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Open AccessArticle

Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

by Yangxiaoyue Liu 1,2, Yaping Yang 1,3,*, Wenlong Jing 4,5,6 and Xiafang Yue 1,3
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Guangzhou Institute of Geography, Guangzhou 510070, China
Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China
Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 31;
Received: 16 November 2017 / Revised: 20 December 2017 / Accepted: 22 December 2017 / Published: 25 December 2017
(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)
Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values. View Full-Text
Keywords: soil moisture; ESA CCI; downscaling; machine learning; monthly soil moisture; ESA CCI; downscaling; machine learning; monthly
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MDPI and ACS Style

Liu, Y.; Yang, Y.; Jing, W.; Yue, X. Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China. Remote Sens. 2018, 10, 31.

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