Automated Production of Medium Spatial Resolution Soil Moisture Remote Sensing Products Based on Google Earth Engine

Soil moisture is a critical parameter in hydrology, meteorology, and agricultural applications. To address the limitations of low model generalization and difficulties in rapid data acquisition, this article integrated a downscaling soil moisture estimation model with the Google Earth Engine (GEE) platform to develop a technique for automated production of large-scale, medium spatial resolution soil moisture remote sensing products. Using Henan Province as a case study, this article produced monthly average soil moisture data with a spatial resolution of 1 km for 2018. It provided a detailed description of the automated production workflow implemented on the GEE platform and presented the 1 km monthly composite soil moisture results for the agricultural regions of Henan Province. The findings revealed that soil moisture in Henan Province generally exhibited a spatial trend of lower values in the north and higher values in the south across most months. In the east-west direction, soil moisture showed no clear gradient, though values in eastern Henan were consistently lower. Additionally, the spatial variations in soil moisture correlated positively with precipitation and evapotranspiration. This article successfully addressed the challenges of low model generalizability and difficulty in rapid data acquisition, achieving the automated generation of soil moisture remote sensing products. This method will fill the research shortcoming and hold significant importance for regional soil moisture estimation and application studies.