res. Atmos. 2011, 116, D4. [Google Scholar] [CrossRef]
- Guanter, L.; Alonso, L.; Gómez-Chova, L.; Amorós-López, J.; Vila, J.; Moreno, J. Estimation of Solar-Induced Vegetation Fluorescence from Space Measurements. Geophys. Res. Lett. 2007, 34, L08401. [Google Scholar] [CrossRef]
- Richter, A.; Burrows, J.P.; Nüß, H.; Granier, C.; Niemeier, U. Increase in Tropospheric Nitrogen Dioxide over China Observed from Space. Nature 2005, 437, 129–132. [Google Scholar] [CrossRef]
- Wecht, K.J.; Jacob, D.J.; Frankenberg, C.; Jiang, Z.; Blake, D.R. Mapping of North American Methane Emissions with High Spatial Resolution by Inversion of SCIAMACHY Satellite Data. J. Geophys. Res. 2014, 119, 7741–7756. [Google Scholar] [CrossRef]
- Ungar, S.; Mandl, D.; Frye, S.; Ong, L.; Young, J. EO-1 Mission: Transition from Technology Demonstration to Science Path Finder. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007. [Google Scholar]
- Middleton, E.M.; Ungar, S.G.; Mandl, D.J.; Ong, L.; Frye, S.W.; Campbell, P.E.; Landis, D.R.; Young, J.P.; Pollack, N.H. The Earth Observing One (EO-1) Satellite Mission: Over a Decade in Space. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 243–256. [Google Scholar] [CrossRef]
- Middleton, E.M.; Campbell, P.K.; Ong, L.; Landis, D.R.; Zhang, Q.; Neigh, C.S.; Fred Huemmrich, K.; Ungar, S.G.; Mandl, D.J.; Frye, S.W.; et al. Hyperion: The first global orbital spectrometer, Earth Observing-1 (EO-1) satellite (2000–2017). In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017. [Google Scholar]
- Ong, C.; Caccetta, M.; Lau, I.; Malthus, T.; Thapur, N. The use of long term earth observation data archives to identify potential vicarious calibration targets in Australia. In Proc. IEEE International Geoscience and Remote Sensing Symposium; IGARSS: Milan, Italy, 2015. [Google Scholar]
- Thenkabail, P.S.; Mariotto, I.; Gumma, M.K.; Middleton, E.M.; Landis, D.R.; Huemmrich, K.F. Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Two band Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 427–439. [Google Scholar] [CrossRef]
- Lencioni, D.E.; Digenis, C.J.; Bicknell, W.E.; Heam, D.R.; Mendenhall, J.A. Design and Performance of the EO-1 Advanced Land Imager. In Proceedings of the Sensors, System, and Next-Gen Sat III, Florence, Italy, 20–23 September 1999; SPIE: Bellingham, DC, USA, 1999. [Google Scholar]
- Mendenhall, J.A.; Hearn, D.R.; Lencioni, D.E. Comparison of the EO-1 Advanced Land Imager Performance with the Landsat Data Continuity Mission Specification; Massachusetts Institute of Technology, Lincoln Laboratory: Cambridge, MA, USA, 2002. [Google Scholar]
- Knight, E.J.; Kvaran, G. Landsat-8 Operational Land Imager Design, Characterization and Performance. Remote Sens. 2014, 6, 10286–10305. [Google Scholar] [CrossRef]
- Gao, B.-C.; Goetz, A.F.H. Column Atmospheric Water Vapor and Vegetation Liquid Water Retrievals from Airborne Imaging Spectrometer Data. J. Geophys. Res. 1990, 95, 3549–3564. [Google Scholar] [CrossRef]
- Gao, B.-C.; Heidebrecht, K.B.; Goetz, A.F.H. Derivation of Scaled Surface Reflectances from AVIRIS Data. Remote Sens. Environ. 1993, 44, 165–178. [Google Scholar] [CrossRef]
- Kaufman, Y.J.; Tanré, D.; Remer, L.A.; Vermote, E.F.; Chu, A.; Holben, B.N. Operational Remote Sensing of Tropospheric Aerosol over Land from EOS Moderate Resolution Imaging Spectroradiometer. J. Geophys. Res. Atmos. 1997, 102, 17051–17067. [Google Scholar] [CrossRef]
- King, M.D.; Menzel, W.P.; Kaufman, Y.J.; Tanré, D.; Gao, B.C.; Platnick, S.; Ackerman, S.A.; Remer, L.A.; Pincus, R.; Hubanks, P.A. Cloud and Aerosol Properties, Precipitable Water, and Profiles of Temperature and Water Vapor from MODIS. IEEE Trans. Geosci. Remote Sens. 2003, 41, 442–456. [Google Scholar] [CrossRef]
- Riggs, G.A.; Hall, D.K.; Román, M.O. Overview of NASA’s MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) Snow-Cover Earth System Data Records. Earth Syst. Sci. Data 2017, 9, 765–777. [Google Scholar] [CrossRef]
- Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.M.; Tucker, C.J.; Stenseth, N.C. Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change. Trends Ecol. Evol. 2005, 20, 503–510. [Google Scholar] [CrossRef]
- Pour, A.B.; Hashim, M. The Application of ASTER Remote Sensing Data to Porphyry Copper and Epithermal Gold Deposits. Ore Geol. Rev. 2012, 44, 1–9. [Google Scholar] [CrossRef]
- Abrams, M.; Yamaguchi, Y. Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration. Remote Sens. 2019, 11, 1394. [Google Scholar] [CrossRef]
- Rowan, L.C.; Hook, S.J.; Abrams, M.J.; Mars, J.C. Mapping Hydrothermally Altered Rocks at Cuprite, Nevada, Using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster), a New Satellite-Imaging System. Econ. Geol. 2003, 98, 1019–1027. [Google Scholar] [CrossRef]
- Toutin, T. ASTER DEMs for Geomatic and Geoscientific Applications: A Review. Int. J. Remote Sens. 2008, 29, 1855–1875. [Google Scholar] [CrossRef]
- Gui, K.; Che, H.; Zheng, Y.; Wang, Y.; Zhang, L.; Zhao, H.; Li, L.; Zhong, J.; Yao, W.; Zhang, X. Seasonal Variability and Trends in Global Type-Segregated Aerosol Optical Depth as Revealed by MISR Satellite Observations. Sci. Total Environ. 2021, 787, 147543. [Google Scholar] [CrossRef] [PubMed]
- Yu, Y.; Kalashnikova, O.V.; Garay, M.J.; Lee, H.; Notaro, M. Identification and Characterization of Dust Source Regions Across North Africa and the Middle East Using MISR Satellite Observations. Geophys. Res. Lett. 2018, 45, 6690–6701. [Google Scholar] [CrossRef]
- Chen, Y.M.; Liang, S.; Wang, J.; Kim, H.Y.; Martonchik, J.V. Validation of MISR Land Surface Broadband Albedo. Int. J. Remote Sens. 2008, 29, 6971–6983. [Google Scholar] [CrossRef]
- Stephens, G.; Winker, D.; Pelon, J.; Trepte, C.; Vane, D.; Yuhas, C.; L’Ecuyer, T.; Lebsock, M. Cloudsat and Calipso within the A-Train: Ten Years of Actively Observing the Earth System. Bull. Am. Meteorol. Soc. 2018, 99, 569–581. [Google Scholar] [CrossRef]
- Huang, J.; Minnis, P.; Yan, H.; Yi, Y.; Chen, B.; Zhang, L.; Ayers, K. Dust Aerosol Effect on Semi-Arid Climate over Northwest China Detected from the A-Train. Atmos. Chem. Phys. 2010, 10, 6863–6872. [Google Scholar] [CrossRef]
- Jiang, J.H.; Su, H.; Zhai, C.; Perun, V.S.; Del Genio, A.; Nazarenko, L.S.; Donner, L.J.; Horowitz, L.; Seman, C.; Cole, J.; et al. Evaluation of Cloud and Water Vapor Simulations in CMIP5 Climate Models Using NASA “A-Train” Satellite Observations. J. Geophys. Res. Atmos. 2012, 117, D14. [Google Scholar] [CrossRef]
- Setvák, M.; Bedka, K.; Lindsey, D.T.; Sokol, A.; Charvát, Z.; Šťástka, J.; Wang, P.K. A-Train Observations of Deep Convective Storm Tops. Atmos. Res. 2013, 123, 229–248. [Google Scholar] [CrossRef]
- Berry, E.; Mace, G.G. Cloud Properties and Radiative Effects of the Asian Summer Monsoon Derived from A-Train Data. J. Geophys. Res. 2014, 119, 9492–9508. [Google Scholar] [CrossRef]
- Frappart, F.; Ramillien, G. Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review. Remote Sens. 2018, 10, 829. [Google Scholar] [CrossRef]
- Yi, Y.; Chen, R.H.; Kimball, J.S.; Moghaddam, M.; Xu, X.; Euskirchen, E.S.; Das, N.; Miller, C.E. Potential Satellite Monitoring of Surface Organic Soil Properties in Arctic Tundra From SMAP. Water Resour. Res. 2022, 58, e2021WR030957. [Google Scholar] [CrossRef]
- Chen, Q.; Wang, F.; Shen, Y.; Zhang, X.; Nie, Y.; Chen, J. Monthly Gravity Field Solutions From Early LEO Satellites’ Observations Contribute to Global Ocean Mass Change Estimates Over 1993∼2004. Geophys. Res. Lett. 2022, 49, e2022GL099917. [Google Scholar] [CrossRef]
- Kvas, A.; Boergens, E.; Dobslaw, H.; Eicker, A.; Mayer-Guerr, T.; Güntner, A. Evaluating Long-Term Water Storage Trends in Small Catchments and Aquifers from a Joint Inversion of 20 Years of GRACE/GRACE-FO Mission Data. Geophys. J. Int. 2024, 236, 1002–1012. [Google Scholar] [CrossRef]
- Rodell, M.; Reager, J.T. Water Cycle Science Enabled by the GRACE and GRACE-FO Satellite Missions. Nat. Water 2023, 1, 47–59. [Google Scholar] [CrossRef]
- Rodell, M.; Li, B. Changing Intensity of Hydroclimatic Extreme Events Revealed by GRACE and GRACE-FO. Nat. Water 2023, 1, 241–248. [Google Scholar] [CrossRef]
- NASEM (National Academy of Sciences). Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space; National Academies Press: Washington, DC, USA, 2018. [Google Scholar]
- St Germain, K.; Scott Schwinger, D.; Murphy, K.; Baynes, K.; Herrmann, N.; Egan, M.; Procaccino, C.; Kim, B.; Whitehurst, A.; McCarthy, L.; et al. NASA’s Earth System Observatory Formulation Progress. In Global Space Conference on Climate Change; NASA: Washington, DC, USA, 2023. [Google Scholar]
- Le Vine, D.M.; Lagerloef, G.S.E.; Yueh, S.; Pellerano, F.; Dinnat, E.; Wentz, F. Aquarius Mission Technical Overview. In Global Space Conference on Climate Change; International Astronautical Federation: Denver, CO, USA, 2006. [Google Scholar]
- Gary Lagerloef, A.; Lindstrom, E. Ocean Salinity and the Aquarius/SAC-D Mission: A New Frontier in Ocean Remote Sensing. Mar. Technol. Soc. J. 2008, 21, 26–30. [Google Scholar] [CrossRef]
- Le Vine, D.M.; Lagerloef, G.S.E.; Torrusio, S.E. Aquarius and Remote Sensing of Sea Surface Salinity from Space. Proc. IEEE 2010, 98, 688–703. [Google Scholar] [CrossRef]
- Grunseich, G.; Subrahmanyam, B.; Wang, B. The Madden-Julian Oscillation Detected in Aquarius Salinity Observations. Geophys Res. Lett 2013, 40, 5461–5466. [Google Scholar] [CrossRef]
- Brown, M.E.; Escobar, V.; Moran, S.; Entekhabi, D.; O’Neill, P.E.; Njoku, E.G.; Doorn, B.; Entin, J.K. NASA’s Soil Moisture Active Passive (SMAP) Mission and Opportunities for Applications Users. Bull. Am. Meteor. Soc. 2013, 94, 1125–1128. [Google Scholar] [CrossRef]
- Entekhabi, D.; Njoku, E.G.; O’Neill, P.E.; Kellogg, K.H.; Crow, W.T.; Edelstein, W.N.; Entin, J.K.; Goodman, S.D.; Jackson, T.J.; Johnson, J.; et al. The Soil Moisture Active Passive (SMAP) Mission. Proc. IEEE 2010, 98, 704–716. [Google Scholar] [CrossRef]
- Watts, J.D.; Farina, M.; Kimball, J.S.; Schiferl, L.D.; Liu, Z.; Arndt, K.A.; Zona, D.; Ballantyne, A.; Euskirchen, E.S.; Parmentier, F.J.W.; et al. Carbon Uptake in Eurasian Boreal Forests Dominates the High-Latitude Net Ecosystem Carbon Budget. Glob. Chang. Biol. 2023, 29, 1870–1889. [Google Scholar] [CrossRef]
- Derksen, C.; Xu, X.; Scott Dunbar, R.; Colliander, A.; Kim, Y.; Kimball, J.S.; Black, T.A.; Euskirchen, E.; Langlois, A.; Loranty, M.M.; et al. Retrieving Landscape Freeze/Thaw State from Soil Moisture Active Passive (SMAP) Radar and Radiometer Measurements. Remote Sens. Environ. 2017, 194, 48–62. [Google Scholar] [CrossRef]
- Rowlandson, T.L.; Berg, A.A.; Roy, A.; Kim, E.; Pardo Lara, R.; Powers, J.; Lewis, K.; Houser, P.; McDonald, K.; Toose, P.; et al. Capturing Agricultural Soil Freeze/Thaw State through Remote Sensing and Ground Observations: A Soil Freeze/Thaw Validation Campaign. Remote Sens. Environ. 2018, 211, 59–70. [Google Scholar] [CrossRef]
- Mishra, A.; Vu, T.; Veettil, A.V.; Entekhabi, D. Drought Monitoring with Soil Moisture Active Passive (SMAP) Measurements. J. Hydrol. 2017, 552, 620–632. [Google Scholar] [CrossRef]
- Sazib, N.; Bolten, J.D.; Mladenova, I.E. Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts over Australia and California. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022, 15, 779–787. [Google Scholar] [CrossRef]
- Zhang, X.; Gibson, J. Using Multi-Source Nighttime Lights Data to Proxy for County-Level Economic Activity in China from 2012 to 2019. Remote Sens. 2022, 14, 1282. [Google Scholar] [CrossRef]
- Biancamaria, S.; Lettenmaiter, D.P.; Pavelsky, T.M. The SWOT Mission and Its Capabilities for Land Hydrology. In Remote Sensing and Water Resources; Cazenave, A., Champollion, N., Benveniste, J., Chen, J., Eds.; Springer: Bern, Switzerland, 2016; pp. 117–147. [Google Scholar]
- NASEM (National Academies of Sciences, Engineering and Medicine). Earth Science and Applications from Space: National Imperatives for the next Decade and Beyond; National Academies Press: Washington, DC, USA, 2007; ISBN 0309103878. [Google Scholar]
- Watters, D.; Battaglia, A. The NASA-JAXA Global Precipitation Measurement Mission—Part I: New Frontiers in Precipitation. Weather 2021, 76, 41–44. [Google Scholar] [CrossRef]
- Watters, D.; Battaglia, A. The NASA-JAXA Global Precipitation Measurement Mission—Part II: New Frontiers in Precipitation Science. Weather 2021, 76, 52–56. [Google Scholar] [CrossRef]
- Los, S.O.; Rosette, J.A.B.; Kljun, N.; North, P.R.J.; Chasmer, L.; Suárez, J.C.; Hopkinson, C.; Hill, R.A.; Van Gorsel, E.; Mahoney, C.; et al. Vegetation Height and Cover Fraction between 60° S and 60° N from ICESat GLAS Data. Geosci. Model. Dev. 2012, 5, 413–432. [Google Scholar] [CrossRef]
- Neuenschwander, A.L.; Urban, T.J.; Gutierrez, R.; Schutz, B.E. Characterization of ICESat/GLAS Waveforms over Terrestial Ecosystems: Implications for Vegetation Mapping. J. Geophys. Res. Biogeosci. 2008, 113. [Google Scholar] [CrossRef]
- Kwok, R.; Cunningham, G.F. ICESat over Arctic Sea Ice: Estimation of Snow Depth and Ice Thickness. J. Geophys. Res. Ocean. 2008, 113. [Google Scholar] [CrossRef]
- Zwally, H.J.; Yi, D.; Kwok, R.; Zhao, Y. ICESat Measurements of Sea Ice Freeboard and Estimates of Sea Ice Thickness in the Weddell Sea. J. Geophys. Res. Ocean. 2008, 113. [Google Scholar] [CrossRef]
- Markus, T.; Neumann, T.; Martino, A.; Abdalati, W.; Brunt, K.; Csatho, B.; Farrell, S.; Fricker, H.; Gardner, A.; Harding, D.; et al. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2): Science Requirements, Concept, and Implementation. Remote Sens. Environ. 2017, 190, 260–273. [Google Scholar] [CrossRef]
- Neuenschwander, A.; Pitts, K. The ATL08 Land and Vegetation Product for the ICESat-2 Mission. Remote Sens. Environ. 2019, 221, 247–259. [Google Scholar] [CrossRef]
- Smith, B.; Fricker, H.A.; Gardner, A.S.; Medley, B.; Nilsson, J.; Paolo, F.S.; Holschuh, N.; Adusumilli, S.; Brunt, K.; Csatho, B.; et al. Pervasive Ice Sheet Mass Loss Reflects Competing Ocean and Atmosphere Processes. Science 2020, 368, 11239–11242. [Google Scholar] [CrossRef]
- Luo, S.; Song, C.; Zhan, P.; Liu, K.; Chen, T.; Li, W.; Ke, L. Refined Estimation of Lake Water Level and Storage Changes on the Tibetan Plateau from ICESat/ICESat-2. Catena 2021, 200, 105177. [Google Scholar] [CrossRef]
- Fricker, H.A.; Arndt, P.; Brunt, K.M.; Datta, R.T.; Fair, Z.; Jasinski, M.F.; Kingslake, J.; Magruder, L.A.; Moussavi, M.; Pope, A.; et al. ICESat-2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica. Geophys. Res. Lett. 2021, 48, e2020GL090550. [Google Scholar] [CrossRef]
- Kacimi, S.; Kwok, R. Arctic Snow Depth, Ice Thickness, and Volume from ICESat-2 and CryoSat-2: 2018–2021. Geophys. Res. Lett. 2022, 49, e2021GL097448. [Google Scholar] [CrossRef]
- Malambo, L.; Popescu, S.C. Assessing the Agreement of ICESat-2 Terrain and Canopy Height with Airborne Lidar over US Ecozones. Remote Sens. Environ. 2021, 266, 112711. [Google Scholar] [CrossRef]
- Simurda, C.; Magruder, L.A.; Markel, J.; Garvin, J.B.; Slayback, D.A. ICESat-2 Applications for Investigating Emerging Volcanoes. Geosciences 2022, 12, 40. [Google Scholar] [CrossRef]
- Hakkarainen, J.; Ialongo, I.; Tamminen, J. Direct Space-Based Observations of Anthropogenic CO2 Emission Areas from OCO-2. Geophys. Res. Lett. 2016, 43, 11,400–11,406. [Google Scholar] [CrossRef]
- Crisp, D.; Pollock, H.; Rosenberg, R.; Chapsky, L.; Lee, R.; Oyafuso, F.; Frankenberg, C.; Dell, C.; Bruegge, C.; Doran, G.; et al. The On-Orbit Performance of the Orbiting Carbon Observatory-2 (OCO-2) Instrument and Its Radiometrically Calibrated Products. Atmos. Meas. Tech. 2017, 10, 59–81. [Google Scholar] [CrossRef]
- Peiro, H.; Crowell, S.; Schuh, A.; Baker, D.F.; O’Dell, C.; Jacobson, A.R.; Chevallier, F.; Liu, J.; Eldering, A.; Crisp, D.; et al. Four Years of Global Carbon Cycle Observed from the Orbiting Carbon Observatory 2 (OCO-2) Version 9 and in Situ Data and Comparison to OCO-2 Version 7. Atmos. Chem. Phys. 2022, 22, 1097–1130. [Google Scholar] [CrossRef]
- Nassar, R.; Mastrogiacomo, J.P.; Bateman-Hemphill, W.; McCracken, C.; MacDonald, C.G.; Hill, T.; O’Dell, C.W.; Kiel, M.; Crisp, D. Advances in Quantifying Power Plant CO2 Emissions with OCO-2. Remote Sens. Envoiron. 2021, 264. [Google Scholar] [CrossRef]
- Veefkind, J.P.; Aben, I.; McMullan, K.; Förster, H.; de Vries, J.; Otter, G.; Claas, J.; Eskes, H.J.; de Haan, J.F.; Kleipool, Q.; et al. TROPOMI on the ESA Sentinel-5 Precursor: A GMES Mission for Global Observations of the Atmospheric Composition for Climate, Air Quality and Ozone Layer Applications. Remote Sens. Environ. 2012, 120, 70–83. [Google Scholar] [CrossRef]
- Carn, S.A.; Fioletov, V.E.; Mclinden, C.A.; Li, C.; Krotkov, N.A. A Decade of Global Volcanic SO2 Emissions Measured from Space. Sci. Rep. 2017, 7, 112579. [Google Scholar] [CrossRef]
- Zeng, J.; Vollmer, B.E.; Wei, J.C.; Ostrenga, D.M.; Johnson, J.E.; Gerasimov, I.V. Sentinel-5P/TROPOMI and S-NPP/OMPS Data Support at GES DISC. In 2018 ATMOS Conference In Proceedings of the Sentinel-5P/TROPOMI and S-NPP/OMPS Data Support at GES DISC 2018, Salzburg, Austria, 25 November 2018; p. 1. [Google Scholar]
- Cusworth, D.H.; Thorpe, A.K.; Ayasse, A.K.; Stepp, D.; Heckler, J.; Asner, G.P.; Miller, C.E.; Yadav, V.; Chapman, J.W.; Eastwood, M.L.; et al. Strong Methane Point Sources Contribute a Disproportionate Fraction of Total Emissions across Multiple Basins in the United States. Proc. Natl. Acad. Sci. USA 2022, 119, e2202338119. [Google Scholar] [CrossRef]
