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.
Highlights
Spatiotemporal continuous soil moisture data are an important index for drought monitoring, global climate change research, and ecological environmental change research
Soil moisture data obtained by inversion of Fengyun 3B (FY-3B) remote sensing data are widely used in drought monitoring and global climate change research
The results showed that the new optimization algorithm was efficient and effective, and the inversion results based on Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Applied-Remote-Sensing on 08 Nov 2021 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use integration of TM and ASAR data were obviously better than inversion individuals, that embodied advantages and potential of soil moisture inversion based on integration of active and passive remote sensing
Summary
Spatiotemporal continuous soil moisture data are an important index for drought monitoring, global climate change research, and ecological environmental change research. It has important theoretical and practical significance for regional water resources, agriculture, and animal husbandry management.[1,2] Remote sensing technology has advantages in terms of full space coverage, continuous time coverage, and low cost. It has become an important tool for acquiring soil moisture data.[3]. The presence of clouds, aerosols, and haze leads to missing value in some regional areas, and it further leads to missing soil moisture data.
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