A Bayesian hierarchical model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images

TL;DR

This paper introduces a Bayesian hierarchical model using a double logistic function to estimate land surface phenology parameters from harmonized Landsat 8 and Sentinel-2 images, providing robust uncertainty quantification.

Contribution

It presents a novel Bayesian model with a double logistic function for LSP parameter estimation, including an open-source R package for efficient analysis of large raster datasets.

Findings

Likelihood choice impacts estimates when data are near bounds

All likelihoods perform similarly with sufficient data

Posterior distributions enable uncertainty propagation

Abstract

We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel-2 (HLS) dataset. Building on previous work, we propose a double logistic function that, once couched within a Bayesian model, yields posterior distributions for all LSP parameters. We assess the efficacy of the Normal, Truncated Normal, and Beta likelihoods to deliver robust LSP parameter estimates. Two case studies are presented and used to explore aspects of the proposed model. The first, conducted over forested pixels within a HLS tile, explores choice of likelihood and space-time varying HLS data availability for long-term average LSP parameter point and uncertainty estimation. The second, conducted on a small area of interest within the HLS tile on an annual time-step, further examines the impact of sample size and choice of likelihood on LSP parameter estimates. Results indicate that while the Truncated Normal and Beta likelihoods are theoretically preferable when the vegetation index is bounded, all three likelihoods performed similarly when the number of index observations is sufficiently large and values are not near the index bounds. Both case studies demonstrate how pixel-level LSP parameter posterior distributions can be used to propagate uncertainty through subsequent analysis. As a companion to this article, we provide an open-source \R package \pkg{rsBayes} and supplementary data and code used to reproduce the analysis results. The proposed model specification and software implementation delivers computationally efficient, statistically robust, and inferentially rich LSP parameter posterior distributions at the pixel-level across massive raster time series datasets.