Filling method for soil moisture based on BP neural network

Xiaoxia Yang; Chengming Zhang; Cui Zhaoyun; Yu Fan; Jing Wang; Yingjuan Han

doi:10.1117/1.JRS.12.042806

24 September 2018 Filling method for soil moisture based on BP neural network

Xiaoxia Yang, Chengming Zhang, Cui Zhaoyun, Yu Fan, Jing Wang, Yingjuan Han

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 4, 042806 (September 2018). https://doi.org/10.1117/1.JRS.12.042806

Abstract

Soil moisture data obtained by inversion of Fengyun 3B remote sensing data, are widely used in drought monitoring and global climate change research, however, some regional data are missing in this data set, which reduces the application effect. Based on backpropagation neural network (BPNN), we established a filling method and filled the missing area with moderate resolution imaging spectroradiometer (MODIS) inversion products, including land surface temperature, normalized difference vegetation index, and albedo. We named it the multilayer BPNN filling algorithm. The algorithm consists of two neural network layers. The first network layer is used for the spatial scaling of MODIS inversion products, and the second network layer uses the scaling products to further generate soil moisture values. We compared the proposed method to a discrete cosine transform and partial least square (DCT-PLS) and a kriging using the same data set. The experiments demonstrate that our method could obtain good filling results in both homogeneous areas and areas with high data variations, whereas DCT-PLS and kriging could only get good filling results in homogeneous areas.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Xiaoxia Yang, Chengming Zhang, Cui Zhaoyun, Yu Fan, Jing Wang, and Yingjuan Han "Filling method for soil moisture based on BP neural network," Journal of Applied Remote Sensing 12(4), 042806 (24 September 2018). https://doi.org/10.1117/1.JRS.12.042806

Received: 28 March 2018; Accepted: 27 August 2018; Published: 24 September 2018

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