Impact of vegetation water content information on soil moisture retrievals in agricultural regions: An analysis based on the SMAPVEX16-MicroWEX dataset

REMOTE SENSING OF ENVIRONMENT

卷:265

Article

Judge, Jasmeet¹  Liu, Pang-Wei^2,3  Monsivais-Huertero, Alejandro⁴  Bongiovanni, Tara⁵  Chakrabarti, Subit⁶  Steele-Dunne, Susan C.⁷  Preston, Daniel¹  Allen, Samantha¹  Bermejo, Jaime Polo⁷  Rush, Patrick¹  DeRoo, Roger⁸  Colliander, Andreas⁹  Cosh, Michael¹⁰ 

[1] Univ Florida, Inst Food & Agr Sci, Ctr Remote Sensing, Dept Agr & Biol Engn, Gainesville, FL 32611 USA

[2] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA

[3] Sci Syst & Applicat Inc, Lanham, MD 20706 USA

[4] Inst Politecn Nacl, Escuela Super Ingn Mecan & Elect Ticoman, Miguel Othon de Mendizabal S-N, Mexico City 07320, DF, Mexico

[5] Univ Texas Austin, Bur Econ Geol, Jackson Sch Geosci, Austin, TX USA

[6] Indigo Ag Inc, Boston, MA USA

[7] Delft Univ Technol, Fac Civil Engn & Geosci, Dept Geosci & Remote Sensing, NL-2628 CN Delft, Netherlands

[8] Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA

[9] CALTECH, NASA, Jet Prop Lab, Pasadena, CA 91125 USA

[10] USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD USA

关键词: SMAP;    Active and passive microwave;    Vegetation water content;    Soil moisture;    SMAPVEX16-IA;    SMAPVEX16-MicroWEX;   

DOI  :  10.1016/j.rse.2021.112623

来源: Elsevier

【 摘 要 】

Soil moisture (SM) retrieval in agricultural regions during the growing seasons is particularly challenging due to high spatial variability and dynamic vegetation conditions. The retrievals have been problematic even when the passive signatures at different spatial scales match well since they depend upon the accuracy of vegetation information such as the vegetation water content (VWC). The VWC used in the Soil Moisture Active Passive (SMAP) single channel retrieval algorithm (SCA) is derived from remotely sensed, climatologically-based Normalized Difference Vegetation Index (NDVI), which does not respond to real-time vegetation dynamics and is prone to saturation. This study explored the differences and seasonal trend in passive signatures and SM at satellite- and field-scales and investigated uncertainties in retrievals arising from different approaches used to estimate VWC from optical and radar indices. It used high temporal resolution, ground-based data collected during the SMAP Validation-Microwave Water, Energy Balance Experiment in 2016 (SMAPVEX16-MicroWEX) during a growing season of corn and soybean. Overall, the brightness temperatures (TB) from SMAP matched well with the upscaled, ground-based TB, with a root mean square differences (RMSDs) of about 5 K. In contrast, the SMAP SM retrievals were worse during rapid vegetation growth in the mid-season, with higher RMSDs compared to the upscaled in situ SM, than those in the late-season. In addition, the ground-based TB from corn and soybean were similar in the early and the late seasons, while their emission differences were > 40 K in the mid season. This indicates the importance of accurate VWC information, particularly during the early and late growing seasons, to account for sub-pixel heterogeneities in agricultural regions. VWC obtained from five optical and radar indices were used in the SMAP SCA for soil moisture retrieval for the entire growing season of corn. The NDVI-based VWC provided SM retrievals that were consistently lower compared to those using in situ VWC, with a higher RMSD of 0.030 m3/m3 and a negative bias of 0.020 m3/m3 for VWC > 4 kg/m2. The Normalized Difference Water Index (NDWI)-derived VWC resulted in lower SM retrieval RMSD of 0.022 m3/m3 when compared with in situ SM. Among the three radar indices, vertically polarized cross-pol ratio (CRvv)-derived VWC provided similar RMSDs in retrieved SM as the NDWI-derived VWC during the growing season. The radar vegetation index (RVI)derived VWC improved in the late season compared to the in situ VWC and resulted in SM retrievals with RMSDs similar to the CRvv-derived retrievals. Results presented here suggest that SMAP SCA SM retrievals could be improved through the use of near-real time NDWI and CRvv-derived vegetation information. Microwave data are available regardless of cloud cover, so the guaranteed availability of CRvv to capture seasonal and interannual variability is advantageous.

【 授权许可】

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