Filling method for soil moisture based on BP neural network
| Autor: | Han Yingjuan, Wang Jing, Yang Xiaoxia, Cui Zhaoyun, Zhang Chengming, Yu Fan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Network architecture
Artificial neural network 0208 environmental biotechnology 02 engineering and technology Network layer Backpropagation Normalized Difference Vegetation Index 020801 environmental engineering Kriging Discrete cosine transform General Earth and Planetary Sciences Environmental science Moderate-resolution imaging spectroradiometer Remote sensing |
| Zdroj: | Journal of Applied Remote Sensing. 12:1 |
| ISSN: | 1931-3195 |
| DOI: | 10.1117/1.jrs.12.042806 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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