Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1

ilustraciones, fotografías a color, mapas

Autores:
Martínez Alayón, Fredy Alberto
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84138
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84138
https://repositorio.unal.edu.co/
Palabra clave:
Recursos naturales
Fenología
Natural resources
Phenology
Radar de apertura sintética (SAR)
Imágenes Sentinel-1
Fenología del arroz
Datos polarimétricos
Exactitud de clasificación
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_f972226771892d1e18d5a48eeb411f53
oai_identifier_str oai:repositorio.unal.edu.co:unal/84138
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
dc.title.translated.eng.fl_str_mv Identification of the phenological state of rice cultivation from Sentinel-1 radar images
title Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
spellingShingle Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
Recursos naturales
Fenología
Natural resources
Phenology
Radar de apertura sintética (SAR)
Imágenes Sentinel-1
Fenología del arroz
Datos polarimétricos
Exactitud de clasificación
title_short Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
title_full Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
title_fullStr Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
title_full_unstemmed Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
title_sort Identificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1
dc.creator.fl_str_mv Martínez Alayón, Fredy Alberto
dc.contributor.advisor.none.fl_str_mv Lizarazo Salcedo, Iván Alberto
dc.contributor.author.none.fl_str_mv Martínez Alayón, Fredy Alberto
dc.subject.lemb.spa.fl_str_mv Recursos naturales
Fenología
topic Recursos naturales
Fenología
Natural resources
Phenology
Radar de apertura sintética (SAR)
Imágenes Sentinel-1
Fenología del arroz
Datos polarimétricos
Exactitud de clasificación
dc.subject.lemb.eng.fl_str_mv Natural resources
Phenology
dc.subject.proposal.spa.fl_str_mv Radar de apertura sintética (SAR)
Imágenes Sentinel-1
Fenología del arroz
Datos polarimétricos
Exactitud de clasificación
description ilustraciones, fotografías a color, mapas
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-04T21:37:33Z
dc.date.available.none.fl_str_mv 2023-07-04T21:37:33Z
dc.date.issued.none.fl_str_mv 2023-01-30
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/84138
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/84138
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.relation.references.spa.fl_str_mv Anderson, F., Ixmuca, A., Herndon, K. E., y Kucera, L. M. (2019). Sar handbook: Background. En Global Forest Observations Initiative (GFOI) Plenary 2019.
Argenti, F., Lapini, A., Bianchi, T., y Alparone, L. (2013). A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geoscience and remote sensing magazine, 1(3):6– 35.
Benes, R. y Riha, K. (2012). Medical image denoising by improved kuan filter. Advances in Electrical and Electronic Engineering, 10(1):43–49.
Bhattacharya, A., Rao, Y. S., y Mandal, D. (2021). Radar remote sensing for crop biophysical parameter estimation. Springer.
Bhogapurapu, N., Dey, S., Bhattacharya, A., Mandal, D., Lopez-Sanchez, J. M., McNairn, H., López-Martı́nez, C., y Rao, Y. S. (2021). Dual-polarimetric descriptors from sentinel-1 grd sar data for crop growth assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 178:20–35.
Bourbigot, M., Johnsen, H., Piantanida, R., y Hajduch, G. (2016). Sentinel-1 Product Definition.
Braun, A. (2019). Radar satellite imagery for humanitarian response. Bridging the gap between technology and application, 10.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Bruce, P., Bruce, A., y Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python. O’Reilly Media.
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., y Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. En ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122.
CCRS (2019). Remote sensing tutorials. Technical report, Canada Centre for Remote Sensing.
Chen, K.-S. (2016). Principles of synthetic aperture radar imaging: a system simulation approach, volumen 2. CRC Press.
Chen, X. (2017). Spatiotemporal processes of plant phenology: simulation and prediction. Springer.
Cihlar, J. (2000). Land cover mapping of large areas from satellites: status and research priorities. International journal of remote sensing, 21(6-7):1093–1114.
Cloude, S. (2009). Polarisation: applications in remote sensing. OUP Oxford.
Congalton, R. G. y Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data Principles and Practices, Second Edition, volumen 1.
Congalton, R. G. y Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data Principles and Practices, Third Edition, volumen 1.
Counce, P. A., Keisling, T. C., y Mitchell, A. J. (2000). A uniform, objectives, and adaptive system for expressing rice development. Crop Sci., 40(2):436–443.
DANE (2018). Manual de uso del marco geoestadı́stico nacional en el proceso estadı́stico.
DANE y FEDEARROZ (2017). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.
DANE y FEDEARROZ (2018). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.
DANE y FEDEARROZ (2019). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadı́sticas - Federación Nacional de Arroceros.
DANE y FEDEARROZ (2020). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.
DANE y FEDEARROZ (2021). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.
