SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022

Abstract

The drawbacks of low-coverage rate in global land inevitably exist in satellite-based daily soil moisture products because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models. To solve this issue, Zhang et al. (2021a) generated seamless global daily soil moisture (SGD-SM 1.0) products for the years 2013–2019. Nevertheless, there are still several shortages in SGD-SM 1.0 products, especially in temporal range, sudden extreme weather conditions and sequential time-series information. In this work, we develop an improved seamless global daily soil moisture (SGD-SM 2.0) dataset for the years 2002–2022, to overcome the above-mentioned shortages. The SGD-SM 2.0 dataset uses three sensors, i.e. AMSR-E, AMSR2 and WindSat. Global daily precipitation products are fused into the proposed reconstructing model. We propose an integrated long short-term memory convolutional neural network (LSTM-CNN) to fill the gaps and missing regions in daily soil moisture products. In situ validation and time-series validation testify to the reconstructing accuracy and availability of SGD-SM 2.0 (R: 0.672, RMSE: 0.096, MAE: 0.078). The time-series curves of the improved SGD-SM 2.0 are consistent with the original daily time-series soil moisture and precipitation distribution. Compared with SGD-SM 1.0, the improved SGD-SM 2.0 outperforms on reconstructing accuracy and time-series consistency. The SGD-SM 2.0 products are recorded in https://doi.org/10.5281/zenodo.6041561 (Zhang et al., 2022).