Soybean crops occupy most areas in Rio Grande do Sul State and are highly dependent on rainfall since most of them are non-irrigated. Rainfall during the harvest period is often insufficient to meet the water demand, making water indicators an important tool for the crops. This study compared two approaches in the parameterization process of TVDI (Temperature-Vegetation Dryness Index) in a subtropical climate region of Brazil. The process used Moderate Resolution Imaging Spectroradiometer (MODIS) images of the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI), with spatial resolutions of 1,000 m and periods of 8-16 d, respectively. The evaporative triangles for the TS/NDVI scatter plots were built either for each image (scene-specific parameterization) or for all images at once (crop-type parameterization). The rainfall data were obtained from meteorological stations located in the study site and the analysis period comprised two contrasting harvests regarding soybean yield (most important crop in the region). The scene-specific parameterization allowed to analyze water status in the study site by inspecting the wet and dry edge of each image and identifying the areas of stress in each one. TVDI crop parameterization showed that the model was able to determine the time and frequency of water stress events during the crop-seasons. TVDI crop-parameterization, therefore, is more consistent for crop monitoring and forecasting purposes.
parameterization; soybean; water status; satellite imagery