Comparing Satellite Soil Moisture Products Using In Situ Observations over an Instrumented Experimental Basin in Romania
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Satellite Soil Moisture Products
2.2.1. CGLS Sentinel-1 SSM1km
2.2.2. CGLS Soil Water Index 1 km
2.2.3. NASA Soil Moisture Active Passive Mission
2.3. Analysis of Soil Moisture Series
2.3.1. Soil Moisture Data Processing
2.3.2. Hydrological Consistency Index
2.3.3. Cumulative Distribution Function Matching Approach
2.3.4. Statistical Tests
- Pearson correlation (R)
- Spearman correlation (rho)
- Root Mean Square Error (RMSE)
- Gi = soil moisture gauge observation (m3/m3)
- G = mean soil moisture gauge observation (m3/m3)
- Si = satellite estimation (m3/m3)
- S = mean satellite estimation (m3/m3)
- n = observation number
- D = difference between ranks of Gi and Si
3. Results
3.1. Analysis of HCI
3.2. CDF and Statistical Tests
4. Discussion
5. Conclusions
- -
- The SP_L4_SM products show the best performances regardless of the considered metrics, as confirmed by the physics-based index (HCI) and some statistical tests widely used in inter-comparison analysis.
- -
- The performance of the SWI1km observations has shown satisfactory results with single-point and spatialized soil moisture estimations over the Experimental Basin, considering both near-surface and root-zone data.
- -
- Among the products with the lowest performance (SSM1km and L2_SM_SP products), the C-band and L-band data fusion operating by the L2_SM_SP products shows a slight improvement over SSM1km at the expense of a lower temporal resumption.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Wasko, C.; Nathan, R. Influence of changes in rainfall and soil moisture on trends in flooding. J. Hydrol. 2019, 575, 432–441. [Google Scholar] [CrossRef]
- Ontel, I.; Irimescu, A.; Boldeanu, G.; Mihailescu, D.; Angearu, C.V.; Nertan, A.; Craciunescu, V.; Negreanu, S. Assessment of soil moisture anomaly sensitivity to detect drought spatio-temporal variability in Romania. Sensors 2021, 21, 8371. [Google Scholar] [CrossRef]
- Singh, N.K.; Emanuel, R.E.; McGlynn, B.L.; Miniat, C.F. Soil moisture responses to rainfall: Implications for runoff generation. Water Resour. Res. 2021, 57, e2020WR028827. [Google Scholar] [CrossRef]
- Robinson, D.A.; Campbell, C.S.; Hopmans, J.W.; Hornbuckle, B.K.; Jones, S.B.; Knight, R.; Ogden, F.; Selker, J.; Wendroth, O. Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vadose Zone J. 2008, 7, 358–389. [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]
- Wang, C.; Fu, B.; Zhang, L.; Xu, Z. Soil moisture–plant interactions: An ecohydrological review. J. Soils Sediments 2019, 19, 1–9. [Google Scholar] [CrossRef]
- Merz, B.; Plate, E.J. An analysis of the effects of spatial variability of soil and soil moisture on runoff. Water Resour. Res. 1997, 33, 2909–2922. [Google Scholar] [CrossRef]
- Brocca, L.; Morbidelli, R.; Melone, F.; Moramarco, T. Soil moisture spatial variability in experimental areas of central Italy. J. Hydrol. 2007, 333, 356–373. [Google Scholar] [CrossRef]
- Brocca, L.; Tullo, T.; Melone, F.; Moramarco, T.; Morbidelli, R. Catchment scale soil moisture spatial–temporal variability. J. Hydrol. 2012, 422, 63–75. [Google Scholar] [CrossRef]
- Zucco, G.; Brocca, L.; Moramarco, T.; Morbidelli, R. Influence of land use on soil moisture spatial–temporal variability and monitoring. J. Hydrol. 2014, 516, 193–199. [Google Scholar] [CrossRef]
- Yu, B.; Liu, G.; Liu, Q.; Wang, X.; Feng, J.; Huang, C. Soil moisture variations at different topographic domains and land use types in the semi-arid Loess Plateau, China. Catena 2018, 165, 125–132. [Google Scholar] [CrossRef]
