Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanc...

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Bibliographic Details
Main Authors: Panpan Yao, Jiancheng Shi, Tianjie Zhao, Hui Lu, Amen Al-Yaari
Format: Article
Language:English
Published: MDPI AG 2017-01-01
Series:Remote Sensing
Subjects:
soil moisture
neural network
long time series
microwave vegetation index
Online Access:http://www.mdpi.com/2072-4292/9/1/35

Internet

http://www.mdpi.com/2072-4292/9/1/35

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