Paper Info
Title
Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method.
Abstract
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 m(3)/m(3)) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m(3)/m(3)). 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.
Year
DOI
Venue
2019
10.3390/rs11030284
REMOTE SENSING
Keywords
Field
DocType
multilayer soil moisture mapping,RF method,remote sensing,ground monitoring
Remote sensing,Water content,Geology
Journal
Volume
Issue
Citations 
11
3
0
PageRank 
References 
Authors
0.34
17
7
Name
Order
Citations
PageRank
Linglin Zeng151.13
Shun Hu200.34
Daxiang Xiang3193.93
Xiang Zhang419534.67
Deren Li562074.26
Lin Li633.21
Tingqiang Zhang700.34