- Robinson, D.A.; Gardner, C.M.K.; Cooper, J.D. Measurement of relative permittivity in sandy soils using TDR, capacitance and theta probes: Comparison, including the effects of bulk soil electrical conductivity. J. Hydrol. 1999, 223, 198–211. [Google Scholar] [CrossRef]
- Di Matteo, L.; Pauselli, C.; Valigi, D.; Ercoli, M.; Rossi, M.; Guerra, G.; Cambi, C.; Ricco, R.; Vinti, G. Reliability of water content estimation by profile probe and its effect on slope stability. Landslides 2018, 15, 173–180. [Google Scholar] [CrossRef]
- Dhakal, M.; West, C.P.; Deb, S.K.; Kharel, G.; Ritchie, G.L. Field calibration of PR2 capacitance probe in Pullman clay-loam soil of Southern High Plains. Agrosystems Geosci. Environ. 2019, 2, 1–7. [Google Scholar] [CrossRef]
- Di Matteo, L.; Spigarelli, A.; Ortenzi, S. Processes in the Unsaturated Zone by Reliable Soil Water Content Estimation: Indications for Soil Water Management from a Sandy Soil Experimental Field in Central Italy. Sustainability 2020, 13, 227. [Google Scholar] [CrossRef]
- Kargas, G.; Londra, P.; Anastasatou, M.; Moustakas, N. The effect of soil iron on the estimation of soil water content using dielectric sensors. Water 2020, 12, 598. [Google Scholar] [CrossRef]
- Ortenzi, S.; Mangoni, M.; Di Matteo, L. Estimating moisture content and hydraulic properties of unsaturated sandy soils of Tiber River (Central Italy): Integrating data from calibrated PR2/6 probe and hydraulic property estimator. Ital. J. Groundw. 2022, 11, 17–25. [Google Scholar] [CrossRef]
- Vereecken, H.; Huisman, J.A.; Pachepsky, Y.; Montzka, C.; Van Der Kruk, J.; Bogena, H.; Weihermüller, L.; Herbst, M.; Martinez, G.; Vanderborght, J. On the spatio-temporal dynamics of soil moisture at the field scale. J. Hydrol. 2014, 516, 76–96. [Google Scholar] [CrossRef]
- Wagner, W.; Blöschl, G.; Pampaloni, P.; Calvet, J.-C.; Bizzarri, B.; Wigneron, J.P.; Kerr, Y. Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Hydrol. Res. 2007, 38, 1–20. [Google Scholar] [CrossRef]
- Dorigo, W.A.; Scipal, K.; Parinussa, R.M.; Liu, Y.Y.; Wagner, W.; De Jeu, R.A.; Naeimi, V. Error characterisation of global active and passive microwave soil moisture datasets. Hydrol. Earth Syst. Sci. 2010, 14, 2605–2616. [Google Scholar] [CrossRef]
- 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]
- Dorigo, W.A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Xaver, A.; Gruber, A.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; et al. The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci. 2011, 15, 1675–1698. [Google Scholar] [CrossRef]
- Zeng, J.; Chen, K.S.; Bi, H.; Chen, Q. A preliminary evaluation of the SMAP radiometer soil moisture product over United States and Europe using ground-based measurements. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4929–4940. [Google Scholar] [CrossRef]
- Beck, H.E.; Pan, M.; Miralles, D.G.; Reichle, R.H.; Dorigo, W.A.; Hahn, S.; Sheffield, J.; Karthikeyan, L.; Balsamo, G.; Parinussa, R.M.; et al. Evaluation of 18 satellite-and model-based soil moisture products using in situ measurements from 826 sensors. Hydrol. Earth Syst. Sci. 2021, 25, 17–40. [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. Discuss. 2021, 25, 5749–5804. [Google Scholar] [CrossRef]
- Al-Yaari, A.; Wigneron, J.P.; Dorigo, W.; Colliander, A.; Pellarin, T.; Hahn, S.; Mialon, A.; Richaume, P.; Fernandex-Moran, R.; Fan, L.; et al. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements. Remote Sens. Environ. 2019, 224, 289–303. [Google Scholar] [CrossRef]
- ASSIMO Project. Available online: http://assimo.meteoromania.ro/ (accessed on 15 June 2024).
