RESUMOEm virtude da crescente demanda mundial por alimentos, um monitoramento eficaz e em larga escala da umidade do solo constitui fator de grande importância para a previsão de safras. Este trabalho teve por objetivo apresentar uma técnica para o cálculo do teor de água no solo, utilizando modelos preditivos de umidade do solo, baseados em dados de radar de abertura sintética (SAR).
This study presents an evaluation on the use of SWJERS-1 data for soil moisture prediction models, comparing values obtained using these data with those obtained during field campaign. We used the models presented by [l] and [2], which relate the backscatter coefficient with soil parameters, such as roughness and moisture. It was impossible to directly invert the model from Oh et al when including the complex number hnction. The model from Dubois et al. was inverted, but it became necessary to insert estimated values of roughness (RMS height) to permit its use with monopolarized images. The results can be considered as adequate.
The Bayesian statistical approach is a well known technique used in computer based image classification, and the Maximum Likelihood Classifier (ML) is one of the most present in literature. The structure of the ML Classifier is such that, in a multidimensional approach, every image used in the classification process has the same effect or contribution, regardless its intrinsic quality. This paper extends the concept of image influence controller in the Modified Global Membership Function (MGMF) to classes reliability factors in order to use class influence controllers instead a single image reliability factor. It is also proposed an estimator for the classes reliability factors based on the Confusion Matrix and its Conditional Kappa coefficient. Two SAR images are used to evaluate the estimator and the classification process that take into account the classes reliability factors.
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