- Pu, D.; Zhu, L.; De Smedt, I.; Li, X.; Sun, W.; Wang, D.; Liu, S.; Li, J.; Shu, L.; Chen, Y.; et al. Response of Anthropogenic Volatile Organic Compound Emissions to Urbanization in Asia Probed with TROPOMI and VIIRS Satellite Observations. Geophys. Res. Lett. 2022, 49, e2022GL099470. [Google Scholar] [CrossRef]
- Fioletov, V.; Mclinden, C.A.; Griffin, D.; Theys, N.; Loyola, D.G.; Hedelt, P.; Krotkov, N.A.; Li, C. Anthropogenic and Volcanic Point Source SO2 Emissions Derived from TROPOMI on Board Sentinel-5 Precursor: First Results. Atmos. Chem. Phys. 2020, 20, 5591–5607. [Google Scholar] [CrossRef]
- Griffin, D.; Zhao, X.; McLinden, C.A.; Boersma, F.; Bourassa, A.; Dammers, E.; Degenstein, D.; Eskes, H.; Fehr, L.; Fioletov, V.; et al. High-Resolution Mapping of Nitrogen Dioxide With TROPOMI: First Results and Validation Over the Canadian Oil Sands. Geophys. Res. Lett. 2019, 46, 1049–1060. [Google Scholar] [CrossRef]
- Guanter, L.; Bacour, C.; Schneider, A.; Aben, I.; Van Kempen, T.A.; Maignan, F.; Retscher, C.; Köhler, P.; Frankenberg, C.; Joiner, J.; et al. The TROPOSIF Global Sun-Induced Fluorescence Dataset from the Sentinel-5P TROPOMI Mission. Earth Syst. Sci. Data 2021, 13, 5423–5440. [Google Scholar] [CrossRef]
- Vîrghileanu, M.; Săvulescu, I.; Mihai, B.A.; Nistor, C.; Dobre, R. Nitrogen Dioxide (No2) Pollution Monitoring with Sentinel-5p Satellite Imagery over Europe during the Coronavirus Pandemic Outbreak. Remote Sens. 2020, 12, 3575. [Google Scholar] [CrossRef]
- Middleton, E.M.; Huemmrich, K.F.; Zhang, Q.; Campbell, P.K.E.; Landis, D.R. Biophysical and Biochemical Characterization and Plant Species Studies; CRC Press: Boca Raton, FL, USA, 2018; Volume 3, pp. 133–179. [Google Scholar]
- Durand, Y.; Chinal, E.; Endemann, M.; Meynart, R.; Reitebuch, O.; Treichel, R. ALADIN Airborne Demonstrator: A Doppler Wind Lidar to Prepare ESA’s ADM-Aeolus Explorer Mission. In Earth Observing Systems XI.; SPIE: Bellingham, DC, USA, 2006; Volume 6296, p. 62961D. [Google Scholar]
- Lux, O.; Lemmerz, C.; Weiler, F.; Marksteiner, U.; Witschas, B.; Rahm, S.; Geiß, A.; Reitebuch, O. Intercomparison of Wind Observations from the European Space Agency’s Aeolus Satellite Mission and the ALADIN Airborne Demonstrator. Atmos. Meas. Tech. 2020, 13, 2075–2097. [Google Scholar] [CrossRef]
- Zhai, X.; Marksteiner, U.; Weiler, F.; Lemmerz, C.; Lux, O.; Witschas, B.; Reitebuch, O. Rayleigh Wind Retrieval for the ALADIN Airborne Demonstrator of the Aeolus Mission Using Simulated Response Calibration. Atmos. Meas. Tech. 2020, 13, 445–465. [Google Scholar] [CrossRef]
- Straume-Lindner, A.G.; Parrinello, T.; von Bismarck, J.; Bley, S.; Wernham, D.; Kanitz, T.; Alvarez, E.; Fischer, P.; de Laurentis, M.; Fehr, T.; et al. ESA’S Wind Mission AEOLUS—Overview, Status and Outlook. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; pp. 755–758. [Google Scholar]
- Kerr, Y.H.; Waldteufel, P.; Wigneron, J.-P.; Martinuzzi, J.; Font, J.; Berger, M. Soil Moisture Retrieval from Space: The Soil Moisture and Ocean Salinity (SMOS) Mission. IEEE Trans. Geosci Remote Sens. 2001, 39, 1729–1735. [Google Scholar] [CrossRef]
- Ge, L.; Hang, R.; Liu, Y.; Liu, Q. Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations. Remote Sens. 2018, 10, 1327. [Google Scholar] [CrossRef]
- Kolassa, J.; Reichle, R.H.; Liu, Q.; Alemohammad, S.H.; Gentine, P.; Aida, K.; Asanuma, J.; Bircher, S.; Caldwell, T.; Colliander, A.; et al. Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique. Remote Sens. Environ. 2018, 204, 43–59. [Google Scholar] [CrossRef]
- Martin, M.J.; King, R.R.; While, J.; Aguiar, A.B. Assimilating Satellite Sea-Surface Salinity Data from SMOS, Aquarius and SMAP into a Global Ocean Forecasting System. Quar. J. R. Meteorol. Soc. 2019, 145, 705–726. [Google Scholar] [CrossRef]
- Morris, E.M.; Wingham, D.J. The Effect of Fluctuations in Surface Density, Accumulation and Compaction on Elevation Change Rates along the EGIG Line, Central Greenland. J. Glaciol. 2011, 57, 416–430. [Google Scholar] [CrossRef]
- Jiang, L.; Nielsen, K.; Andersen, O.B.; Bauer-Gottwein, P. CryoSat-2 Radar Altimetry for Monitoring Freshwater Resources of China. Remote Sens. Environ. 2017, 200, 125–139. [Google Scholar] [CrossRef]
- Helm, V.; Humbert, A.; Miller, H. Elevation and Elevation Change of Greenland and Antarctica Derived from CryoSat-2. Cryosph 2014, 8, 1539–1559. [Google Scholar] [CrossRef]
- Laxon, S.W.; Giles, K.A.; Ridout, A.L.; Wingham, D.J.; Willatt, R.; Cullen, R.; Kwok, R.; Schweiger, A.; Zhang, J.; Haas, C.; et al. CryoSat-2 Estimates of Arctic Sea Ice Thickness and Volume. Geophys. Res. Lett. 2013, 40, 732–737. [Google Scholar] [CrossRef]
- Kern, M.; Cullen, R.; Berruti, B.; Bouffard, J.; Casal, T.; Drinkwater, M.R.; Gabriele, A.; Lecuyot, A.; Ludwig, M.; Midthassel, R.; et al. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) High-Priority Candidate Mission. Cryos 2020, 14, 2235–2251. [Google Scholar] [CrossRef]
- Friis-Christensen, E.; Lühr, H.; Hulot, G. SWARM: A Constellation to Study the Earth’s Magnetic Field. Earth Planets Space 2006, 58, 351–358. [Google Scholar] [CrossRef]
- Olsen, N.; Friis-Christensen, E.; Floberghagen, R.; Alken, P.; Beggan, C.D.; Chulliat, A.; Doornbos, E.; Da Encarnação, J.T.; Hamilton, B.; Hulot, G.; et al. The SWARM Satellite Constellation Application and Research Facility (SCARF) and SWARM Data Products. Earth Planets Space 2013, 65, 1189–1200. [Google Scholar] [CrossRef]
- Yau, A.W.; James, H.G. CASSIOPE Enhanced Polar Outflow Probe (e-POP) Mission Overview. Space Sci. Rev. 2015, 189, 3–14. [Google Scholar] [CrossRef]
- Masek, J.G.; Wulder, M.A.; Markham, B.; McCorkel, J.; Crawford, C.J.; Storey, J.; Jenstrom, D.T. Landsat 9: Empowering Open Science and Applications through Continuity. Remote Sens. Environ. 2020, 248, 111968. [Google Scholar] [CrossRef]
- Goward, S.N.; Masek, J.G.; Loveland, T.R.; Dwyer, J.L.; Williams, D.L.; Arvidson, T.; Rocchio, L.E.P.; Irons, J.R. Semi-Centennial of Landsat Observations & Pending Landsat 9 Launch. Photogramm. Eng. Remote Sens. 2021, 87, 533–539. [Google Scholar] [CrossRef]
- Digenis, C.J. The EO-1 Mission and the Advanced Land Imager. Lincoln. Labs. J. 2005, 15, 161–164. [Google Scholar]
- Fahnestock, M.; Scambos, T.; Moon, T.; Gardner, A.; Haran, T.; Klinger, M. Rapid Large-Area Mapping of Ice Flow Using Landsat 8. Remote Sens. Environ. 2016, 185, 84–94. [Google Scholar] [CrossRef]
- Chen, Z.; Chi, Z.; Zinglersen, K.B.; Tian, Y.; Wang, K.; Hui, F.; Cheng, X. A New Image Mosaic of Greenland Using Landsat-8 OLI Images. Sci. Bull. 2020, 65, 522–524. [Google Scholar] [CrossRef]
- Li, D.; Shangguan, D.; Anjum, M.N. Glacial Lake Inventory Derived from Landsat 8 OLI in 2016–2018 in China-Pakistan Economic Corridor. ISPRS Int. J. Geoinf. 2020, 9, 294. [Google Scholar] [CrossRef]
- Williamson, A.G.; Banwell, A.F.; Willis, I.C.; Arnold, N.S. Dual-Satellite (Sentinel-2 and Landsat 8) Remote Sensing of Supraglacial Lakes in Greenland. Cryosph 2018, 12, 3045–3065. [Google Scholar] [CrossRef]
- Halberstadt, A.R.W.; Gleason, C.J.; Moussavi, M.S.; Pope, A.; Trusel, L.D.; DeConto, R.M. Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification. Remote Sens. 2020, 12, 1327. [Google Scholar] [CrossRef]
- Baumann, S.; Anderson, B.; Chinn, T.; MacKintosh, A.; Collier, C.; Lorrey, A.M.; Rack, W.; Purdie, H.; Eaves, S. Updated Inventory of Glacier Ice in New Zealand Based on 2016 Satellite Imagery. J. Glaciol. 2021, 67, 13–26. [Google Scholar] [CrossRef]
- Racoviteanu, A.E.; Nicholson, L.; Glasser, N.F. Surface Composition of Debris-Covered Glaciers across the Himalaya Using Linear Spectral Unmixing of Landsat 8 OLI Imagery. Cryosph. 2021, 15, 4557–4588. [Google Scholar] [CrossRef]
- Tuckett, P.A.; Ely, J.C.; Sole, A.J.; Livingstone, S.J.; Davison, B.J.; Melchior van Wessem, J.; Howard, J. Rapid Accelerations of Antarctic Peninsula Outlet Glaciers Driven by Surface Melt. Nat. Commun. 2019, 10, 4311. [Google Scholar] [CrossRef] [PubMed]
- Dozier, J. Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper. Remote Sens. Environ. 1989, 28, 9–22. [Google Scholar] [CrossRef]
- Hall, D.K.; Ormsby, J.P.; Bindschadler, R.A.; Siddalingaiah, H. Characterization of Snow and Ice Reflectance Zones on Glaciers Using Landsat Thematic Mapper Data. Ann. Glaciol. 1987, 9, 104–108. [Google Scholar] [CrossRef]
- Crawford, C.J.; Manson, S.M.; Bauer, M.E.; Hall, D.K. Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development. Remote Sens. Environ. 2013, 135, 224–233. [Google Scholar] [CrossRef]
- Wulder, M.A.; Roy, D.P.; Radeloff, V.C.; Loveland, T.R.; Anderson, M.C.; Johnson, D.M.; Healey, S.; Zhu, Z.; Scambos, T.A.; Pahlevan, N.; et al. Fifty Years of Landsat Science and Impacts. Remote Sens. Environ. 2022, 280, 113195. [Google Scholar] [CrossRef]
- Justice, C.O.; Román, M.O.; Csiszar, I.; Vermote, E.F.; Wolfe, R.E.; Hook, S.J.; Friedl, M.; Wang, Z.; Schaaf, C.B.; Miura, T.; et al. Land and Cryosphere Products from Suomi NPP VIIRS: Overview and Status. J. Geophys. Res. Atmos. 2013, 118, 9753–9765. [Google Scholar] [CrossRef] [PubMed]
- Schroeder, W.; Oliva, P.; Giglio, L.; Csiszar, I.A. The New VIIRS 375 m Active Fire Detection Data Product: Algorithm Description and Initial Assessment. Remote Sens. Environ. 2014, 143, 85–96. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Baugh, K.; Zhizhin, M.; Hsu, F.C.; Ghosh, T. VIIRS Night-Time Lights. Int. J. Remote Sens. 2017, 38, 5860–5879. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Baugh, K.E.; Zhizhin, M.; Hsu, F.-C. Why VIIRS Data Are Superior to DMSP for Mapping Nighttime Lights. Proc Asia-Pac. Advan Netw. 2013, 35, 62. [Google Scholar] [CrossRef]
- Bennett, M.M.; Smith, L.C. Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics. Remote Sens. Environ. 2017, 192, 176–197. [Google Scholar] [CrossRef]
- Mann, M.L.; Melaas, E.K.; Malik, A. Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India. Remote Sens. 2016, 8, 711. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Zhizhin, M.; Hsu, F.C.; Baugh, K.E. VIIRS Nightfire: Satellite Pyrometry at Night. Remote Sens. 2013, 5, 4423–4449. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Zhizhin, M.; Baugh, K.; Hsu, F.C.; Ghosh, T. Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data. Energies 2016, 9, 14. [Google Scholar] [CrossRef]
- Franklin, M.; Chau, K.; Cushing, L.J.; Johnston, J.E. Characterizing Flaring from Unconventional Oil and Gas Operations in South Texas Using Satellite Observations. Environ. Sci Technol. 2019, 53, 2220–2228. [Google Scholar] [CrossRef] [PubMed]
- Elvidge, C.D.; Zhizhin, M.; Baugh, K.; Hsu, F.C. Automatic Boat Identification System for VIIRS Low Light Imaging Data. Remote Sens. 2015, 7, 3020–3036. [Google Scholar] [CrossRef]
- Straka, W.C.; Seaman, C.J.; Baugh, K.; Cole, K.; Stevens, E.; Miller, S.D. Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management. Remote Sens. 2015, 7, 971–989. [Google Scholar] [CrossRef]
- Chen, Y.; Hantson, S.; Andela, N.; Coffield, S.R.; Graff, C.A.; Morton, D.C.; Ott, L.E.; Foufoula-Georgiou, E.; Smyth, P.; Goulden, M.L.; et al. California Wildfire Spread Derived Using VIIRS Satellite Observations and an Object-Based Tracking System. Sci. Data 2022, 9, 249. [Google Scholar] [CrossRef] [PubMed]
- Kogan, F.; Goldberg, M.; Schott, T.; Guo, W. Suomi NPP/VIIRS: Improving Drought Watch, Crop Loss Prediction, and Food Security. Int. J. Remote Sens. 2015, 36, 5373–5383. [Google Scholar] [CrossRef]
- Theobald, D.M.; Kennedy, C.; Chen, B.; Oakleaf, J.; Baruch-Mordo, S.; Kiesecker, J. Earth Transformed: Detailed Mapping of Global Human Modification from 1990 to 2017. Earth Syst. Sci. Data 2020, 12, 1953–1972. [Google Scholar] [CrossRef]
- Wanyama, D.; Wimberly, M.C.; Mensah, F. Patterns and Drivers of Disturbance in Tropical Forest Reserves of Southern Ghana. Environ. Res. Lett. 2023, 18, 064022. [Google Scholar] [CrossRef]
- Huff, A.K.; Kondragunta, S.; Zhang, H.; Hoff, R.M. Monitoring the Impacts of Wildfires on Forest Ecosystems and Public Health in the Exo-Urban Environment Using High-Resolution Satellite Aerosol Products from the Visible Infrared Imaging Radiometer Suite (VIIRS). Environ. Health Insights 2015, 9, EHI-S19590. [Google Scholar] [CrossRef]
- Facchinelli, F.; Pappalardo, S.E.; Codato, D.; Diantini, A.; Della Fera, G.; Crescini, E.; De Marchi, M. Unburnable and Unleakable Carbon in Western Amazon: Using VIIRS Nightfire Data to Map Gas Flaring and Policy Compliance in the Yasuni Biosphere Reserve. Sustainability 2020, 12, 58. [Google Scholar] [CrossRef]
- Waigl, C.F.; Stuefer, M.; Prakash, A.; Ichoku, C. Detecting High and Low-Intensity Fires in Alaska Using VIIRS I-Band Data: An Improved Operational Approach for High Latitudes. Remote Sens. Environ. 2017, 199, 389–400. [Google Scholar] [CrossRef]
- Wang, W.; Cao, C. NOAA-20 and S-NPP VIIRS Thermal Emissive Bands on-Orbit Calibration Algorithm Update and Long-Term Performance Inter-Comparison. Remote Sens. 2021, 13, 448. [Google Scholar] [CrossRef]
- Lyapustin, A.; Wang, Y.; Choi, M.; Xiong, X.; Angal, A.; Wu, A.; Doelling, D.R.; Bhatt, R.; Go, S.; Korkin, S.; et al. Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records. Remote Sens. Environ. 2023, 295, 113717. [Google Scholar] [CrossRef]
- Ndikumana, E.; Minh, D.H.T.; Nguyen, H.T.D.; Baghdadi, N.; Courault, D.; Hossard, L.; Moussawi, I. El Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sens. 2018, 10, 1394. [Google Scholar] [CrossRef]
- Nandy, S.; Srinet, R.; Padalia, H. Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India. Geophys. Res. Lett. 2021, 48, e2021GL093799. [Google Scholar] [CrossRef]
- Raspini, F.; Bianchini, S.; Ciampalini, A.; Del Soldato, M.; Solari, L.; Novali, F.; Del Conte, S.; Rucci, A.; Ferretti, A.; Casagli, N. Continuous, Semi-Automatic Monitoring of Ground Deformation Using Sentinel-1 Satellites. Sci. Rep. 2018, 8. [Google Scholar] [CrossRef] [PubMed]
- Rateb, A.; Abotalib, A.Z. Inferencing the Land Subsidence in the Nile Delta Using Sentinel-1 Satellites and GPS between 2015 and 2019. Sci Total Environ. 2020, 729, 138868. [Google Scholar] [CrossRef] [PubMed]
- Olen, S.; Bookhagen, B. Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series. Remote Sens. 2018, 10, 1272. [Google Scholar] [CrossRef]
- Zhang, M.; Chen, F.; Liang, D.; Tian, B.; Yang, A. Use of Sentinel-1 Grd Sar Images to Delineate Flood Extent in Pakistan. Sustainability 2020, 12, 5784. [Google Scholar] [CrossRef]
- Gomez, C.; Dharumarajan, S.; Féret, J.B.; Lagacherie, P.; Ruiz, L.; Sekhar, M. Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping. Remote Sens. 2019, 11, 565. [Google Scholar] [CrossRef]
- Rapinel, S.; Mony, C.; Lecoq, L.; Clément, B.; Thomas, A.; Hubert-Moy, L. Evaluation of Sentinel-2 Time-Series for Mapping Floodplain Grassland Plant Communities. Remote Sens. Environ. 2019, 223, 115–129. [Google Scholar] [CrossRef]
- Grabska, E.; Hostert, P.; Pflugmacher, D.; Ostapowicz, K. Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens. 2019, 11, 1197. [Google Scholar] [CrossRef]
- Hemmerling, J.; Pflugmacher, D.; Hostert, P. Mapping Temperate Forest Tree Species Using Dense Sentinel-2 Time Series. Remote Sens. Environ. 2021, 267, 112743. [Google Scholar] [CrossRef]
- Mandanici, E.; Bitelli, G. Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens. 2016, 8, 1014. [Google Scholar] [CrossRef]
- Steinhausen, M.J.; Wagner, P.D.; Narasimhan, B.; Waske, B. Combining Sentinel-1 and Sentinel-2 Data for Improved Land Use and Land Cover Mapping of Monsoon Regions. Int. J. Appl. Earth Obs. Geoinfor. 2018, 73, 595–604. [Google Scholar] [CrossRef]
- Phiri, D.; Simwanda, M.; Salekin, S.; Nyirenda, V.R.; Murayama, Y.; Ranagalage, M. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sens. 2020, 12, 2291. [Google Scholar] [CrossRef]
- Topaloǧlu, R.H.; Sertel, E.; Musaoǧlu, N. Assessment of Classification Accuracies of Sentinel-2 and Landsat-8 Data for Land Cover/Use Mapping. Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci. 2016, 41, 1055–1059. [Google Scholar] [CrossRef]
- Mazzia, V.; Khaliq, A.; Chiaberge, M. Improvement in Land Cover and Crop Classification Based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN). Appl. Sci. 2020, 10, 238. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Segarra, J.; Buchaillot, M.L.; Araus, J.L.; Kefauver, S.C. Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agron 2020, 10, 641. [Google Scholar] [CrossRef]
- Vuolo, F.; Neuwirth, M.; Immitzer, M.; Atzberger, C.; Ng, W.T. How Much Does Multi-Temporal Sentinel-2 Data Improve Crop Type Classification? Int. J. Appl. Earth Obs. Geoinfor. 2018, 72, 122–130. [Google Scholar] [CrossRef]
- Wulder, M.A.; Loveland, T.R.; Roy, D.P.; Crawford, C.J.; Masek, J.G.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Belward, A.S.; Cohen, W.B.; et al. Current Status of Landsat Program, Science, and Applications. Remote Sens. Environ. 2019, 225, 127–147. [Google Scholar] [CrossRef]
- Yang, X.; Chen, Y.; Wang, J. Combined Use of Sentinel-2 and Landsat 8 to Monitor Water Surface Area Dynamics Using Google Earth Engine. Remote Sens. Lett. 2020, 11, 687–696. [Google Scholar] [CrossRef]
- Misra, G.; Cawkwell, F.; Wingler, A. Status of Phenological Research Using Sentinel-2 Data: A Review. Remote Sens. 2020, 12, 2760. [Google Scholar] [CrossRef]
- Claverie, M.; Ju, J.; Masek, J.G.; Dungan, J.L.; Vermote, E.F.; Roger, J.C.; Skakun, S.V.; Justice, C. The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set. Remote Sens. Environ. 2018, 219, 145–161. [Google Scholar] [CrossRef]
- Claverie, M.; Masek, J.G.; Ju, J.; Dungan, J.L. Harmonized Landsat-8 Sentinel-2 (HLS) Product User’s Guide Ver: 1.3; NASA Goddard Space Flight Center: Beltsville, MD, USA, 2018.
