Improvements to a MODIS global terrestrial evapotranspiration algorithm

Qiaozhen Mu; Maosheng Zhao; Steven W. Running

doi:10.1016/j.rse.2011.02.019

Improvements to a MODIS global terrestrial evapotranspiration algorithm

Qiaozhen Mu, Maosheng Zhao, Steven W. Running

W. A. Franke College of Forestry and Conservation

University of Montana

Research output: Contribution to journal › Article › peer-review

2322 Scopus citations

Abstract

MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519-536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km² land surface ET dataset for the 109.03Millionkm² global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8×10³km³, agrees very well with other reported estimates of 65.5×10³km³ over the terrestrial land surface, which is much higher than 45.8×10³km³ estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39mm day^-1 to 0.33mmday^-1 driven by tower meteorological data, and from 0.40mmday^-1 to 0.31mmday^-1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10-30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.

Original language	English
Pages (from-to)	1781-1800
Number of pages	20
Journal	Remote Sensing of Environment
Volume	115
Issue number	8
DOIs	https://doi.org/10.1016/j.rse.2011.02.019
State	Published - Aug 15 2011

Funding

This research was financially supported by the NASA Earth Observing System MODIS project (grant NNX08AG87A ). Eddy covariance flux tower sites are part of both the AmeriFlux and Fluxnet networks. We gratefully acknowledge the efforts of researchers at these sites and thank them for making their data available. Sites are funded through grants from the U.S. Department of Energy (DOE) Office of Biological and Environmental Research (BER) unless otherwise noted. Data collection at the ARM SGP Burn, ARM SGP Control and ARM SGP Main sites is supported by DOE of BER (contract number DE-AC02-05CH11231 ) as part of the Atmospheric Radiation Measurement Program, and at Bartlett AmeriFlux site funded by the USDA Forest Service, NASA, and DOE through the Northeastern Regional Center of the National Institute for Climate Change Research. Data collection and research is led by W. Oechel at Atqasuk, Ivotuk and Sky Oaks Old sites (funded by National Science Foundation), by T. Meyers at the Audubon Research Ranch, Bondville and Fort Peck sites, by A. Goldstein at Blodgett Research site, by T. Martin at the Donaldson site, by T. Kolb, S. Dore and M. Montes-Helu at Flagstaff Unmanaged Forest and Flagstaff Wildfire sites (supported by USDA CREES NRI 2004-35111-15057 and USDA NRI 2008-35101-19076), by K. Clark at the Fort Dix site (funded by USFS), by M. Litvak at Freeman Ranch Mesquite Juniper site, by T. Griffis and J. Baker at Rosemount G19 Alternative Management Corn Soybean Rotation and Rosemount G21 Conventional Management Corn Soybean Rotation, by R. Scott at Kendall Grassland site, by S. Saleska, J. W. Munger, S. Wofsy and L. Hutyra at LBA Tapajos KM67 Mature Forest (funded by NASA), by H. da Rocha, M. Goulden and S. Miller at LBA Tapajos KM83 Logged Forest, by J. Hadley at Little Prospect Hill site, S. Verma at Mead Irrigated, Mead Irrigated Rotation and Mead Rainfed sites, by B. Law at Metolius New Young Pine, Metolius First Young Pine and Metolius Intermediate Pine sites (funded by the Office of Science (BER), DOE (Grant no. DE-FG02-06ER64318)), by L. Gu at Missouri Ozark site, by D. Dragoni at Morgan Monroe State Forest site, by R. K. Monson at Niwot Ridge site, by J. Chen at Ohio Oak Openings and Wisconsin Mature Red Pine sites, by A. Desai, P. Bolstad and B. Cook at Sylvania Wilderness site (funded by Office of Science (BER), DOE Terrestrial Carbon Processes program, grant number DE-FG02-00ER63023), by D. Baldocchi at Tonzi Ranch site, by P. Curtis at UMBS, by M. Goulden at UCI 1850, UCI 1930, UCI 1964, UCI 1964wet, UCI 1981, UCI 1989, UCI 1998 sites, by R. Coulter at Walnut River site, by K. Bible at Wind River Crane site, by K. Davis at Willow Creek (funded by the DOE's National Institute for Global Environmental Change program (NIGEC), and is currently supported by USDA-FS joint venture agreement 09-JV-11242306-105 and Wisconsin Focus on Energy under A. Desai at U. Wisconsin).

Funders	Funder number
DE-AC02-05CH11231
09-JV-11242306-105
National Aeronautics and Space Administration	NNX08AG87A
CREES NRI 2004-35111-15057, NRI 2008-35101-19076
DE-FG02-00ER63023, UCI 1981, DE-FG02-06ER64318, UCI 1930
Biological and Environmental Research
U.S. Forest Service-Retired

Keywords

Evapotranspiration
MODIS
Soil surface evaporation
Stomatal conductance
Vegetation cover fraction

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10.1016/j.rse.2011.02.019

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@article{8748d0d856094928bfae6e85a2f6e221,

title = "Improvements to a MODIS global terrestrial evapotranspiration algorithm",

abstract = "MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519-536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km2 land surface ET dataset for the 109.03Millionkm2 global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8×103km3, agrees very well with other reported estimates of 65.5×103km3 over the terrestrial land surface, which is much higher than 45.8×103km3 estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39mm day-1 to 0.33mmday-1 driven by tower meteorological data, and from 0.40mmday-1 to 0.31mmday-1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6\% and 24.1\% of the ET measured from towers, within the range (10-30\%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.",

keywords = "Evapotranspiration, MODIS, Soil surface evaporation, Stomatal conductance, Vegetation cover fraction",

author = "Qiaozhen Mu and Maosheng Zhao and Running, \{Steven W.\}",

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doi = "10.1016/j.rse.2011.02.019",

language = "English",

volume = "115",

pages = "1781--1800",

journal = "Remote Sensing of Environment",

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T1 - Improvements to a MODIS global terrestrial evapotranspiration algorithm

AU - Mu, Qiaozhen

AU - Zhao, Maosheng

AU - Running, Steven W.

PY - 2011/8/15

Y1 - 2011/8/15

N2 - MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519-536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km2 land surface ET dataset for the 109.03Millionkm2 global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8×103km3, agrees very well with other reported estimates of 65.5×103km3 over the terrestrial land surface, which is much higher than 45.8×103km3 estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39mm day-1 to 0.33mmday-1 driven by tower meteorological data, and from 0.40mmday-1 to 0.31mmday-1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10-30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.

AB - MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519-536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km2 land surface ET dataset for the 109.03Millionkm2 global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8×103km3, agrees very well with other reported estimates of 65.5×103km3 over the terrestrial land surface, which is much higher than 45.8×103km3 estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39mm day-1 to 0.33mmday-1 driven by tower meteorological data, and from 0.40mmday-1 to 0.31mmday-1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10-30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.

KW - Evapotranspiration

KW - MODIS

KW - Soil surface evaporation

KW - Stomatal conductance

KW - Vegetation cover fraction

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JO - Remote Sensing of Environment

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ER -

Improvements to a MODIS global terrestrial evapotranspiration algorithm

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