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 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 ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=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 geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.

Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the US National Ecological Observatory Network (NEON), there is increasing need for methods that are both flexible and automatable across numerous images with diverse land cover. FlexBRDF corrects for BRDF effects in groups of flightlines, with key user-selectable features including kernel selection, land cover stratification (we employ NDVI), and use of a reference solar zenith angle (SZA). We demonstrate FlexBRDF using a series of nine long (150-400 km) AVIRIS-Classic flightlines collected on 22 May 2013 over Southern California, where rough terrain, diverse land cover, and a wide range of solar illumination yield significant BRDF effects, and then test the approach on additional AVIRIS-Classic data from California, AVIRIS-Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Based on comparisons of overlap areas between adjacent flightlines, correction algorithms built from multiple flightlines concurrently performed better than corrections built for single images (RMSE improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common SZA among a group of flightlines also improved performance. While BRDF corrections tailored to individual sites may be preferred for local studies, FlexBRDF is compatible with bulk processing of large datasets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions.