Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning

•Deep learning models predicted soil moisture well with limited SMAP samples.•Transfer learning improved predictions with additional samples from ERA5-land.•Transfer ConvLSTM performed the best with over 90% variation explained.•The predictive ability of different factors was widely investigated.•Transfer learning is advocated for datasets with limited samples like SMAP. The skillful soil moisture (SM) for the Soil Moisture Active Passive (SMAP) L4 product can provide substantial value for many practical applications including ecosystem management and precision agriculture. Deep learning (DL) models provide powerful methods for hydrologic variables’ prediction such as SM. However, the sample size of daily SM in the SMAP product is quite small, which may lead to overfitting and further impact the accuracy of DL models. From this, we first tested whether excellent predictive performance can be achieved with limited SMAP samples by the Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) models, which are frequent used for hydrologic prediction. Then we pre-trained the DL models in the source domain (ERA5-land) and fine-tuned them in the target domain (SMAP). The results show that the transfer ConvLSTM model had the highest R2 ranging from 0.909 to 0.916 and the lowest RMSE ranging from 0.0239 to 0.0247 for the lead time of 3, 5 and 7 days, and the regression lines between the predicted and the observed SM were closer to the ideal line (y = x) than all the other DL models. All the performances of transfer DL models were better than those of their corresponding DL models without transfer learning and some regions witnessed an increased explained variation over 20%. The predictive ability of different factors (i.e., lagged SM, soil temperature, season, and precipitation) has been widely discussed in this paper. According the results, we advocate for applying cross-source transfer learning with DL models for SM prediction in newly built datasets.