用农田边界信息提升卫星温度假分辨率，精度更高且带不确定性评估。
Downscaling land surface temperature data using edge detection and block-diagonal Gaussian process regression
- 通过边缘检测提取农田边界，构建分块高斯过程模型捕捉空间结构
- 在真实数据上实现从100米到30米的温度假分辨率，误差显著降低
- 适合农业监测、城市热环境和气候研究等需要高精度温度的应用
准确获取高分辨率地表温度（LST）对估算蒸散量（植物耗水量）至关重要，是农业应用的核心指标。本文针对美国宇航局ECOSTRESS任务的低分辨率LST数据，提出一种新型统计降尺度方法，以陆地8号（Landsat 8）的高分辨率数据作为农田结构代理。通过边缘检测识别农田边界，揭示空间域中的块状结构特征。提出一种分块对角高斯过程（BDGP）模型，既保留各农田间温度独立性以提升计算效率，又考虑了ECOSTRESS观测中“支持尺度变化”的问题。基于该模型进行高斯过程回归，实现从100米到30米的高分辨率LST估计，并提供不确定性量化。实验表明该方法能生成可靠、高精度的温度分布图，具有农业管理、城市规划与气候研究等广泛应用前景。
原文摘要 · Abstract (English)
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.
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