On the Downscaling of Actual Evapotranspiration Maps Based on Combination of MODIS and Landsat-Based Actual Evapotranspiration Estimates

Remote Sensing

Ramesh K. Singh³  Gabriel B. Senay¹  Naga M. Velpuri³  Stefanie Bohms⁴  James P. Verdin¹  George P. Petropoulos² 

[1] U.S. Geological Survey Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA; E-Mails:;ASRC Federal InuTeq, Contractor to the U.S. Geological Survey Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA; E-Mail;ASRC Federal InuTeq, Contractor to the U.S. Geological Survey Earth Resources Observation and Science Center, 47914 252nd Street, Sioux Falls, SD 57198, USA; E-Mail:;Stinger Ghaffarian Technologies Inc., Contractor to the U.S. Geological Survey Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA; E-Mail:

关键词: Colorado River Basin;    downscaling;    evapotranspiration;    Landsat;    LinZI method;    merging;    MODIS;    WaterSMART;   

DOI  :  10.3390/rs61110483

来源: mdpi

【 摘 要 】

Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.