Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Remote Sensing

Daxiang Xiang¹  Lin Li²  Tingqiang Zhang²  Linglin Zeng³  Xiang Zhang⁴  Deren Li⁴  Shun Hu⁵ 

[1] Changjiang River Scientific Research Institute, Changjiang River Water Resources Commission, Wuhan 430015, China;Guangxi Key Laboratory Cultivation Base of Water Engineering Materials, Guangxi Institute of Water Resources Research, Nanning 530023, China;Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China;

关键词: multilayer soil moisture mapping;    RF method;    remote sensing;    ground monitoring;   

DOI  :  10.3390/rs11030284

来源: DOAJ

【 摘 要 】

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m³/m³) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m³/m³). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

【 授权许可】

Unknown