Evaluation and Exploitation of Retrieval Algorithms for Estimating Biophysical Crop Variables Using Sentinel-2, Venus, and PRISMA Satellite Data

• 作者: Angelo PALOMBO Deepak UPRETI Guijun YANG Hao YANG Raffaele CASA Simone PASCUCCI Stefano PIGNATTI Wenjiang HUANG
• 来源期刊: 测绘学报（英文版）
• 年，卷（期）: 2020,（4）
• 页码范围: 79-88
• 页数: 10页
• 语种: 英文

摘要

This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite ( e. g., PRISMA, EnMAP, and GF-5). Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index ( LAI) and Leaf chlorophyll content ( Cab) were evaluated:non-kernel-based and kernel-based Machine Learning Regression Algorithms ( MLRA ); Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season. Results show that for Sentinel-2 data, Gaussian Process Regression ( GPR) was the best performing algorithm for both LAI (R2=0.89 and RMSE=0.59) and Cab (R2=0.70 and RMSE=8.31). Whereas, for PRISMA simulated data the Kernel Ridge Regression ( KRR) was the best performing algorithm among all the other MLRA ( R2=0.91 and RMSE=0.51) for LAI and ( R2=0.83 and RMSE=6.09) for Cab, respectively. Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands, which are used as tie-points in the PROSAIL inversion, are extremely useful for an accurate retrieving of crop biophysical parameters.