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Partitioning of evapotranspiration in remote sensing-based models

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Abstract
Satellite based retrievals of evapotranspiration (ET) are widely used for assessments of global and regional scale surface fluxes. However, the partitioning of the estimated ET between soil evaporation, transpiration, and canopy interception regularly shows strong divergence between models, and to date, remains largely unvalidated. To examine this problem, this paper considers three algorithms: the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MODIS), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), and the Global Land Evaporation Amsterdam Model (GLEAM). Surface flux estimates from these three models, obtained via the WACMOS-ET initiative, are compared against a comprehensive collection of field studies, spanning a wide range of climates and land cover types. Overall, we find errors between estimates of field and remote sensing-based soil evaporation (RMSD = 90-114%, r(2) = 0.14-0.25, N = 35), interception (RMSD = 62-181%, r(2) = 0.39-0.85, N = 13), and transpiration (RMSD = 54-114%, r(2) = 0.33-0.55, N = 35) are relatively large compared to the combined estimates of total ET (RMSD = 35-49%, r(2) = 0.61-0.75, N = 35). Errors in modeled ET components are compared between land cover types, field methods, and precipitation regimes. Modeled estimates of soil evaporation were found to have significant deviations from observed values across all three models, while the characterization of vegetation effects also influences errors in all three components. Improvements in these estimates, and other satellite based partitioning estimates are likely to lead to better understanding of the movement of water through the soil-plant-water continuum.
Keywords
Evapotranspiration, Partitioning, Modeling, Remote sensing, Transpiration, Soil evaporation, TROPICAL RAIN-FOREST, GLOBAL TERRESTRIAL EVAPOTRANSPIRATION, SOIL-MOISTURE, WATER-BALANCE, SAP-FLOW, PORE-SCALE, PLANT TRANSPIRATION, CANOPY EVAPORATION, CHIHUAHUAN DESERT, INTERCEPTION LOSS

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Chicago
Talsma, Carl J, Stephen P Good, Carlos Jimenez, Brecht Martens, Joshua B Fisher, Diego Gonzalez Miralles, Matthew F McCabe, and Adam J Purdy. 2018. “Partitioning of Evapotranspiration in Remote Sensing-based Models.” Agricultural and Forest Meteorology 260-261: 131–143.
APA
Talsma, C. J., Good, S. P., Jimenez, C., Martens, B., Fisher, J. B., Gonzalez Miralles, D., McCabe, M. F., et al. (2018). Partitioning of evapotranspiration in remote sensing-based models. AGRICULTURAL AND FOREST METEOROLOGY, 260-261, 131–143.
Vancouver
1.
Talsma CJ, Good SP, Jimenez C, Martens B, Fisher JB, Gonzalez Miralles D, et al. Partitioning of evapotranspiration in remote sensing-based models. AGRICULTURAL AND FOREST METEOROLOGY. 2018;260-261:131–43.
MLA
Talsma, Carl J, Stephen P Good, Carlos Jimenez, et al. “Partitioning of Evapotranspiration in Remote Sensing-based Models.” AGRICULTURAL AND FOREST METEOROLOGY 260-261 (2018): 131–143. Print.
@article{8566824,
  abstract     = {Satellite based retrievals of evapotranspiration (ET) are widely used for assessments of global and regional scale surface fluxes. However, the partitioning of the estimated ET between soil evaporation, transpiration, and canopy interception regularly shows strong divergence between models, and to date, remains largely unvalidated. To examine this problem, this paper considers three algorithms: the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MODIS), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), and the Global Land Evaporation Amsterdam Model (GLEAM). Surface flux estimates from these three models, obtained via the WACMOS-ET initiative, are compared against a comprehensive collection of field studies, spanning a wide range of climates and land cover types. Overall, we find errors between estimates of field and remote sensing-based soil evaporation (RMSD = 90-114%, r(2) = 0.14-0.25, N = 35), interception (RMSD = 62-181%, r(2) = 0.39-0.85, N = 13), and transpiration (RMSD = 54-114%, r(2) = 0.33-0.55, N = 35) are relatively large compared to the combined estimates of total ET (RMSD = 35-49%, r(2) = 0.61-0.75, N = 35). Errors in modeled ET components are compared between land cover types, field methods, and precipitation regimes. Modeled estimates of soil evaporation were found to have significant deviations from observed values across all three models, while the characterization of vegetation effects also influences errors in all three components. Improvements in these estimates, and other satellite based partitioning estimates are likely to lead to better understanding of the movement of water through the soil-plant-water continuum.},
  author       = {Talsma, Carl J and Good, Stephen P and Jimenez, Carlos and Martens, Brecht and Fisher, Joshua B and Gonzalez Miralles, Diego and McCabe, Matthew F and Purdy, Adam J},
  issn         = {0168-1923},
  journal      = {AGRICULTURAL AND FOREST METEOROLOGY},
  keywords     = {Evapotranspiration,Partitioning,Modeling,Remote sensing,Transpiration,Soil evaporation,TROPICAL RAIN-FOREST,GLOBAL TERRESTRIAL EVAPOTRANSPIRATION,SOIL-MOISTURE,WATER-BALANCE,SAP-FLOW,PORE-SCALE,PLANT TRANSPIRATION,CANOPY EVAPORATION,CHIHUAHUAN DESERT,INTERCEPTION LOSS},
  language     = {eng},
  pages        = {131--143},
  title        = {Partitioning of evapotranspiration in remote sensing-based models},
  url          = {http://dx.doi.org/10.1016/j.agrformet.2018.05.010},
  volume       = {260-261},
  year         = {2018},
}

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