Crop Reflectance Estimate Errors from the SAIL Model Due to Spatial and Temporal Variability of Canopy and Soil Characteristics

Radiative transfer models must be combined with crop growth models for the latter to be recalibrated on each point of a regional domain using remote sensing data. However, radiative transfer models have parameters depending on vegetation and soil characteristics which vary spatially and temporally at a regional scale, but are generally unknown. Their estimation leads to reflectance simulation errors. These errors are analyzed in this article for the visible and near-infrared part of the spectrum using the SAIL model for sugar beets. Two ways for estimating SAIL parameters were compared: One does not require any knowledge of magnitude and variability. The other one is based on rules developed from parameter distribution analysis and extra knowledge on soils and climate. The reflectance estimation error was very high for LAI<2.0 when no previous knowledge was assumed. Defining parameter values from soil, climate, and crop development knowledge considerably reduced errors and would be useful for application of reflectance models at the regional scale. The use of vegetation indices reduced the effects of parameter value uncertainty, but still benefited from improved parameter estimation. It was found that simple classification of crop and notably soil parameter values would be useful for application of reflectance models at the regional scale. Future work will deal with quantifying consequences when reflectance models are coupled to crop growth models for regional yield estimation.