Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under spars...

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Bibliographic Details
Main Authors: Leran Han, Chunmei Wang, Qiyue Liu, Gengke Wang, Tao Yu, Xingfa Gu, Yunzhou Zhang
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Remote Sensing
Subjects:
soil moisture
multi-source fusion
Bayesian maximum entropy framework
Online Access:https://www.mdpi.com/2072-4292/12/23/3916

Internet

https://www.mdpi.com/2072-4292/12/23/3916

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