- International Soil Moisture Network Data Access. Available online: https://ismn.earth/en/dataviewer/ (accessed on 15 June 2024).
- Bazzi, H.; Baghdadi, N.; El Hajj, M.; Zribi, M.; Belhouchette, H. A comparison of two soil moisture products S 2 MP and copernicus-SSM over southern France. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 3366–3375. [Google Scholar] [CrossRef]
- Dari, J.; Morbidelli, R.; Saltalippi, C.; Massari, C.; Brocca, L. Spatial-temporal variability of soil moisture: Addressing the monitoring at the catchment scale. J. Hydrol. 2019, 570, 436–444. [Google Scholar] [CrossRef]
- Cui, C.; Xu, J.; Zeng, J.; Chen, K.S.; Bai, X.; Lu, H.; Chen, Q.; Zhao, T. Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sens. 2017, 10, 33. [Google Scholar] [CrossRef]
- Minea, G.; Moroşanu, G.A. Research of water balance at hydrological micro-scale in the Aldeni Experimental Basin (Romania). Forum Geogr. 2014, XIII, 185–192. [Google Scholar] [CrossRef]
- Mătreață, S. Influenta Factorilor Fizico-Geografici Asupra Scurgerii Apei in Bazine Hidrografice Mici cu Exemplificari pe Raurile din Romania. Ph.D. Thesis, Romanian Academy of Geography, Bucharest, Romania, 2009; p. 341. [Google Scholar]
- Minea, G.; Adler, M.J.; Pătru, G. A hydrometric and hydrological approach test at microscale. Procedia Environ. Sci. 2016, 32, 275–280. [Google Scholar] [CrossRef]
- Minea, G.; Moroşanu, G.A. Micro-scale hydrological field experiments in Romania. Open Geosci. 2016, 8, 154–160. [Google Scholar] [CrossRef]
- Minea, G.; Ciobotaru, N.; Ioana-Toroimac, G.; Mititelu-Ionuș, O.; Neculau, G.; Gyasi-Agyei, Y.; Rodrigo-Comino, J. Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas. Sci. Rep. 2022, 12, 9393. [Google Scholar] [CrossRef]
- Jipa, D.C.; Olariu, C.; Marinescu, N.; Olteanu, R.; Brustur, T. A late Neogene marker sequence in the Dacian Basin (Paratethys Realm). Genetic and stratigraphic significance. GeoEcomarina 2007, 13, 121–138. [Google Scholar]
- Jipa, D.; Olariu, C. Dacian Basin, Depositional architecture and sedimentary history of paratethys sea. GeoEcomarina Spec. Publ. 2009, 3, 268. [Google Scholar]
- Minea, G.; Ioana-Toroimac, G.; Moroşanu, G. The dominant runoff processes on grassland versus bare soil hillslopes in a temperate environment-An experimental study. J. Hydrol. Hydromech. 2019, 67, 297–304. [Google Scholar] [CrossRef]
- Minea, G.; Adler, M.J.; Moroşanu, G.; Neculau, G. The relationship between flow rates and land use at plot scale in the Voinesti experimental basin (Romania). Sci. Papers. Ser. E Land Reclam. Earth Obs. Surv. Environ. Eng. 2015, 4, 88–94. [Google Scholar]
- Colliander, A.; Jackson, T.J.; Bindlish, R.; Chan, S.; Das, N.; Kim, S.B.; Cosh, M.H.; Dunbar, R.S.; Danng, 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]
- Maftei, C.; Chevallier, P.; Ciurea, C.; Rosu, L. Considerations Concerning the Characteristics of Permeability of the Podzolic soil in Voinesti Catchment. “Ovidius” university annals Constanza. Ser. Civ. Eng. 2002, 1, 525. [Google Scholar]
- Wagner, W.; Sabel, D.; Doubkova, M.; Bartsch, A.; Pathe, C. The potential of Sentinel-1 for monitoring soil moisture with a high spatial resolution at global scale. In Proceedings of the Earth Observation and Water Cycle Science, Frascati, Italy, 18–20 November 2009; Volume 3, p. 60. [Google Scholar]
- Balenzano, A.; Mattia, F.; Satalino, G.; Lovergine, F.P.; Palmisano, D.; Peng, J.; Marzahn, P.; Wegmuller, U.; Cartus, O.; Dabrowska-Zielinska, K.; et al. Sentinel-1 soil moisture at 1 km resolution: A validation study. Remote Sens. Environ. 2021, 263, 112554. [Google Scholar] [CrossRef]
- Wagner, W. Soil Moisture Retrieval from ERS Scatterometer Data; European Commission, Joint Research Centre, Space Applications Institute: Brussels, Belgium, 1998. [Google Scholar]
- 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]
- Naeimi, V. Model Improvements and Error Characterization for Global ERS and METOP Scatterometer Soil Moisture Data. Ph.D. Thesis, Technische Universität Wien, Vienna, Austria, 2009. [Google Scholar]
- Melzer, T. Vegetation modelling in WARP 6.0. In Proceedings of the EUMETSAT Meteorological Satellite Conference, Vienna, Austria, 16–20 September 2013. [Google Scholar]
- Vreugdenhil, M.; Dorigo, W.A.; Wagner, W.; De Jeu, R.A.; Hahn, S.; Van Marle, M.J. Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval. IEEE Trans. Geosci. Remote Sens. 2016, 54, 3513–3531. [Google Scholar] [CrossRef]
- Hahn, S.; Reimer, C.; Vreugdenhil, M.; Melzer, T.; Wagner, W. Dynamic characterization of the incidence angle dependence of backscatter using metop ASCAT. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 2348–2359. [Google Scholar] [CrossRef]
- Bauer-Marschallinger, B.; Freeman, V.; Cao, S.; Paulik, C.; Schaufler, S.; Stachl, T.; Modanesi, S.; Massari, C.; Ciabatta, L.; Brocca, L.; et al. Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles. IEEE Trans. Geosci. Remote Sens. 2019, 57, 520–539. [Google Scholar] [CrossRef]
- Wagner, W.; Lemoine, G.; Rott, H. A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data. Remote Sens. Environ. 1999, 70, 191–207. [Google Scholar] [CrossRef]
- Albergel, C.; Rüdiger, C.; Pellarin, T.; Calvet, J.-C.; Fritz, N.; Froissard, F.; Suquia, D.; Petitpa, A.; Piguet, B.; Martin, E. From near-surface to root-zone soil moisture using an exponential filter: An assessment of the method based on in-situ observations and model simulations. Hydrol. Earth Syst. Sci. 2008, 12, 1323–1337. [Google Scholar] [CrossRef]
- Ceballos, A.; Scipal, K.; Wagner, W.; Martínez-Fernández, J. Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero Basin, Spain. Hydrol. Process. 2005, 19, 1549–1566. [Google Scholar] [CrossRef]
- de Lange, R.; Beck, R.; van de Giesen, N.; Friesen, J.; de Wit, A.; Wagner, W. Scatterometer-derived soil moisture calibrated for soil texture with a one-dimensional water-flow model. IEEE Trans. Geosci. Remote Sens. 2008, 46, 4041–4049. [Google Scholar] [CrossRef]
- Paulik, C.; Dorigo, W.; Wagner, W.; Kidd, R. Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network. Int. J. Appl. Earth Obs. Geoinf. 2014, 30, 1–8. [Google Scholar] [CrossRef]
- Bauer-Marschallinger, B.; Paulik, C.; Hochstöger, S.; Mistelbauer, T.; Modanesi, S.; Ciabatta, L.; Massari, C.; Brocca, L.; Wagner, W. Soil moisture from fusion of scatterometer and SAR: Closing the scale gap with temporal filtering. Remote Sens. 2018, 10, 1030. [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.; Jackson, T.; Johnson, J.; et al. The soil moisture active passive (SMAP) mission. Proc. IEEE 2010, 98, 704–716. [Google Scholar] [CrossRef]
- Wang, J.R.; Engman, E.T.; Mo, T.; Schmugge, T.J.; Shiue, J. The effects of soil moisture, surface roughness, and vegetation on L-band emission and backscatter. IEEE Trans. Geosci. Remote Sens. 1987, GE-25, 825–833. [Google Scholar] [CrossRef]
- Das, N.N.; Entekhabi, D.; Dunbar, R.S.; Chaubell, M.J.; Colliander, A.; Yueh, S.; Jagdhuber, T.; Chen, F.; Corw, W.; O’neil, P.E.; et al. The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product. Remote Sens. Environ. 2019, 233, 111380. [Google Scholar] [CrossRef]
- National Snow and Ice Data Center. SMAP L4 Global 3-Hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 7; Data set ID: SPL4SMGP; National Snow and Ice Data Center: Boulder, CO, USA, 2015. [Google Scholar] [CrossRef]
- Reichle, R.; Koster, R.; De Lannoy, G.; Crow, W.; Kimball, J. Level 4 Surface and Root Zone Soil Moisture (L4_SM) Data Product; Technical Note; NASA: Washington, DC, USA, 2014. [Google Scholar]
- Brocca, L.; Hasenauer, S.; Lacava, T.; Melone, F.; Moramarco, T.; Wagner, W.; Dorigo, W.; Matgen, P.; Martínez-Fernández, J.; Llorens, P.; et al. Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sens. Environ. 2011, 115, 3390–3408. [Google Scholar] [CrossRef]
- Fan, J.; Han, Q.; Tan, S.; Li, J. Evaluation of six satellite-based soil moisture products based on in situ measurements in Hunan Province, Central China. Front. Environ. Sci. 2022, 10, 829046. [Google Scholar] [CrossRef]
- Dente, L.; Vekerdy, Z.; Wen, J.; Su, Z. Maqu network for validation of satellite-derived soil moisture products. Int. J. Appl. Earth Obs. Geoinf. 2012, 17, 55–65. [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]
- Western, A.W.; Zhou, S.L.; Grayson, R.B.; McMahon, T.A.; Blöschl, G.; Wilson, D.J. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. J. Hydrol. 2004, 286, 113–134. [Google Scholar] [CrossRef]
- Penna, D.; van Meerveld, H.J.; Oliviero, O.; Zuecco, G.; Assendelft, R.S.; Dalla Fontana, G.; Borga, M. Seasonal changes in runoff generation in a small forested mountain catchment. Hydrol. Process. 2015, 29, 2027–2042. [Google Scholar] [CrossRef]
- Paciolla, N.; Corbari, C.; Al Bitar, A.; Kerr, Y.; Mancini, M. Irrigation and precipitation hydrological consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 remotely sensed soil moisture products. Remote Sens. 2020, 12, 3737. [Google Scholar] [CrossRef]
- Reichle, R.H.; Koster, R.D. Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett. 2004, 31, L19501. [Google Scholar] [CrossRef]
- Brocca, L.; Melone, F.; Moramarco, T.; Wagner, W.; Albergel, C. Scaling and filtering approaches for the use of satellite soil moisture observations. In Remote Sensing of Energy Fluxes and Soil Moisture Content; CRC Press: Boca Raton, FL, USA, 2013; Volume 411, p. 426. [Google Scholar]
- Karthikeyan, L.; Pan, M.; Wanders, N.; Kumar, D.N.; Wood, E.F. Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons. Adv. Water Resour. 2017, 109, 236–252. [Google Scholar] [CrossRef]
- Singh, A.; Gaurav, K.; Meena, G.K.; Kumar, S. Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India. Remote Sens. 2020, 12, 2266. [Google Scholar] [CrossRef]
- Bai, L.; Lv, X.; Li, X. Evaluation of two SMAP soil moisture retrievals using modeled-and ground-based measurements. Remote Sens. 2019, 11, 2891. [Google Scholar] [CrossRef]
- Albergel, C.; De Rosnay, P.; Gruhier, C.; Muñoz-Sabater, J.; Hasenauer, S.; Isaksen, L.; Kerr, T.; Wagner, W. Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sens. Environ. 2012, 118, 215–226. [Google Scholar] [CrossRef]
- Schmidt, T.; Schrön, M.; Li, Z.; Francke, T.; Zacharias, S.; Hildebrandt, A.; Peng, J. Comprehensive quality assessment of satellite-and model-based soil moisture products against the COSMOS network in Germany. Remote Sens. Environ. 2024, 301, 113930. [Google Scholar] [CrossRef]
- Wang, L.; Qu, J.J. Satellite remote sensing applications for surface soil moisture monitoring: A review. Front. Earth Sci. China 2009, 3, 237–247. [Google Scholar] [CrossRef]
- Wang, Y.; Zhao, H.; Fan, J.; Wang, C.; Ji, X.; Jin, D.; Chen, J. A Review of Earth’s Surface Soil Moisture Retrieval Models via Remote Sensing. Water 2023, 15, 3757. [Google Scholar] [CrossRef]
- Bauer-Marschallinge, B.; Massart, S. Quality Assessment Report, Update 2023. Soil Water Index Collection 1 km Version 1.0. Copernicus, 2023. Available online: https://land.copernicus.eu/en/technical-library/quality-assessment-report-update-2023-soil-water-index-version-1/@@download/file (accessed on 15 June 2024).