- Chen, Y.; Sun, K.; Li, W.; Hu, X.; Li, P.; Bai, T. Vicarious Calibration of Fengyun-3D MERSI-II at Railroad Valley Playa Site: A Case for Sensors with Large View Angles. Remote Sens. 2021, 13, 1347. [Google Scholar] [CrossRef]
- Moon, M.; Richardson, A.D.; Friedl, M.A. Multiscale Assessment of Land Surface Phenology from Harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam Imagery. Remote Sens. Environ. 2021, 266, 112716. [Google Scholar] [CrossRef]
- Bolton, D.K.; Gray, J.M.; Melaas, E.K.; Moon, M.; Eklundh, L.; Friedl, M.A. Continental-Scale Land Surface Phenology from Harmonized Landsat 8 and Sentinel-2 Imagery. Remote Sens. Environ. 2020, 240, 111685. [Google Scholar] [CrossRef]
- Nguyen, H.T.T.; Doan, T.M.; Tomppo, E.; McRoberts, R.E. Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam. Remote Sens. 2020, 12, 1367. [Google Scholar] [CrossRef]
- Sánchez-Zapero, J.; Camacho, F.; Martínez-Sánchez, E.; Gorroño, J.; León-Tavares, J.; Benhadj, I.; Toté, C.; Swinnen, E.; Muñoz-Sabater, J. Global Estimates of Surface Albedo from Sentinel-3 OLCI and SLSTR Data for Copernicus Climate Change Service: Algorithm and Preliminary Validation. Remote Sens. Environ. 2023, 287, 113460. [Google Scholar] [CrossRef]
- Donlon, C.; Berruti, B.; Buongiorno, A.; Ferreira, M.H.; Féménias, P.; Frerick, J.; Goryl, P.; Klein, U.; Laur, H.; Mavrocordatos, C.; et al. The Global Monitoring for Environment and Security (GMES) Sentinel-3 Mission. Remote Sens. Environ. 2012, 120, 37–57. [Google Scholar] [CrossRef]
- Shen, M.; Duan, H.; Cao, Z.; Xue, K.; Qi, T.; Ma, J.; Liu, D.; Song, K.; Huang, C.; Song, X. Sentinel-3 OLCI Observations of Water Clarity in Large Lakes in Eastern China: Implications for SDG 6.3.2 Evaluation. Remote Sens. Environ. 2020, 247, 111950. [Google Scholar] [CrossRef]
- Vanhellemont, Q.; Ruddick, K. Atmospheric Correction of Sentinel-3/OLCI Data for Mapping of Suspended Particulate Matter and Chlorophyll-a Concentration in Belgian Turbid Coastal Waters. Remote Sens. Environ. 2021, 256, 112284. [Google Scholar] [CrossRef]
- Guzinski, R.; Nieto, H.; Sanchez, J.M.; Lopez-Urrea, R.; Boujnah, D.M.; Boulet, G. Utility of Copernicus-Based Inputs for Actual Evapotranspiration Modeling in Support of Sustainable Water Use in Agriculture. IEEE J. Sel Top. Appl. Earth Obs. Remote Sens. 2021, 14, 11466–11484. [Google Scholar] [CrossRef]
- Kravitz, J.; Matthews, M.; Bernard, S.; Griffith, D. Application of Sentinel 3 OLCI for Chl-a Retrieval over Small Inland Water Targets: Successes and Challenges. Remote Sens. Environ. 2020, 237, 111562. [Google Scholar] [CrossRef]
- Sobrino, J.A.; Irakulis, I. A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations around the World from Sentinel-3 SLSTR Data. Remote Sens. 2020, 12, 2052. [Google Scholar] [CrossRef]
- Zheng, Y.; Zhang, G.; Tan, S.; Feng, L. Research on Progress of Forest Fire Monitoring with Satellite Remote Sensing. Agric. Rural. Stud. 2023, 1, 0008. [Google Scholar] [CrossRef]
- Legeais, J.F.; Meyssignac, B.; Faugère, Y.; Guerou, A.; Ablain, M.; Pujol, M.I.; Dufau, C.; Dibarboure, G. Copernicus Sea Level Space Observations: A Basis for Assessing Mitigation and Developing Adaptation Strategies to Sea Level Rise. Front. Mar. Sci. 2021, 8, 704721. [Google Scholar] [CrossRef]
- Jiang, M.; Xu, K.; Wang, J. Evaluation of Sentinel-6 Altimetry Data over Ocean. Remote Sens. 2023, 15, 12. [Google Scholar] [CrossRef]
- Donlon, C.J.; Cullen, R.; Giulicchi, L.; Vuilleumier, P.; Francis, C.R.; Kuschnerus, M.; Simpson, W.; Bouridah, A.; Caleno, M.; Bertoni, R.; et al. The Copernicus Sentinel-6 Mission: Enhanced Continuity of Satellite Sea Level Measurements from Space. Remote Sens. Environ. 2021, 258, 112395. [Google Scholar] [CrossRef]
- Vangi, E.; D’amico, G.; Francini, S.; Giannetti, F.; Lasserre, B.; Marchetti, M.; Chirici, G. The New Hyperspectral Satellite Prisma: Imagery for Forest Types Discrimination. Sensors 2021, 21, 1182. [Google Scholar] [CrossRef] [PubMed]
- Cogliati, S.; Sarti, F.; Chiarantini, L.; Cosi, M.; Lorusso, R.; Lopinto, E.; Miglietta, F.; Genesio, L.; Guanter, L.; Damm, A.; et al. The PRISMA Imaging Spectroscopy Mission: Overview and First Performance Analysis. Remote Sens. Environ. 2021, 262, 112499. [Google Scholar] [CrossRef]
- Shaik, R.U.; Periasamy, S.; Zeng, W. Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications. Remote Sens. 2023, 15, 1378. [Google Scholar] [CrossRef]
- Mouroulis, P.; Green, R.O. Review of High Fidelity Imaging Spectrometer Design for Remote Sensing. Opt. Eng. 2018, 57, 1. [Google Scholar] [CrossRef]
- Guarini, R.; Loizzo, R.; Longo, F.; Mari, S.; Scopa, T.; Varacalli, G. Overview of the PRISMA space and ground segment and its hyperspectral products. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA; 2017; pp. 431–434. [Google Scholar]
- Bresciani, M.; Giardino, C.; Fabbretto, A.; Pellegrino, A.; Mangano, S.; Free, G.; Pinardi, M. Application of New Hyperspectral Sensors in the Remote Sensing of Aquatic Ecosystem Health: Exploiting PRISMA and DESIS for Four Italian Lakes. Resources 2022, 11, 8. [Google Scholar] [CrossRef]
- Chirico, R.; Mondillo, N.; Laukamp, C.; Mormone, A.; Di Martire, D.; Novellino, A.; Balassone, G. Mapping Hydrothermal and Supergene Alteration Zones Associated with Carbonate-Hosted Zn-Pb Deposits by Using PRISMA Satellite Imagery Supported by Field-Based Hyperspectral Data, Mineralogical and Geochemical Analysis. Ore Geol. Rev. 2023, 152, 105244. [Google Scholar] [CrossRef]
- Müller, R.; Avbelj, J.; Carmona, E.; Eckardt, A.; Gerasch, B.; Graham, C.L.; Günther, B.; Heiden, U.; Ickes, J.; Kerr, G.; et al. The New Hyperspectral Sensor Desis on the Multi-Payload Platform Muses Installed on the ISS. Proc (ISPRS) Int Archiv Photogram. Remote Sens. Spat. Inform. Sci. 2016, 2016, 461–467. [Google Scholar]
- Krutz, D.; Müller, R.; Knodt, U.; Günther, B.; Walter, I.; Sebastian, I.; Säuberlich, T.; Reulke, R.; Carmona, E.; Eckardt, A.; et al. The Instrument Design of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors 2019, 19, 1622. [Google Scholar] [CrossRef]
- Alonso, K.; Bachmann, M.; Burch, K.; Carmona, E.; Cerra, D.; de los Reyes, R.; Dietrich, D.; Heiden, U.; Hölderlin, A.; Ickes, J.; et al. Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS). Sensors 2019, 19, 4471. [Google Scholar] [CrossRef] [PubMed]
- Aneece, I.; Foley, D.; Thenkabail, P.; Oliphant, A.; Teluguntla, P. New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022, 15, 7846–7858. [Google Scholar] [CrossRef]
- Matsunaga, T.; Iwasaki, A.; Tsuchida, S.; Iwao, K.; Nakamura, R.; Yamamoto, H.; Kato, S.; Obata, K.; Kashimura, O.; Tanii, J.; et al. HISUI Status toward FY2019 Launch. In Proceedings of the IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 160–163. [Google Scholar]
- Sousa, D.; Small, C. Topological Generality and Spectral Dimensionality in the Earth Mineral Dust Source Investigation (EMIT) Using Joint Characterization and the Spectral Mixture Residual. Remote Sens. 2023, 15, 2295. [Google Scholar] [CrossRef]
- Thorpe, A.K.; Green, R.O.; Thompson, D.R.; Brodrick, P.G.; Chapman, J.W.; Elder, C.D.; Irakulis-Loitxate, I.; Cusworth, D.H.; Ayasse, A.K.; Duren, R.M.; et al. Attribution of Individual Methane and Carbon Dioxide Emission Sources Using EMIT Observations from Space. Sci. Adv. 2023, 9, eadh2391. [Google Scholar] [CrossRef]
- Yavuz, E.; Kuzu, L.; Uğurdoğan, S.; Saral, A. Investigation of Aerosol Direct Radiative Forcing during a Dust Storm Using a Regional Climate Model over Turkiye. Sigma J. Eng. Nat. Sci. 2023, 41, 35–41. [Google Scholar] [CrossRef]
- Bender, H.A.; Mouroulis, P.; Dierssen, H.M.; Painter, T.H.; Thompson, D.R.; Smith, C.D.; Gross, J.; Green, R.O.; Haag, J.M.; Van Gorp, B.E.; et al. Snow and Water Imaging Spectrometer: Mission and Instrument Concepts for Earth-Orbiting CubeSats. J. Appl. Remote Sens. 2018, 12, 1. [Google Scholar] [CrossRef]
- Green, R.O.; Mahowald, N.; Ung, C.; Thompson, D.R.; Bator, L.; Bennet, M.; Bernas, M.; Blackway, N.; Bradley, C.; Cha, J.; et al. The Earth Surface Mineral Dust Source Investigation: An Earth Science Imaging Spectroscopy Mission. In Proceedings of the 2020 IEEE Aerospace Conference, Big Sky, MT, USA, 7–14 March 2020. [Google Scholar]
- Fisher, J.B.; Lee, B.; Purdy, A.J.; Halverson, G.H.; Dohlen, M.B.; Cawse-Nicholson, K.; Wang, A.; Anderson, R.G.; Aragon, B.; Arain, M.A.; et al. ECOSTRESS: NASA’s Next Generation Mission to Measure Evapotranspiration from the International Space Station. Water Resour Res. 2020, 56, e2019WR026058. [Google Scholar] [CrossRef]
- Kohli, G.; Lee, C.M.; Fisher, J.B.; Halverson, G.; Variano, E.; Jin, Y.; Carney, D.; Wilder, B.A.; Kinoshita, A.M. Ecostress and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sens. 2020, 12, 4126. [Google Scholar] [CrossRef]
- Anderson, M.C.; Yang, Y.; Xue, J.; Knipper, K.R.; Yang, Y.; Gao, F.; Hain, C.R.; Kustas, W.P.; Cawse-Nicholson, K.; Hulley, G.; et al. Interoperability of ECOSTRESS and Landsat for Mapping Evapotranspiration Time Series at Sub-Field Scales. Remote Sens. Environ. 2021, 252, 112189. [Google Scholar] [CrossRef]
- Cawse-Nicholson, K.; Anderson, M.C.; Yang, Y.; Yang, Y.; Hook, S.J.; Fisher, J.B.; Halverson, G.; Hulley, G.C.; Hain, C.; Baldocchi, D.D.; et al. Evaluation of a CONUS-Wide ECOSTRESS DisALEXI Evapotranspiration Product. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021, 14, 10117–10133. [Google Scholar] [CrossRef]
- Fisher, J.B.; Dohlen, M.B.; Halverson, G.H.; Collison, J.W.; Pearson, C.; Huntington, J.L. Remotely Sensed Terrestrial Open Water Evaporation. Sci. Rep. 2023, 13, 8174. [Google Scholar] [CrossRef]
- Doughty, C.E.; Keany, J.M.; Wiebe, B.C.; Rey-Sanchez, C.; Carter, K.R.; Middleby, K.B.; Cheesman, A.W.; Goulden, M.L.; da Rocha, H.R.; Miller, S.D.; et al. Tropical Forests Are Approaching Critical Temperature Thresholds. Nature 2023, 621, 105–111. [Google Scholar] [CrossRef] [PubMed]
- Hu, T.; Hulley, G.C.; Mallick, K.; Szantoi, Z.; Hook, S. Comparison between the ASTER and ECOSTRESS Global Emissivity Datasets. Int. J. Appl. Earth Obs. Geoinform. 2023, 118, 103227. [Google Scholar] [CrossRef]
- O’Gorman, P.A.; Schneider, T. The Physical Basis for Increases in Precipitation Extremes in Simulations of 21st-Century Climate Change. Proc. Natl. Acad. Sci. USA 2009, 106, 14773–14777. [Google Scholar] [CrossRef] [PubMed]
- Sugiyama, M.; Shiogama, H.; Emori, S. Precipitation Extreme Changes Exceeding Moisture Content Increases in MIROC and IPCC Climate Models. Proc. Natl. Acad. Sci. USA 2010, 107, 571–575. [Google Scholar] [CrossRef] [PubMed]
- O’Gorman, P.A. Precipitation Extremes Under Climate Change. Curr. Clim. Chang. Rep. 2015, 1, 49–59. [Google Scholar] [CrossRef] [PubMed]
- Donat, M.G.; Lowry, A.L.; Alexander, L.V.; O’Gorman, P.A.; Maher, N. More Extreme Precipitation in the World’s Dry and Wet Regions. Nat. Clim. Chang. 2016, 6, 508–513. [Google Scholar] [CrossRef]
- Dagan, G.; Stier, P.; Watson-Parris, D. Analysis of the Atmospheric Water Budget for Elucidating the Spatial Scale of Precipitation Changes Under Climate Change. Geophys. Res. Lett. 2019, 46, 10504–10511. [Google Scholar] [CrossRef]
- Allan, R.P.; Barlow, M.; Byrne, M.P.; Cherchi, A.; Douville, H.; Fowler, H.J.; Gan, T.Y.; Pendergrass, A.G.; Rosenfeld, D.; Swann, A.L.S.; et al. Advances in Understanding Large-Scale Responses of the Water Cycle to Climate Change. Ann. N. Y. Acad. Sci. 2020, 1472, 49–75. [Google Scholar] [CrossRef]
- Johnson, W.R.; Hook, S.J.; Foote, M.; Eng, B.T.; Jau, B. Infrared instrument support for HyspIRI-TIR. In Proceedings of the SPIE Optical Engineering Applications, San Diego, CA, USA, 30 January 2012; Volume 85, p. 851102. [Google Scholar] [CrossRef]
- Bruno, J.M.; Hook, S.J.; Johnson, W.R.; Foote, M.C.; Paine, C.G.; Pannell, Z.W.; Smythe, R.F.; Kuan, G.M.; Jakoboski, J.K.; Eng, B.T. PHyTIR—A Prototype Thermal Infrared Radiometer. In Proceedings of the 2013 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 2–9 March 2013; pp. 1–11. [Google Scholar]
- Duncanson, L.; Kellner, J.R.; Armston, J.; Dubayah, R.; Minor, D.M.; Hancock, S.; Healey, S.P.; Patterson, P.L.; Saarela, S.; Marselis, S.; et al. Aboveground Biomass Density Models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission. Remote Sens. Environ. 2022, 270, 112845. [Google Scholar] [CrossRef]
- Potapov, P.; Li, X.; Hernandez-Serna, A.; Tyukavina, A.; Hansen, M.C.; Kommareddy, A.; Pickens, A.; Turubanova, S.; Tang, H.; Silva, C.E.; et al. Mapping Global Forest Canopy Height through Integration of GEDI and Landsat Data. Remote Sens. Environ. 2021, 253, 112165. [Google Scholar] [CrossRef]
- Schneider, F.D.; Ferraz, A.; Hancock, S.; Duncanson, L.I.; Dubayah, R.O.; Pavlick, R.P.; Schimel, D.S. Towards Mapping the Diversity of Canopy Structure from Space with GEDI. Environ. Res. Lett. 2020, 15, 115006. [Google Scholar] [CrossRef]
- Marselis, S.M.; Keil, P.; Chase, J.M.; Dubayah, R. The Use of GEDI Canopy Structure for Explaining Variation in Tree Species Richness in Natural Forests. Environ. Res. Lett. 2022, 17, 045003. [Google Scholar] [CrossRef]
- Zhao, X.; Chen, J.M.; Zhang, Y.; Jiao, Z.; Liu, L.; Qiu, F.; Zang, J.; Cao, R. Global Mapping of Forest Clumping Index Based on GEDI Canopy Height and Complementary Data. ISPRS J. Photogram. Remote Sens. 2024, 209, 1–16. [Google Scholar] [CrossRef]
- Vogeler, J.C.; Fekety, P.A.; Elliott, L.; Swayze, N.C.; Filippelli, S.K.; Barry, B.; Holbrook, J.D.; Vierling, K.T. Evaluating GEDI Data Fusions for Continuous Characterizations of Forest Wildlife Habitat. Front. Remote Sens. 2023, 4, 1196554. [Google Scholar] [CrossRef]
- Torresani, M.; Rocchini, D.; Alberti, A.; Moudrý, V.; Heym, M.; Thouverai, E.; Kacic, P.; Tomelleri, E. LiDAR GEDI Derived Tree Canopy Height Heterogeneity Reveals Patterns of Biodiversity in Forest Ecosystems. Ecol. Inf. 2023, 76, 102082. [Google Scholar] [CrossRef] [PubMed]
- Krieger, G.; Morera, A.; Fiedler, H.; Hajnsek, I.; Werner, M.; Younis, M.; Zink, M. TanDEM-X: A satellite formation for high-resolution SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3317–3341. [Google Scholar] [CrossRef]
- Farmonov, N.; Amankulova, K.; Szatmári, J.; Urinov, J.; Narmanov, Z.; Nosirov, J.; Mucsi, L. Combining PlanetScope and Sentinel-2 Images with Environmental Data for Improved Wheat Yield Estimation. Int. J. Digit. Earth 2023, 16, 847–867. [Google Scholar] [CrossRef]
- Xiao, J.; Fisher, J.B.; Hashimoto, H.; Ichii, K.; Parazoo, N.C. Emerging Satellite Observations for Diurnal Cycling of Ecosystem Processes. Nat Plants 2021, 7, 877–887. [Google Scholar] [CrossRef]
- Chang, Y.; Xiao, J.; Li, X.; Middel, A.; Zhang, Y.; Gu, Z.; Wu, Y.; He, S. Exploring Diurnal Thermal Variations in Urban Local Climate Zones with ECOSTRESS Land Surface Temperature Data. Remote Sens. Environ. 2021, 263, 112544. [Google Scholar] [CrossRef]
- Pereira, G.; Longo, K.M.; Freitas, S.R.; Mataveli, G.; Oliveira, V.J.; Santos, P.R.; Rodrigues, L.F.; Cardozo, F.S. Improving the South America Wildfires Smoke Estimates: Integration of Polar-Orbiting and Geostationary Satellite Fire Products in the Brazilian Biomass Burning Emission Model (3BEM). Atmos. Environ. 2022, 273, 118954. [Google Scholar] [CrossRef]
- Chatzopoulos-Vouzoglanis, K.; Reinke, K.J.; Soto-Berelov, M.; Jones, S.D. One Year of Near-Continuous Fire Monitoring on a Continental Scale: Comparing Fire Radiative Power from Polar-Orbiting and Geostationary Observations. Int. J. Appl. Earth Obs. Geoinfor. 2023, 117, 103214. [Google Scholar] [CrossRef]
- de Bruin, H.A.R.; Trigo, I.F. A New Method to Estimate Reference Crop Evapotranspiration from Geostationary Satellite Imagery: Practical Considerations. Water 2019, 11, 382. [Google Scholar] [CrossRef]
- Tran, N.N.; Huete, A.; Nguyen, H.; Grant, I.; Miura, T.; Ma, X.; Lyapustin, A.; Wang, Y.; Ebert, E. Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites. Remote Sens. 2020, 12, 2494. [Google Scholar] [CrossRef]
- Goodman, S.J.; Schmit, T.J.; Daniels, J. The GOES-R Series: A New Generation of Geostationary Environmental Satellites, 1st ed.; Redmon, R., Ed.; Academic Press: New York, NY, USA, 2019. [Google Scholar]
- Schmit, T.J.; Lindstrom, S.S.; Gerth, J.J.; Gunshor, M.M. Applications of the 16 Spectral Bands on the Advanced Baseline Imager (ABI). J. Oper. Meteorol. 2018, 6, 33–46. [Google Scholar] [CrossRef]
- Khan, A.M.; Stoy, P.C.; Douglas, J.T.; Anderson, M.; Diak, G.; Otkin, J.A.; Hain, C.; Rehbein, E.M.; McCorkel, J. Reviews and Syntheses: Ongoing and Emerging Opportunities to Improve Environmental Science Using Observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites. Biogeosci 2021, 18, 4117–4141. [Google Scholar] [CrossRef]
- Bateman, M.; Mach, D. Preliminary Detection Efficiency and False Alarm Rate Assessment of the Geostationary Lightning Mapper on the GOES-16 Satellite. J. Appl. Remote Sens. 2020, 14, 1. [Google Scholar] [CrossRef]
- Thompson, K.B.; Bateman, M.G.; Mecikalski, J.R. Signatures of Oceanic Wind Events in Geostationary Cloud Top Temperature and Lightning Data. Weather Forecast. 2021, 36, 407–423. [Google Scholar] [CrossRef]
- Brodehl, S.; Müller, R.; Schömer, E.; Spichtinger, P.; Wand, M. End-to-End Prediction of Lightning Events from Geostationary Satellite Images. Remote Sens. 2022, 14, 3760. [Google Scholar] [CrossRef]
- Ingmann, P.; Veihelmann, B.; Langen, J.; Lamarre, D.; Stark, H.; Courrèges-Lacoste, G.B. Requirements for the GMES Atmosphere Service and ESA’s Implementation Concept: Sentinels-4/-5 and -5p. Remote Sens. Environ. 2012, 120, 58–69. [Google Scholar] [CrossRef]
- Schaepman, M.E.; Ustin, S.L.; Plaza, A.J.; Painter, T.H.; Verrelst, J.; Liang, S. Earth System Science Related Imaging Spectroscopy-An Assessment. Remote Sens. Environ. 2009, 113, S123–S137. [Google Scholar] [CrossRef]
- Fassnacht, F.E.; Latifi, H.; Stereńczak, K.; Modzelewska, A.; Lefsky, M.; Waser, L.T.; Straub, C.; Ghosh, A. Review of Studies on Tree Species Classification from Remotely Sensed Data. Remote Sens. Environ. 2016, 186, 64–87. [Google Scholar] [CrossRef]
- Ustin, S.L.; Roberts, D.A.; Gamon, J.A.; Asner, G.P.; Green, R.O. Using imaging spectroscopy to study ecosystem processes and properties. BioSci 2004, 54, 523–534. [Google Scholar] [CrossRef]
- Rocchini, D.; Santos, M.J.; Ustin, S.L.; Féret, J.B.; Asner, G.P.; Beierkuhnlein, C.; Dalponte, M.; Feilhauer, H.; Foody, G.M.; Geller, G.N.; et al. The Spectral Species Concept in Living Color. J. Geophys. Res. Biogeosci. 2022, 127, e2022JG007026. [Google Scholar] [CrossRef]
- Clark, R.N. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy; USGS: Denver, CO, USA, 1999.
- Clark, R.N.; Swayze, G.A.; Carlson, R.; Grundy, W.; Noll, K. Spectroscopy from Space. Nat. Ecol. Evol. 2022, 6, 506–519. [Google Scholar] [CrossRef]
- Stenberg, B.; Viscarra Rossel, R.A.; Mouazen, A.M.; Wetterlind, J. Visible and Near Infrared Spectroscopy in Soil Science. Adv. Agron. 2010, 107, 164–213. [Google Scholar]
- Nocita, M.; Stevens, A.; van Wesemael, B.; Aitkenhead, M.; Bachmann, M.; Barthès, B.; Ben Dor, E.; Brown, D.J.; Clairotte, M.; Csorba, A.; et al. Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring. Adv. Agron. 2015, 132, 139–159. [Google Scholar] [CrossRef]
- Dozier, J.; Green, R.O.; Nolin, A.W.; Painter, T.H. Interpretation of Snow Properties from Imaging Spectrometry. Remote Sens. Environ. 2009, 113, S25–S37. [Google Scholar] [CrossRef]
- Green, R.O.; Painter, T.H.; Roberts, D.A.; Dozier, J. Measuring the Expressed Abundance of the Three Phases of Water with an Imaging Spectrometer over Melting Snow. Water Resour. Res. 2006, 42, W10402. [Google Scholar] [CrossRef]
- Painter, T.H.; Dozier, J.; Roberts, D.A.; Davis, R.E.; Green, R.O. Retrieval of Subpixel Snow-Covered Area and Grain Size from Imaging Spectrometer Data. Remote Sens. Environ. 2003, 85, 64–77. [Google Scholar] [CrossRef]
- Porder, S.; Asner, G.P.; Vitousek, P.M. Ground-Based and Remotely Sensed Nutrient Availability across a Tropical Landscape. Proc. Natl. Acad. Sci. USA 2005, 102, 10909–10912. [Google Scholar] [CrossRef]
- Kokaly, R.F.; Graham, G.E. Multiscale Hyperspectral Imaging of the Orange Hill Porphyry Copper Deposit, Alaska, USA, with Laboratory-, Field-, and Aircraft-Based Imaging Spectrometers. Spectr. Geol. Remote Sens. 2017, 17, 923–943. [Google Scholar]
- Ehlmann, B.L.; Mustard, J.F.; Fassett, C.I.; Schon, S.C.; Head, J.W.; Des Marais, D.J.; Grant, J.A.; Murchie, S.L. Clay Minerals in Delta Deposits and Organic Preservation Potential on Mars. Nat. Geosci. 2008, 1, 355–358. [Google Scholar] [CrossRef]
- Craig, S.E.; Lohrenz, S.E.; Lee, Z.; Mahoney, K.L.; Kirkpatrick, G.J.; Schofield, O.M.; Steward, R.G. Use of Hyperspectral Remote Sensing Reflectance for Detection and Assessment of the Harmful Alga, Karenia brevis. Appl. Opt. 2006, 45, 5414–5425. [Google Scholar] [CrossRef] [PubMed]
- Dekker, A.G.; Phinn, S.R.; Anstee, J.; Bissett, P.; Brando, V.E.; Casey, B.; Fearns, P.; Hedley, J.; Klonowski, W.; Lee, Z.P.; et al. Intercomparison of Shallow Water Bathymetry, Hydro-Optics, and Benthos Mapping Techniques in Australian and Caribbean Coastal Environments. Limnol. Ocean. Methods 2011, 9, 396–425. [Google Scholar] [CrossRef]
- Chase, A.P.; Boss, E.; Cetinić, I.; Slade, W. Estimation of Phytoplankton Accessory Pigments From Hyperspectral Reflectance Spectra: Toward a Global Algorithm. J. Geophys. Res. Ocean. 2017, 122, 9725–9743. [Google Scholar] [CrossRef]
- Clark, R.N.; Swayze, G.A.; Leifer, I.; Livo, K.E.; Kokaly, R.; Hoefen, T.; Lundeen, S.; Eastwood, M.; Green, R.O.; Pearson, N.; et al. A Method for Quantitative Mapping of Thick Oil Spills Using Imaging Spectroscopy; U.S. Geological Survey Open File Report 2010-1167; U.S. Geological Survey: Denver, CO, USA, 2010.
- Duren, R.M.; Thorpe, A.K.; Foster, K.T.; Rafiq, T.; Hopkins, F.M.; Yadav, V.; Bue, B.D.; Thompson, D.R.; Conley, S.; Colombi, N.K.; et al. California’s Methane Super-Emitters. Nature 2019, 575, 180–184. [Google Scholar] [CrossRef] [PubMed]
- Dennison, P.E.; Charoensiri, K.; Roberts, D.A.; Peterson, S.H.; Green, R.O. Wildfire Temperature and Land Cover Modeling Using Hyperspectral Data. Remote Sens. Environ. 2006, 100, 212–222. [Google Scholar] [CrossRef]
- Veraverbeke, S.; Dennison, P.; Gitas, I.; Hulley, G.; Kalashnikova, O.; Katagis, T.; Kuai, L.; Meng, R.; Roberts, D.; Stavros, N. Hyperspectral Remote Sensing of Fire: State-of-the-Art and Future Perspectives. Remote Sens. Environ. 2018, 216, 105–121. [Google Scholar] [CrossRef]
- Asner, G.P.; Martin, R.E. Spectral and Chemical Analysis of Tropical Forests: Scaling from Leaf to Canopy Levels. Remote Sens. Environ. 2008, 112, 3958–3970. [Google Scholar] [CrossRef]
- Asner, G.P.; Martin, R.E. Canopy Phylogenetic, Chemical and Spectral Assembly in a Lowland Amazonian Forest. New Phytol. 2011, 189, 999–1012. [Google Scholar] [CrossRef]
- Kokaly, R.F.; King, T.V.V.; Hoefen, T.M. Surface Mineral Maps of Afghanistan Derived from HyMap Imaging Spectrometer Data, Version 2; USGS Afghanistan Project Product No. 186; US Geological Survey: Denver, CO, USA, 2013.
- Denis, A.; Stevens, A.; van Wesemael, B.; Udelhoven, T.; Tychon, B. Soil Organic Carbon Assessment by Field and Airborne Spectrometry in Bare Croplands: Accounting for Soil Surface Roughness. Geoderma 2014, 226–227, 94–102. [Google Scholar] [CrossRef]
- Blanchard, F.; Bruneau, A.; Laliberté, E. Foliar Spectra Accurately Distinguish Most Temperate Tree Species and Show Strong Phylogenetic Signal. Am. J. Bot. 2024, e16314. [Google Scholar] [CrossRef] [PubMed]
- Roberts, D.A.; Gardner, M.; Church, R.; Ustin, S.; Scheer, G.; Green, R.O.; Hall, E. Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models. Remote Sens. Environ. 1998, 65, 267–279. [Google Scholar] [CrossRef]
- Feret, J.B.; Asner, G.P. Tree Species Discrimination in Tropical Forests Using Airborne Imaging Spectroscopy. IEEE Trans. Geosci Remote Sens. 2013, 51, 73–84. [Google Scholar] [CrossRef]
- Roth, K.L.; Roberts, D.A.; Dennison, P.E.; Alonzo, M.; Peterson, S.H.; Beland, M. Differentiating Plant Species within and across Diverse Ecosystems with Imaging Spectroscopy. Remote Sens. Environ. 2015, 167, 135–151. [Google Scholar] [CrossRef]
- Meerdink, S.K.; Roberts, D.A.; Roth, K.L.; King, J.Y.; Gader, P.D.; Koltunov, A. Classifying California Plant Species Temporally Using Airborne Hyperspectral Imagery. Remote Sens. Environ. 2019, 232, 111308. [Google Scholar] [CrossRef]
- Green, R.O.; Dozier, J.; Roberts, D.; Painter, T. Spectral Snow-Reflectance Models for Grain-Size and Liquid-Water Fraction in Melting Snow for the Solar-Reflected Spectrum. Ann. Glaciol. 2017, 34, 71–73. [Google Scholar] [CrossRef]
- Serbin, S.P.; Dillaway, D.N.; Kruger, E.L.; Townsend, P.A. Leaf Optical Properties Reflect Variation in Photosynthetic Metabolism and Its Sensitivity to Temperature. J. Exp. Bot. 2012, 63, 489–502. [Google Scholar] [CrossRef] [PubMed]
- Gamon, J.A.; Huemmrich, K.F.; Wong, C.Y.S.; Ensminger, I.; Garrity, S.; Hollinger, D.Y.; Noormets, A.; Peñuelask, J. A Remotely Sensed Pigment Index Reveals Photosynthetic Phenology in Evergreen Conifers. Proc. Natl. Acad. Sci. USA 2016, 113, 13087–13092. [Google Scholar] [CrossRef]
- Schweiger, A.K.; Cavender-Bares, J.; Townsend, P.A.; Hobbie, S.E.; Madritch, M.D.; Wang, R.; Tilman, D.; Gamon, J.A. Plant Spectral Diversity Integrates Functional and Phylogenetic Components of Biodiversity and Predicts Ecosystem Function. Nat. Ecol. Evol. 2018, 2, 976–982. [Google Scholar] [CrossRef]
- Kokaly, R.F.; Asner, G.P.; Ollinger, S.V.; Martin, M.E.; Wessman, C.A. Characterizing Canopy Biochemistry from Imaging Spectroscopy and Its Application to Ecosystem Studies. Remote Sens. Environ. 2009, 113, S78–S91. [Google Scholar] [CrossRef]
- Schweiger, A.K.; Laliberté, E. Plant Beta-Diversity Across Biomes Captured by Imaging Spectroscopy. Nat. Commun. 2022, 13, 2767. [Google Scholar] [CrossRef] [PubMed]
- Schweiger, A.K.; Schütz, M.; Risch, A.C.; Kneubühler, M.; Haller, R.; Schaepman, M.E. How to Predict Plant Functional Types Using Imaging Spectroscopy: Linking Vegetation Community Traits, Plant Functional Types and Spectral Response. Methods Ecol. Evol. 2017, 8, 86–95. [Google Scholar] [CrossRef]
- Gitelson, A.; Solovchenko, A. Generic Algorithms for Estimating Foliar Pigment Content. Geophys. Res. Lett. 2017, 44, 9293–9298. [Google Scholar] [CrossRef]
- Hestir, E.L.; Brando, V.E.; Bresciani, M.; Giardino, C.; Matta, E.; Villa, P.; Dekker, A.G. Measuring Freshwater Aquatic Ecosystems: The Need for a Hyperspectral Global Mapping Satellite Mission. Remote Sens. Environ. 2015, 167, 181–195. [Google Scholar] [CrossRef]
- Swayze, G.A.; Smith, K.S.; Clark, R.N.; Sutley, S.J.; Pearson, R.M.; Vance, J.S.; Hageman, P.L.; Briggs, P.H.; Meier, A.L.; Singleton, M.J.; et al. Using Imaging Spectroscopy to Map Acidic Mine Waste. Environ. Sci Technol 2000, 34, 47–54. [Google Scholar] [CrossRef]
- Kudela, R.M.; Palacios, S.L.; Austerberry, D.C.; Accorsi, E.K.; Guild, L.S.; Torres-Perez, J. Application of Hyperspectral Remote Sensing to Cyanobacterial Blooms in Inland Waters. Remote Sens. Environ. 2015, 167, 196–205. [Google Scholar] [CrossRef]
- Ong, C.; Carrère, V.; Chabrillat, S.; Clark, R.; Hoefen, T.; Kokaly, R.; Marion, R.; Souza Filho, C.R.; Swayze, G.; Thompson, D.R. Imaging Spectroscopy for the Detection, Assessment and Monitoring of Natural and Anthropogenic Hazards. Surv. Geophys. 2019, 40, 431–470. [Google Scholar] [CrossRef]
- Dennison, P.E.; Thorpe, A.K.; Pardyjak, E.R.; Roberts, D.A.; Qi, Y.; Green, R.O.; Bradley, E.S.; Funk, C.C. High Spatial Resolution Mapping of Elevated Atmospheric Carbon Dioxide Using Airborne Imaging Spectroscopy: Radiative Transfer Modeling and Power Plant Plume Detection. Remote Sens. Environ. 2013, 139, 116–129. [Google Scholar] [CrossRef]
- Cusworth, D.H.; Duren, R.M.; Thorpe, A.K.; Pandey, S.; Maasakkers, J.D.; Aben, I.; Jervis, D.; Varon, D.J.; Jacob, D.J.; Randles, C.A.; et al. Multisatellite Imaging of a Gas Well Blowout Enables Quantification of Total Methane Emissions. Geophys. Res. Lett. 2021, 48, e2020GL090864. [Google Scholar] [CrossRef]
- Séférian, R.; Berthet, S.; Chevallier, M. Assessing the Decadal Predictability of Land and Ocean Carbon Uptake. Geophys. Res. Lett. 2018, 45, 2455–2466. [Google Scholar] [CrossRef]
- Heinze, C.; Meyer, S.; Goris, N.; Anderson, L.; Steinfeldt, R.; Chang, N.; Le Quéré, C.; Bakker, D.C.E. The Ocean Carbon Sink—Impacts, Vulnerabilities and Challenges. Earth Syst. Dynam. 2015, 6, 327–358. [Google Scholar] [CrossRef]
- Heinze, C.; Blenckner, T.; Martins, H.; Rusiecka, D.; Döscher, R.; Gehlen, M.; Gruber, N.; Holland, E.; Hov, Ø.; Joos, F.; et al. The Quiet Crossing of Ocean Tipping Points. Proc. Nat. Acad. Sci. USA 2021, 118, e2008478118. [Google Scholar] [CrossRef] [PubMed]
