基于核岭回归算法的PROSAIL模型反演高空间分辨率叶面积指数

High-resolution leaf area index inversion based on the Kernel Ridge Regression algorithm and PROSAIL model

准确估算叶面积指数(LAI)在生态、环境和气候变化研究方面具有重要作用。依靠卫星遥感技术能够获取大范围LAI产品,但其空间分辨率较低且依赖大量地面实测数据,难以满足高精度、大范围研究的需求。本研究基于30 m空间分辨率地表反射率数据,在不依赖大量地面实测数据的情况下,提出基于核岭回归算法的PROSAIL物理模型反演LAI,首先对PROSAIL模型的输入参数进行敏感性分析,以确定输入参数并生成模拟数据集,从而建立模拟反射率与LAI之间的核岭回归反演模型,进行高空间分辨率LAI反演,并与基于多层感知机的PROSAIL模型、基于随机森林回归的PROSAIL模型进行对比分析。结果表明:基于核岭回归的PROSAIL模型获得了最高的LAI反演精度,模型决定系数(R2)为0。8089,均方根误差(RMSE)为0。2492,基于多层感知机和随机森林回归的PROSAIL模型反演精度较差,模型R2分别为0。7726和0。7118,RMSE分别为0。2781和0。2432。研究认为基于核岭回归的PROSAIL模型可以有效提升LAI反演精度,为快速准确的区域性高空间分辨率LAI反演提供了技术和方法。

Accurate estimation of leaf area index(LAI)plays an important role in ecological,environmental and climate change research.Large-scale LAI estimates can be obtained from satellite remote sensing technology,but they rely on a large amount of ground-measured data with and they have low spatial resolution,which often does not meet the needs of high-precision and large-scale research.In this study,using surface reflectance data with a spatial resolution of 30 m,we tested an inversion method combining the Kernel Ridge Regression(KRR)algorithm and the PROSAIL physical model to invert LAI without a large number of ground measured data.First,the sensitivity analysis was performed on the input parameters of the PROSAIL model to determine the input parameters and generate the simulated data sets.Then,the KRR model inversion between the simulated reflectance and LAI was established.For comparison,we linked two other models,the Multilayer Perceptron(MLP)algorithm and the Random Forest Regression(RFR)algorithm,with the PROSAIL model,to perform high spatial resolution LAI inversion.Finally,we used ground measured data to compare the outputs and performance of the three inversion models.We found that the LAI inversion accuracy of the KRR-PROSAIL model was the highest with an R2 of 0.8089 and root-mean-square error(RMSE)of 0.2492.The inversion accuracies of the PROSAIL model linked with MLP and RFR were inferior with R2values of 0.7726 and 0.7118,respectively and RMSE values of 0.2781and 0.2432,respectively.Based on this study we recommend the combination of the Kernel Ridge Regression algorithm and PROSAIL models to invert satellite data to LAI for improved accuracy and high spatial resolution of the inverted LAI data.This methodology provides a method for rapid and accurate inversion of regional highprecision LAI information.