The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collects thermal observations from the International Space Station to support evapotranspiration (ET) research at fine spatial resolutions (70 m x 70 m). Initial ET estimates from ECOSTRESS Collection 1 have been used in a wide range of scientific studies and applications, though subsequent analyses identified areas for improvement. This study provides an overview of updates to ECOSTRESS Collection 2 ET and presents an accuracy assessment of ET and auxiliary variables against in situ data from AmeriFlux. Key updates in Collection 2 include: four independent model estimates of ET and improved auxiliary forcing data. We find the multi-model ensemble ET estimate achieves a root mean square error (RMSE) of 109 Wm-2 for instantaneous observations and 1.5 mm/day for daily retrievals. When considering uncertainty in energy balance closure approaches for site-level data, the RMSE improves to 48 Wm-2 for instantaneous ET. We observe variable performance based on time of day of ECOSTRESS image acquisition, climate and vegetation type. Evaluation of auxiliary data highlight limitations in down-scaled net radiation and relative humidity, contributing to a diurnal hysteresis in ET estimates. We provide accuracy metrics and model sensitivity to auxiliary data to facilitate user confidence, data adoption, interpretation, and applications. ECOSTRESS is the only instrument capable of providing ET at different times of day at high spatial scales; thus, this work is an important step toward enhancing the capabilities of satellite-driven ET models in resolving diurnal ET variations and guiding directions for future improvements.

Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.