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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 933–938, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-933-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 933–938, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-933-2022
 
30 May 2022
30 May 2022

SPATIAL DOWNSCALING OF SMAP SOIL MOISTURE USING THE MODIS AND SRTM OBSERVATIONS

J. D. Mohite1, S. A. Sawant1, A. Pandit1, and S. Pappula2 J. D. Mohite et al.
  • 1TCS Research and Innovation, Tata Consultancy Services, Mumbai, India
  • 2TCS Research and Innovation, Tata Consultancy Services, Hyderabad, India

Keywords: Soil Moisture, Downscaling, SMAP, Machine Learning, MODIS

Abstract. The main objective of this study is the spatial downscaling of Soil Moisture Active Passive (SMAP) soil moisture (36 km) using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) products. The study was conducted over India during the post-monsoon (i.e., Rabi) season Daily SMAP soil moisture (SM) data was composited to 3 days to cover the entire study area. MODIS data for the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Albedo, and Land Surface Temperature (LST) were similarly obtained by constructing a three day composite. SMAP soil moisture was used as a dependent variable, whereas, MODIS NDVI, NDWI, Albedo, LST, and SRTM elevation were used as independent variables in a regression analysis for downscaling of SMAP soil moisture. The coefficient of determination (R2) was used to evaluate the performance of multi-variate linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). Each method was used to test the performance of monthly and seasonal models. RFR outperformed MLR and SVR for monthly and seasonal models. Furthermore, a comparison of monthly and seasonal models revealed that the model created on Jan. data performed best (R2=0.80), while R2 of 0.73, 0.61, 0.75, and 0.76 were attained using RFR for seasonal, Dec., Feb., and Mar. models, respectively. In addition, in-situ soil moisture data was used to validate downscaled soil moisture (1 km). Comparison between downscaled soil moisture and in-situ soil moisture showed good agreement with a difference ranging between −9.3 to 7.4 %.