Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops

作者：Martin Danner, Katja Berger, Matthias Wocher, Wolfram Mauser, Tobias Hank · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2021 · DOI：10.1016/j.isprsjprs.2021.01.017 · 被引用次数：164 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Leaf Properties and Growth Measurement

With an upcoming unprecedented stream of imaging spectroscopy data, there is a rising need for tools and software applications exploiting the spectral possibilities to extract relevant information on an operational basis. In this study, we investigate the potential of a scientific processor designed to quantify biophysical and biochemical crop traits from spectroscopic imagery of the upcoming Environmental Mapping and Analysis Program (EnMAP) satellite. Said processor relies on a hybrid retrieval workflow executing pre-trained machine learning regression models fast and efficiently based on training data from a lookup table of synthetic vegetation spectra and their associated parameterization of the well-known radiative transfer model (RTM) PROSAIL. The established models provide spatial information about leaf area index (LAI), average leaf inclination angle (ALIA), leaf chlorophyll content (Cab) and leaf mass per area (Cm). In contrast to using site-specific training data, the approach facilitates a universal application without the need to integrate a priori information into the processor. Four machine learning algorithms, namely artificial neural networks (ANN), random forest regression (RFR), support vector machine regression (SVR), and Gaussian process regression (GPR), were found to estimate biophysical and biochemical variables of unseen targets with high performance (relative error scores < 10%). ANNs excelled in terms of accuracy, model size and execution time when t...