关键词: 黄河三角洲, GEE, 时序遥感, 土壤盐渍化, XGBoost

Abstract: In this study, the Yellow River Delta is taken as the research area. Based on the remote sensing image and ground measured salt data in 2022, the remote sensing spectral index and band reflectance with strong correlation with salt data are extracted as modeling factors. Multiple linear regression, random forest, BP neural network and XGBoost regression method are used to construct soil salt inversion model, and the optimal model is selected to carry out long-term inversion analysis of soil salt content in the study area from 2001 to 2020. The results show that: ①Through correlation analysis, 8 spectral information(CRSI, DVI, ENDVI, MSAVI, NDSI, NDVI, SI-T, near infrared band)related to soil salt content are screened out, which are significantly correlated at the level of P<0.01. ②Compared with the prediction accuracy of the four inversion models, the XGBoost algorithm has a stable prediction ability, and the inversion effect of soil salinity in the study area is the best. The values of R² and RMSE in the validation set are 0.84 and 3.066. ③According to the soil salt content from low to high, the salinization grade of the study area is divided into four grades (Ⅱ, Ⅲ, Ⅳ and Ⅴ). In the past 20 years, the total area of saline soil in the study area showed a downward trend, reducing by 20.7% of the total area of the study area.

Key words: Yellow River Delta, GEE, time series remote sensing, soil salinization, XGBoost