MONITORING TEMPORARY CROPS IN IPU-CE USING SAR TIME SERIES IMAGES AND MACHINE LEARNING

Autor/innen

DOI:

https://doi.org/10.4215/rm2025.e24006

Schlagwörter:

Monitoring Land Use and Land Cover, SAR Data Cubes, Caatinga Biome

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.

Autor/innen-Biografien

  • Ana Larissa Ribeiro De Freitas, National Institute for Space Research, São José dos Campos (SP), Brazil

    PhD student in Remote Sensing (INPE) with an emphasis on Monitoring Temporary Crops and the use of SAR, Geographer (UFC, 2017) and Master in Remote Sensing (INPE, 2021). Postgraduate in Business Intelligence, Big Data and Analytics (UNOPAR, 2022). Develops research in the Caatinga and Cerrado, using Earth Observation Data Cube for Classification of Time Series of SAR images with Deep Learning. Obtained support for her master's degree from the Funbio Scholarship Program - Conserving the Future 2019. Interested in the following topics: Remote Sensing, SAR, Temporary Crops, Monitoring, Data Cube, Land Use and Cover Dynamics.

  • Fabio Furlan Gama, National Institute for Space Research, São José dos Campos (SP), Brazil

    PhD in Remote Sensing from the National Institute for Space Research (2007). Experience in systems development and SAR radar applications, using interferometry and polarimetry, for applications in cartography, surface deformation and forest inventory.

  • Felipe Carvalho de Souza, National Institute for Space Research, São José dos Campos (SP), Brazil

    PhD student in Remote Sensing (INPE) with an emphasis on Temporary Crop Monitoring and the use of SAR, Geographer (UFC, 2017) and Master in Remote Sensing (INPE, 2021). Postgraduate in Business Intelligence, Big Data and Analytics (UNOPAR, 2022). She conducts research in the Caatinga and Cerrado, using the Earth Observation Data Cube to classify SAR image time series with Deep Learning. She received support for her master's degree from the Funbio Scholarship Program - Conserving the Future 2019. Interests: Remote Sensing, SAR, Temporary Crops, Monitoring, Data Cube, Land Use and Land Cover Dynamics.

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Veröffentlicht

2025-05-26

Ausgabe

Bd. 24 (2025)

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