Mapping Drivers of Greenness: Spatial Variable Selection for MODIS Vegetation Indices

TL;DR

This paper introduces a Bayesian spatially varying coefficient model with tensor product B-splines and group lasso priors to identify environmental drivers of vegetation greenness from MODIS data, improving interpretability and spatial understanding.

Contribution

It develops a novel spatially varying regression framework with Bayesian group lasso priors for variable selection and effect mapping in vegetation studies.

Findings

Effect maps reveal dominant environmental controls across landscapes.

Model accurately recovers relevant predictors and their spatial effects.

Provides uncertainty quantification for predictor effects.

Abstract

Understanding how environmental drivers relate to vegetation condition motivates spatially varying regression models, but estimating a separate coefficient surface for every predictor can yield noisy patterns and poor interpretability when many predictors are irrelevant. Motivated by MODIS vegetation index studies, we examine predictors from spectral bands, productivity and energy fluxes, observation geometry, and land surface characteristics. Because these relationships vary with canopy structure, climate, land use, and measurement conditions, methods should both model spatially varying effects and identify where predictors matter. We propose a spatially varying coefficient model where each coefficient surface uses a tensor product B-spline basis and a Bayesian group lasso prior on the basis coefficients. This prior induces predictor level shrinkage, pushing negligible effects toward zero while preserving spatial structure. Posterior inference uses Markov chain Monte Carlo and provides uncertainty quantification for each effect surface. We summarize retained effects with spatial significance maps that mark locations where the 95 percent posterior credible interval excludes zero, and we define a spatial coverage probability as the proportion of locations where the credible interval excludes zero. Simulations recover sparsity and achieve prediction. A MODIS application yields a parsimonious subset of predictors whose effect maps clarify dominant controls across landscapes.