- Roberts, D.A.; Bradley, E.S.; Cheung, R.; Leifer, I.; Dennison, P.E.; Margolis, J.S. Mapping Methane Emissions from a Marine Geological Seep Source Using Imaging Spectrometry. Remote Sens. Environ. 2010, 114, 592–606. [Google Scholar] [CrossRef]
- Thorpe, A.K.; Frankenberg, C.; Aubrey, A.D.; Roberts, D.A.; Nottrott, A.A.; Rahn, T.A.; Sauer, J.A.; Dubey, M.K.; Costigan, K.R.; Arata, C.; et al. Mapping Methane Concentrations from a Controlled Release Experiment Using the next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG). Remote Sens. Environ. 2016, 179, 104–115. [Google Scholar] [CrossRef]
- Thorpe, A.K.; Frankenberg, C.; Thompson, D.R.; Duren, R.M.; Aubrey, A.D.; Bue, B.D.; Green, R.O.; Gerilowski, K.; Krings, T.; Borchardt, J.; et al. Airborne DOAS Retrievals of Methane, Carbon Dioxide, and Water Vapor Concentrations at High Spatial Resolution: Application to AVIRIS-NG. Atmos. Meas. Tech. 2017, 10, 3833–3850. [Google Scholar] [CrossRef]
- Ayasse, A.K.; Thorpe, A.K.; Roberts, D.A.; Funk, C.C.; Dennison, P.E.; Frankenberg, C.; Steffke, A.; Aubrey, A.D. Evaluating the Effects of Surface Properties on Methane Retrievals Using a Synthetic Airborne Visible/Infrared Imaging Spectrometer next Generation (AVIRIS-NG) Image. Remote Sens. Environ. 2018, 215, 386–397. [Google Scholar] [CrossRef]
- Rohrschneider, R.R.; Wofsy, S.; Franklin, J.E.; Benmergui, J.; Soto, J.; Davis, S.B. The MethaneSAT Mission. In Proceedings of the 35th Small Satellite Conference, Logan, UT, USA, 7–12 August 2021. [Google Scholar]
- Jacob, D.J.; Varon, D.J.; Cusworth, D.H.; Dennison, P.E.; Frankenberg, C.; Gautam, R.; Guanter, L.; Kelley, J.; McKeever, J.; Ott, L.E.; et al. Quantifying Methane Emissions from the Global Scale Down to Point Sources Using Satellite Observations of Atmospheric Methane. Atmos. Chem. Phys. 2022, 22, 9617–9646. [Google Scholar] [CrossRef]
- Kellogg, K.; Rosen, P.; Barela, P.; Sagi, R.; Kumar, R.; Hoffman, P.; Edelstein, W.; Shen, Y.; Sreekantha, C.V.; Bhan, R.; et al. NASA-ISRO Synthetic Aperture Radar (NISAR) Mission. IEEE Aero Conf. 2020, 1–21. [Google Scholar] [CrossRef]
- Rosen, P.A.; Kim, Y.; Kumar, R.; Misra, T.; Bhan, R.; Sagi, V.R. Global Persistent SAR Sampling with the NASA-ISRO SAR (NISAR) Mission. In Proceedings of the 2017 IEEE Radar Conference, Seattle, DC, USA, 8–12 May 2017. [Google Scholar]
- Albinet, C.; Whitehurst, A.S.; Jewell, L.A.; Bugbee, K.; Laur, H.; Murphy, K.J.; Frommknecht, B.; Scipal, K.; Costa, G.; Jai, B.; et al. A Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP) for Biomass, NISAR, and GEDI. Surv. Geophys. 2019, 40, 1017–1027. [Google Scholar] [CrossRef]
- Sedehi, M.; Carbone, A.; Imbembo, E.; Heliere, F.; Rommen, B.; Fehringer, M.; Scipal, K.; Leanza, A.; Simon, T.; Willemsen, P. Biomass—A Fully Polarimetric P-Band SAR ESA Mission. In Proceedings of the 13th Europ Conf Synthetic Aperture Radar, online, 29 March–1 April 2021. [Google Scholar]
- Moreno, J.F. The Fluorescence Explorer (FLEX) Mission: From Spectral Measurements to High-Level Science Products. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; IEEE: Brussels, Belgium, 2021; pp. 115–118. [Google Scholar] [CrossRef]
- Drusch, M.; Moreno, J.; Del Bello, U.; Franco, R.; Goulas, Y.; Huth, A.; Kraft, S.; Middleton, E.M.; Miglietta, F.; Mohammed, G.; et al. The FLuorescence EXplorer Mission Concept-ESA’s Earth Explorer 8. IEEE Trans. Geosci Remote Sens. 2017, 55, 1273–1284. [Google Scholar] [CrossRef]
- Mohammed, G.H.; Colombo, R.; Middleton, E.M.; Rascher, U.; van der Tol, C.; Nedbal, L.; Goulas, Y.; Pérez-Priego, O.; Damm, A.; Meroni, M.; et al. Remote Sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in Vegetation: 50 years of Progress. Remote Sens. Environ. 2019, 231, 111177. [Google Scholar] [CrossRef]
- Naethe, P.; Julitta, T.; Chang, C.Y.Y.; Burkart, A.; Migliavacca, M.; Guanter, L.; Rascher, U. A Precise Method Unaffected by Atmospheric Reabsorption for Ground-Based Retrieval of Red and Far-Red Sun-Induced Chlorophyll Fluorescence. Agric. Meteorol 2022, 325, 109152. [Google Scholar] [CrossRef]
- Joiner, J.; Yoshida, Y.; Vasilkov, A.P.; Schaefer, K.; Jung, M.; Guanter, L.; Zhang, Y.; Garrity, S.; Middleton, E.M.; Huemmrich, K.F.; et al. The Seasonal Cycle of Satellite Chlorophyll Fluorescence Observations and Its Relationship to Vegetation Phenology and Ecosystem Atmosphere Carbon Exchange. Remote Sens. Environ. 2014, 152, 375–391. [Google Scholar] [CrossRef]
- Joiner, J.; Yoshida, Y.; Anderson, M.; Holmes, T.; Hain, C.; Reichle, R.; Koster, R.; Middleton, E.; Zeng, F.W. Global Relationships among Traditional Reflectance Vegetation Indices (NDVI and NDII), Evapotranspiration (ET), and Soil Moisture Variability on Weekly Timescales. Remote Sens. Environ. 2018, 219, 339–352. [Google Scholar] [CrossRef] [PubMed]
- Kraft, S.; Del Bello, U.; Bouvet, M.; Drusch, M.; Moreno, J. FLEX: ESA’s Earth Explorer 8 Candidate Mission. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 7125–7128. [Google Scholar]
- Coppo, P.; Taiti, A.; Pettinato, L.; Francois, M.; Taccola, M.; Drusch, M. Fluorescence Imaging Spectrometer (FLORIS) for ESA FLEX Mission. Remote Sens. 2017, 9, 649. [Google Scholar] [CrossRef]
- Gamon, A.; Peñuelas, J.; Field, C.B. A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency. Remote Sens. Environ. 1992, 41, 35–44. [Google Scholar] [CrossRef]
- Middleton, E.M.; Huemmrich, K.F.; Landis, D.R.; Black, T.A.; Barr, A.G.; McCaughey, J.H. Photosynthetic Efficiency of Northern Forest Ecosystems Using a MODIS-Derived Photochemical Reflectance Index (PRI). Remote Sens. Environ. 2016, 187, 345–366. [Google Scholar] [CrossRef]
- Suess, M.; De Whitte, E.; Rommen, B. Earth Explorer 10 Candidate Mission Harmony. In Proceedings of the EUSAR 2022 14th Europ Conf Synthetic Aperture Radar, Leipzig, Germany, 25 July 2022; pp. 150–153. [Google Scholar]
- Ciani, D.; Sabatini, M.; Buongiorno Nardelli, B.; Lopez Dekker, P.; Rommen, B.; Wethey, D.S.; Yang, C.; Liberti, G.L. Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies. Remote Sens. 2023, 15, 1163. [Google Scholar] [CrossRef]
- Lee, C.M.; Glen, N.F.; Stavros, E.N.; Luvll, J.; Yuen, K.; Hain, C.; Schollaert Uz, S. Systematic integration of application into the surface biology and geology (SBG) earth mission architecture study. JGR Biogeosci. 2022, 127, e2021JG006720. [Google Scholar] [CrossRef]
- Schneider, F.D.; Morsdorf, F.; Schmid, B.; Petchey, O.L.; Hueni, A.; Schimel, D.S.; Schaepman, M.E. Mapping Functional Diversity from Remotely Sensed Morphological and Physiological Forest Traits. Nat. Commun. 2017, 8, 1441. [Google Scholar] [CrossRef]
- Cavender-Bares, J.; Gamon, J.A.; Townsend, P.A. Remote Sensing of Plant Biodiversity; SpringerOpen: New York, NY, USA, 2020. [Google Scholar]
- Schimel, D.S.; Poulter, B. The Earth in Living Color-NASA’s Surface Biology and Geology Designated Observable. In Proceedings of the 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 5–12 March 2022. [Google Scholar]
- Green, R.O.; Sen, A.; Pearson, J.C.; Mourlouis, P.; Patel, S.; Sullivan, P.; Werne, T.; Brenner, M.; McKinley, I.; Liggett, E.; et al. Surface Biology and Geology (SBG) Visible to Short Wavelength Infrared (VSWIR) Wide Swath Instrument Concept. In Proceedings of the 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 5–12 March 2022; pp. 1–10. [Google Scholar]
- Shaw, L.A.; Geier, S.; McKinley, I.M.; Bernas, M.A.; Gharakhanian, M.; Dergevorkian, A.; Eastwood, M.L.; Mouroulis, P.; Green, R.O. Design, alignment, and laboratory calibration of the Compact Wide Swath Imaging Spectrometer II (CWIS-II). In Imaging Spectrometry XXV: Applications, Sensors, and Processing, Proceedings of the SPIE Optical Engineering Applications, San Diego, CA, USA, 21–26 August 2022; SPIE: Bellingham, DC, USA, 2022; Volume 12235, p. 1223502. [Google Scholar]
- Basilio, R.R.; Hook, S.J.; Zoffoli, S.; Buongiorno, M.F. Surface Biology and Geology (SBG) Thermal Infrared (TIR) Free-Flyer Concept. In Proceedings of the 2022 IEEE Aerospace Conference (AERO), Big Sky, MT, USA, 5–12 March 2022; pp. 1–9. [Google Scholar]
- Thompson, J.O.; Williams, D.B.; Ramsey, M.S. The Expectations and Prospects for Quantitative Volcanology in the Upcoming Surface Biology and Geology (SBG) Era. Earth Space Sci. 2023, 10, e2022EA002817. [Google Scholar] [CrossRef]
- Shreevastava, A.; Hulley, G.; Thompson, J. Algorithms for Detecting Sub-Pixel Elevated Temperature Features for the NASA Surface Biology and Geology (SBG) Designated Observable. J. Geophys. Res. Biogeosci. 2023, 128, e2022JG007370. [Google Scholar] [CrossRef]
- Kornfeld, R.P.; Arnold, B.W.; Gross, M.A.; Dahya, N.T.; Klipstein, W.M.; Gath, P.F.; Bettadpur, S. GRACE-FO: The Gravity Recovery and Climate Experiment Follow-On Mission. J. Spacecr. Rocket. 2019, 56, 931–951. [Google Scholar] [CrossRef]
- Wiese, D.N.; Bienstock, B.; Blackwood, C.; Chrone, J.; Loomis, B.D.; Sauber, J.; Rodell, M.; Baize, R.; Bearden, D.; Case, K.; et al. The Mass Change Designated Observable Study: Overview and Results. Earth Space Sci 2022, 9, e2022EA002311. [Google Scholar] [CrossRef]
- Durand, Y.; Bazalgette Courrèges-Lacoste, G.; Pachot, C.; Pasquet, A.; Chanumolu, A.; Meijer, Y.; Fernandez, V.; Lesschaeve, S.; Spilling, D.; Dussaux, A.; et al. Copernicus CO2M Mission for Monitoring Anthropogenic Carbon Dioxide Emissions from Space: Payload Status. In Proceedings of the Sensors, Systems, and Next-Generation Satellites XXVI, Dubrovnik, Croatia, 28 October 2022; p. 8. [Google Scholar]
- Kuhlmann, G.; Broquet, G.G.; Marshall, J.; Clément, V.; Löscher, A.; Meijer, Y.; Brunner, D. Detectability of CO2 Emission Plumes of Cities and Power Plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) Mission. Atmos. Meas. Tech. 2019, 12, 6695–6719. [Google Scholar] [CrossRef]
- Nieke, J.; Despoisse, L.; Gabriele, A.; Weber, H.; Strese, H.; Ghasemi, N.; Gascon, F.; Alonso, K.; Boccia, V.; Tsonevska, B.; et al. The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME): An Overview of Its Mission, System and Planning Status. In Proceedings of the Sensors, Systems, and Next-Generation Satellites XXVII, Amsterdam, The Netherlands, 22 November 2023; Babu, S.R., Heliere, A., Kimura, T., Eds.; SPIE: Bellingham, DC, USA, 2023; Volume 12729. [Google Scholar]
- Celesti, M.; Rast, M.; Adams, J.; Boccia, V.; Gascon, F.; Isola, C.; Nieke, J. The Copernicus Hyperspectral Imaging Mission for the Environment (Chime): Status and Planning. In Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; pp. 5011–5014. [Google Scholar]
- Kilic, L.; Prigent, C.; Aires, F.; Heygster, G.; Pellet, V.; Jimenez, C. Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer. Remote Sens. 2020, 12, 1060. [Google Scholar] [CrossRef]
- Lavergne, T.; Piñol Solé, M.; Down, E.; Donlon, C. Towards a Swath-to-Swath Sea-Ice Drift Product for the Copernicus Imaging Microwave Radiometer Mission. Cryos 2021, 15, 3681–3698. [Google Scholar] [CrossRef]
- Jiménez, C.; Tenerelli, J.; Prigent, C.; Kilic, L.; Lavergne, T.; Skarpalezos, S.; Høyer, J.L.; Reul, N.; Donlon, C. Ocean and Sea Ice Retrievals from an End-To-End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements. J. Geophys. Res. Ocean. 2021, 126, e2021JC017610. [Google Scholar] [CrossRef]
- Turpe, K.R.; Casey, K.A.; Crawford, C.J.; Guild, L.S.; Kieffer, H.; Lin, G.; Kokaly, R.; Shrestha, A.K.; Anderson, C.; Ramaseri Chandra, S.N.; et al. Calibration and Validation for the Surface Biology and Geology (SBG) Mission Concept: Recommendations for a Multi-Sensor System for Imaging Spectroscopy and Thermal Imagery. JBG Biogeosci. 2023, 128, e2023jg007452. [Google Scholar] [CrossRef]
- Tymstra, C.; Stocks, B.J.; Cai, X.; Flannigan, M.D. Wildfire Management in Canada: Review, Challenges and Opportunities. Prog. Disaster Sci. 2020, 5, 100045. [Google Scholar] [CrossRef]
- Velasco Hererra, V.M.; Soon, W.; Pérez-Moreno, C.; Velasco Herrera, G.; Martell-Dubois, R.; Rosique-de la Cruz, L.; Fedorov, V.M.; Cerdeira-Estrada, S.; Bongelli, E.; Zúñiga, E. Past and Future of Wildfires in Northern Hemisphere’s Boreal Forests. Ecol. Manag. 2022, 504, 119859. [Google Scholar] [CrossRef]
- Crowley, M.A.; Stockdale, C.A.; Johnston, J.M.; Wulder, M.A.; Liu, T.; McCarty, J.L.; Rieb, J.T.; Cardille, J.A.; White, J.C. Towards a Whole-System Framework for Wildfire Monitoring Using Earth Observations. Glob. Chang. Biol. 2023, 29, 1423–1436. [Google Scholar] [CrossRef] [PubMed]
- McFayden, C.B.; Hope, E.S.; Boychuk, D.; Johnston, L.M.; Richardson, A.; Coyle, M.; Sloane, M.; Cantin, A.S.; Johnston, J.M.; Lynham, T.J. Canadian Fire Management Agency Readiness for WildFireSat: Assessment and Strategies for Enhanced Preparedness. Fire 2023, 6, 73. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat Satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Wu, Z.; Snyder, G.; Vadnais, C.; Arora, R.; Babcock, M.; Stensaas, G.; Doucette, P.; Newman, T. User Needs for Future Landsat Missions. Remote Sens. Environ. 2019, 231, 111214. [Google Scholar] [CrossRef]
- Gillespie, A.; Rokugawa, S.; Matsunaga, T.; Cothern, J.S.; Hook, S.; Kahle, A.B. A Temperature and Emissivity Separation Algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Images. IEEE Trans. Geosci. Remote Sens. Symp. 1998, 36, 1113–1126. [Google Scholar] [CrossRef]
- Gustafson, W.T.; Gillespie, A.R.; Yamada, G.J. Revisions to the ASTER Temperature/Emissivity Separation Algorithm. In Proceedings of the Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 25–29 September 2006; pp. 770–775. [Google Scholar]
- Jones, M.W.; Peters, G.; Gasser, T.; Andrew, R.M.; Schwingshackl, C.; Gütschow, J.; Houghton, R.A.; Friedlingstein, P.; Pongratz, J.; Le Quere, C. National contributions to climate change due to historical emissions of carbon dioxide, methane, and nitrous oxide since 1950. Sci. Data 2023, 10, 155. [Google Scholar] [CrossRef]
- Ritchie, H.; Rosado, P.; Roser, M. Greenhouse Gas Emissions. Published Online at OurWorldInData.org. Available online: https://ourworldindata.org/greenhouse-gas-emissions (accessed on 15 May 2024). 2020 updated January 2024. Original data from Jones et al., 2024 with major processing by Our World in Data, a project of the Global Change Data Lab, a non-profit organization based in Oxford, England and is a registered charity in the United Kingdom.
