基于国产资源一号02D卫星和机器学习算法的钦州湾滨海土壤盐分反演

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Abstract The relevant feature parameters extracted from the domestic ZY1-02D multispectral satellite were used to characterize the soil salinity over the coastal area of Qinzhou Bay with the support of Ada Boot, LightGBM, XGBoost, RFR, and CatBoost machine learning algorithms. The performance of each model was evaluated with the coefficient of determination (R²) and root mean square error (RMSE). The results show that the soil total salt content in the research area was measured to range from 0.740 to 10.352g/kg with an average of 1.739g/kg. Model simulation results demonstrate that CatBoost had the best predictive performance over AdaBoost, LightGBM, XGBoost, and RFR, and combined CatBoost with the highest accuracy (R²=0.8317, RMSE=0.396g/kg); and of all variables in a group, the mean of texture features was most sensitive to soil salinity and made the highest contribution; The soil salt content was simulated to range from 0to 8.784g/kg, with an average of 2.478g/kg, in which mild salinity mainly occurred in the western part of the study area and scattered in the eastern part. The combination of domestic resource satellite remote sensing data and CatBoost model has shown good performance in retrieving soil salinity in the coastal area of Qinzhou Bay, providing a new approach to characterizing coastal soil salinity at a large-scale.

Key words： soil salinity remote sensing quantitative inversion machine learning ZY-1imagery Qinzhou bay

Received: 22 May 2023

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	TIAN Yi-chao
	ZHENG Dan-lin
	ZHANG Qiang
	LU Fang
	HUANG You-ju
	TAO Jin
	ZHANG Ya-li
	LIN Jun-liang
	YAO Gui-zhao
	YAO Yuan-yuan

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TIAN Yi-chao,ZHENG Dan-lin,ZHANG Qiang等. Inversion of coastal soil salinity in Qinzhou Bay based on domestic ZY1-02D satellite and machine learning algorithm[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(1): 371-385.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I1/371

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