Multi‐Sensor Approach for High Space and Time Resolution Land Surface Temperature

作者：Ankur R. Desai, Anam M. Khan, Ting Zheng, Sreenath Paleri, Brian Butterworth, Temple R. Lee, Joshua B. Fisher, Glynn Hulley, Tania Kleynhans, Aaron Gerace, Philip A. Townsend, Paul C. Stoy, Stefan Metzger · 发表于：Earth and Space Science · 年份：2021 · DOI：10.1029/2021ea001842 · 被引用次数：42 · 研究领域：Urban Heat Island Mitigation、Climate variability and models、Cryospheric studies and observations

Abstract Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower ( r 2 = 0.79, RMSE = 3.5 K) and airborne ( r 2 = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some imp...