Band | W | Ka | K | Ku | X | C | S | L | P |
---|---|---|---|---|---|---|---|---|---|
Frequency, GHz | 75–100 | 40–100 | 18–26.5 | 12.5–18 | 8.0–12.5 | 3.90–8.0 | 1.55–3.90 | 0.39–1.55 | 0.216–0.45 |
Wavelength, cm | 0.27–0.40 | 0.30–0.75 | 1.67–1.11 | 1.7–2.4 | 2.4–3.75 | 3.75–7.5 | 7.5–5 | 15–30 | 30–130 |
Satellite/ Instrument | Launch Date(s) | Orbit Type | Altitude, km | Swath, km | Equatorial Crossing Time | UV 350–400 nm; VNIR 400–1500 nm; GSD km | SWIR, 1500–2500 nm, GSD km | Mid-IR 3.0–6.0 μm, TIR, 6–15.0 μm, GSD km |
---|---|---|---|---|---|---|---|---|
NOAA TIROS-N AVHRR-1 | 1978–1981 | polar, LEO Sun synchronous | 850 | 2900 | 14:30A | 615 nm, 912 nm @ 1.1 km | -- | 3.74 μm 11.0 μm @ 1.1 km |
NOAA 6, 8, 10 AVHRR | 1979–1987 1983–1985 1986–2001 | polar, LEO Sun synchronous | 840, 820 810 | ~2700 | 14:30A 07:30D 07:30D | 615, 912 nm @1.1 km | -- | 3.74 μm, 11.0 μm @1.1 km |
NOAA 7, 9, 11, 12, 14 AVHRR/2 | 1986–2001 1988–2004 1991–2007 1994–2007 | polar, LEO Sun synchronous | 860, 850 843 804, 844 | 3000 | 14:30A 14:30A 04:10A 05:10A 09:30A | 615, 912 nm @1.1 km, | -- | 3.74, 10.8, 12.0 μm @1.1 km |
NOAA 15,16, 17, 18,19 AVHRR-3 | 1998–2024 2000–2014 2005–2024 2009–2024 | polar, LEO Sun synchronous | 813 810 854 870 | 3000 | 07:29D 09:03D 10:34D 08:22D | 630, 862 nm @1.1 km | 1.61 μm @1.1km | 3.74, 10.80, 12.00 μm @1.1 km |
METOP-A, B, C, AVHRR-3 | 2006–2021 2012–2024 2018–2027 | polar, LEO Sun synchronous | 827 827 817 | 3000 | 07:50D 09:31D 09:31D | 630, 862 nm @1.1 km | 1.61 μm @1.1 km | 3.74, 10.8, 12.0 μm @1.1 km |
Satellite Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency, Days | Equatorial Crossing Time | UV 240–400 nm, VNIR, 400–1500 nm, SWIR 1500–2500 nm; GSD m | MidIR 3.0–6.0 TIR 6.0–15.0 μm, GSD | Radar and Microwave |
---|---|---|---|---|---|---|---|---|---|
ATSR/ERS 1 ATSAR-2/ERS-2 | 1991–2000, 1995–2008 | Polar, LEO Sun synchronous | 785 | 500 | 3 d TIR & 6 d SWIR, 3 d NIR & 6 d VIS | 10:30D | ATSR-2: 550, 659, 865 nm; ATSR-1 &-2: 1610 nm @ 1 km GSD | ATSR-1 & -2: 3.70, 10.85, 12.00 μm @ 1 km GSD | ATSR-1, microwave sounder 23.8, 35.6 GHz @ 1 km GSD |
GOME/ ERS-2 | 1995–2008 | polar LEO Sun synchronous | 785 | 120 or 960 | 3 d NIR & 6 d VIS | 10:30D | [240–295, 290–405, 400–605, 590–790 (1024 channels)], [292–402, 402–597, 597–790 nm (pol, 1 channel)] @ 40 × 40 km2 @ larger swath, or 40 × 320 km2 @ smaller swath | -- | -- |
CHRIS/PROBA-1 | 2001–2022, 2001–2024 | polar LEO Sun synchronous | 615 | Mode 1–4 13 km, Mode 5 6.5 km | 7 d | 07:30D | 400–1030 nm, 150 channels, selectable Mode 1 up to 63 channels over 5 angles (−55, −36, 0, 36, 55) @36 m GSD. Mode 2–4, 18 bands @ 18 m GSD, Mode 5, 37 bands @ 18 m GSD. | -- | Single frequency Ku band 13.575 GHz and bandwidth 350 GHZ. @15 km GSD in SAR model, along track resolution is 250 m. |
Satellite /Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency Days | Equatorial Crossing Time | UV, VNIR, 400–1500 nm; GSD, m | SWIR, 1500 2500 nm, GSD m | Thermal IR 5.5–15 μm |
---|---|---|---|---|---|---|---|---|---|
MERIS (Medium Resolution Imaging Spectrometer) ENVISAT | 2002– 2012 | Polar, LEO Sun synchronous | 774 | 1150 | 1–3 d | 10:00D | 412.5, 442.5, 490, 510, 560, 620, 685, 681.25, 708.75, 753.75, 760.625, 778.75, 865, 885, 900 nm @300 m basic GSD, 1200 m reduced resolution | -- | -- |
SCanning Imaging Absorption spectroMeter for Atmosphic CartograpHY (SCIAMACHY)/ENVISAT | 2002– 2012 | Polar, LEO Sun synchronous | 774 | limb scanning, 500 km horizontal, 3 km vertical; Nadir scanning 960 | 3 d | 10:00D | [214–334, 300–412, 383–628, 595–812, 773–1063, 971–1773 nm] with 1024 channels each. [310–2380 nm] with 7 bands | 1934–2044, 2259–2386 nm, with 1024 bands each | -- |
JAXA Greenhouse Gases Observing Satellite (GOSAT aka IBUKI) | 2009– 2024 | Polar, LEO Sun synchronous | 666 | 1150 | 1–3 d | 10:00D | TANSO-CAI 380, 674, 870 nm @ 0.5 km; TANSO-FTS, 775–757 nm @ 10.5 km | TANSO-CAI 1600 nm 15 1.5 km; Tanso-FTS: 1720–1560 nm @ 10.5 km | TANSO—FTS, 14.28–5.55 μm, @ 10.5 km |
JAXA GOSAT-2 (IBUKI-2) | 2018– 2024 nadir, limb, and solar/lunar occultation | Polar, LEO Sun synchronous | 613 | 790 | 3 d | 06:00D | TANSO/CAI-2, 343 nm Fore, 380 nm Aft, 443 nm Fore, 550 nm Aft, 674 nm Fore & Aft, 869 nm Fore & Aft, 0.5 km GSD; TANSO/FTS/2 772–753 nm @10.5 km | TANSO/CAI-2 1630 nm Fore & Aft @ 1.5 km; TANSO-FTS/2 1560–1690 nm, 1920–2380 nm @ 10.5 km | TANSO-FTS/2 5.6–8.4 μm and 8.4–14.3 μm @ 10.5 km |
Landsat-7 ETM+ | 1999–2022 | Polar, LEO Sun synchronous | 705 | 185 | 16 d | 10:00 | 500–900 nm PAN @15 m GSD; 480, 560, 660, 830 nm @ 30 m GSD | 1650, 2200 nm, 30 m GSD | 11.45 μm @ 60 m GSD |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude/ Swath km | Repeat Frequency, Days | Equatorial Crossing Time | UV, VNIR, 400–1500 nm; GSD m | SWIR, 1500–2500 nm, GSD m | Mid IR 3.0–6.0 μm, Thermal IR, 11–15 μm |
---|---|---|---|---|---|---|---|---|
NMP Hyperion/EO-1 | 2001– 2017 | Polar, LEO Sun synchronous | 691/7.7 | yearly | 9:45D | 357–1000, 900–1600 nm @10 nm/band = 120 bands @ 30 m GSD | 1600–2576 nm @ 10 nm/band = 95 bands @30 m GSD | -- |
NMP ALI/EO-1 | 2001– 2007 | Polar, LEO Sun synchronous | 691/37 | 80 d | 09:45D | 480–690 PAN @ 10 m GSD, 443, 482, 565, 660, 790, 867, 1250 nm @ 30 m GSD | 1650, 2215 nm @ 30 m GSD | -- |
MODIS/Terra & Aqua | 2000– 2027 | Polar, LEO Sun synchronous | 705/2230 | 1, 2 d | 10:30D 10:30A | 645, 858 nm @ 250 m GSD, 469, 555, 1240 nm @ 500 m GSD, 412, 443, 488, 531, 551, 667, 678, 748, 870, 905, 936, 940, 1375 nm @ 1 km GSD | -- | 3.750, 3.959, 4.050, 4.515, 6.715 μm @ 1 km GSD; 11.030, 12.020, 13.335, 13.635, 13.935, 14.235 μm @ 1l, GSD |
CERES/Terra & Aqua | 2000– 2022 | Polar, LEO Sun synchronous | 705/60 | 16 d | 10:30D | 560, 660, 810 nm @ 15 m GSD, | 2165, 2205, 22,690, 2339, 2395 nm @ 30 m GSD | 8.30, 8.65, 9.10, 10, 60, 11.30 μm @ 90 m GSD |
Satellite /Instrument | Launch Date | Orbit Type | Altitude/ Swath km | Repeat Frequency, Days | Equatorial Crossing Time | UV, VNIR, 400–1500 nm; GSD m | SWIR 1500–2500 nm, GSD m | Mid IR 3.0–6.0 μm, Thermal IR, 8–12 μm |
---|---|---|---|---|---|---|---|---|
ASTER/Terra | 2000–2022 | Polar, LEO Sun synchronous | 705/60 | 16 d | 10:30D | 3 bands, 560, 660, 810 nm @ 15 m GSD, | 4 bands, 2165, 2205, 2339, 2395 nm @ 30 m GSD | 5 bands, 8.30, 8.65, 9.10, 10,60, 11.30 μm @ 90 m GSD |
MISR/Terra | 2000–2027 | Polar, LEO Sun synchronous | 705/380 | 9 d, daylight | 10:30D | 4 bands, 446.4, 557.5, 671.7, 866.4 nm, @ 9 view angles ±26.1°, ±45.6°, ±60.0°, ±70.5° nadir @ 250 m GSD off-nadir @ 275 m GSD | -- | -- |
AIRS/Aqua | 2002–2026 | Polar, LEO Sun synchronous | 705/1650 | daily | 10:30A | 4 bands, 425, 630, 715, 815 nm @ 2.3 km GSD | -- | 3 bands, 4.175, 7.21, 12.1 μm @13.5 km GSD |
TES-Nadir, TES- limb on Aura | 2004–2025 | Polar, LEO Sun synchronous | 705/885 (nadir), effective resolution 300 (limb) | nadir 16 d, limb 3 d | 13:40A | -- | -- | 4 bands of 43,750 channels nadir, 4 bands 162, 162 channels limb 11.11–15.38, 8.70–12.20, 5.13–9.09, 3.28–5.26 μm @ 0.53 × 0.53 km nadir, @ 2.3 km limb, lowest altitude |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency, Days | Equatorial Crossing Time | Radar and Microwave, Type(s), Frequency(s), GSD, m |
---|---|---|---|---|---|---|---|
Gravity Recovery and Climate Experiment (GRACE) NASA/DLR | 2002–2017 | 89° drifting orbit | 485 | -- | -- | varies | High Accuracy Inter-Satellite Ranging System, HAIRS, K-band 24 GHz ~1 cm and Ka-band 32 GHz ~9 mm. SuperSTAR Accelerometers. Blackjack GPS system. |
Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) NASA/DLR | 2018– 2026 | 89° drifting orbit | 490 | -- | gridded geolocated monthly, and time averaged | varies | High Accuracy Inter-satellite Ranging System, HAIRS, K-band 24 GHz ~1 cm and Ka-band 32 GHz ~9 mm. SuperSTAR Accelerometers. Tri GPS (GPS, Galileo, and GLONASS) |
Aquarius/SAC-D, Joint NASA/Argentina, ocean salinity | 2011– 2015 | Polar, LEO Sun synchronous | 657 | 390 | 7d | 18:00 on ascending track | 3 L-band radiometers and 1 L-band scatterometer, operating at 1.412 and 1.2 GHz, respectively, @ 150 km |
NASA Soil Moisture Active/Passive (SMAP) | 2015– 2026 | Polar, LEO Sun synchronous | 685 | 1000 | 8 d repeating ground track | 06:00D | 1.41 GHz, L-band. microwave radiometer @ 40 km GSD, co-aligned with SAR 1.26 GHZ full Pol HH, VV, HV, VH, @ 30 km unprocessed, 3 m km processed; 6 m antenna |
Surface Water and Ocean Topography (SWOT) CNES and NASA | 2022– 2026 | 78° Drifting orbit | 891 | 120 | 90% globe w/2x sampling every 21 d | varies | KaRIN is Ka (35.5 GHz) radar altimeter, w/2 antennas @ 50 m horizontal GSD on land, @1km on ocean. Microwave altimeter provides water vapor correction in 3 frequencies at 18.7, 23.8, 34.6 GHz. |
Global Precipitation Measurement Constellation, NASA and JAXA. Core Observatory | 2014– 2030 | 62° Drifting orbit | 398 km; raised to 442 km on 7–8 November 2023 | 245 [email protected] GHz and 125 km @35.5 GHz | Near global 5 days/latitudes > ±65° not covered; FMI near global 2 days, latitudes > ±70° not covered | varies | Dual frequency Precipitation Radar (DPR) Ku band 13.6 GHz and Ka band 35.55 GHz; GPM Microwave Imager (GMI) 13 bands 10.65, 18.7, 23.8, 36.5, 89, 166.5, 183.31 ± 7, 183.31 ± 3 GHz with vertical polarization; 5 bands 10.65, 18.7, 36.5, 89.0, 166.5 GHz with horizontal polarization. |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency, Days | Equatorial Crossing Time | UV 270–400 nm, VNIR 400–1500 nm, SWIR 1500–2500 nm | Radar and Microwave, Type(s), Frequency(s), GSD, m |
---|---|---|---|---|---|---|---|---|
NASA GLAS/ ICESat (LiDAR) | 2003–2010 | 94° drifting | 600 | -- | 91 d | varies | 2 bands 532, 1064 nm Lidar @ 66 m GSD samples at 170 m intervals and 10 cm vertical, surface to cloud top at 200 m. | -- |
NASA ATLAS/ ICESAT-2 (LIDAR) | 2018–2026 | 94° drifting | 481–495 | -- | 183d | varies | 1 band, 1064 nm lidar @ 66 m GSD horizontal samples at 170 m intervals and 10 cm vertical surface to 200 m cloud top | -- |
Orbiting Carbon Observatory-2 (OCO-2) | 2014–2026 | Polar, LEO Sun synchronous | 705 | x-track 10 km, 3 pointing modes | Global, 1 month | 13:30A | -- | 3 bands with 1024 channels, 1594–1619 nm with 0.08 nm resolution, 2024–2082 nm with 0.10 nm resolution @1.29 km X-track × 2.25 km along-track |
Sentinel 5 Precursor (5P); Tropospheric Monitoring Instrument (TROPOMI) (Trails VIIRS on Suomi NPP by 3.5 min.) | 2017–2025; free flyer | Polar, LEO Sun synchronous | 824 | 2600 | <1 d | 13:30A | TROPOMI 5 Hyperspectral regions UV1270–300 nm, UV2 300–400 nm with 0.39 nm resolution, VIS 400–500 nm with 0.58 nm resolution, NIR 700–775 nm with 1.03 nm resolution, SWIR3 2305–2385 nm with 2.01 nm resolution, 7 km GSD, relaxed to 50 km for wavelengths <300 nm | -- |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency Days | Equatorial Crossing Time | UV, VNIR, 400–1500 nm; GSD m | Radar and Microwave, Band Type(s), Frequency(s), GSD, m |
---|---|---|---|---|---|---|---|---|
ESA Earth Explorer #1 Gravity Field and Stead-State Ocean Circulation (GOCE) | 2009–2023 | Polar, LEO Sun synchronous | 230 | -- | -- | 6:00A | -- | 3-AXIS Electrostatic Gravity Gradiometer (EGG) with 6 accelerometers, Laser Retro-Reflector (LRR), Lagrange receiver Satellite-to Satellite Tracking System (SSTI), capable of tracking 12 GPS satellites for accurate positioning. |
ESA Earth Explorer # 5 Aeolius with ALADIN (Atmsopheric Laser Doppler Instrument) | 2018–2023 | Polar, LEO Sun synchronous | 320 | 87 | weekly | 06:00D | Aladin, 325 nm laser transmitter looking 35° off nadir. Pulsed laser source 50 Hz averaging over 87 km along track. High spectral resolution lidar (HSRL) @15 m GSD. 2 detectors discriminate Mei and Rayleigh winds | -- |
ESA Earth Explorer # 2, Soil Moisture and Ocean Salinity (SMOS) | 2009, 2025 | polar, LEO, sun-synchronous | 758 | 1050 | global soil moisture 3d; salinity monthly | 06:00D | -- | Microwave Imaging Radiometers with Aperture Synthesis (MIRAS), passive 2-D interferometric polarimetric radiometer; L-band SAR, w/several polarimetric modes; 1.413 GHz; 50 km GSD; can be degraded for salinity to 15 km grid |
ESA Earth Explorer # 3, SIRAL/ Cryosat-2 | 2010–2028 | LEO drifting, 92° | 717 | -- | 369d, binned latitudes, monthly | varies | -- | SIRAL has a 13.575 GHZ frequency, 350 MHz bandwidth, and a pulse repetition frequency dependent on the operating mode SIRAL has along track resolution @15 km GSD, in SAR mode alongtrack resolution is250 m @ 0.45 km GSD |
ESA Earth Explorer #4 SWARM constellation (A, B, C), Canada’s CASSIOPE integrated as SWARM E | A,B,C 2013–2025, 2018–2021 | A,C 67.75°, B 87.75°, SWARM E 80.97° | A,C @ 462, B 511, SWARM E 670 | SWARM A, B. C each had 7 identical instrument packages, Accelerometer (ACC), Absolute Scalar Magnetometer (ASM), Electric Field Instrument EFI), GPS Receiver (GPS), Laser Retro-Reflector (LRR),Star Tracker Set (STR), and Vector Field Magnetometer (VFM). CASSIOPE had 8 instruments: Imaging and Rapid-screening Ion Mass Spectrometer (IRS) Suprathermal Electron Imager (SEI), Fast Auroral Imager (DAI), Magnetic Field Instrument (MFI) Radio Receiver Instrument (RRI), GPS Attitude and POsitioning Experiment (GAP), Coherent Electromagnetic radiation (CER) |
Satellite /Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency Days | Equatorial Crossing Time | UV/VNIR, 400–1500 nm; GSD m | SWIR, GSD, m | MidWave IR 3.0–6.0 μm; Thermal IR 6.0–14.0 μm, GSD m |
---|---|---|---|---|---|---|---|---|---|
Landsat 8, 9 | 2013– 2024, 2021– 2031 | polar, LEO, sun-synchronous | 705 | 185 | 16 (8 d together) | 10:11D | OLI, 1 PAN 500–680 nm @ 15 m; 5 bands (444, 480, 560, 655, 865 nm) @30 m GSD | OLI 2 SWIR bands 1610, 2200 nm @ 30 m GSD | TIRS 2 bands at 10.895, 12.519 μm @ 100 m GSD |
Visible Infrared Imaging Radiometer Suite (VIIRS), on NASA’s Suomi NPP | 2012– 2028 Extended | polar, LEO, sun-synchronous | 833 | 3000 | 1, 8, 16 d | 13:25A | VIIRS I-Bands: 1 day/night band 500–900 nm @ 375 m GSD; 2 bands at 640, 865 nm @ 375 m GSD; 9 bands, 412, 445, 488, 555, 672, 746, 865, 1240, 1378 nm @ 750 m GSD) | VIIRS, I-band, 1 at 1.61 μm @ 375 m; M-bands, 2 at 1.61, 2.250 μm @ 750 m | VIIRS I-bands, 2 at 3.74, 11.45 μm @ 375 m GSD, M-bands 5 at 3.70, 4.05, 8.55, 10.763, 12.013 μm @ 750 m GSD |
VIIRS on NOAA 20, 21 (previusly JPSS-1 & JPSS-2) | 2017– 2027, 2023– 2030 | polar, LEO, sun-synchronous | 834 | 3000 | 1, 8, 16 d | 13:25A | VIIRS I-Bands, 1 day/night band 500–900 nm @ 375 m GSD; 2 bands at 640, 865 nm @ 375 m GSD); M-bands, 9 at 412. 445, 488, 555, 672, 746, 965, 1240, 1378 nm @ 750 m GSD | VIIRS, I-band, 1 at 1.61 μm @ 375 m; M-bands, 2 at 1.61, 2.250 μm @ 750 m | VIIRS I-bands, 3 at 3.74, 11.45 μm @ 375 m GSD; M-bands, 5 at 3.70, 4.05, 8.55, 10.763, 12.013 μm @ 750 m GSD |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency, Days | Equatorial Crossing Time | VNIR, 400–1500 nm; GSD m; SWIR 1500–2500 nm, GSD m | MidIR 3.0–6.0 μm, GSD km, TIR 6-15.0 μm, GSD, km | Radar & Microwave, bands or Frequencies, GSD, km |
---|---|---|---|---|---|---|---|---|---|
Sentinel 1A, 1B, 1C, 1D Interferometric SAR | 1A 2014–2024, 1B 2016 2022 1C 2024–2031, 1D 2025–2032 | polar, LEO, sun synch-ronous | 693 | depends on mode 80, 250, 400 km | extra wide swath 8 d, 12 d each, 180° apart | 06:00D | -- | -- | 1 SAR C band at 5.405 GHz side looking 15°–45° off nadir, Dual polarization, SAR imager 4–80 m GSD mode dependent, interferometric SAR (nSAR), 20 km GSD. |
Sentinel 2A and 2B and Sentinel 2C and 2D | 2A 2015–2024, 2B 2017–2024, 2C 2024–2031, 2D 2028–2033 | polar, LEO, sun synch-ronous | 786 | 290 | 10 d each, together 5 d | 10:30D | Multi-spectral Instrument (MSI) 4 bands at 490, 560, 665, 842 nm @ 10 m; 4 bands at 705, 740, 783, 865 nm @ 20 m; 2 bands at 1610, 2190 nm @ 20 m GSD. Includes 3 calibration bands 443, 945, 1375, nm bands @ 60 m GSD | -- | -- |
