Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and In Situ Observations
2.2. Soil Moisture Products and Data Preprocessing
2.3. In Situ and Triple Collocation-Based Validations
2.4. Evaluating the Capabilities of Precipitation and Irrigation Detection
3. Results
3.1. Overall Performance of SM Products
3.2. Specific Behaviors of SM Products
3.3. TC-Based Comparison of SM Products
3.4. Diverse SM Responses to Precipitation Events
3.5. Diverse SM Responses to Irrigation Events
4. Discussion
4.1. Practices for Optimal Product and Algorithm Selection
4.2. Implications for Improving the Retrieval Algorithm
4.3. Recommendations for Validation of Soil Moisture Products
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Seneviratne, S.I.; Corti, T.; Davin, E.L.; Hirschi, M.; Jaeger, E.B.; Lehner, I.; Orlowsky, B.; Teuling, A.J. Investigating soil moisture—Climate interactions in a changing climate: A review. Earth-Sci. Rev. 2010, 99, 125–161. [Google Scholar] [CrossRef]
- Brocca, L.; Ciabatta, L.; Massari, C.; Camici, S.; Tarpanelli, A. Soil Moisture for Hydrological Applications: Open Questions and New Opportunities. Water 2017, 9, 140. [Google Scholar] [CrossRef]
- Walker, J.P.; Willgoose, G.R.; Kalma, J.D. In situ measurement of soil moisture: A comparison of techniques. J. Hydrol. 2004, 293, 85–99. [Google Scholar] [CrossRef]
- Dorigo, W.; Himmelbauer, I.; Aberer, D.; Schremmer, L.; Petrakovic, I.; Zappa, L.; Preimesberger, W.; Xaver, A.; Annor, F.; Ardö, J.; et al. The International Soil Moisture Network: Serving Earth system science for over a decade. Hydrol. Earth Syst. Sci. 2021, 25, 5749–5804. [Google Scholar] [CrossRef]
- Mo, T.; Choudhury, B.J.; Schmugge, T.J.; Wang, J.R.; Jackson, T.J. A model for microwave emission from vegetation-covered fields. J. Geophys. Res. Earth Surf. 1982, 87, 11229–11237. [Google Scholar] [CrossRef]
- Wigneron, J.-P.; Jackson, T.; O’Neill, P.; De Lannoy, G.; de Rosnay, P.; Walker, J.; Ferrazzoli, P.; Mironov, V.; Bircher, S.; Grant, J.; et al. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sens. Environ. 2017, 192, 238–262. [Google Scholar] [CrossRef]
- Dorigo, W.A.; Scipal, K.; Parinussa, R.M.; Liu, Y.Y.; Wagner, W.; de Jeu, R.A.M.; Naeimi, V. Error characterisation of global active and passive microwave soil moisture datasets. Hydrol. Earth Syst. Sci. 2010, 14, 2605–2616. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.Y.; Dorigo, W.A.; Parinussa, R.M.; de Jeu, R.A.M.; Wagner, W.; McCabe, M.F.; Evans, J.P.; van Dijk, A.I.J.M. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ. 2012, 123, 280–297. [Google Scholar] [CrossRef]
- Dong, J.; Crow, W.; Reichle, R.; Liu, Q.; Lei, F.; Cosh, M.H. A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model. Geophys. Res. Lett. 2019, 46, 6604–6613. [Google Scholar] [CrossRef]
- Khandan, R.; Wigneron, J.-P.; Bonafoni, S.; Pour Biazar, A.; Gholamnia, M. Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review. Remote Sens. 2022, 14, 770. [Google Scholar] [CrossRef]
- Gruber, A.; De Lannoy, G.; Albergel, C.; Al-Yaari, A.; Brocca, L.; Calvet, J.-C.; Colliander, A.; Cosh, M.; Crow, W.; Dorigo, W.; et al. Validation practices for satellite soil moisture retrievals: What are (the) errors? Remote Sens. Environ. 2020, 244, 111806. [Google Scholar] [CrossRef]
- Schaefer, G.L.; Cosh, M.H.; Jackson, T.J. The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). J. Atmos. Ocean. Technol. 2007, 24, 2073–2077. [Google Scholar] [CrossRef]
