Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 21; p. 5355
• Main Authors: Zhang, Ying, Hou, Jinliang, Huang, Chunlin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Accuracy; Algorithms; Artificial neural networks; ASCAT; Bayesian analysis; Datasets; Environment models; Environmental hazards; Environmental management; Gauges; Ground stations; Ground-based observation; Learning algorithms; Long short-term memory; Machine learning; Meteorological data; Meteorological satellites; Moisture effects; Neural networks; Precipitation; Rain; Rainfall; Rainfall measurement; Remote sensing; River basins; Rivers; Salinity; Satellites; SM2RAIN; SMAP; SMOS; Soil moisture; Soils; Spatial discrimination; Spatial resolution; Topography; Water balance; Weather stations; Wireless sensor networks; Heihe River; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14215355

Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm).