Reply on RC2

Abstract

Satellite-based daily soil moisture products inevitably exist the drawbacks of low-coverage rate in global land, because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models. To solve this issue, Zhang et al. (2021) 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 on temporal range, sudden extreme weather condition, and sequential time-series information. In this work, we develop an improved seamless global daily soil moisture (SGD-SM 2.0) dataset from 2002 to 2022, to overcome above shortages. SGD-SM 2.0 uses three sensors AMSR-E, AMSR2 and WindSat. Global daily precipitation products are assimilated 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 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 consistency 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. SGD-SM 2.0 products are recorded at https://doi.org/10.5281/zenodo.6041561 (Zhang et al., 2022).