- Ling, X.; Huang, Y.; Guo, W.; Wang, Y.; Chen, C.; Qiu, B.; Ge, J.; Qin, K.; Xue, Y.; Peng, J. Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China. Hydrol. Earth Syst. Sci. 2021, 25, 4209–4229. [Google Scholar] [CrossRef]
- Colliander, A.; Jackson, T.J.; Bindlish, R.; Chan, S.; Das, N.; Kim, S.B.; Cosh, M.H.; Dunbar, R.S.; Dang, L.; Pashaian, L.; et al. Validation of SMAP surface soil moisture products with core validation sites. Remote Sens. Environ. 2017, 191, 215–231. [Google Scholar] [CrossRef]
- Fu, H.; Zhou, T.; Sun, C. Evaluation and Analysis of AMSR2 and FY3B Soil Moisture Products by an In Situ Network in Cropland on Pixel Scale in the Northeast of China. Remote Sens. 2019, 11, 868. [Google Scholar] [CrossRef] [Green Version]
- Liu, J.; Chai, L.; Lu, Z.; Liu, S.; Qu, Y.; Geng, D.; Song, Y.; Guan, Y.; Guo, Z.; Wang, J.; et al. Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas. Remote Sens. 2019, 11, 792. [Google Scholar] [CrossRef] [Green Version]
- Oozeer, Y.; Fletcher, C.G.; Champagne, C. Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada. Remote Sens. 2020, 12, 1455. [Google Scholar] [CrossRef]
- Wu, Z.; Feng, H.; He, H.; Zhou, J.; Zhang, Y. Evaluation of Soil Moisture Climatology and Anomaly Components Derived From ERA5-Land and GLDAS-2.1 in China. Water Resour. Manag. 2021, 35, 629–643. [Google Scholar] [CrossRef]
- Chan, S.K.; Bindlish, R.; O’Neill, P.E.; Njoku, E.; Jackson, T.; Colliander, A.; Chen, F.; Burgin, M.; Dunbar, S.; Piepmeier, J.; et al. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4994–5007. [Google Scholar] [CrossRef]
- Scipal, K.; Holmes, T.; de Jeu, R.; Naeimi, V.; Wagner, W. A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophys. Res. Lett. 2008, 35, L24403. [Google Scholar] [CrossRef] [Green Version]
- Su, C.-H.; Ryu, D.; Crow, W.T.; Western, A.W. Beyond triple collocation: Applications to soil moisture monitoring. J. Geophys. Res. Atmos. 2014, 119, 6419–6439. [Google Scholar] [CrossRef]
- Chen, F.; Crow, W.; Colliander, A.; Cosh, M.H.; Jackson, T.J.; Bindlish, R.; Reichle, R.; Chan, S.K.; Bosch, D.D.; Starks, P.J.; et al. Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 10, 489–502. [Google Scholar] [CrossRef]
- Chen, F.; Crow, W.T.; Bindlish, R.; Colliander, A.; Burgin, M.S.; Asanuma, J.; Aida, K. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation. Remote Sens. Environ. 2018, 214, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Wu, X.; Lu, G.; Wu, Z.; He, H.; Scanlon, T.; Dorigo, W. Triple Collocation-Based Assessment of Satellite Soil Moisture Products with In Situ Measurements in China: Understanding the Error Sources. Remote Sens. 2020, 12, 2275. [Google Scholar] [CrossRef]
- Dorigo, W.A.; Gruber, A.; De Jeu, R.A.M.; Wagner, W.; Stacke, T.; Loew, A.; Albergel, C.; Brocca, L.; Chung, D.; Parinussa, R.M.; et al. Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens. Environ. 2015, 162, 380–395. [Google Scholar] [CrossRef]
- Kim, H.; Parinussa, R.; Konings, A.G.; Wagner, W.; Cosh, M.H.; Lakshmi, V.; Zohaib, M.; Choi, M. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sens. Environ. 2018, 204, 260–275. [Google Scholar] [CrossRef]
- Zheng, J.; Zhao, T.; Lü, H.; Shi, J.; Cosh, M.H.; Ji, D.; Jiang, L.; Cui, Q.; Lu, H.; Yang, K.; et al. Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China. Remote Sens. Environ. 2022, 271, 112891. [Google Scholar] [CrossRef]
- Gruber, A.; Su, C.-H.; Crow, W.; Zwieback, S.; Dorigo, W.A.; Wagner, W. Estimating error cross-correlations in soil moisture data sets using extended collocation analysis. J. Geophys. Res. Atmos. 2016, 121, 1208–1219. [Google Scholar] [CrossRef] [Green Version]
