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.
References
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Brito, P., Chaves, M., Carvalho, H., Souza, F., Silva, B., Ferreira, K., Santos, R., and de Queiroz, G. R. (2023). Uso de séries temporais para classificação de uso e cobertura da terra em Petrolina, Pernambuco.
Claudino-Sales, V., Lima, E., Diniz, S., and Cunha, F. (2020). Megageomorfologia do planalto da ibiapaba, estado do Ceará: uma introdução. William Morris Davis-Revista de Geomorfologia, 1(1):186–209.
Cloude, S. R. and Pottier, E. (1996). A review of target decomposition theorems in radar polarimetry. IEEE transactions on geoscience and remote sensing, 34(2):498–518.
Crawford, M. M., Tuia, D. and Yang, H. L. (2013) “Active Learning: Any Value for Classification of Remotely Sensed Data?” Proceedings of the IEEE, vol. 101, no. 3, pp. 593–608, doi: 10.1109/JPROC.2012.2231951.
De Freitas, A. L. R., Rabelo, F. D. B., da Silva, E. V., and Lima, E. C. (2017). Técnicas de geoprocessamento para delimitação de sistemas físico-naturais e ações de planejamento: o caso do município de Ipu-ce. Os Desafios da Geografia Física na Fronteira do Conhecimento, 1:5709–5714.
Diniz, J. M. F. d. S., Gama, F. F., and Adami, M. (2022). Evaluation of polarimetry and interferometry of sentinel-1a sar data for land use and land cover of the brazilian amazon region. Geocarto International, 37(5):1482–1500.
Doblas, J., Shimabukuro, Y., Sant’Anna, S., Carneiro, A., Aragão, L., and Almeida, C. (2020). Optimizing near real-time detection of deforestation on tropical rainforests using sentinel-1 data. Remote Sensing, 12(23):3922.
ESA (2024). S1 processing. European Space Agency (ESA). Accessed on 14 December 2024.
European Space Agency (2021-2022). Copernicus sentinel-1 data [2021, 2022]. Accessed via the Copernicus Open Access Hub.
European Space Agency (2024). Sentinel-1 SAR User Guide. Online; accessed 14 May 2024.
European Space Agency (ESA) (2024). Sentinel-1 mission overview. Accessed: 2024- 12-13.
Ferreira, K. R., Queiroz, G. R., Vinhas, L., Marujo, R. F., Simões, R. E., Picoli, M. C., Camara, G., Cartaxo, R., Gomes, V. C., Santos, L. A., et al. (2020). Earth observation data cubes for Brazil: Requirements, methodology and products. Remote Sensing, 12(24):4033.
Gama, F. F., Wiederkehr, N. C., and da Conceição Bispo, P. (2022). Removal of ionosphe- ric effects from sigma naught images of the alos/palsar-2 satellite. Remote Sensing, 14(4):962.
Ganem, K. A., Dutra, A. C., de Oliveira, M. T., de Freitas, R. M., Grecchi, R. C., da Silva Pinto Vieira, R. M., Arai, E., Silva, F. B., Sampaio, C. B. V., Duarte, V., and Shimabu- kuro, Y. E. (2020). Mapping caatinga vegetation using optical earth observation data: Opportunities and challenges. Revista Brasileira de Cartografia, 72(50th).
Goudie, A. (2004). Encyclopedia of geomorphology. Routledge.
Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis, volume 2. Springer Science & Business Media.
ICMBIO (2017). Plano de Manejo: Encarte 4 - Contexto Regional. http://www. icmbio.gov.br/parnaubajara/planos-de-manejo. Accessed: 26 nov. 2024.
IPECE (2024). IPECE DATA - Sistema de Informações Geossocioeconômicas do Ceará. http://ipecedata.ipece.ce.gov.br/ipece-data-web/ module/perfil-municipal.xhtml. Accessed: 26 nov. 2024.
Kasischke, E. S., Melack, J. M., and Dobson, M. C. (1997). The use of imaging radars for ecological applications—a review. Remote sensing of environment, 59(2):141–156.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological cybernetics, 43(1):59–69.
Le Toan, T. and Floury, N. (1998). On the retrieval of forest biomass from sar data. EUROPEAN SPACE AGENCY-PUBLICATIONS-ESA SP, 441:213–214.
Lee, J.-S., Jurkevich, L., Dewaele, P., Wambacq, P., and Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote sensing reviews, 8(4):313–340.
