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Abstract Remote sensing-based soil moisture content inversion is an indispensable procedure in drought monitoring; however, the image acquisition process is often influenced by bad weather such as cloud cover and snowfall, or sensor performance defects, which causes missing data. The existing filtering interpolation methods based on time series images have a high requirement on input data and thus are difficult to be widely applied, while the spatial interpolation methods do not work well for the images with missing blocks. In view of the above problems, this paper proposes a missing data filling method based on optimum interpolation, which predicts and fills missing data with the ground observation data as a reference. The authors selected Ningxia as the study area and obtained the soil moisture content in multiple periods using the VCADI index, and conducted missing pixel interpolation using the proposed method with the ground observation data of 16 national meterological stations. Experimental results show that the proposed method performs well in all regions with different levels of missing data. The authors simulated the images with missing blocks and different levels of missing data, and compared the performances between the inverse distance weighted interpolation method, the Kriging interpolation method and the optimum interpolation method. Experimental results show that the method proposed by the authors can obtain more accurate interpolation results.
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| Keywords
soil moisture content
remote sensing inversion
optimum interpolation
missing data
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Corresponding Authors:
Heng DONG
E-mail: junli_geo@126.com;simondong@whut.edu.cn
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Issue Date: 30 May 2018
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2018,
Vol. 30
), Heng DONG2(
