Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 6; p. 683
• Main Authors: Qu, Yuquan, Zhu, Zhongli, Chai, Linna, Liu, Shaomin, Montzka, Carsten, Liu, Jin, Yang, Xiaofan, Lu, Zheng, Jin, Rui, Li, Xiang, Guo, Zhixia, Zheng, Jie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21-03-2019
• Subjects: AMSR-E; AMSR2; Artificial intelligence; Bias; Brightness; Brightness temperature; Climate change; Climate system; Computer simulation; Data collection; Digital Elevation Models; Exploration; Forest soils; Geosphere; Global climate; Japanese space program; Laboratories; Land surface temperature; Machine learning; Mean square errors; Neural networks; Precipitation; Qinghai–Tibet plateau; random forest; Remote sensing; Root-mean-square errors; Scanning; SMAP; Soil moisture; Soil temperature; Soils; Spatial distribution; Temperature effects; Temporal variations; Time series; Trend analysis; Vegetation; Beijing China; Tibetan Plateau; Tibet; Asia; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11060683

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.