ABSTRACT

A general framework for the integration of multi-sensor data for dry and fresh biomass retrieval is proposed and tested in Alpine meadows and pastures. To this purpose, hyperspectral spectroradiometer (as simulation of hyperspectral imagery) and biomass samples were collected in field campaigns and Copernicus Sentinel-1 Interferometric Wide (IW) swath SAR backscattering coefficients were used. First, a genetic algorithm feature selection was performed on hyperspectral data, and afterwards the resulting most sensitive bands where combined with SAR data within a support vector regression (SVR) model. The most sensitive hyperspectral bands were mainly located in different regions of the SWIR range for both fresh and dry biomass, and in the red and near-infrared regions mainly for dry biomass, but with less influence for fresh biomass. The R² correlation values between the sampled and the estimated biomass range from 0.24 to 0.71. The relatively low performances are mainly related to the saturation effect in the optical bands, as well as to the paucity of points for high values of biomass. The methodology allows a better understanding of the interaction between grassland systems and the electromagnetic spectrum by offering a model with a reduced number of narrow bands in the context of a multi-sensor integration.

摘要

提出了用于干燥和新鲜生物量检索的多传感器数据集成的通用框架，并在高山草甸和牧场进行了测试。为此，在野战中收集了高光谱光谱仪（作为高光谱图像的模拟）和生物质样品，并使用了Copernicus Sentinel-1干涉宽幅（IW）地带SAR背向散射系数。首先，对高光谱数据执行遗传算法特征选择，然后在支持向量回归（SVR）模型中将所得的最敏感波段与SAR数据结合在一起。对于新鲜和干燥生物量，最敏感的高光谱带主要位于SWIR范围的不同区域，对于干燥生物量，主要位于红色和近红外区域，但对新鲜生物量的影响较小。这  采样的生物量与估计的生物量之间的R ²相关值范围为0.24至0.71。相对较低的性能主要与光波段中的饱和效应有关，也与高生物质值的点少有关。通过在多传感器集成的情况下提供减少数量的窄带模型，该方法可以更好地了解草地系统与电磁频谱之间的相互作用。