Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Qiang Zhang,Qiangqiang Yuan,Yuan Wang,Jie Li,Fujun Sun,Liangpei Zhang

doi:10.5194/essd-13-1385-2021

Abstract

Abstract. High-quality and long-term soil moisture products are significant for hydrologic monitoring and agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land (just about 30 %–80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling. Through the proposed framework, we generate the seamless daily global (SGD) AMSR2 long-term soil moisture products from 2013 to 2019. To further validate the effectiveness of these products, three verification methods are used as follows: (1) in situ validation, (2) time-series validation, and (3) simulated missing-region validation. Results show that the seamless global daily soil moisture products have reliable cooperativity with the selected in situ values. The evaluation indexes of the reconstructed (original) dataset are a correlation coefficient (R) of 0.685 (0.689), root-mean-squared error (RMSE) of 0.097 (0.093), and mean absolute error (MAE) of 0.079 (0.077). The temporal consistency of the reconstructed daily soil moisture products is ensured with the original time-series distribution of valid values. The spatial continuity of the reconstructed regions is in accordance with the spatial information (R: 0.963–0.974, RMSE: 0.065–0.073, and MAE: 0.044–0.052). This dataset can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).

Highlights

Surface soil moisture is a crucial Earth land characteristic in describing the hydrologic cycle system (Wigneron et al, 2003; Lievens et al, 2015)
The acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land, due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling
The daily soil moisture products are saved in NetCDF4 format

Summary

Introduction

Surface soil moisture is a crucial Earth land characteristic in describing the hydrologic cycle system (Wigneron et al, 2003; Lievens et al, 2015) It can be applied for monitoring droughts and floods in agriculture (Samaniego et al, 2018) and geologic hazards (Long et al, 2014). In the regions close to the Equator, or in the permafrost region, the degree of missing soil moisture data is more serious (Zeng et al, 2015; Santi et al, 2018). This phenomenon greatly disturbs subsequent soil moisture applications, espe-

Methods

Results

Discussion

Conclusion