Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval

Soil moisture is one of the important parameters in Earth system models. In recent years, the retrieval based on machine learning and data fusion of multisource satellite observation data has become one of the effective methods to obtain soil moisture information at a large scale. However, most retrieval studies need to download remote sensing original data first, then preprocess, train the retrieval models, and finally generate products in the offline environment. In order to meet the requirements of long temporal series of large-scale area retrieval, and with the widespread use of machine learning in retrieval studies, the amount of remote sensing data and necessary computing resources are gradually increasing. Moreover, studies usually use a single machine learning retrieval model for the entire study area, which lacks the consideration of geographical differences and spatial heterogeneity of soil moisture. Therefore, we established a geo-intelligent soil moisture retrieval framework completely based on the cloud environment. In this study, a variety of machine learning algorithms were used to fuse multisource observation data mainly including Moderate-Resolution Imaging Spectroradiometer (MODIS) data and other auxiliary data, and the Continental United States (CONUS) was taken as the experimental area to generate soil moisture data with a resolution of 500 m. In addition, this study combines geographical correlation with machine learning models to cope with the spatial heterogeneity of surface soil moisture (SSM). Overall, based on site-based validation, the retrieval model trained under the framework performed well, with an estimation accuracy of 0.716 and 0.0383
$\text{m}^{3} \cdot \text{m}^{-3}$
in terms of coefficient of determination (
$R^{2}$
) and unbiased root-mean-square error (ubRMSE). The establishment of the cloud retrieval framework provides convenience for the whole retrieval process and also provides a new idea for other retrieval studies of geoscience parameters.