MONITORING TEMPORARY CROPS IN IPU-CE USING SAR TIME SERIES IMAGES AND MACHINE LEARNING
Abstract
In recent decades, remote sensing techniques have advanced considerably, enabling effective monitoring of land use, agricultural productivity, and the environmental impacts of agricultural expansion in the Caatinga biome. This study integrates H-αlpha decomposition products developed by Cloude-Pottier, Sigma-0 backscatter, polarization ratio, and vegetation indices derived from Sentinel-1 data cube from July 2021 to August 2022. The methodological approach enhanced the quality and heterogeneity of training samples, resulting in a more accurate and spatially distributed classified map. These findings contribute to a deeper understanding of agricultural dynamics in Ipu-CE, especially in areas with multiple crop cycles, and provide valuable insights to support sustainable agricultural monitoring and policymaking in semi-arid regions.
Keywords: Monitoring Land Use and Land Cover, SAR Data Cubes, Caatinga Biome.
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