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

Authors :
Yuquan Qu
Zhongli Zhu
Linna Chai
Shaomin Liu
Carsten Montzka
Jin Liu
Xiaofan Yang
Zheng Lu
Rui Jin
Xiang Li
Zhixia Guo
Jie Zheng
Source :
Remote Sensing, Vol 11, Iss 6, p 683 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

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.

Details

Language :
English
ISSN :
20724292
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.29d05761edd04d34a1137f9fb2cab946
Document Type :
article
Full Text :
https://doi.org/10.3390/rs11060683