Remote sensing of evapotranspiration using automated calibration: Development and testing in the state of Florida.

摘要

Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested.;The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps.