Downscaling land surface temperature data using edge detection and block-diagonal Gaussian process regression

TL;DR

This paper introduces a novel block-diagonal Gaussian process model that leverages edge detection on high-resolution Landsat data to accurately downscale and estimate land surface temperature from coarser satellite data, with applications in agriculture and climate.

Contribution

The paper develops a new block-diagonal Gaussian process approach that models agricultural field structures for high-resolution LST downscaling, incorporating edge detection and change of support considerations.

Findings

High-resolution LST estimates with quantified uncertainty

Effective capture of agricultural field boundaries

Demonstrated practicality in real satellite data applications

Abstract

Accurate and high-resolution estimation of land surface temperature (LST) is crucial in estimating evapotranspiration, a measure of plant water use and a central quantity in agricultural applications. In this work, we develop a novel statistical method for downscaling LST data obtained from NASA's ECOSTRESS mission, using high-resolution data from the Landsat 8 mission as a proxy for modeling agricultural field structure. Using the Landsat data, we identify the boundaries of agricultural fields through edge detection techniques, allowing us to capture the inherent block structure present in the spatial domain. We propose a block-diagonal Gaussian process (BDGP) model that captures the spatial structure of the agricultural fields, leverages independence of LST across fields for computational tractability, and accounts for the change of support present in ECOSTRESS observations. We use the resulting BDGP model to perform Gaussian process regression and obtain high-resolution estimates of LST from ECOSTRESS data, along with uncertainty quantification. Our results demonstrate the practicality of the proposed method in producing reliable high-resolution LST estimates, with potential applications in agriculture, urban planning, and climate studies.