DANE, D. (2014). Censo nacional agropecuario 2014.
Davidson, A., Fisette, T., McNairn, H., y Daneshfar, B. (2014). Handbook on remote sensing for agricultural statistics. Número August.
Dey, S., Bhogapurapu, N., Bhattacharya, A., Mandal, D., Lopez-Sanchez, J. M., McNairn, H., y Frery, A. C. (2021). Rice phenology mapping using novel target characterization parameters from polarimetric sar data. International Journal of Remote Sensing, 42(14):5515–5539.
Dong, J. y Xiao, X. (2016). Evolution of regional to global paddy rice mapping methods: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 119:214–227.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., y Moore, B. (2016). Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote Sensing of Environment, 185:142–154.
EIU (2021). Global economics world commodity forecasts. Technical report, The Economist Intelligence Unit.
ESA (2007). ASAR Product Handbook. European Space Agency.
ESA (2012). ESA’s radar observatory mission for GMES operational services, volumen 1.
ESA (2013a). Sentinel-1 sar technical guide. Último acceso: 16 de NOVIEMBRE de 2021.
ESA (2013b). Sentinel-1 user guides. Último acceso: 16 de NOVIEMBRE de 2021.
European Space Agency (2013). Sentinel-1 User Handbook, Issue 1 Revision 2. (1):1–64.
FAO (2018). Fao rice market monitor (rmm). Technical report, Food and Agriculture Organization of the United Nations.
FEDEARROZ-FNA, editor (2014). Manejo integrado del cultivo de arroz. FEDERACIÓN NACIONAL DE ARROCEROS FONDO NACIONAL DEL ARROZ, Área Técnica, Bogotá D. C., Colombia.
FEDEARROZ-FNA, editor (2017). IV CENSO NACIONAL ARROCERO 2016. FEDERACIÓN NACIONAL DE ARROCEROS FONDO NACIONAL DEL ARROZ, División de Investigaciones Económicas, Bogotá D. C., Colombia.
Filipponi, F. (2019). Sentinel-1 grd preprocessing workflow. En Multidisciplinary Digital Publishing Institute Proceedings, volumen 18, p. 11.
Garcés, G. y Medina, J. (2017). LA FISIOLOGÍA DEL CULTIVO DEL ARROZ EN EL PROGRAMA AMTEC. Technical report, FEDEARROZ - Fondo Nacional del Arroz, Bogotá.
Garcı́a, S. A. y Martı́nez, L. J. (2010). Método para identificación de cultivos de arroz (Oryza sativa L.) con base en imágenes de satélite. Agronomı́a Colombiana., 28(2):281–290.
Gareth, J., Daniela, W., Trevor, H., y Robert, T. (2013). An introduction to statistical learning: with applications in R. Spinger.
Girón, Girón, R. y Hernández, Torrez, F. (2017). Identificación de cultivos de arroz empleando imágenes Sentinel-1. Análisis Geográficos, 53:57–67.
Gislason, P. O., Benediktsson, J. A., y Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern recognition letters, 27(4):294–300.
Gobernación-Tolima, editor (2019). PLAN DEPARTAMENTAL DE EXTENSIÓN AGROPECUARIA-PDEA. Gobernación del Tolima, Ibagué, Colombia.
GSARS, editor (2017). Handbook on Remote Sensing for Agricultural Statistics. Global Strategy to improve Agricultural and Rural Statistics (GSARS), Roma - Italia.
Hajduch, G., Bourbigot, M., Johnsen, H., y Piantanida, R. (2021). Sentinel-1 Product Specification.
Harrison, B., Jupp, D., Coppa, I., Grant, I., Hudson, D., Phinn, S., Smith, C., Mueller, N., Forster, B., y Lewis, M. (2016). Earth Observation: Data, Processing and Applications. data-basics and acquisition. volume 1A. Australia and New Zealand CRC for Spatial Information.
Harrison, B., Jupp, D., Coppa, I., Grant, I., Hudson, D., Phinn, S., Smith, C., Mueller, N., Forster, B., y Lewis, M. (2017). Earth Observation: Data, Processing and Applications. data-image interpretation. volume 1B. Australia and New Zealand CRC for Spatial Information.
IGAC (2005). Interpretación visual de imágenes de sensores remotos y su aplicación en levantamientos de cobertura y uso de la tierra. Instituto Geográfico Agustı́n Codazzi. Centro de Investigación y Desarrollo de Información Geográfica.
Jarvis, A., Rubiano Mejı́a, J. E., Nelson, A., Farrow, A., y Mulligan, M. (2004). Practical use of srtm data in the tropics: Comparisons with digital elevation models generated cartographic data.
Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.