- Bauer-Marschallinge, B.; Massart, S. Quality Assessment Report, Update 2023. Surface Soil Moisture Collection 1 km Version 1.0. Copernicus, 2023. Available online: https://land.copernicus.eu/en/technical-library/validation-report-update-2023-surface-soil-moisture-version-1/@@download/file (accessed on 15 June 2024).
- Oliva, R.; Daganzo, E.; Kerr, Y.H.; Mecklenburg, S.; Nieto, S.; Richaume, P.; Gruhier, C. SMOS radio frequency interference scenario: Status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band. IEEE Trans. Geosci. Remote Sens. 2012, 50, 1427–1439. [Google Scholar] [CrossRef]
- Mohammed, P.N.; Aksoy, M.; Piepmeier, J.R.; Johnson, J.T.; Bringer, A. SMAP L-band microwave radiometer: RFI mitigation prelaunch analysis and first year on-orbit observations. IEEE Trans. Geosci. Remote Sens. 2016, 54, 6035–6047. [Google Scholar] [CrossRef]
- Bauer-Marschallinge, B.; Schaufler, S.; Navacchi, C. Validation Report. Surface Soil Moisture Collection 1 km Version 1.0. Copernicus, 2018. Available online: https://land.copernicus.eu/en/technical-library/validation-report-surface-soil-moisture-version-1/@@download/file (accessed on 15 June 2024).
- Mazzariello, A.; Albano, R.; Lacava, T.; Manfreda, S.; Sole, A. Intercomparison of recent microwave satellite soil moisture products on European ecoregions. J. Hydrol. 2023, 626, 130311. [Google Scholar]
- Nash, J.; Sutcliffe, J. River flow forecasting through conceptual models, part I—A discussion and principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
- Tavakol, A.; Rahmani, V.; Quiring, S.M.; Kumar, S.V. Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States. Remote Sens. Environ. 2019, 229, 234–246. [Google Scholar] [CrossRef]
- Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E.A.H. Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States. J. Hydrol. 2017, 546, 393–404. [Google Scholar] [CrossRef]
- Yinglan, A.; Wang, G.; Hu, P.; Lai, X.; Xue, B.; Fang, Q. Root-zone soil moisture estimation based on remote sensing data and deep learning. Environ. Res. 2022, 212, 113278. [Google Scholar]
P > 0 | P < 0 | |
---|---|---|
ΔSM > ξ | A+ | A− |
ΔSM < ξ | A− | A+ |
−ξ < ΔSM < +ξ | n/a | n/a |
SSM1km | SWI1km T-Value = 2 | L2_SM_SP am | L2_SM_SP apm | SP_L4_SM ssm | |
---|---|---|---|---|---|
nT | 842 | 1973 | 537 | 506 | 2191 |
% A− | 58 | 29 | 43 | 45 | 25 |
% A+ | 42 | 71 | 57 | 55 | 75 |
In Situ Dataset | Statistical Test | Grid Pixel | SSM1km | SWI1km | T-Values |
---|---|---|---|---|---|
Probe P1 θn_10 | R | 1 | 0.444 | 0.566 | 5 |
rho | 0.477 | 0.666 | |||
RMSE (m3/m3) | 0.191 | 0.168 | |||
n | 471 | 1220 | |||
Probe P2 θn_10 | R | 1 | 0.397 | 0.528 | 2 |
rho | 0.410 | 0.583 | |||
