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Landsat images and crop model for evaluating water stress of rainfed soybean

Sayago, Silvina, Ovando, Gustavo, Bocco, Mónica
Remote sensing of environment 2017 v.198 pp. 30-39
Glycine max, Landsat, agricultural land, available water capacity, crop models, evapotranspiration, growing season, monitoring, normalized difference vegetation index, phenology, remote sensing, soil water, soil water content, soil water deficit, soybeans, surface temperature, temporal variation, vegetation cover, water stress, Argentina
Soil water content is a vital resource that plays a central role in agricultural areas. In Argentina the soybean (Glycine max (L.) Merrill) is the most important crop, considering the economic yield and the sown area. Actually, remote sensing allows continuous monitoring of crops and to evaluate the impact of water stress in their development. The combination of Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) is an indicator that provides information about the condition of the vegetation and surface soil moisture content. In this study we evaluate relationships between indicators of crop water stress and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat, for sites with rainfed soybean in the agricultural central zone of Córdoba (Argentina).Field data were acquired continuously throughout the whole growing season. For each sample date and plot, data of percent green vegetation cover, soil moisture content and phenology were registered. The use of a simulation crop model allowed obtaining indicators of crop water stress indices: (1) soil water deficit, (2) crop water use vs. reference crop evapotranspiration, and (3) fraction of the available water capacity that is readily available.The NDVI/LST spaces presented a trapezoidal form, which indicated that TVDI will have similar sensitivity for the full range of NDVI and showed temporal changes of wet and dry edges.When soybean cover exceeds 60%, the combination of TVDI with (2) and (3) can enhance the ability of detecting crop water stress conditions (R²=0.62 and 0.82, respectively). However, the relationship between TVDI and (1) showed low correlation values (R²=0.21).