%0 Dataset
%T SGD-SM 2.0: Seamless Dataset for Long term Improvement of Global Daily Soil Moisture (2002-2022)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/5233d05a-0465-4109-a1bc-d46918d6be10
%W NCDC
%R 10.5281/zenodo.6041561
%A Yuan Qiangqiang
%A Jin Taoyong
%K Soil moisture;SGD-SM 2.0;soil moisture products;precipitation products
%X Due to the limitations of satellite orbit coverage and soil moisture retrieval modes, satellite based daily soil moisture products inevitably have the disadvantage of low global land coverage. The SGD-SM 2.0 dataset uses three sensors, namely AMSR-E, AMSR2, and WindSat. The global daily precipitation product is integrated into the proposed reconstruction model. An integrated Long Short Term Memory Convolutional Neural Network (LSTM-CNN) was proposed to fill in the gaps and missing areas in daily soil moisture products. In situ validation and time series validation have demonstrated the reconstruction accuracy and usability of SGD-SM 2.0 (R: 0.672, RMSE: 0.096, MAE: 0.078). The time series curve of the improved SGD-SM 2.0 is consistent with the original daily time series distribution of soil moisture and precipitation. Compared with SGD-SM 1.0, the improved SGD-SM 2.0 has significant advantages in reconstruction accuracy and time series consistency.