Spatiotemporal Seamless Soil Moisture Data Generation by Integrating Microwave Remote Sensing and Model Simulation

Objectives Soil moisture is an important parameter to measure the material and energy exchange between soil and atmosphere. It is a key environmental factor in the fields of hydrology, meteorology, agriculture and so on. Remote sensing inversion and model simulation are two basic means to obtain global soil moisture data. Remote sensing inversion can obtain soil moisture data with relatively high resolution, but there are often time-space gaps while land surface model can simulate spatiotemporal continuous data, but the spatial resolution is often coarse. Both two data cannot meet the fine observation of soil moisture. Therefore, it is very important to develop an effective method to obtain high-resolution, spatiotemporal seamless soil moisture data.

Methods Focusing on their complementary advantages, we propose a global seamless soil moisture data generation method integrating microwave remote sensing and model simulation. Specifically, for the vacant area of 9 km soil moisture data of soil moisture active passive satellite, GLDAS Noah 0.25° model assimilation data is introduced to establish the spatiotemporal fusion model between them. Through this model, Noah data is downscaled to 9 km to fill the vacant area. As for some areas that have not been filled, Noah data is used for fitting, interpolation and filling, and the residual correction is further carried out based on Poisson equation method, and then the spatiotemporal seamless 9 km daily soil moisture data is generated.

Results We evaluated the experimental results by simulation experiment evaluation, site data evaluation and triple collocation evaluation. Experiments show that the global seamless soil moisture data produced in this paper performs well in all evaluation methods.

Conclusions This method can effectively combine the spatial resolution advantage of remote sensing observation with the spatiotemporal continuity advantage of model simulation, provide spatiotemporal seamless global soil moisture data, and better meet the needs of global scale water cycle monitoring and water resources management.