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.

翻译：准确且高分辨率的地表温度（LST）估算对于估算蒸散发（衡量植物水分利用的关键指标，也是农业应用中的核心参数）至关重要。本研究提出了一种新颖的统计方法，用于对NASA ECOSTRESS任务获取的LST数据进行降尺度处理，并以Landsat 8任务的高分辨率数据作为农业田块结构的建模代理。通过Landsat数据，我们利用边缘检测技术识别农田边界，从而捕捉空间域中固有的块状结构。我们提出了一种块对角高斯过程（BDGP）模型，该模型能够捕捉农田的空间结构，利用不同田块间LST的独立性以实现计算可行性，并考虑ECOSTRESS观测中存在的支撑域变化问题。我们使用所构建的BDGP模型进行高斯过程回归，从ECOSTRESS数据中获得高分辨率LST估计值及其不确定性量化。研究结果证明了该方法在生成可靠高分辨率LST估计值方面的实用性，在农业、城市规划和气候研究等领域具有潜在应用价值。