辐射传输耦合集成学习的堆叠模型在多源遥感数据融合反演土壤含水率的潜力OA

Soil moisture content(SMC)is a key parameter in agricultural water management and drought monitoring,and accurate estimation of SMC is crucial for promoting sustainable agricultural development.Currently,due to the excessive reliance of empirical models on statistical relationships,their universality and portability are poor.Although physical models have clear mechanisms,they often need to be simplified in complex environments.Therefore,the accuracy of single model estimation of SMC is still facing challenges.It is necessary to explore a hybrid modeling strategy integrating experience and physical mechanisms to improve the stability and generalization of SMC estimation.In response to this issue,taking the Agricultural Eighth Division of Xinjiang Production and Construction Corps as the research area,using Sentinel-1/2 satellite as the data source,and ground measured hyperspectral data as the auxiliary,the radiative transfer model and ensemble learning was coupled,and a Sentinel-1/2 fusion inversion soil moisture content model was proposed based on soil radiative transfer model(BSM).This method firstly calibrated the parameter range of the BSM model based on measured soil hyperspectral data,and then combined the spectral response function and BSM model to simulate Sentinel-2 reflectance,applied it to model training,and verified the measured data.Finally,the validation results would be used as input variables for the second layer of the stacked model,combined with the Sentinel-1 radar backscatter coefficient and its texture features,to estimate SMC using XGboost,GRBT,and AdaBoost.The results showed that in the calibration of the parameter range of the BSM model,the model parameters lat and lon exhibited a good linear relationship(R2=0.545),and the accuracy was improved as R2=0.368 compared with the first layer inversion model of soil moisture content without calibration parameters(R2=-1.143).The fusion algorithm based on soil radiative transfer model and Sentinel-1 satellite had better accuracy in retrieving SMC than the machine learning model,with a validation set determination coefficient R2 of 0.816.In the hybrid model constructed by integrating the backscatter coefficient and texture features of Sentinel-1 radar with Sentinel-2 multispectral,AdaBoost showed the best inversion accuracy,with a validation set R2 of 0.816,verifying the complementary potential of empirical and physics based models in the inversion of SMC.The research result emphasized that hybrid methods combined the generalization ability of physical models with the flexibility of empirical models,providing technological means for real-time monitoring of soil moisture and sustainable management of land resources in the research area.