Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

作者：Katja Berger, Jochem Verrelst, Jean‐Baptiste Féret, Tobias Hank, Matthias Wocher, Wolfram Mauser, Gustau Camps‐Valls · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2020 · DOI：10.1016/j.jag.2020.102174 · 被引用次数：188 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Isotope Analysis in Ecology

Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information co...