MACHINE LEARNING ALGORITHMS IN GRASSLAND MONITORING: UTILIZING MULTI-TEMPORAL SENTINEL-1 SAR AND WEATHER DATA

This study investigates the efficacy of two feed-forward neural networks (MLP and RBF) and an SVM algorithm in monitoring grasslands. Employing six input parameters - Sentinel-1 SAR intensity, texture, temperature, precipitation, global radiation, and evapotranspiration - we aimed to determine their influence on model accuracy. Our research highlights the superior stability and accuracy of MLP and RBF NNs over SVM, with MLP NNs marginally outperforming RBF NNs. The most effective results were achieved using a comprehensive input set. Nevertheless, reducing the number of inputs led to a decrease in data dimensionality and consequently, model accuracy. The research indicates potential accuracy improvements with additional factors like soil type and grassland management. Conducting field studies concurrent with image acquisition is crucial for understanding the diverse grassland conditions and their effects on scattering mechanisms.