RMSE (m3/m3) | 0.226 | 0.157 | |||
Bias | |||||
n | 473 | 1232 | |||
Probe P3 θn_10 | R | 1 | 0.478 | 0.600 | 5 |
rho | 0.522 | 0.700 | |||
RMSE (m3/m3) | 0.148 | 0.126 | |||
n | 503 | 1275 | |||
Probe P4 θn_10 | R | 1 | 0.429 | 0.586 | 5 |
rho | 0.459 | 0.652 | |||
RMSE (m3/m3) | 0.160 | 0.135 | |||
n | 471 | 1213 | |||
Probe P5 θn_10 | R | 3 | 0.345 | 0.584 | 5 |
rho | 0.333 | 0.670 | |||
RMSE (m3/m3) | 0.149 | 0.112 | |||
n | 490 | 1164 | |||
Probe P6 θn_10 | R | 2 | 0.377 | 0.560 | 5 |
rho | 0.411 | 0.667 | |||
RMSE (m3/m3) | 0.152 | 0.129 | |||
n | 451 | 1084 |
Dataset | Statistical Test | SSM1km | SWI1km | T-Values | L2_SM_SP am | L2_SM_SP apm | SP_L4_SM ssm | SP_L4_SM rz |
---|---|---|---|---|---|---|---|---|
R | 0.431 | 0.573 | 5 | 0.418 | 0.421 | 0.651 | - | |
rho | 0.494 | 0.678 | 0.526 | 0.522 | 0.764 | - | ||
RMSE (m3/m3) | 0.135 | 0.115 | 0.155 | 0.157 | 0.104 | - | ||
n | 529 | 1339 | 370 | 347 | 1383 | - | ||
R | - | 0.706 | 5 | - | - | - | - | |
rho | - | 0.717 | - | - | - | - | ||
RMSE (m3/m3) | - | 0.145 | - | - | - | - | ||
n | - | 1314 | - | - | - | - | ||
R | - | 0.672 | 10 | - | - | - | - | |
rho | - | 0.686 | - | - | - | - | ||
RMSE (m3/m3) | - | 0.105 | - | - | - | - | ||
n | - | 1339 | - | - | - | - | ||
R | - | 0.648 | 10 | - | - | - | - | |
rho | - | 0.681 | - | - | - | - | ||
RMSE (m3/m3) | - | 0.117 | - | - | - | - | ||
n | - | 1340 | - | - | - | - | ||
R | - | 0.551 | 15 | - | - | - | - | |
rho | - | 0.648 | - | - | - | - | ||
RMSE (m3/m3) | - | 0.124 | - | - | - | - | ||
n | - | 1339 | - | - | - | - | ||
R | - | - | - | - | - | - | 0.703 | |
rho | - | - | - | - | - | 0.752 | ||
RMSE (m3/m3) | - | - | - | - | - | 0.124 | ||
n | - | - | - | - | - | 1361 |
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
Ortenzi, S.; Cencetti, C.; Mincu, F.-I.; Neculau, G.; Chendeş, V.; Ciabatta, L.; Massari, C.; Di Matteo, L. Comparing Satellite Soil Moisture Products Using In Situ Observations over an Instrumented Experimental Basin in Romania. Remote Sens. 2024, 16, 3283. https://doi.org/10.3390/rs16173283
Ortenzi S, Cencetti C, Mincu F-I, Neculau G, Chendeş V, Ciabatta L, Massari C, Di Matteo L. Comparing Satellite Soil Moisture Products Using In Situ Observations over an Instrumented Experimental Basin in Romania. Remote Sensing. 2024; 16(17):3283. https://doi.org/10.3390/rs16173283
Chicago/Turabian StyleOrtenzi, Sofia, Corrado Cencetti, Florentina-Iuliana Mincu, Gianina Neculau, Viorel Chendeş, Luca Ciabatta, Christian Massari, and Lucio Di Matteo. 2024. "Comparing Satellite Soil Moisture Products Using In Situ Observations over an Instrumented Experimental Basin in Romania" Remote Sensing 16, no. 17: 3283. https://doi.org/10.3390/rs16173283