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

• Published in: Earth and space science (Hoboken, N.J.) Vol. 8; no. 10
• Main Authors: Desai, Ankur R., Khan, Anam M., Zheng, Ting, Paleri, Sreenath, Butterworth, Brian, Lee, Temple R., Fisher, Joshua B., Hulley, Glynn, Kleynhans, Tania, Gerace, Aaron, Townsend, Philip A., Stoy, Paul, Metzger, Stefan
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.10.2021; American Geophysical Union (AGU)
• Subjects: Aircraft; Atmosphere; CHEESEHEAD19; Drones; ECOSTRESS; GOES; Heat; Land surface temperature; Landsat; Orbits; Radiation; Remote sensing; Satellites; Sensors; Space stations; Temperature; United States > US; Wisconsin
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2021EA001842

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 (r2 = 0.79, RMSE = 3.5 K) and airborne (r2 = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST. Plain Language Summary The temperature of the Earth’s surface over land—land surface temperature (LST)—is an important variable to observe and forecast. Variation in LST over space and time at scales of meters and hours influence processes in the atmosphere, soils, vegetation, and water. For the worldwide coverage of LST, we rely on Earth‐observing satellites. However, there are trade offs in how finely LST can be observed over space versus how often LST can be observed over time, given the characteristics of any one satellite's orbit, not to mention the obscuring effect of clouds. Therefore, methods are needed that enable data from multiple satellites as well as aircraft and towers if we want to observe LST at high space and time resolution. Here, we develop such an approach and test its accuracy over a test bed of extensive LST observations made by towers, drones, and aircraft during a field experiment in Northern Wisconsin USA. Key Points Fusion of satellites with models for high space and time resolution land surface temperature needed for many surface‐atmosphere studies Developed an approach that evaluates well across array of towers and aircraft observations from an intensive field experiment Additional downscaling with airborne hyperspectral imagery further refines the identification of hot spots as evaluated with drone observations