Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach

摘要: Abstract An optimal use of the global scale actual evapotranspiration (AET) products for various hydro-meteorological applications requires a systematic characterization their uncertainties. This study presents first application an extended triple collocation (TC) approach to provide mutually uncorrelated absolute and relative error structure among three readily available AET (MOD16, GLEAM, GLDAS) on point spatial within extent Asia. The physical evaluation results GLDAS MOD16 exhibited reasonable accuracy compared in-situ with mean Index Agreement >0.71, 0.59 0.58, respectively, thereby yielding Root Mean Square Error between ∼4–13 mm/8 day over nine AsiaFlux sites representing forest, rice paddy, grassland biomes. Theoretical uncertainty assessment four dataset combinations revealed that average ∼1.5–5.5 mm/8 day random was contributed from AET, reducing other datasets. GLEAM performed consistently better least uncertainties forest paddy surfaces where had almost similar errors as those obtained while showed high all vegetation conditions. Interestingly, datasets large (>25%) low tall canopies. A spatially merged product generated agreement in order GLDAS > GLEAM > MOD16 47%, 42% 11% area. Overall, TC quality is step forward develop near real-time accurate by processing theoretical current algorithms.