A spatial downscaling method for SMAP soil moisture considering vegetation memory and spatiotemporal fusion

Abstract

Soil moisture (SM) plays an essential role in the hydrological cycle, drought monitoring, and water resources management. However, passive microwave remote sensing products offer coarse spatial resolutions (approximately 25–40 km), greatly limiting their applications. In this study, we considered vegetation memory and increased the amount of vegetation data by spatiotemporal fusion, integrated lagged vegetation index and fusion data as model inputs, and downscaled the spatial resolution of SMAP SM using the Random Forest (RF) model as well as multi-source remote sensing data. The downscaled SM was validated with 30 in situ SM measurements and daily precipitation data and explored the contributions of NDVIlagged and NDVIfused in downscaling. The results show that the downscaled SM is highly consistent with the in situ SM measurements. The correlation ranges from 0.628 to 0.849 with an average of 0.689. The root mean square error (RMSE) ranges from 0.024 to 0.094 m3/m3 with an average of 0.057 m3/m3. The proposed method leverages the strengths of the RF model, fusion vegetation data, and lagged vegetation data to improve high-resolution SM accuracy.

Keywords

• Soil moisture downscaling
• SMAP
• random forest
• vegetation memory
• spatiotemporal fusion