Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product
Root zone soil moisture (RZSM) is a key variable controlling the soil-vegetation-atmosphere exchanges. Its estimation is vital for monitoring hydrological, meteorological and agricultural processes. A number of large-scale products exist but with a coarse resolution (>1 km), which is not suitable...
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Elsevier
2025-06-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425002215 |
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| author | Nadia Ouaadi Abdelghani Chehbouni Emna Ayari Bouchra Ait Hssaine Jamal ElFarkh Michel Le Page Salah Er-Raki Aaron Boone |
| author_facet | Nadia Ouaadi Abdelghani Chehbouni Emna Ayari Bouchra Ait Hssaine Jamal ElFarkh Michel Le Page Salah Er-Raki Aaron Boone |
| author_sort | Nadia Ouaadi |
| collection | DOAJ |
| description | Root zone soil moisture (RZSM) is a key variable controlling the soil-vegetation-atmosphere exchanges. Its estimation is vital for monitoring hydrological, meteorological and agricultural processes. A number of large-scale products exist but with a coarse resolution (>1 km), which is not suitable for plot-scale studies. The aim of this work is to map RZSM, for the first time, at very high spatial resolution using a very high spatial resolution surface soil moisture (SSM) product and a recursive exponential filter. SSM is estimated from Sentinel-1 data using the water cloud model at a resolution of approximately 50 m. The approach was evaluated on a database consisting of 12 fields, including 7 winter wheat and 5 summer maize fields, irrigated using different techniques. The results show that the approach performs reasonably well using Sentinel-1 SSM product with correlation coefficient (R) between 0.3 and 0.82, root-mean-square error (RMSE) between 0.05 and 0.12 m3/m3 and a bias in the range −0.1–0.07 m3/m3, at 15–20 cm depth. This is equivalent to R = 0.6, RMSE = 0.12 m3/m3 and bias = 0.07 m3/m3 using the entire database, which is quite low compared to the use of in situ SSM measurements (R = 0.81, RMSE = 0.07 m3/m3 and bias = 0.03 m3/m3). This is related to inaccuracies in the SSM product, where fields with good SSM estimation also resulted in good RZSM estimation and conversely. In addition to SSM, the approach is also sensitive to its time constant T. Analysis of RZSM sensitivity to T shows that the optimum T value depends on soil texture, climate and measurement depth. In particular, low optimum T values (1 day) are obtained for loamy and sandy loam soils, while higher values (5–10 days) are optimal for soils with a high clay fraction, at 15–20 cm depth. These values increase with soil depth and are influenced by seasonal atmospheric demand. Combined to reasonable statistical metrics, the spatial variability depicted by the RZSM maps opens up prospects for high-resolution RZSM mapping from Sentinel-1 SSM data using a simple approach over annual crops. This is of prime relevance for agricultural applications requiring very high-resolution estimation at plot scale, such as crop yield, irrigation and fertilizer management, as well as for the assessment of inter-plot variability. |
| format | Article |
| id | doaj-art-31055a4db5c648eea7fa40504b0a11ff |
| institution | OA Journals |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Agricultural Water Management |
| spelling | doaj-art-31055a4db5c648eea7fa40504b0a11ff2025-08-20T01:55:31ZengElsevierAgricultural Water Management1873-22832025-06-0131410950710.1016/j.agwat.2025.109507Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture productNadia Ouaadi0Abdelghani Chehbouni1Emna Ayari2Bouchra Ait Hssaine3Jamal ElFarkh4Michel Le Page5Salah Er-Raki6Aaron Boone7CRSA, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Corresponding author.CRSA, Mohammed VI Polytechnic University, Ben Guerir, MoroccoVPE, Swedish University of Agricultural Sciences, Umeå, SwedenCRSA, Mohammed VI Polytechnic University, Ben Guerir, MoroccoCRSA, Mohammed VI Polytechnic University, Ben Guerir, MoroccoCESBIO, University of Toulouse, IRD/CNRS/UPS/CNES, Toulouse, FranceCRSA, Mohammed VI Polytechnic University, Ben Guerir, Morocco; CAB, Centre AgroBiotech-URL-CNRST-05, Cadi Ayyad University, Marrakech, MoroccoGMME/SURFACE, Meteo-France/CNRM, Toulouse, FranceRoot zone soil moisture (RZSM) is a key variable controlling the soil-vegetation-atmosphere exchanges. Its estimation is vital for monitoring hydrological, meteorological and agricultural processes. A number of large-scale products exist but with a coarse resolution (>1 km), which is not suitable for plot-scale studies. The aim of this work is to map RZSM, for the first time, at very high spatial resolution using a very high spatial resolution surface soil moisture (SSM) product and a recursive exponential filter. SSM is estimated from Sentinel-1 data using the water cloud model at a resolution of approximately 50 m. The approach was evaluated on a database consisting of 12 fields, including 7 winter wheat and 5 summer maize fields, irrigated using different techniques. The results show that the approach performs reasonably well using Sentinel-1 SSM product with correlation coefficient (R) between 0.3 and 0.82, root-mean-square error (RMSE) between 0.05 and 0.12 m3/m3 and a bias in the range −0.1–0.07 m3/m3, at 15–20 cm depth. This is equivalent to R = 0.6, RMSE = 0.12 m3/m3 and bias = 0.07 m3/m3 using the entire database, which is quite low compared to the use of in situ SSM measurements (R = 0.81, RMSE = 0.07 m3/m3 and bias = 0.03 m3/m3). This is related to inaccuracies in the SSM product, where fields with good SSM estimation also resulted in good RZSM estimation and conversely. In addition to SSM, the approach is also sensitive to its time constant T. Analysis of RZSM sensitivity to T shows that the optimum T value depends on soil texture, climate and measurement depth. In particular, low optimum T values (1 day) are obtained for loamy and sandy loam soils, while higher values (5–10 days) are optimal for soils with a high clay fraction, at 15–20 cm depth. These values increase with soil depth and are influenced by seasonal atmospheric demand. Combined to reasonable statistical metrics, the spatial variability depicted by the RZSM maps opens up prospects for high-resolution RZSM mapping from Sentinel-1 SSM data using a simple approach over annual crops. This is of prime relevance for agricultural applications requiring very high-resolution estimation at plot scale, such as crop yield, irrigation and fertilizer management, as well as for the assessment of inter-plot variability.http://www.sciencedirect.com/science/article/pii/S0378377425002215Root zone soil moistureSurface soil moistureRadar remote sensingExponentiel filterCrops |
| spellingShingle | Nadia Ouaadi Abdelghani Chehbouni Emna Ayari Bouchra Ait Hssaine Jamal ElFarkh Michel Le Page Salah Er-Raki Aaron Boone Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product Agricultural Water Management Root zone soil moisture Surface soil moisture Radar remote sensing Exponentiel filter Crops |
| title | Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product |
| title_full | Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product |
| title_fullStr | Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product |
| title_full_unstemmed | Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product |
| title_short | Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product |
| title_sort | root zone soil moisture mapping at very high spatial resolution using radar derived surface soil moisture product |
| topic | Root zone soil moisture Surface soil moisture Radar remote sensing Exponentiel filter Crops |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425002215 |
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