Location: Southwest Watershed Research CenterTitle: Estimating riparian and agricultural actual evapotranspiration by reference evapotranspiration and MODIS Enhanced Vegetation Index
|NAGLER, P.L. - Us Geological Survey (USGS)|
|GLENN, E.P. - Commonwealth Scientific And Industrial Research Organisation (CSIRO)|
|NGUYEN, U. - University Of Arizona|
|Scott, Russell - Russ|
|DOODY, T. - Commonwealth Scientific And Industrial Research Organisation (CSIRO)|
Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/22/2013
Publication Date: 8/5/2013
Citation: Nagler, P., Glenn, E., Nguyen, U., Scott, R.L., Doody, T. 2013. Estimating riparian and agricultural actual evapotranspiration by reference evapotranspiration and MODIS Enhanced Vegetation Index. Remote Sensing. 5:3849-3871. https://doi.org/10.3390/rs5083849.
Interpretive Summary: River basins frequently support both irrigated agriculture and natural vegetation along rivers. To accurately account for the water used by the crops and natural vegetation, new methods incorporating readily-available satellite data are needed. We developed such a method using freely and globally available satellite data from NASA in combination with local weather data. The algorithm capably predicted crop and natural riparian vegetation water use (termed evapotranspiration) and then further validated over five different irrigation districts worldwide with predictions within 10% of measured results in each case. The algorithm provides more accurate information to farmers and land managers for improved water resource management.
Technical Abstract: Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in these districts. We developed a general algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the MODIS sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from literature reports. The algorithm was based on an equation of the form ETa = ETo [a(1-e-bEVI) – c], where the term (1-e-bEVI) is derived from the Beer-Lambert Law to express light reflection from a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa (r2 = 0.73) across riparian plants and crops. It was then tested against independent data for five irrigation districts and two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a mean non-significant (P = 0.89) difference between measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to give a final predictive equation that can be applied across crops and riparian plant associations for monitoring of individual irrigation districts or for conducting global water use assessments for this mixed agricultural and riparian biome.