Liao, H., Wdowinski, S., and Li, S. (2020). Regional-scale hydrological monitoring of wetlands with sentinel-1 insar observations: Case study of the south florida everglades. Remote Sensing of Environment, 251:112051.
Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3):18–22.
López Martinez, C. (2003). Multidimensional speckle noise. Modelling and filtering re- lated to sar data. Universitat Politècnica de Catalunya.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, no. 1, pp. 321–357, 2002.
MapBiomas (2024). ATBD – ENTENDA CADA ETAPA. Online; accessed 25 May 2024.
Markham, C. G. (1967). Climatological aspects of drought in northeastern Brazil. University of California, Berkeley.
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., and Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and remote sensing magazine, 1(1):6–43.
Paradella, W. R., Knust, S. S. A., Dos Santos, A. R., Silva, A., Rabelo, T. N., and Oliveira, C. (2009). Influence of macroscale and microscale surface roughness on multi- beam radarsat-1 data: implications for geological mapping in the curaçá valley (Brazil). Photo interprétation (Paris), 45(2).
Pelletier, C., et al. (2016). Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sensing of Environment, 187:156-168.
Pelletier, C., Webb, G. I., and Petitjean, F. (2019). Temporal convolutional neural network for the classification of satellite image time series. Remote Sensing, 11(5):523.
Picoli, M. C. A., Câmara, G., Sanches, I., Simões, R., Carvalho, A., Maciel, A., Coutinho, A., Esquerdo, J., Antunes, J., Begotti, R. A., et al. (2018). Big earth observation time series analysis for monitoring brazilian agriculture. ISPRS journal of photogrammetry and remote sensing, 145:328–339.
Rodriguez, J. M. M. and da Silva, E. V. (2002). A classificação das paisagens a partir de uma visão geossistêmica. Mercator, 1(1).
Sano, E. E., Bolfe, É . L., Parreiras, T. C., Bettiol, G. M., Vicente, L. E., Sanches, I. D. A., and Victoria, D. d. C. (2023). Estimating double cropping plantations in the brazilian cerrado through planetscope monthly mosaics. Land, 12(3):581.
Sano, E. E., Ferreira, L., Asner, G., and Steinke, E. (2007). Spatial and temporal pro- babilities of obtaining cloud-free landsat images over the brazilian tropical savanna. International Journal of Remote Sensing, 28(12):2739–2752.
Santos, L. A., Ferreira, K. R., Camara, G., Picoli, M. C., & Simoes, R. E. (2021). Quality control and class noise reduction of satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 177:75-88.
Simões, R., Camara, G., Queiroz, G., Souza, F., Andrade, P. R., Santos, L., Carvalho, A., and Ferreira, K. (2021). Satellite image time series analysis for big earth observation data. Remote Sensing, 13(13):2428.
Simões, R., Picoli, M. C., Camara, G., Maciel, A., Santos, L., Andrade, P. R., Sánchez, A., Ferreira, K., and Carvalho, A. (2020). Land use and cover maps for Mato Grosso state in brazil from 2001 to 2017. Scientific Data, 7(1):34.
SNAP (2024). Sentinel Application Platform - SNAP 10.0.0. https://step.esa. int/main/download/snap-download/. Online; accessed 25 May 2024.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the royal statistical society: Series B (Methodological), 36(2):111–133.
Tomppo, E., Antropov, O., and Praks, J. (2019). Cropland classification using sentinel-1 time series: Methodological performance and prediction uncertainty assessment. Re- mote Sensing, 11(21):2480.
Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C., Thiel, C., Rossi, C., Syriou, A., and Giuliani, G. (2019). Towards sentinel-1 sar analysis-ready data: A best practices assessment on preparing backscatter data for the cube. Data, 4(3):93.
Ulaby, F. T., Moore, R. K., and Fung, A. K. (1982). Microwave remote sensing: Active and passive. volume 2-radar remote sensing and surface scattering and emission theory.
Von Bertalanffy, L. (1975). Teoria geral dos sistemas. Vozes Petrópolis.
Barra, A., Monserrat, O., Mazzanti, P., Esposito, C., Crosetto, M., and Scarascia Mugnozza, G. (2016). First insights on the potential of sentinel-1 for landslides detection. Geomatics, Natural Hazards and Risk, 7(6):1874–1883.
Bertrand, G. and Bertrand, C. (2007). Uma geografia transversal e de travessias: o meio ambiente através dos territórios e das temporalidades. Maringá: Massoni, 360.
Brazil (2024). Institutes the national policy for the recovery of the caatinga vegetation. Pending approval in the Chamber of Deputies after Senate approval.
Brito, P., Chaves, M., Carvalho, H., Souza, F., Silva, B., Ferreira, K., Santos, R., and de Queiroz, G. R. (2023). Uso de séries temporais para classificação de uso e cobertura da terra em Petrolina, Pernambuco.
Claudino-Sales, V., Lima, E., Diniz, S., and Cunha, F. (2020). Megageomorfologia do planalto da ibiapaba, estado do Ceará: uma introdução. William Morris Davis-Revista de Geomorfologia, 1(1):186–209.
Cloude, S. R. and Pottier, E. (1996). A review of target decomposition theorems in radar polarimetry. IEEE transactions on geoscience and remote sensing, 34(2):498–518.
Crawford, M. M., Tuia, D. and Yang, H. L. (2013) “Active Learning: Any Value for Classification of Remotely Sensed Data?” Proceedings of the IEEE, vol. 101, no. 3, pp. 593–608, doi: 10.1109/JPROC.2012.2231951.
De Freitas, A. L. R., Rabelo, F. D. B., da Silva, E. V., and Lima, E. C. (2017). Técnicas de geoprocessamento para delimitação de sistemas físico-naturais e ações de planejamento: o caso do município de Ipu-ce. Os Desafios da Geografia Física na Fronteira do Conhecimento, 1:5709–5714.
Diniz, J. M. F. d. S., Gama, F. F., and Adami, M. (2022). Evaluation of polarimetry and interferometry of sentinel-1a sar data for land use and land cover of the brazilian amazon region. Geocarto International, 37(5):1482–1500.
Doblas, J., Shimabukuro, Y., Sant’Anna, S., Carneiro, A., Aragão, L., and Almeida, C. (2020). Optimizing near real-time detection of deforestation on tropical rainforests using sentinel-1 data. Remote Sensing, 12(23):3922.
ESA (2024). S1 processing. European Space Agency (ESA). Accessed on 14 December 2024.
European Space Agency (2021-2022). Copernicus sentinel-1 data [2021, 2022]. Accessed via the Copernicus Open Access Hub.
European Space Agency (2024). Sentinel-1 SAR User Guide. Online; accessed 14 May 2024.
European Space Agency (ESA) (2024). Sentinel-1 mission overview. Accessed: 2024- 12-13.
Ferreira, K. R., Queiroz, G. R., Vinhas, L., Marujo, R. F., Simões, R. E., Picoli, M. C., Camara, G., Cartaxo, R., Gomes, V. C., Santos, L. A., et al. (2020). Earth observation data cubes for Brazil: Requirements, methodology and products. Remote Sensing, 12(24):4033.
Gama, F. F., Wiederkehr, N. C., and da Conceição Bispo, P. (2022). Removal of ionosphe- ric effects from sigma naught images of the alos/palsar-2 satellite. Remote Sensing, 14(4):962.
Ganem, K. A., Dutra, A. C., de Oliveira, M. T., de Freitas, R. M., Grecchi, R. C., da Silva Pinto Vieira, R. M., Arai, E., Silva, F. B., Sampaio, C. B. V., Duarte, V., and Shimabu- kuro, Y. E. (2020). Mapping caatinga vegetation using optical earth observation data: Opportunities and challenges. Revista Brasileira de Cartografia, 72(50th).
Goudie, A. (2004). Encyclopedia of geomorphology. Routledge.
Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis, volume 2. Springer Science & Business Media.
ICMBIO (2017). Plano de Manejo: Encarte 4 - Contexto Regional. http://www. icmbio.gov.br/parnaubajara/planos-de-manejo. Accessed: 26 nov. 2024.
IPECE (2024). IPECE DATA - Sistema de Informações Geossocioeconômicas do Ceará. http://ipecedata.ipece.ce.gov.br/ipece-data-web/ module/perfil-municipal.xhtml. Accessed: 26 nov. 2024.
Kasischke, E. S., Melack, J. M., and Dobson, M. C. (1997). The use of imaging radars for ecological applications—a review. Remote sensing of environment, 59(2):141–156.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological cybernetics, 43(1):59–69.
Le Toan, T. and Floury, N. (1998). On the retrieval of forest biomass from sar data. EUROPEAN SPACE AGENCY-PUBLICATIONS-ESA SP, 441:213–214.
Lee, J.-S., Jurkevich, L., Dewaele, P., Wambacq, P., and Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote sensing reviews, 8(4):313–340.
Liao, H., Wdowinski, S., and Li, S. (2020). Regional-scale hydrological monitoring of wetlands with sentinel-1 insar observations: Case study of the south florida everglades. Remote Sensing of Environment, 251:112051.
Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3):18–22.
López Martinez, C. (2003). Multidimensional speckle noise. Modelling and filtering re- lated to sar data. Universitat Politècnica de Catalunya.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, no. 1, pp. 321–357, 2002.
MapBiomas (2024). ATBD – ENTENDA CADA ETAPA. Online; accessed 25 May 2024.
Markham, C. G. (1967). Climatological aspects of drought in northeastern Brazil. University of California, Berkeley.
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., and Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and remote sensing magazine, 1(1):6–43.
Paradella, W. R., Knust, S. S. A., Dos Santos, A. R., Silva, A., Rabelo, T. N., and Oliveira, C. (2009). Influence of macroscale and microscale surface roughness on multi- beam radarsat-1 data: implications for geological mapping in the curaçá valley (Brazil). Photo interprétation (Paris), 45(2).
Pelletier, C., et al. (2016). Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sensing of Environment, 187:156-168.
Pelletier, C., Webb, G. I., and Petitjean, F. (2019). Temporal convolutional neural network for the classification of satellite image time series. Remote Sensing, 11(5):523.
Picoli, M. C. A., Câmara, G., Sanches, I., Simões, R., Carvalho, A., Maciel, A., Coutinho, A., Esquerdo, J., Antunes, J., Begotti, R. A., et al. (2018). Big earth observation time series analysis for monitoring brazilian agriculture. ISPRS journal of photogrammetry and remote sensing, 145:328–339.
Rodriguez, J. M. M. and da Silva, E. V. (2002). A classificação das paisagens a partir de uma visão geossistêmica. Mercator, 1(1).
Sano, E. E., Bolfe, É . L., Parreiras, T. C., Bettiol, G. M., Vicente, L. E., Sanches, I. D. A., and Victoria, D. d. C. (2023). Estimating double cropping plantations in the brazilian cerrado through planetscope monthly mosaics. Land, 12(3):581.
Sano, E. E., Ferreira, L., Asner, G., and Steinke, E. (2007). Spatial and temporal pro- babilities of obtaining cloud-free landsat images over the brazilian tropical savanna. International Journal of Remote Sensing, 28(12):2739–2752.
Santos, L. A., Ferreira, K. R., Camara, G., Picoli, M. C., & Simoes, R. E. (2021). Quality control and class noise reduction of satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 177:75-88.
Simões, R., Camara, G., Queiroz, G., Souza, F., Andrade, P. R., Santos, L., Carvalho, A., and Ferreira, K. (2021). Satellite image time series analysis for big earth observation data. Remote Sensing, 13(13):2428.
Simões, R., Picoli, M. C., Camara, G., Maciel, A., Santos, L., Andrade, P. R., Sánchez, A., Ferreira, K., and Carvalho, A. (2020). Land use and cover maps for Mato Grosso state in brazil from 2001 to 2017. Scientific Data, 7(1):34.
SNAP (2024). Sentinel Application Platform - SNAP 10.0.0. https://step.esa. int/main/download/snap-download/. Online; accessed 25 May 2024.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the royal statistical society: Series B (Methodological), 36(2):111–133.
Tomppo, E., Antropov, O., and Praks, J. (2019). Cropland classification using sentinel-1 time series: Methodological performance and prediction uncertainty assessment. Re- mote Sensing, 11(21):2480.
Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C., Thiel, C., Rossi, C., Syriou, A., and Giuliani, G. (2019). Towards sentinel-1 sar analysis-ready data: A best practices assessment on preparing backscatter data for the cube. Data, 4(3):93.
Ulaby, F. T., Moore, R. K., and Fung, A. K. (1982). Microwave remote sensing: Active and passive. volume 2-radar remote sensing and surface scattering and emission theory.
Von Bertalanffy, L. (1975). Teoria geral dos sistemas. Vozes Petrópolis.
Published
26/05/2025
How to Cite
DE FREITAS, Ana Larissa Ribeiro; GAMA, Fabio Furlan; SOUZA, Felipe Carvalho de.
MONITORING TEMPORARY CROPS IN IPU-CE USING SAR TIME SERIES IMAGES AND MACHINE LEARNING.
Mercator, Fortaleza, v. 24, may 2025.
ISSN 1984-2201.
Available at: <http://www.mercator.ufc.br/mercator/article/view/e24006>. Date accessed: 17 aug. 2025.
doi: https://doi.org/10.4215/rm2025.e24006.
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