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Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification
Author(s) -
Zhang Kun,
Zhu Gaofeng,
Ma Jinzhu,
Yang Yuting,
Shang Shasha,
Gu Chunjie
Publication year - 2019
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr023485
Subject(s) - evapotranspiration , sobol sequence , biome , algorithm , moderate resolution imaging spectroradiometer , sensitivity (control systems) , markov chain , range (aeronautics) , computer science , environmental science , artificial intelligence , mathematics , machine learning , statistics , satellite , physics , ecology , materials science , astronomy , ecosystem , electronic engineering , monte carlo method , composite material , biology , engineering
Accurate estimation of terrestrial evapotranspiration ( E ) is critical to understand the world's energy and water cycles. MOD16 is the core algorithm of the widely used global E data set (the Moderate Resolution Imaging Spectroradiometer [MODIS] E product). However, it exhibits considerable uncertainties in some regions. Based on the data from 175 flux towers, we identified the key parameters of the MOD16 algorithm using the Sobol’ sensitivity analysis method across biomes. The output of the MOD16 algorithm was sensitive to eight parameters. Among them, β, which is treated as a constant (0.2 kPa) across biomes in the original MOD16 algorithm, was identified as the parameter to which the algorithm was most sensitive. We used the differential‐evolution Markov chain method to obtain the proper posterior distributions for each key parameter across a range of biomes. The values of the key parameters for the different biomes were accurately estimated by differential‐evolution Markov chain in comparison with data from the flux towers. We then evaluated the performances of the original MOD16 and the optimized MOD16 and compared them at multiple spatial scales (i.e., site, catchment, and global). We obtained relatively consistent and more reliable E simulations using the optimized MOD16 at all three scales. In the future, more attention should be paid to uncertainties in the algorithm's structure and its parameterizations of soil moisture constraint, canopy resistance, and energy partitioning.