Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

Remote Sensing

Pamela L. Nagler¹  Edward P. Glenn³  Uyen Nguyen⁴  Russell L. Scott² 

[1] Sonoran Desert Research Station, Southwest Biological Science Center, US Geological Survey, 1110 E. South Campus Drive, Room 123, Tucson, AZ 85721, USA;USDA-Agricultural Research Service, 2000 E. Allen Rd., Tucson, AZ 85719, USA; E-Mail:;CSIRO Land and Water, Waite Road, Gate 4, Glen Osmund, SA 5064, Australia; E-Mail:;Environmental Research Laboratory, University of Arizona, 2601 E. Airport Dr., Tucson, AZ 85706, USA; E-Mails:

关键词: remote sensing;    leaf area index;    water balance;    alfalfa;    saltcedar;    common reed;    mesquite;   

DOI  :  10.3390/rs5083849

来源: mdpi

【 摘 要 】

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 irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ET_a) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ET_o). The algorithm was calibrated with five years of ET_a data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ET_a data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ET_a = ET_o [a(1 − e^−bEVI) − c], where the term (1 − e^−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ET_a across riparian plants and crops (r² = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ET_a data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ET_a of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland