Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements

Land evapotranspiration (ET) is a key component of terrestrial ecosystems, as it is the nexus of hydrological, energy, and carbon cycles. Satellite-based observations are commonly utilized to provide high-resolution, large-scale ET estimates. The ground-based validation of such large-scale estimates is necessary to ensure that remotely sensed ET characteristics are accurate, and to extend their various applications. The Global Land-surface Evaporation Amsterdam Methodology (GLEAM) combines a wide range of multi-satellite observations to estimate daily actual evaporation through a process-based methodology. In this study, we focused on evaluating a daily GLEAM 0.25° ET product using in-situ eddy covariance (EC) ET data (2003–2005) as a benchmark at eight sites from the Chinese Flux Observation and Research Network (ChinaFLUX), which contains several biome types (croplands, grasslands, shrublands, savannas, and forests) across China at a range of temporal scales (from daily, to monthly, to annual). The results indicated that the ET products of the Global Land-surface Evaporation Amsterdam Methodology (GLEAM ET) over different time scales can estimate actual ET with reasonable accuracy. GLEAM showed high skill scores for most of the land-cover types except at the Xishuangbanna forest site (XSBN), where significantly systematic bias was detected at each individual temporal scale. Overall, GLEAM ET products were closer to the EC observations at the three grassland sites than at the four forest sites or the cropland site. GLEAM significantly overestimated the EC measurements at the four forest sites and one cropland site, while a slight underestimation occurred at the three grassland sites; there was a year-long systematic overestimation for GLEAM at the four forest sites. The daily GLEAM ET aggregated by monthly and annual data agreed more closely with EC measurements than those taken at the daily timescale. The results also showed a high average correlation coefficient (r) with in-situ EC observations at all sites, at daily (r = 0.71), monthly (r = 0.86), and annual (r = 0.79) time scales in addition to ET season-dependent characteristics for satellite estimation errors. The results presented here contribute to further assessment of the quality and uncertainty of GLEAM ET products, which may benefit future advancements in the ET algorithm and its product quality.