- Ma, H.; Zeng, J.; Chen, N.; Zhang, X.; Cosh, M.H.; Wang, W. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations. Remote Sens. Environ. 2019, 231, 111215. [Google Scholar] [CrossRef]
- Zhang, R.; Kim, S.; Sharma, A. A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote Sens. Environ. 2019, 223, 82–94. [Google Scholar] [CrossRef]
- Karthikeyan, L.; Pan, M.; Konings, A.G.; Piles, M.; Fernandez-Moran, R.; Kumar, D.N.; Wood, E.F. Simultaneous retrieval of global scale Vegetation Optical Depth, surface roughness, and soil moisture using X-band AMSR-E observations. Remote Sens. Environ. 2019, 234, 111473. [Google Scholar] [CrossRef]
- Kim, H.; Wigneron, J.-P.; Kumar, S.; Dong, J.; Wagner, W.; Cosh, M.H.; Bosch, D.D.; Collins, C.H.; Starks, P.J.; Seyfried, M.; et al. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sens. Environ. 2020, 251, 112052. [Google Scholar] [CrossRef]
- Zhang, K.; Li, X.; Zheng, D.; Zhang, L.; Zhu, G. Estimation of Global Irrigation Water Use by the Integration of Multiple Satellite Observations. Water Resour. Res. 2022, 58, e2021WR030031. [Google Scholar] [CrossRef]
- Zhang, K.; Wang, Q.; Chao, L.; Ye, J.; Li, Z.; Yu, Z.; Yang, T.; Ju, Q. Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. J. Hydrol. 2019, 574, 903–914. [Google Scholar] [CrossRef]
- Sun, P.; Zhang, Q.; Cheng, C.; Singh, V.P.; Shi, P. ENSO-induced drought hazards and wet spells and related agricultural losses across Anhui province, China. Nat. Hazards 2017, 89, 963–983. [Google Scholar] [CrossRef]
- Dorigo, W.A.; Xaver, A.; Vreugdenhil, M.; Gruber, A.; Hegyiová, A.; Sanchis-Dufau, A.D.; Zamojski, D.; Cordes, C.; Wagner, W.; Drusch, M. Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone J. 2013, 12, vzj2012.0097. [Google Scholar] [CrossRef]
- Yang, J.; Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 2021, 13, 3907–3925. [Google Scholar] [CrossRef]
- Owe, M.; de Jeu, R.; Walker, J. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1643–1654. [Google Scholar] [CrossRef] [Green Version]
- Koike, T.; Nakamura, Y.; Kaihotsu, I.; Davaa, G.; Matsuura, N.; Tamagawa, K.; Fujii, H. Development of an Advanced Microwave Scanning Radiometer (AMSR-E) Algorithm for Soil Moisture and Vegetation Water Content. Proc. Hydraul. Eng. 2004, 48, 217–222. [Google Scholar] [CrossRef] [Green Version]
- Mladenova, I.; Jackson, T.; Njoku, E.; Bindlish, R.; Chan, S.; Cosh, M.; Holmes, T.; de Jeu, R.; Jones, L.; Kimball, J.; et al. Remote monitoring of soil moisture using passive microwave-based techniques—Theoretical basis and overview of selected algorithms for AMSR-E. Remote Sens. Environ. 2014, 144, 197–213. [Google Scholar] [CrossRef]
- Kim, S.; Liu, Y.Y.; Johnson, F.M.; Parinussa, R.M.; Sharma, A. A global comparison of alternate AMSR2 soil moisture products: Why do they differ? Remote Sens. Environ. 2015, 161, 43–62. [Google Scholar] [CrossRef]
- Yao, P.; Lu, H.; Shi, J.; Zhao, T.; Yang, K.; Cosh, M.H.; Gianotti, D.J.S.; Entekhabi, D. A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019). Sci. Data 2021, 8, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Q.; Yuan, Q.; Li, J.; Wang, Y.; Sun, F.; Zhang, L. Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019. Earth Syst. Sci. Data 2021, 13, 1385–1401. [Google Scholar] [CrossRef]
- Al Bitar, A.; Mialon, A.; Kerr, Y.H.; Cabot, F.; Richaume, P.; Jacquette, E.; Quesney, A.; Mahmoodi, A.; Tarot, S.; Parrens, M.; et al. The global SMOS Level 3 daily soil moisture and brightness temperature maps. Earth Syst. Sci. Data 2017, 9, 293–315. [Google Scholar] [CrossRef] [Green Version]
- Fernandez-Moran, R.; Al-Yaari, A.; Mialon, A.; Mahmoodi, A.; Al Bitar, A.; De Lannoy, G.; Rodriguez-Fernandez, N.; Lopez-Baeza, E.; Kerr, Y.; Wigneron, J.-P. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product. Remote Sens. 2017, 9, 457. [Google Scholar] [CrossRef] [Green Version]
- Fan, X.; Liu, Y.; Gan, G.; Wu, G. SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data. Remote Sens. Environ. 2020, 247, 111914. [Google Scholar] [CrossRef]
- Chew, C.C.; Small, E.E. Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture. Geophys. Res. Lett. 2018, 45, 4049–4057. [Google Scholar] [CrossRef] [Green Version]
- Bartalis, Z.; Wagner, W.; Naeimi, V.; Hasenauer, S.; Scipal, K.; Bonekamp, H.; Figa, J.; Anderson, C. Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT). Geophys. Res. Lett. 2007, 34, L20401. [Google Scholar] [CrossRef] [Green Version]
- Dorigo, W.; Wagner, W.; Albergel, C.; Albrecht, F.; Balsamo, G.; Brocca, L.; Chung, D.; Ertl, M.; Forkel, M.; Gruber, A.; et al. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ. 2017, 203, 185–215. [Google Scholar] [CrossRef]
- Gruber, A.; Scanlon, T.; van der Schalie, R.; Wagner, W.; Dorigo, W. Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data 2019, 11, 717–739. [Google Scholar] [CrossRef] [Green Version]
- Rodell, M.; Houser, P.R.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.-J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M.; et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [Google Scholar] [CrossRef] [Green Version]
- Gelaro, R.; McCarty, W.; Suárez, M.J.; Todling, R.; Molod, A.; Takacs, L.; Randles, C.A.; Darmenov, A.; Bosilovich, M.G.; Reichle, R.; et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 2017, 30, 5419–5454. [Google Scholar] [CrossRef] [PubMed]
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horanyi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
- Xu, L.; Chen, N.; Zhang, X.; Moradkhani, H.; Zhang, C.; Hu, C. In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sens. Environ. 2020, 254, 112248. [Google Scholar] [CrossRef]
- Kim, H.; Lakshmi, V.; Kwon, Y.; Kumar, S.V. First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model. Environ. Res. Lett. 2021, 16, 074041. [Google Scholar] [CrossRef]
- Muñoz-Sabater, J.; Dutra, E.; Agustí-Panareda, A.; Albergel, C.; Arduini, G.; Balsamo, G.; Boussetta, S.; Choulga, M.; Harrigan, S.; Hersbach, H.; et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 2021, 13, 4349–4383. [Google Scholar] [CrossRef]
- Reichle, R.H.; De Lannoy, G.J.M.; Liu, Q.; Ardizzone, J.V.; Colliander, A.; Conaty, A.; Crow, W.; Jackson, T.J.; Jones, L.A.; Kimball, J.S.; et al. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements. J. Hydrometeorol. 2017, 18, 2621–2645. [Google Scholar] [CrossRef]
- Qiu, J.; Dong, J.; Crow, W.T.; Zhang, X.; Reichle, R.H.; De Lannoy, G.J.M. The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis. Hydrol. Earth Syst. Sci. 2021, 25, 1569–1586. [Google Scholar] [CrossRef]
- Huffman, G.J.; Bolvin, D.T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Nelkin, E.J.; Sorooshian, S.; Tan, J.; Xie, P. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). In Algorithm Theoretical Basis Document (ATBD) Version; NASA/GSFC: Greenbelt, MD, USA, 2020. [Google Scholar]
- Zhang, Z.; Chatterjee, A.; Ott, L.; Reichle, R.; Feldman, A.F.; Poulter, B. Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model. Remote Sens. 2022, 14, 2405. [Google Scholar] [CrossRef]
- Peng, J.; Loew, A.; Merlin, O.; Verhoest, N.E.C. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 2017, 55, 341–366. [Google Scholar] [CrossRef]
- Peng, J.; Albergel, C.; Balenzano, A.; Brocca, L.; Cartus, O.; Cosh, M.H.; Crow, W.T.; Dabrowska-Zielinska, K.; Dadson, S.; Davidson, M.W.; et al. A roadmap for high-resolution satellite soil moisture applications—Confronting product characteristics with user requirements. Remote Sens. Environ. 2020, 252, 112162. [Google Scholar] [CrossRef]
- Liu, Y.; Liu, Y.; Wang, W. Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis. Remote Sens. Environ. 2019, 220, 1–18. [Google Scholar] [CrossRef]
- Dong, J.; Crow, W.T.; Bindlish, R. The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals. Geophys. Res. Lett. 2018, 45, 758–765. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Walker, V.A.; Hornbuckle, B.K.; Cosh, M.H.; Prueger, J.H. Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt. Remote Sens. 2019, 11, 2488. [Google Scholar] [CrossRef] [Green Version]
- Colliander, A.; Cosh, M.H.; Kelly, V.R.; Kraatz, S.; Bourgeau-Chavez, L.; Siqueira, P.; Roy, A.; Konings, A.G.; Holtzman, N.; Misra, S.; et al. SMAP Detects Soil Moisture Under Temperate Forest Canopies. Geophys. Res. Lett. 2020, 47, e2020GL089697. [Google Scholar] [CrossRef]
- Feldman, A.; Gianotti, D.; Dong, J.; Akbar, R.; Crow, W.; McColl, K.; Nippert, J.; Tumber-Dávila, S.J.; Holbrook, N.M.; Rockwell, F.; et al. Satellites capture soil moisture dynamics deeper than a few centimeters and are relevant to plant water uptake. Earth Space Sci. Open Arch. 2022. [Google Scholar] [CrossRef]
- Gao, L.; Sadeghi, M.; Ebtehaj, A. Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite. Remote Sens. Environ. 2020, 239, 111662. [Google Scholar] [CrossRef]
- Xing, Z.; Fan, L.; Zhao, L.; De Lannoy, G.; Frappart, F.; Peng, J.; Li, X.; Zeng, J.; Al-Yaari, A.; Yang, K.; et al. A first assessment of satellite and reanalysis estimates of surface and root-zone soil moisture over the permafrost region of Qinghai-Tibet Plateau. Remote Sens. Environ. 2021, 265, 112666. [Google Scholar] [CrossRef]
- Pierdicca, N.; Comite, D.; Camps, A.; Carreno-Luengo, H.; Cenci, L.; Clarizia, M.P.; Costantini, F.; Dente, L.; Guerriero, L.; Mmollfulleda, A.; et al. The Potential of Spaceborne GNSS Reflectometry for Soil Moisture, Biomass, and Freeze–Thaw Monitoring: Summary of a European Space Agency-Funded Study. IEEE Geosci. Remote Sens. Mag. 2021, 2–32. [Google Scholar] [CrossRef]
- Kim, H.; Lakshmi, V. Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture. Geophys. Res. Lett. 2018, 45, 8272–8282. [Google Scholar] [CrossRef] [Green Version]
- Eroglu, O.; Kurum, M.; Boyd, D.; Gurbuz, A.C. High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sens. 2019, 11, 2272. [Google Scholar] [CrossRef] [Green Version]
- Yueh, S.H.; Shah, R.; Chaubell, M.J.; Hayashi, A.; Xu, X.; Colliander, A. A Semiempirical Modeling of Soil Moisture, Vegetation, and Surface Roughness Impact on CYGNSS Reflectometry Data. IEEE Trans. Geosci. Remote Sens. 2020, 60, 1–17. [Google Scholar] [CrossRef]
- Lei, F.; Senyurek, V.; Kurum, M.; Gurbuz, A.C.; Boyd, D.; Moorhead, R.; Crow, W.T.; Eroglu, O. Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations. Remote Sens. Environ. 2022, 276, 113041. [Google Scholar] [CrossRef]
- Fan, X.; Liu, Y.; Tao, J.; Weng, Y. Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression. Remote Sens. 2015, 7, 488–511. [Google Scholar] [CrossRef] [Green Version]
- Dong, J.; Crow, W.T.; Tobin, K.J.; Cosh, M.H.; Bosch, D.D.; Starks, P.J.; Seyfried, M.; Collins, C.H. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sens. Environ. 2020, 242, 111756. [Google Scholar] [CrossRef]
No. | Latitude | Longitude | Elevation | Land Cover | Water Fraction | Mean SM (cm·cm−3) |
---|---|---|---|---|---|---|
1 | 34.45°N | 116.33°E | 44.2 m | Cropland | Low | 0.170 ± 0.081 |
2 | 33.79°N | 115.73°E | 37.0 m | Cropland | Low | 0.230 ± 0.067 |
3 | 33.49°N | 116.20°E | 29.3 m | Cropland | Low | 0.227 ± 0.108 |
4 | 33.93°N | 116.75°E | 31.6 m | Cropland | Low | 0.188 ± 0.091 |
5 | 33.28°N | 116.55°E | 27.1 m | Cropland | Low | 0.193 ± 0.103 |
6 | 33.14°N | 117.87°E | 16.6 m | Cropland | High | 0.317 ± 0.028 |
7 | 32.64°N | 116.24°E | 24.6 m | Cropland | Low | 0.310 ± 0.049 |
8 | 32.44°N | 116.79°E | 25.7 m | Cropland | High | 0.276 ± 0.122 |
9 | 32.86°N | 117.55°E | 24.6 m | Cropland | Low | 0.293 ± 0.034 |
10 | 32.13°N | 118.29°E | 31.5 m | Cropland | Low | 0.328 ± 0.041 |
11 | 32.46°N | 118.46°E | 35.0 m | Cropland | Low | 0.390 ± 0.034 |
12 | 31.68°N | 115.88°E | 95.4 m | Forest | High | 0.319 ± 0.025 |
13 | 31.40°N | 116.34°E | 86.4 m | Forest | Low | 0.301 ± 0.052 |
14 | 31.28°N | 117.29°E | 20.2 m | Cropland | Low | 0.307 ± 0.077 |
15 | 31.72°N | 118.36°E | 22.5 m | Cropland | High | 0.231 ± 0.070 |
16 | 31.57°N | 118.49°E | 23.2 m | Cropland | High | 0.307 ± 0.107 |
17 | 31.08°N | 118.18°E | 26.8 m | Forest | Low | 0.184 ± 0.107 |
18 | 30.30°N | 118.13°E | 193.4 m | Forest | Low | 0.243 ± 0.081 |
19 | 30.88°N | 118.32°E | 34.9 m | Forest | Low | 0.268 ± 0.067 |
20 | 30.68°N | 118.41°E | 56.0 m | Forest | Low | 0.356 ± 0.022 |
SM Products | Resolution | Depth | Source | Time | Category |
---|---|---|---|---|---|
AMSR2 LPRM X/C1/C2 | 25 km | <2 cm | NASA | 1:30 a.m./1:30 p.m. | RS |
AMSR2 JAXA | 25 km | <2 cm | JAXA | 1:30 a.m./1:30 p.m. | RS |
AMSR2 NPD | 25 km | <2 cm | NASA | 1:30 a.m./1:30 p.m. | RS |
AMSR2 SCA | 25 km | <2 cm | NASA | 1:30 a.m./1:30 p.m. | RS |
SMOS L3 | 25 km | <5 cm | ESA | 6 a.m./6 p.m. | RS |
SMOS IC | 25 km | <5 cm | INRA-CESBIO | 6 a.m./6 p.m. | RS |
SMAP SCA-H/V/DCA | 36 km | <5 cm | NASA | 6 a.m./6 p.m. | RS |
CYGNSS | 36 km | <5 cm | UCAR/CU | daily at 8 a.m. | RS |
MetOp-B ASCAT | 25 km | <2 cm | EUMETSAT | 9:30 a.m./9:30 p.m. | RS |
ESA CCI | 0.25° | <5 cm | ESA | daily at 8 a.m. | RS + Model |
GLDAS Noah | 0.25° | 0–10 cm | NASA | subdaily at 8 a.m. | Model |
MERRA2 | 0.5° × 0.625° | 0–5 cm | NASA | hourly at 8:30 a.m. | Model |
ERA5 | 0.25° | 0–7 cm | ECWMF | hourly at 8 a.m | Model + RS |
ERA5 Land | 0.1° | 0–7 cm | ECWMF | hourly at 8 a.m | Model |
SMAP L4 | 9 km | 0–5 cm | NASA | subdaily at 9:30 a.m. | Model + RS |
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Fan, X.; Lu, Y.; Liu, Y.; Li, T.; Xun, S.; Zhao, X. Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events. Remote Sens. 2022, 14, 3339. https://doi.org/10.3390/rs14143339
Fan X, Lu Y, Liu Y, Li T, Xun S, Zhao X. Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events. Remote Sensing. 2022; 14(14):3339. https://doi.org/10.3390/rs14143339
Chicago/Turabian StyleFan, Xingwang, Yanyu Lu, Yongwei Liu, Tingting Li, Shangpei Xun, and Xiaosong Zhao. 2022. "Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events" Remote Sensing 14, no. 14: 3339. https://doi.org/10.3390/rs14143339
APA StyleFan, X., Lu, Y., Liu, Y., Li, T., Xun, S., & Zhao, X. (2022). Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events. Remote Sensing, 14(14), 3339. https://doi.org/10.3390/rs14143339