Consistent regression of biophysical parameters with kernel methods
This work addresses the problem of consistent regression for biophysical parameter estimation, which is relevant for researchers needing to incorporate specific constraints into their models.
This paper proposes a novel statistical regression framework that incorporates consistency constraints, offering both linear and nonlinear (kernel-based) formulations with closed-form analytical solutions. The models effectively utilize driver information while minimizing dependence on auxiliary variables, successfully demonstrating performance in estimating chlorophyll content.
This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are introduced, and both imply closed-form analytical solutions. The models exploit all the information from a set of drivers while being maximally independent of a set of auxiliary, protected variables. We successfully illustrate the performance in the estimation of chlorophyll content.