Inverting soil salinity of farmland in Xinjiang by integrating Sentinel-1/2 and environmental variables

Soil salinization is an important factor that jeopardizes agricultural production and ecological environment. Rapid and accurate acquisition of soil salinity information in farmland is instructive for sustainable agricultural development and land resource management. In order to improve the accuracy of soil salinity prediction under vegetation cover conditions by satellite remote sensing, the eighth Agricultural Division of Xinjiang Production and Construction Corps was taken as the study area in this study. The soil surface (0-20 cm) samples were collected under high fractional vegetation cover conditions in July and August 2023, respectively, and synchronized satellite images were acquired. Sentinel-1, Sentinel-2 and environment variables provide 3 different types of explanatory variables. The dataset A (polarization indices, spectral indices), dataset B (polarization indices, environment variables), dataset C (spectral indices, environment variables), and dataset D (polarization indices, spectral indices, environment variables) were constructed separately from different combinations of Sentinel-1 radar information, Sentinel-2 multispectral information and environment variables. Then, three integrated machine learning algorithms, namely adaptive boosting (AdaBoost), gradient boost regression Tree (GBRT) and eXtreme gradient boosting tree (XGBoost), were applied to construct soil salinity inversion models based on different datasets. The results showed that Models constructed from dataset B (polarization indices and environmental variables) and C (spectral indices and environmental variables) achieved higher prediction accuracies compared to dataset A (polarization indices and spectral indices). It is shown that when environmental variables are involved in the prediction of soil salinity, the model effect is more effective than the model constructed by polarization and spectral indices suggesting that the model effects are more effective than those constructed from polarization and spectral indices. When environmental variables were applied to dataset D together with polarization indices and spectral indices, the prediction accuracy of all models constructed based on dataset D are generally higher than those constructed on dataset A, B, and C, and that the synergy of environmental variables with radar data and multispectral data can effectively improve the model accuracy. Radar information, spectral information and environmental variables are complementary in soil salinity prediction. Based on the correlation analysis, it can be seen that radar information, spectral information and environmental variables can be used as effective characteristic variables for soil salinity prediction in the study area. It was worth noting that the correlation between topographic factors and land surface temperature with soil salinity is relatively high, with the highest correlation between elevation and surface soil salinity (r = 0.52). Considering the spatial characteristics of soil salinity distribution in the study area can provide effective characteristic variables for soil salinity prediction under vegetation cover condition. In all datasets, the XGBoost had the best performance, followed by GBRT, and the AdaBoost had a large validation error. The D-XGBoost model having the highest accuracy with a validation set R² of 0.72, an RMSE of 2.40 g/kg, and an MAE of 1.29 g/kg. The integrated learning algorithms based on the combination of multiple source variables has a strong nonlinear fitting ability. XGBoost can better model the complex nonlinear relationship between soil salinity content and remote sensing information, environmental factors, and obtain ideal fitting results. The joint application of multi-source remote sensing data and integrated learning algorithms can obtain the ideal soil salinity inversion accuracy under vegetation cover conditions. This study provides an effective technical means for real-time dynamic monitoring of soil salinity by satellite remote sensing in farmland to optimize irrigation strategies and manage saline soils comprehensively in Xinjiang.