Downscaling Modis Evapotranspiration via Cokriging in Wellton-Mohawk Irrigation and Drainage District, Yuma, AZ

Abstract

Evapotranspiration (ET) is a key parameter for irrigation planning and management, and it is a crucial factor for water conservation practices considering the challenges associated with agricultural water availability. Field ET determination is the most accurate, but remains to be expensive and limited in scope. On the other hand, remote sensing is becoming an alternative tool for the estimation of ET. Operational ET algorithms, like the Moderate Resolution Imaging Spectroradiometer (MODIS)-based ET, are now successful at generating ET estimates globally at 1km resolution, however their intent is not management of agriculture irrigation. This research was done to develop an integrated method for downscaling MODIS ET appropriate for farm-level applications using geostatistical and remote sensing techniques. The proposed methodology was applied in the Wellton-Mohawk Irrigation and Drainage District of Yuma, Arizona. In a first effort, ET data was downscaled from standard 1-km-MODIS to a medium 250-m-spatial resolution via cokriging using Land Surface Temperature and Enhanced Vegetation Index as covariates. Results showed consistent downscaled ET with a variance greater than the variance of the coarse scale input and nearly similar mean values. This 250m product can serve larger irrigation districts in developed countries, where plot size is fairly large and regular. However, the size and shapes of most farms in developing countries makes the 250m ET challenging. For this reason, the second part of this work was done to downscale global scale 1km ET to 30m farm level application for irrigation use. This approach involved the generation of daily vegetation indices (VI) at 30m in order to support the downscaling of MODIS 1km ET. Landsat and MODIS reflectances were combined with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm and the resulting VI data was used as a covariate to downscale ET with the cokriging approach. The results showed that the MODIS ET data seriously underestimates ET over irrigated areas. To correct this problem the MODIS data was then adjusted using field measured values to make it useful for operational purposes. The proposed geospatial method was applied to different growth stages of cotton and results were validated with actual ET from The Arizona Meteorological Network (AZMET) and published consumptive use of water for the area. The adjusted downscaled ET was comparable to these two published data (maximum error of 33%). This methodology is a practical alternative in areas where there is no ancillary data to estimate ET and it is expected to help in the planning of irrigation agriculture that will lead to improved agricultural productivity and irrigation efficiency.