Sentenel 3A and 3B, Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) and Sentinel-3 radar altimeter. | 3A 2016–2026, 3B 2018– 2028 | polar, LEO, sun synch-ronous | 814.5 | OLCI = 1270, off nadir view avoids sunglint; SLSTR nadir swath 1400 km all viewing; diurnal viewing @740 km | OLCI global 1 mo. for 30 km ave. spacing or 10d for 100 km ave. spacing. SLSTR global coverage monthly, or 10 d w/100 km ave. spacing. | 10:00D | OLCI, push-broom side-to-side telescopes. 2 bands 761.25, 767.5 nm at 2.5 nm, 1 band 764.275 nm at 3.75 nm; 9 bands 412.5, 442.5, 490, 510, 620, 665, 708.25, 885, 900 nm at 10 nm, @ 300 m; 2 bands 940, 1020 nm at 40 nm @300 m GSD SLSTR, 6 bands 555, 659, 865, 1375, 1610, 2250 nm @ 500 m GSD | SLSTR, 3 bands 3.74, 10.85, 12.02 μm @ 1 km | SRAL SAR altimeter dual frequency C-band ~5 cm and 5.4 GHz and a Ku-band ~2 cm at 13.58 GHz 20 km GSD. MicroWave Radiometer 2 channels K band ~0.8 cm and 23.8 GHZ; Ka band ~0.27 cm and 36.5 GHz; nadir viewing 20 km GSD. Includes a Precise Orbit Determination package. |
Sentinel 6A and 6B, Michael Freilich, Jason Continuity of Service (Jason-CS A and Jason-CS B) | 6A 2020–2027, 6B 2025–2032, 6C 2030–2036 | 66° drifting orbit | 1336 | 290 | 10d | -- | -- | -- | POSEIDON-4, 2-band SAR altimeter, Ku ~2 cm and 13.575 GHz and C ~5 cm and 5.31 GHz; w/12 m antenna. A 3-band Multi-frequency 18.7, 23.8 and 34 GHz microwave radiometer, approximately Ku-, K, Ka bands. Advanced Microwave Radiometer for Climate, (AMR-C), with High-Resolution Microwave Radiometer (HRMR) mm-wave channels at 90, 130, 160 GHz (λ = ~31, 6.1, 3.7 μm) |
Satellite Characteristics | Landsat 8/OLI-TRS | Sentinel 2A/MSI | Sentinel-2B/MSI | |
---|---|---|---|---|
11 February 2013 | 23 June 2015 | 7 March 2017 | ||
Equatorial Crossing Time, Descending orbit | 10:00 | 10:30 | 10:30 | |
Spatial Resolution, GSD | 30 m OLI/100 m TIRS | 10 m/20 m/60 m (for different bands) | ||
Swath/Field of View | 180 km/15° | 290 km/20.6° | ||
Spectral bands, Central Wavelength | Ultra blue | 443 nm | 443 nm (60 m) | |
Visible | 482, 561, 655 nm | 490 (10 m), 560 (10 m), 665 (20 m) | ||
Red edge | -- | 705 (20 m), 740 (20 m), 783 nm (20 m) | ||
NIR | 865 nm | 842 (10 m), 865 nm (20 m) | ||
SWIR | 1373 nm | 1375 nm (60 m) | ||
Water Vapor | -- | 945 nm (60 m) | ||
Thermal | 10.9, 12.0 μm | -- |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude | Swath, km | Repeat Frequency Days | Equatorial Crossing Time | UV/VNIR, 400–1500 nm, GSD, m | SWIR, 1500-2500 nm, GSD, m |
---|---|---|---|---|---|---|---|---|
Italian Space Agency (ASI) PRecursore IperSpettraie della Missione Applicativa (PRISMA) | 2019–2025 | polar, LEO, sun- synchro- nous | 615 | 30 | 29d | 10:30D | 66 bands 400–1010 nm at 10 nm bandwidth @ 30 m GSD | 171 bands 920–2505 nm at 10 nm bandwidth @ 30 m GSD |
German Space Agency (DLR) Environmental Monitoring and Analysis Program (EnMAP) | 2022–2026 | polar, LEO, sun- synchro- nous | 653 | 30 | 27d | 11:00D | 92 bands 420–1000 nm; 6.5 nm ave. bandwidth @30 m GSD | 48 bands SWIR 1: 900–1380 nm; 28 bands SWIR 2: 1480–1760 nm; 50 bands SWIR3: 1950–2450 nm, 10 nm ave. band-width @30 m GSD |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude km | Swath, km | Repeat Frequency, Days | VSWIR 400–2500 nm at 10 nm Resolution @ GSD | TIR 6–15.0 μm, GSD, m |
---|---|---|---|---|---|---|---|
DLR Earth Sensing Imaging Spectrometer (DESIS) | 2017– 2024 | 51.6° drifting orbit | 407 | 30 | 3 d spatial repeat, but 63 d for time of day repeat (TOD) | 240 VNIR (450–915 nm) with ~1.9 nm bandwidths, @ 30 m GSD | -- |
HISUI Hyperspectral imager and Multispectral imager; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japanese Space Agency (JAXA). | 2019– 2024 | 51.6° drifting orbit | 407 | 20 | 3 d spatial repeat, but 63d for TOD | 185 bands 400–250 nm, with 10.0–12.5 nm spectral resolution, IFOV is 20 m × 30 m | -- |
Earth Surface Mineral Dust Source Investigation (EMIT). NASA Earth Ventures Instrument (EVI-4) | 2022– 2026 | 51.6° drifting orbit | 407 | 75 | 3 d spatial repeat, but 63d for TOD | 286 bands 380–2500 nm at 7.4 nm sampling, @60 m GSD | -- |
NASA Orbiting Carbon Observatory (OCO3), ESSP-6 mission | 2019– 2029 | 51.6° drifting orbit | 407 | 4.5 × 4.5; Pointing 40 km both sides ISS ground track | 3 d spatial repeat, but 63d for TOD | -- | -- |
NASA ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) EVI-2 | 2018– 2026 | 51.6° drifting orbit | 407 | 384 | 4d repeat w/63d exact repeat for time of day | -- | 5 bands at 8.285 *, 8.785, 9.060 *, 10.552, 12.001 μm at 69 m × 38 m GSD * bands unavailable after 5 May 2019 |
NASA/U. Maryland, Global Ecosystem Dynamics Investigation (GEDI), EVI-2 | 2018– 2031, removed 3/2023–7/2024, returns until January 2030 | 51.6° drifting orbit | 407 | 7 | 3 d spatial repeat, but 63 d for TOD | 3 Nd:YAG lasers emitting at 1064 nm light, split into 7 beams, dithered to produce 14 ground track spot beams, 25 m footprints, spaced 500 m x-track and 60 m along track. Pointing strategy for targets. | -- |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude km | Swath, km | Repeat Frequency Days | Equatorial Crossing Time | Panchromatic Resolution, GSD m | VNIR, Resolution, 400–1500 nm, GSD m | Radar, Microwave, m GSD |
---|---|---|---|---|---|---|---|---|---|
Satellite pour l’Observation de la Terre (SPOT), 6,7 | 2012, 2014, 10 year life | Polar LEO sun synchro-nous | 679 | 60 | 26 d each, with pair 13 d | 10:00D | 1 BAND 450–745 nm @1.5 m GSD | 4 bands 485, 560, 660, 825 nm @ 2.0 m GSD | -- |
Pléiades 1A and 1B (CNES) | 2011–2024, 2012–2024 | Polar LEO sun synchronous | 694 | 20, at NADIR | 26d each, with pair 13 d | 10:00D | 1 band at 480–930 nm @ 50 cm GSD | 4 bands 490, 550, 660, 850 nm @ 2.0 m GSD | -- |
Pléiades NEO, 4 satellites at 90° (CNES) | 2020 (pair 2), 2022 (pair 2) | PolarLEO sun synchronous | 620 | 14, at NADIR | 4 w/6.5 day repeat | 10:30D | 1 band at 450–800 nm @ 30 cm GSD | 6 bands at 425, 485, 560, 655, 725, 825 nm @ 1.2 m GSD | -- |
TanDEM-X (DLR, data provided by Astrium/Infoterra) | 2010–2026 | Polar LEO sun synchronous | 505 | 10–100, mode dependent | 06:00D | -- | -- | x-band SAR (9.65 GHz) @ 1–16 m GSD mode dependent Multipolarization | |
PlanetScope Constellation of 200+ Dove Cubesats, 3-band sensors PS-2 and 4-band PS2.SD. | 2014- | Polar LEO sun synchronous | 475–525 | 24 × 8 km or 24 × 16 km scene based | daily, at Nadir, global | 9:30–11:30D | -- | Originally 3-band RGB satellites, now 4 band B, G, R, NIR, @ 3.0–4.1 m GSD | -- |
PlanetScope Constellation of ~195 SuperDove satellites. called SPB.SD imagers | 2018- | Polar LEO sun synchronous | 504 | 32.5 × 19.6 km scene based | ~daily at Nadir, global | 9:30–11:30D | -- | 8 bands, B (443, 490), G (531, 565), Y 610, R 665, Red- edge 705, NIR 865 @ 3.7 m GSD | -- |
PlanetScope Constellation of 21 SkySats | 2014–2025 | Polar LEO sun synchronous | 450 | Sats 1,2 8 km, Sats 3 15 are 5.8 km, Sats 16 21 are 20.5 km × 5.9, all at Nadir | 4–5 day individual revisit; Constellation ave. 6–7 images/day global | 10:30D (Sats 1–6, 14–16) 13:00A (Sats 1, 2, 8–13) | 1 pan 400–900 nm @ 0.5 m GSD | 4 VNIR bands at 482.5, 555, 650, 820 @ 2.0 m GSD | -- |
PlanetScope RapidEye-4; a 5-satellite constellation, originally launched in 2008 | 2008- Planet acquired in 2015, retired 2020 | Polar LEO sun synchronous | 630 | 77 | daily with pointing; 5.5 d at nadir over mid latitudes | 11:00 + 1 h D | -- | Multispectral Imager (MSI) 5 VNIR bands at 465, 555, 660, 710, 820, at 6.5 m GSD st Nadir, resampled to 5 m on ortho products | 1 pan 400–900 nm @ 0.5 m GSD |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude km | Swath, km | Acquisition Periods | VNIR, SWIR 400– 1500 nm, GSD, m | Mid IR 3.0–6.0 μm TIR, 6–15.0 μm, GSD, km |
---|---|---|---|---|---|---|---|
JAXA Himawari 8 and 9 3rd Generation, contains Advanced Himawari imager (HMI) and a data collection service. | 2014– 2030, 2016– 2030 | Japan, western Pacific, 140.7° | 35,786 | Full disk and regional | Multi modes, 2.5 min to 10 min full disk | Advanced Himawari Imager (AHI) 1 VIS Red at 645 nm @0.5 km, 3 VNIR 455, 510, 860 nm @ 1 km GSD, 2 SWIR 1610, 2260 nm @ 2 km GSD | 10 AHI 3.85, 6.25, 6.95, 7.35, 8.60, 9.63, 10.45, 11.20, 12.35, 13.30 μm @ 2 km GSD. Data transfer uses Ka band (18.1–18.4 GHz) |
NOAA GOES 16 (GOES- East), 17 (GOES West), 18 (GOES West, replaced GOES 17) Advanced Baseline Imager (ABI), Geostationary Lightning Mapper (GLM) | 2016– 2030, 2017– 2032, 2022– 2033 | GOES, 16 over Eastern US (75.2° W), GOES 2017/2018 over western US (137.0° W) | 35,786 | Full disk and regional | multi-modes from 30 s to 15 min | ABI VIS 1 band 640 nm @ 0.5 km, 3 VNIR bands 470, 865, 1378 nm @ 1k GSD, 2 SWIR bands 1610, 2250 nm @ 2 km GSD. GLM measures flash intensity against threshold background, optical energy over 2 × 2 km grid cell. Units of Average Flash Area in km2 and Total Optical Energy per grid cell per time period (5 min) in femtojoules (fJ, 10−15 J) | ABI 10 bands, 3.90, 6.19, 6.90, 7.3, 8.4, 9.6, 10.3, 11.2, 12.3, 13.3 μm @ 2 km GSD |
EUMETSAT METOP Third Generation, MTG Flexible Combined Imager (FCI) on MTG-I1, MTG-I2. Lightning Imager (LI) on MTG-I1, MTG I2. | MTG-I 1 2022– 2030, MTG- I 2 2026– 2036 | GEO 0° Latitude | 37,786 | Full disc and regional | Multi modes, 2.5 min to 10 min full disk | Flexible Combined Imager (FCI), 2 channels 640, 2200 nm @ 500 m GSD and 6 bands 444, 510, 640, 865, 914, 1380, 1610, 2250 nm @ 1 km GSD. Lightning Imager (LI), measures pulse intensity over a narrow band at 777.4 nm with a 4.5 km GSD | FCI, 2 bands at 3.8, 10.50 @ 1 km GSD in hi res mode. and 8 bands 3.8, 6.30, 7.35, 8.70, 9.66, 10.50, 12.30, 13.3 μm @ 2.0 km GSD |
EUMETSAT METOP Third Generation, MTG-S) Sounder. the Infrared Sounder (IRS) and Sentinel 4 UVN will be flown on the MTG-S1. | MTG-S 1 2025– 2035; MTG-S 2 ~10 years after launch of S 1 | GEO over 0° Latitude | 75,779 | Full disc and regional | Multi modes, 2.5 to 10 min for full disk | -- | IRS Fourier transform Interferometer Sounder w/large detector arrays, 4.44–6.25 μm and 8.26–14.70 μm at 0.604 cm−1 wave number resolution, @ 4 km GSD |
Sentinel 4A, 4B, Ultraviolet, Visible, Near-Infrared Imager (UVN) Flown on the MTG-S1 with the Infrared Sounder (IRS) | A 2025–2032 B launch~ 2034 | GEO, 30–65° N latitude, 30° W–45° E longitude. | 37,786 | 60 min cycle | -- | UVN. 190 bands Ultraviolet 305–400 nm, 200 bands Visible 400–500 nm @ 0.5 nm wavelength resolution; 21 bands NIR 750–775 nm at 1.2 nm wavelength resolution @ 8 × 8 km GSD | -- |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude | Swath, km | Repeat Frequency Days | Equatorial Crossing Time | UV/VNIR/SWIR, 400–2500 nm, GSD; an TIR 6.0–12.0 μm @GSD in km | Radar and Microwave Bands, Wavelengths, Frequencies |
---|---|---|---|---|---|---|---|---|
PACE Ocean Color Instrument (OCI) and 2 polarimeters (SPEXone and HARP2) | 2024–2027 | polar, LEO, sun-synchron ous | 676 | OCI = 2663 at 20° Tilt to avoid sunglint, SPEXone = 100 km, HARP2 = 1556 km | OCI = 1–2 d; SPEXone = 30 d, Harp2 = 2 d | 12:00D, | OCI 46 bands from 342.5–887 nm at 5 nm resolution, 7 bands NIR-SWIR bands 940, 1038, 1250, 1378,1615, 2130, 2260 nm @ 1 km GSD. SPEXone is a multiangle polarimeter, measures intensity angle and polarization linearity, 385–770 nm in 2–4 nm steps (yielding about 150 bands) at 5 view angles (−57°,−20°, 0°, 20°, 57°) @ 2.5 km2 GSD; also bands in same range as the 7 OCI bands but in 15–45nm steps yielding about 30–88 bands; HARP-2 has 4 bands 441, 549, 669, 873 nm with bandwidths of 15, 12, 16, 43 nm with 10 view angles at 440, 550, 870 nm at 3 angles 0, 45°, 90° at 2.6 km GSD. | -- |
Carbon Mapper Tanager satellites from Planet | first 2 Tanager satellites, 2024; Phase 2 expansion 2025; 5 yrs | Polar, LEO Sun Sychro-nous | 405 | 18 km, measured in 1200 km wide-strips | TBD | TBD | Pushbroom VSWIR imaging spectrometer 300–2500 nm with 5 nm spectral sampling @30 m GSD | -- |
MethaneSat Environmental Defense Fund and New Zealand Space Agency | launched 3/4/2024, 5 yrs | 51.6° Drifting Orbit | 590 | 200 | 3–4 days global | TBD | VSWIR 2 HgCdTe detectors, #1 with 2 bands having 28 channels between 1249–1305 nm and 42 channels between 1598–1683nm w/0.2 nm resolution and 0.6nm spectral sampling @ 100 × 400 m GSD. SNR 190. #2 VSWIR HgCdTe detectors: 400 channels from 1583–1683 nm with 0.25 nm resolution and 0.08 nm sampling. @100 × 400 m GSD and SNR 190. | -- |
Thermal infraRed Imaging Satellite for High resolution Natural resource Assessment (TRISHNA) French CNES/ Indian ISRO Space Agencies. | 2026–2031 | polar, LEO, sun-synchro-nous | 761 | 932 | 3 d | 12:30D | 4 bands (485, 555, 670, 860, 1380, 1650 nm) @ 57 m GSD and 4 bands TIR 4 bands, (8.6, 9.1, 10.4, 11.6 µm) @ 57 m GSD | -- |
NASA-Indian Space Research Oeganization (NASA ISRO) joint NISAR Mission | 2024–2026 | polar, LEO, sun-synchro-nous | 747 | 242 | 12 A and D (6 day for both) | 06:00D 18:00A | -- | Dual frequency SAR multi-polarimetric modes, GSD mode dependent 2–7 m. Side looking 33–47°. S-band 3.162–3.237 GHz (~9 cm) and 3–24 m GSD; and L band 1.215–1.3 GHz (24 cm) and 3–48 m GSD. |
Satellite /Instrument | Launch Date | Orbit Type | Altitude | Swath km | Repeat Frequency Days | Equatorial crossing Time | VNIR/SWIR, 400 2500 nm, GSD, km | MIDIR 3.0–6.0 μm, TIR 6.0–14.0 μm, @ km GSD | Radar and Microwave Bands, Wavelengths, Frequencies, GSD km |
---|---|---|---|---|---|---|---|---|---|
METOP-SG-Polar Weather Satellite 1A, 2A | 2025–2033, 2032–2040 | Polar, LEO, Sun-synchro-nous | 835 | METImage 2670 km, 3MI 2200 km, IASI-NG 2000 km, S-5 2715 km, MWS 2300 km | 3MI D in sunlight; MET- Image, D, VSWIR, 2x D TIR. MWS near 2 x D | 09:30D | 3MI: 9 channels w/polarization 410, 443, 490, 555, 670, 865, 1370, 1650, 2130 nm, 3 channels no pol: 763, 765, 910 nm @ 4 km GSD. METImage 11 VNIR/SWIR Channels 443, 555, 668, 751.5, 762.7, 865, 914, 1240, 1375, 1630, 2250 nm @0.5 km | METImage 9 bands: 3.74, 3.959, 4.05, 6.725, 7.325, 8.54, 10.69, 12.02, 13.345 μm @ 0.5 km; IASI-NG sounder w/16,921 channels 4.62–15.50 μm @4 × 412 km spaced GSD, within 100 × 100 km2 grid. | MWS 24 channels, single pol (V or H), at frequencies: 54.4, 54.94, 55.5, 57.290344 ± 0.3222 + 0.022, 57.290344 ± 0.3222 ± 0.010, 57.290344 ± 0.3222 ± 0.0045, 89, 164–167, 183.311 ± 7.0, 183.311 ± 4.5, 183.311 ± 3.0, 183.311 ± 1.8, 183.311 + 1.0, 229.0 GHz@ GSD 17 GSD channels 89–229 GHZ, 20 km for channels 50–59 GHZ, 40 km for channels 23.8–31.4 GHz. |
METOP-SG-Polar Weather Satellite 1B, 2B | 2026–2034, 2033–2041 | Polar, LEO, Sun- synchro-nous | 835 | ICI, MWI = 1700 km, SCA 2 swaths @ 660 km separated by 525 km gap. 3 looks at 45, 90, 135°. MWI = 1700 km swath | ICI, MWI D, SCA ~1.5 D | 09:30D | -- | -- | ICI 11 frequency channels 183.31 ± 7.0, 183.31 ± 3.4, 183.31 ± 2.0, 243.2 ± 2.5, +325.15 + 9.5, 325.15 ± 3.5, 325.15 + 1.5, 448 ± 7.2, 448 ± 3.0, 448 ± 1.4, 664 ± 4.2 GHz.; SCA C-band, 5.355 GHZ side looking, 25 km sampling at 12.5 km intervals, hi res mode 15–20 km w/6.25 km sampling intervals all @ 15 km GSD; MWI 18 frequencies 26 channels, 18.7, 23.8, 31.4, 50.3, 52.7, 53.24, 53.75, 89, 118.7503 ± 3.2, 118.7503 ± 2.1, 118.7503 ± 1.4, 118.7503 ± 1.2, 165.5 ± 0.725, 183.31 ± 7.0, 183.31 ± 6.1, 183.31 ± 4.9, 183.31 ± 3.4, 183.31 ± 2.0 GHz, GSD wavelength dependent |
Sentinel-5 flown on METOP-SG-1A, SG-2A | 2025–2033, 2932–2040 | Polar, LEO, Sun-synch-ronous | 835 | 2715 | daily | 09:30D | UVNS: 7 channels, 30 bands from 270–300 nm @ 1 nm sampling, 140 bands from 300–370 nm @0.5 nm sampling, 260 bands from 370–500 nm @0.5 nm sampling, 6 bands from 685–710 nm @ 0.4 nm sampling, 7 bands from 745–773 nm @ 0.4 nm sampling, 340 bands from 1590–1675 nm @ 0.25 nm sampling, 320 bands from 2305–2385 nm @ 0.25 nm sampling. @ 7 km GSD, degraded to 28 km for 270–300 nm. | -- | -- |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude | Swath, km | Repeat Frequency Days | Equatorial crossing Time | UV/VNIR, 400–1500 nm, GSD m | TIR GSD μm | Radar and Micro- Wave, Bands Wavelengths, Frequencies |
---|---|---|---|---|---|---|---|---|---|
ESA Earth Explorer Missions: #6 Earth Cloud, Aerosol, and Radiation Mission (EarthCARE) | 2024– 2027, 3.5 year life | polar, LEO, sun-synch-ronous | 394 | MSI = 150 BBR = 3 view angle, 10 km each | 25 d | 14:00D | Multi-Spectral Imager (MSI) 4 bands 670, 865 nm, 1.67, 2.21 μm @ 500 m; ATLID Measures 1 band at 354.8 nm | MSI TIR 8.8, 10.8, 12.0 μm @ 500 m | Broad-Band Radiometer (BBR) 2 channels, 0.25–4.0,0.25–50 μm at 3 view angles (+ 50° Fore, AFT and nadir) with 10 km footprint @ 1 km GSD; CPR 1 band at 94.05 GHz at@ 500 m. |
BIOMASS | 2025, 5- year life | polar, LEO, sun-synch-ronous | 660 | 50–30 km | 25 d | 06:00D | -- | -- | SAR P-Band ~70 cm wavelength and 435 MHz; SAR, Quad polarized Interferometric. 12 M antenna; side looking incidence angle 25° @50–60 m GSD. |
ESA Earth Explorer # 8, Fluorescence Explorer (FLEX) flys in tandem w/Sentinel-3 | 2026, 3.5 year life | polar, LEO, sun-synch-ronous | 800 | 150 | 27 d | 10:00D | FLORIS 3 bands with 2800 channels from 500–780, 110 channels from 686–697, and 10 channels from 759–769 at 0.1 nm, and bands with 750–1500 channels from 500–2000, sampled at 1–2 nm resolution; all @ 300 m | -- | -- |
ESA Earth Explorer #10, Harmony, 1A and 1B, flys in formation with Sentinel-1, 350 km front and behind. Definition stage. | 2029– 2034 | 98.2° polar sun Synchro-nous | 693 | TBD | 27 d | 06:00D | -- | Muiltiview VIS/TIR imager TBD | receive only, C-band SAR from Sentinel 1. |
Satellite /Instrument | Original NASEM Recommendation | Launch Date | Orbit Type | Alttude, km | Swath, km | Repeat Frequency, Days | Equatorial crossing Time | VNIR, SWIR, 400–1500 nm, 1500–2500 nm, GSD m | TIR 3.0–13.0 μm, GSD m |
---|---|---|---|---|---|---|---|---|---|
NASA Surface Biology and Geology (SBGTIR) | Hyperspectral imagery in the visible and shortwave infrared and Multiband or Hyperspectral imagery in the Thermal IR | 2029, 3 year life | polar, LEO, sun synchro- nous | 665 | VNIR camera 630. TIR TBD up to 935 | 2–3 d | 1:30D | Visible Near-Infrared VNIR 2-band camera from ASI (665, 835), @ 30 m; 1 SWIR band at 1.65 μm @ 60 m | TIR 8 bands TBD, between 3.0–12 μm, at <60 m GSD |
NASA Surface Biology and Geology (SBG- VSWIR) | delayed, 2029– 2032 | polar, LEO, sun synchro- nous | 623 | 185 | 16 d | 11:00D | VNIR 52 bands 380–900, 10 nm sampling @ 30 m GSD, SWIR 1605 bands from 900–2500 nm @ 30 m GSD | -- | |
Mass Change and Geosciences International Constellation (MAGIC-1, MAGIC-2) NASA & ESA Joint venture | “Either of two ranging techniques (microwave and optical) that are being evaluated as part of mission planning. GRACE-FO uses a two frequency (24 GHz) and Ka-(32 GHz) high accuracy inter-satellite ranging system and Tri-G GPS (GPS, Galielieo, GLONASS) system. | 2029 first pair, second TBD | 70° Drifting orbit | 397 | -- | -- | -- | MicroSTAR, a 3 axis accelerometer to correct non-gravitational acceleration error along 3 ultra sensitive axes. Laser Tracking Instrument (LTI) with 4-corner cube reflectors with lasers at 1064 nm wavelength for tracking the other satellite in pair. | -- |
Surface Deformation and Change | Interferometric Synthetic Aperture Radar, (InSAR) with ionospheric correction | TBD program still in Pre Phase A | -- | -- | -- | -- | -- | -- | -- |
Atmosphere Observing System (AOS) AOS-Sky | Aerosols: Backscatter lidar and multichannel multiangle/polarization imaging radiometer flown together; Clouds, Convection and Precipitation: Dual-frequency radar, multifrequency passive microwave and sub-mm radiometer | 2031 | 2 satellite polar sun synchronous orbit; 2 in drifting 55° inclined orbits | 450 | AOS-sky MWR 750 km, SKY Multiangle Polarimeter 300 km, FIR Imaging Radiometer 100 km. AOS HAWCSat ALI 200 km limb scanner. SHOW (SAWCSat) 63 km. TICFIRE 640 km w/30 km vertical. | ALI (SAWCSat) 24 d; SHOW (SAWCSat) global 2 d; FIR Imaging Radiometer global 2 d; | AOS-Sky 13:30D | AOS-Sky Lidar range 532 nm and 1064 nm, polarization sensitive; w/30 m vertical resolution, 350 m footprint, SKY Multi-angle Polarimeter (MAP) 9 bands 380–1570 nm: 2 channels UV 350–390 nm, VIS 410–750 nm for aerosol and spectral cloud. 1350–1400 nm for cirrus clouds, 3 channels from 870–1570 nm for bispectral cloud plus Hyperangle 670–870 nm, 900–960 nm for H2Ov in bispectral clouds, at 0.5 km GSD. HAWCSat Aerosol limb Imager (ALI) 10 channels 610–1560 nm limb scanning and 0.25 km vertical resolution. HAWCSat-SHOW limb viewing imager 1362–1368.32 nm range, 0.13 km vertical resolution | AOS sky: high frequency MWR 89–113 GHz, 3-channel 183, 325 GHz, 2- channel 640–700 GHz nadir viewing @ 10 km GSD, FIR Imaging Radiometer (TICFIRE) 6 bands 7.5–50 μm (7.5–9.5, 10–12, 12–14, 27.35–29.75, 22.5–27.5, 30–50 μm), AOS-Sky Radar, nadir viewing Doppler radar Ka band 35 GHz or 94 GHz (W-band) @2 km horizontal and @500 m GSD. |
Atmosphere Observing System (AOS) AOS-Storm | Storm 2029, HAWCsat in 2031 | drifting 55° inclined orbits | 430 | AOS-Storm MWR 700 km, Aerosol Limb imager 200 km, Water Vapor 430 km. Limb imager 63 km. Ku AOS Doppler SAR 255 km. AOS PMM TBD. | AOS STORM MWR global 2 d. PMM MWR global 2 d. PMM Doppler Radar global 5.5 d. | various | AOS-Storm MWR (SAPHR-NG) passive microwave 89–113, 184 (6 channels), 325 (3 channels) GHz @ 3–10 km GSD; AOS-Lidar nadir viewing, 2 band 532, 1064 nm polarizationsensitive lidar @ 350 m footprint, 30 m vertical. AOS PMM TBD | AOS-Storm MWR 89, 183, 325 GHz @ 3–10 km GSD; AOS Doppler Radar (JAXA), Ku (13.6 GHz) wide-swath doppler radar @ 500 m vertical resolution and 5 km | |
GSD. |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency Days | Equatorial crossing Time | VSWIR 400–2500 nm; TIR 7.5–13 μm GSD m or km |
---|---|---|---|---|---|---|---|
Copernicus Expansion Mission, Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) | CO2M-A 2026–2033, CO2M-B 2027–2034, CO2M-C, 2029–2036 | Polar sun synchronous | 735 | CO2I/NO2I 250, MAP swath increases with view angle, oversampling keeps pixel resolution ~1 km; CLIM 465 | 11 d | 11:30D | CO2I & NO2I 405–490 nm @ 0.6 nm resolution, 747–773 nm @0.12 nm, 1590–1675 nm @0.30 nm, 1990–2095 nm w/0.35 nm resolution at 0.8 km GSD. MAP 410, 443, 490, 555, 670, 753, 865 nm scanned along track fore, aft, ±60°, 12 x-track detectors, ~1km GSD. CLIM 670, 753 nm @ 174 m GSD, 1370 nm @ 348 m GSD |
Copernicus Sentinel Expansion Mission Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), A, B | A 2029-2034, B 2031–2037 | Polar sun synchronous | 786 | 290 | 25 d, together 12.5 d | 10:45D | ~210 bands 400–2500 nm @ ≤10 nm; 20–30 m GSD band dependent. |
Copernicus Expansion mission Land Surface Temperature Monitoring (LSTM), 1A, 1B | A 2029–2034, B 2031–2038 | Polar sun synchronous | 640 | 600–700 km | 4 d with 1 instrument; 2 d with 2 instruments 1 d with 4 instruments | 12:30A | Primary mission 11 bands, VSWIR 490, 665, 865, 945, 1380, 1610 nm @ 30 m and 8.6, 8.9, 9.2, 10.9, 12.0 μm. Secondary mission 13 bands, TIR 8.2, 9.1, 8.63, 12.63, 7.5, 12.2, 9.0, 9.8, 10.5, 10.95, 12.3, 9.3, 9.53 μm, all @ 30–50 m GSD, band dependent. |
Satellite/ Instrument | Launch Date | Orbit Type | Altitude km | Swath, km | Repeat Frequency Days | Equatorial crossing Time | Radar/Microwave Bands, Wavelengths, Frequencies |
---|---|---|---|---|---|---|---|
Copernicus Expansion Mission Observation System for Europe L-band (ROSE-L) A, B Complements C-Band Sentinel-1 | A: 2030– 2036 B: 2931– 2038 | Polar LEO sun-synchro- nous | 693 | 80–400 km mode dependent | days-weeks, mode dependent | 18:00A 06:00D | L-band SAR (1.2575 GHz) polaremetric and interferometric, side looking 15–45° off Nadir, best resolution is 5 m |
Copernicus Expansion Mission Copernicus Imaging Microwave Radiometer (CIMR); A, B fly in loose formation with METOP-SG-B | A 2029- 2036, B 2031– 2036 | Quasi-polar, circular and sun synchronous at constant incidence angle of 55.5° around the poles | 830 | 1900 | sub-daily “no hole” polar cover; 95% global coverage daily | dawn /dusk circular orbit; 06:00D | Conical scanning w/8 m reflector antenna, 5-band multifrequency microwave radiometer; Dual polarization V and H, in Ka (36.5), Ku (18.7), C (6.875), X (10.65), L (1.4135) GHz. GSD for Ka, Ku 2.5 km, for C, X 7.5 km, and L 30 km pixel resolution. |
Copernicus Expansion Mission Copernicus polaR Ice and Snow Topography ALtimeter-CRISTAL A, B | A 2028– 2034, B 2031– 2036 | drifting 92° polar orbit, will not measure beyond 81.5° N & S | 717 | NA | IRIS 367 d, AMR-CR monthly at 30 km or 10 d at 100 km | various | IRIS Dual frequency SAR altimeter Ku 13.5, Ka 35.75GHz @ 10 km GSD in SAR mode and 80 km in along-track mode. Microwave Radiometer (AMR-CR) for atmospheric corrections and surface type characterization with 3 bands: Ka 35.75, Ku 18.7, 34 GHz @ 25 km GSD. |
Satellite /Instrument | Launch Date | Orbit Type | Altitude, km | Swath, km | Repeat Frequency Days | Equatorial crossing Time | UV/VNIR/SWIR, 380–2500 nm, GSD in m | MID IR 3.0–6.0 µm, TIR 6.0–14.0 μm. Various GSD | Radar and Microwave Bands. Wavelengths, Frequencies, GSD Various |
---|---|---|---|---|---|---|---|---|---|
JAXA GOSAT-GW | 2024–2031 | Polar, LEO, sun synchronous | 666 | AMSRE3 = 1450, TANSO-3 selectable 911 for wide or 90 for focus | 3d | 13:30 A | TANSO-3, Bands, band1,~450 nm, <0.5 nm (NO2), band 2 ~760 nm <0.05 nm Band 3 ~1600 nm <0.2 nm spectra resolution, for CO2 and CH4 | -- | AMSRE3 11 frequencies 6.9–183 GHz with 21 channels, GSD changes with frequency. |
Canadian WildFireSat, Pre Phaes A planning | around 2030, 5 years | Polar or GEO, or 2 sats. | n/a | n/a | daily or subdaily | 18:00 ± 2 h | Multispectral VNIR, possibly like Landsat-8 or Sentinel 2 | Uncooled IR Bolometer technology. Possible MWIR 3.1–4.8 μm ~400 m GSD, Likely 2 IR micro-bolometers 10–12 μm range, @400 m GSD | -- |
4th Generation Himawari-10 GEO satellite | 2028–2038 | GEO @ 140.7° E | 35,786 | Full disk of Earth, Continental l, Targeted | seconds, minutes to hourly depending on mode | Full disk = 10 min, regional 1000 km × 1000 km every 2.5 min (up to 4 specified). High res areas 1000 km × 500 km every 30 s | GHMI: 470 (<1 km), 550 (<1), 660 (<0.5), 860 (<1), 1380 (<2), 1610 (<1) in km. 2255 (<1 km) in nm | GHMI: 3.85 (<1 km), 5.15 (<km), 6.25 (<2 km 6.95 (<2 km) μm. 7.35, 8.60, 9.625, 10.4, 11.2, 12.40, 13.3μm (all <2 km). GHMS 1689–2250 cm−1, 680–109 cm−1 | -- |
GOES-XO GOES EAST, WEST, and Continental | XO-East 2032–2047, XO- West 2035–2050, XO- Central 2035–2050. | GEOS East/West will have improved imager, hyperspectral IR sounder for ocean color and atmospheric condition. GOES Central will have a sounder and an atmospheric composition instrument. | 35,786 | Full disk of Earth, U.S. CONUS, targeted | full disk and U.S. CONUS better than 15 min and 5 min sampling. | Seconds to hourly, depending on mode | GXI Imager: 18 channels, improve ABI with 1 new band (910 nm) for WV in lower troposphere (daylight), improved day/night band, 3.9 μm @at 1km and 640 nm @ 250 m GSD | -- | improve ABI with 1 new band (5.15 μm) for WV in lower troposphere, near the ground |
Landsat-Next (L-10), w/3 Platforms, LandIS instrument | 2030–2038 | Polar, LEO sun synchronous | 653 | 164 | 16 (6 d w/3 together) | 10:00 | LandIS (bandwidth, nm) 5 bands: 490 (65), 560 (35), 665 (30), 842 (115), 1610 (90) nm, @ 12 m GSD, 10 bands 443 (20), 600 (30), 620 (20), 650 (35), 705 (15), 740 (15), 865 (20), 985 (20), 1035 (20), 1090 (20), 2038 (25), 2198 (20) @ 20 m GSD. Cal bands 412, 945, 1375 nm @ 60 m GSD | -- | LandIS (bandwidths, nm) 5 bands 8300 (250), 8600 (350), 9100 (350), 11,300 (550), 12,000 (550) nm @60 m GSD |
L-8/L-9 | https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites | L10 | https://landsat.gsfc.nasa.gov/satellites/ | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Band | λ Region | Purpose | GSD | λ, nm | λ Center | Band Width | Band | λ Region | Purpose | GSD | λ, nm | λ Center | Band Width |
1 | Violet | Improve aerosol retrieval & CDOM inland & coastal water | 60 | 402–422 | 412 | 20 | |||||||
1 | Blue 1 | Coastal/Aerosols | 30 | 430–450 | 440 | 20 | 2 | Blue | Landsat Heritage Coastal/Aaerosols | 20 | 433–453 | 443 | 20 |
2 | Blue2 | water | 30 | 450–510 | 480 | 60 | 3 | Blue | Landsat Heritage | 10 | 457.5–522.5 | 490 | 65 |
3 | Green | Carotenoids | 30 | 530–590 | 560 | 60 | 4 | Green | Landsat Heritage | 10 | 542.5–577.5 | 560 | 35 |
8 | Panchromatic | pan shaprening | 15 | 500–680 | 590 | 180 | |||||||
5 | Yellow | Chlorosis, Plant stress | 20 | 590–610 | 600 | 20 | |||||||
6 | Orange | Phycocyanin detection, Harmful algal blooms (HABs) | 20 | 610–630 | 620 | 20 | |||||||
7 | Red 1 | Phycocyanin, chlorophyll | 20 | 640–660 | 650 | 20 | |||||||
4 | Red | Chlorophyll | 30 | 640–670 | 655 | 30 | 8 | Red 2 | Landsat Heritage Chlorophyll | 10 | 650–680 | 665 | 30 |
9 | Red Edge 1 | LAI, Chlorophyll, plant stress (Sentinel-2 (S-2)) | 20 | 697.5–712.5 | 705 | 15 | |||||||
10 | Red Edge 2 | LAI, Chlorophyll, plant stress (S-2) | 20 | 732.5–747.5 | 740 | 15 | |||||||
11 | NIR Broad | 10 NDVI (S-2) | 10 | 789.5–894.5 | 842 | 115 | |||||||
5 | Near-infrared (NIR) | NIR plateau | 30 | 850–880 | 865 | 30 | 12 | NIR 1 | Landsat Heritage | 20 | 855–875 | 865 | 20 |
13 | Water Vapor | Atmospheric corrections, Land Surface Temperature, Surface Reflectance (S-2) | 60 | 935–955 | 945 | 20 | |||||||
14 | Liquid Water | Liquid water | 20 | 975–995 | 985 | 20 | |||||||
15 | Snow/Ice 1 | Snow grain size for water resources | 20 | 1025–1045 | 1035 | 20 | |||||||
16 | Snow/Ice 2 | Ice absorption, | 20 | 1080–1100 | 1090 | 20 | |||||||
snow grain size | |||||||||||||
9 | NIR | Cirrus | 30 | 1360–1380 | 1370 | 20 | 17 | NIR | Landsat Heritage Cirrus | 60 | 1360–1390 | 1375 | 30 |
6 | Shortwave Infrared (SWIR) 1 | Water absorption | 30 | 1570–1650 | 1610 | 80 | 18 | SWIR 1 | Landsat Heritage | 10 | 1555–1655 | 1610 | 90 |
19 | SWIR 2a | Landsat Heritage subdivided for Cellulose/crop residue | 20 | 2025.5–2050.5 | 2038 | 25 | |||||||
20 | SWIR 2b | Landsat Heritage subdivided for Cellulose/crop residue | 20 | 2088–2128 | 2108 | 40 | |||||||
7 | Shortwave Infrared (SWIR) 2 | separation minerals, cellulose/crop residue | 30 | 2110–2290 | 2200 | 180 | 21 | SWIR 2c | Landsat Heritage subdivided for Cellulose/crop residue | 20 | 2191–2231 | 2211 | 40 |
22 | TIR 1 | Mineral surface composition (ASTER) | 60 | 8175–8425 | 8300 | 250 | |||||||
23 | TIR 2 | Emissivity separation, volcanoes (SO2) (MODIS/ASTER) | 60 | 8475–8725 | 8600 | 250 | |||||||
24 | TIR 3 | Mineral surface composition (ASTER) | 60 | 8925–9275 | 9100 | 350 | |||||||
10 | Thermal infrared TIRS 1 | Radiance, Temperature | 100 | 10600–11,190 | 10,895 | 590 | 25 | TIR 4 | Landsat Heritage Surface temperature, carbonates | 60 | 11,025–11,575 | 11300 | 550 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ustin, S.L.; Middleton, E.M. Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications. Sensors 2024, 24, 3488. https://doi.org/10.3390/s24113488
Ustin SL, Middleton EM. Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications. Sensors. 2024; 24(11):3488. https://doi.org/10.3390/s24113488
Chicago/Turabian StyleUstin, Susan L., and Elizabeth McPhee Middleton. 2024. "Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications" Sensors 24, no. 11: 3488. https://doi.org/10.3390/s24113488
APA StyleUstin, S. L., & Middleton, E. M. (2024). Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications. Sensors, 24(11), 3488. https://doi.org/10.3390/s24113488