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Vegetation Indices are important for agricultural monitoring and the assessment of biophysical attributes of different crops. In this context, the objective of this study was to evaluate the relationships between normalized difference vegetation indices (NDVIs) obtained by different terrestrial remote sensors (active and passive), with respect to biophysical aspects of soybeans and beans. With the NDVI GS (GreenSeeker 505 handheld active sensor), it was possible to obtain a better leaf area index and height estimate for both crops, as well chlorophyll index for soybeans. The NDVI FS (FieldSpec4 model Standard-Res passive sensor) is highlight in being able to estimate values for intercepted photosynthetically active radiation, mainly for soybeans. By using NDVIs with active and passive terrestrial remote sensors, it was possible to create models to estimate the biophysical parameters for soybeans and beans. [ABSTRACT FROM AUTHOR]

The objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Paraná State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops. [ABSTRACT FROM AUTHOR]