The content of this article comes from the "Surveying and Mapping Bulletin" No. 3 of 2024, drawing review number: GS Jing (2024) No. 0499
Remote sensing estimation method of cadmium (Cd) content in cultivated soil based on GF-1 data
ZHANG Longqi1,2, GUO Yunkai1,2, DONG Shengguang3, LIU Xinliang1
1. School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, Hunan 410114, China;2. Institute of Surveying, Mapping and Remote Sensing Application Technology, Changsha University of Science and Technology, Changsha, Hunan 410114;3. The Second Institute of Surveying and Mapping of Hunan Province, Changsha, Hunan 410009
Funds: Natural Resources Research Project of Hunan Province (2021-02); Research Booster Program of Changsha University of Science and Technology(2019QJCZ007); Open Fund for Key Laboratory of Highway Engineering of the Ministry of Education (KFJ100109); National Natural Science Foundation of China (41671446)
Key words: cultivated soil, Cd content, GF-1, spectral characteristics, Inversion model
Citation Format: ZHANG Longqi, GUO Yunkai, DONG Shengguang, et al. Remote Sensing Estimation Method of Cadmium (Cd) Content in Cultivated Soil Based on GF-1 Data[J]. Bulletin of Surveying and Mapping, 2024(3): 8-12,94.doi: 10.13474/j.cnki.11-2246.2024.0302
Abstract:In this paper, a variety of spectral transformations and regression analysis methods were used to explore the feasibility of using GF-1 satellite images to monitor soil cadmium (Cd) content in cultivated soil. Firstly, according to the original image data of GF-1, four sets of spectral images were generated by performing reciprocal logarithmic, square root and arcsine square root transformations after completing the preprocessing and eliminating vegetation information. Then, correlation analysis and multiple regression analyses were carried out with the spectral statistics and Cd content of each set of images in the 5m buffer of the sampling point. The linear regression model constructed by the adaptive reweighted regression method with the highest coefficient of determination (>95%) was selected as the remote sensing estimation model. The remote sensing estimation results showed abnormal estimates in the paddy field water accumulation and marginal areas. After analyzing the causes, the author uses the linear interpolation method to obtain the final estimation results. Correlation analysis and modeling accuracy show that the method is feasible and is expected to be applied to actual soil quality monitoring and land management.
About authorAbout author:ZHANG Longqi (1977—), male, Ph.D., lecturer, mainly engaged in teaching and scientific research in remote sensing monitoring and traffic GIS. E-mail: [email protected]
First instance: Yang Ruifang review: Song Qifan
Final Judge: Jin Jun
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