Retrieval of Spatio-temporal Evaporation by Integrating Landsat OLI Optical and Thermal Data

Soil-Vegetation-Atmosphere (SVAT) Transfer Models are capable of providing continuous predictions of evapotranspiration (ET). However, providing these models with reliable spatio-temporal information of vegetation and soil properties remains challenging. Thus, combining optical and thermal satellite information might assists to overcome this challenge when using SVAT models. In this study, using a radiative transfer model of solar and sky radiation (RTMo), we simulate Landsat 8 reflectance bands (2-7). We then apply a numerical optimization approach to invert the model and retrieve the corresponding canopy attributes leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm), leaf brown material (Cs), Leaf Area Index (LAI) and the leaf angle distribution function in the canopy at overpass time. The retrievals are then directly used as inputs into our SVAT model of choice, Soil Canopy Observations of Photochemistry and Energy Fluxes (SCOPE). Using a model for transfer of thermal radiation emitted by vegetation and soil (RTMt), we proceed to simulate Landsat radiance for the corresponding reflectance data using a lookup table (LUT). These variables were then used to develop a crop factor (Kc) map. A reference ET was generated and applied to the Kc map to obtain actual ET. We proceeded to interpolate the ET between the image acquisition dates to have a complete time series. The retrieval maps for the specific variables captured seasonal variability patterns for the respective variables. The generated KC map showed similar trend with the LAI maps. There was an underestimation of actual ET when the simulation was not constrained to the thermal information. The interpolation of ET between acquisition image dates reflected the seasonal trends. Key Word: SVAT, optical, thermal, remote sensing, evapotranspiration