7/26/2023 0 Comments Svat model 11002A reference ET was generated and applied to the Kc map to obtain actual ET. These variables were then used to develop a crop factor (Kc) map. 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). The retrievals are then directly used as inputs into our SVAT model of choice, Soil Canopy Observations of Photochemistry and Energy Fluxes (SCOPE). 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. 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). However, providing these models with reliable spatio-temporal information of vegetation and soil properties remains challenging. Key Word: SVAT, optical, thermal, remote sensing, evapotranspirationĪbstract = "Soil-Vegetation-Atmosphere (SVAT) Transfer Models are capable of providing continuous predictions of evapotranspiration (ET). The interpolation of ET between acquisition image dates reflected the seasonal trends. There was an underestimation of actual ET when the simulation was not constrained to the thermal information. The generated KC map showed similar trend with the LAI maps. The retrieval maps for the specific variables captured seasonal variability patterns for the respective variables. We proceeded to interpolate the ET between the image acquisition dates to have a complete time series. In this study, using a radiative transfer model of solar and sky radiation (RTMo), we simulate Landsat 8 reflectance bands (2-7). Thus, combining optical and thermal satellite information might assists to overcome this challenge when using SVAT models. Soil-Vegetation-Atmosphere (SVAT) Transfer Models are capable of providing continuous predictions of evapotranspiration (ET).
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