组合光学和微波遥感的耕地土壤含盐量反演OA北大核心CSTPCD

The safeguarding of cultivated land is paramount in ensuring national food security,sustainable eco-nomic and social development,and the preservation of the ecological environment.Rapid and accurate acquisi-tion of cultivated soil salinity and spatial distribution information is imperative for the protection of cultivated land.This study focuses on cultivated land in Pingluo County,Ningxia,China,as the research object and discuss-es the feasibility of combining optical remote sensing and microwave remote sensing to predict the accuracy of soil salt content compared with single remote sensing data.The methodology involved the extraction of the spec-tral indices from Landsat 9 OLI and radar polarization combination indices from Sentinel-1.Variable projection importance and gray correlation degree were used to screen and combine characteristic variables.Three machine learning algorithms(back propagation neural network,support vector machine,and random forest)were used to construct the soil salt content prediction model.The best model was used to predict the spatial distribution of the soil salt content in cultivated land.The results show the following facts:(1)The model,validated using the vari-able projection importance method for screening variables,generally exhibited a higher determination coefficient(R2)than the model established using the gray correlation method for characteristic variables.(2)Using the ran-dom forest algorithm,the model combining the spectral index and radar polarization combination index demon-strated the best effect.The modeling set exhibited an R2 of 0.791 and a root mean square error(RMSE)of 1.016.This represented an increase in R2 by 0.065 and 0.085 compared with the single data source model,with corre-sponding decreases in RMSE by 0.147 and 0.189.The validation set showed an R2 of 0.780 and an RMSE of 1.132,indicating a respective increase in R2 by 0.091 and 0.237 and a decrease in RMSE by 0.175 and 0.377 com-pared with the single data source model.(3)The distribution range of mildly salinized and moderately salinized soil of cultivated land in Pingluo County covered wide areas,accounting for 23.77%and 33.54%,respectively,whereas severely salinized soil constituted 15.37%.This underscores the effectiveness of modeling by combining multisource remote sensing data in improving the prediction accuracy of soil salt content.The outcomes offer a valuable technical reference for predicting soil salt content in arid areas and contribute to the sustainable develop-ment of local agriculture.