Jensen, J. R. (2015). Introductory digital image processing: a remote sensing perspective.
Jin, Y.-Q. y Xu, F. (2013). Polarimetric scattering and SAR information retrieval. John Wiley & Sons.
Khabbazan, S., Vermunt, P., Steele-Dunne, S., Ratering Arntz, L., Marinetti, C., van der Valk, D., Iannini, L., Molijn, R., Westerdijk, K., y van der Sande, C. (2019). Crop monitoring using sentinel-1 data: A case study from the netherlands. Remote Sensing, 11(16):1887.
Kim, Y. y van Zyl, J. J. (2009). A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing, 47(8):2519– 2527.
Kordi, F. y Yousefi, H. (2022). Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images. Remote Sensing Applications: Society and Environment, 27:100812.
Krishnakumar, V., Monserrat, O., Crosetto, M., y Crippa, B. (2018). Atmospheric phase delay in sentinel sar interferometry.
Lee, J.-S., Jurkevich, L., Dewaele, P., Wambacq, P., y Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote sensing reviews, 8(4):313–340.
Lopez-Sanchez, J. M., Cloude, S. R., y Ballester-Berman, J. D. (2011). Rice phenology monitoring by means of sar polarimetry at x-band. IEEE Transactions on Geoscience and Remote Sensing, 50(7):2695–2709.
Lopez-Sanchez, J. M., Vicente-Guijalba, F., Ballester-Berman, J. D., y Cloude, S. R. (2014). Influence of incidence angle on the coherent copolar polarimetric response of rice at x-band. IEEE Geoscience and Remote Sensing Letters, 12(2):249–253.
Luo, Y. y Flett, D. (2018). Sentinel-1 data border noise removal and seamless synthetic aperture radar mosaic generation. En Multidisciplinary Digital Publishing Institute Proceedings, volumen 2, p. 330.
Ma, L., Li, M., Ma, X., g, L., Du, P., y Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130:277–293.
Ma, L., Li, M., Ma, X., g, L., Du, P., y Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130:277–293.
Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., McNairn, H., y Rao, Y. S. (2020a). Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 sar data. Remote Sensing of Environment, 247:111954.
Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y. S., y Frery, A. C. (2020b). A radar vegetation index for crop monitoring using compact polarimetric sar data. IEEE Transactions on Geoscience and Remote Sensing, 58(9):6321–6335.
Mandal, D., Vaka, D. S., Bhogapurapu, N. R., Vanama, V., Kumar, V., Rao, Y. S., y Bhattacharya, A. (2019). Sentinel-1 slc preprocessing workflow for polarimetric applications: A generic practice for generating dual-pol covariance matrix elements in snap s-1 toolbox.
Maxwell, A. E. y Warner, T. A. (2020). Thematic classification accuracy assessment with inherently uncertain boundaries: An argument for center-weighted accuracy assessment metrics. Remote Sensing, 12(12).
Meier, U. (1997). Growth stages of mono-and dicotyledonous plants. Blackwell Wissenschafts-Verlag.
Mestre-Quereda, A., Lopez-Sanchez, J. M., Vicente-Guijalba, F., Jacob, A. W., y Engdahl, M. E. (2020). Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:4070–4084.
Mosleh, M. K., Hassan, Q. K., y Chowdhury, E. H. (2015). Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors, 15(1):769–791.
Nwanganga, F. y Chapple, M. (2020). Practical Machine Learning in R, volumen 148.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., y Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148:42–57.
Ouchi, K. (2013). Recent trend and advance of synthetic aperture radar with selected topics. Remote Sensing, 5(2):716–807.
Palakuru, M., Khadar Babu, S., y Chaube, N. R. (2021). Exploring potential of c band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity. Environmental Science and Pollution Research, 28:42001–42013.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., y Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pérez, N. y Ortı́z, U. (2009). Imágenes Aster en la discriminación de áreas de uso agrı́cola en Colombia. Rev. Fac. Nac. Agron., 62(1):4923–4935.
Piantanida, R., Miranda, N., y Hadjduch, G. (2017). Thermal denoising of products generated by the s-1 ipf. Sentinel-1 Mission Perform. Centre, Ramonville-Saint-Agne, France, Tech. Rep. MPC-0392.
Pohl, C. y Van Genderen, J. (2016). Remote sensing image fusion: A practical guide. Crc Press.
Qiu, B., Li, W., Tang, Z., Chen, C., y Qi, W. (2015). Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecological Indicators, 56:79–86.
Quiñones, J. (2011). Clasificación automática de los estados de desarrollo del arroz a partir de imágenes de RADARSAT-2. Tesis de máster, Universidad Nacional de Colombia.
Raschka, S. y Mirjalili, V. (2019a). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.
Raschka, S. y Mirjalili, V. (2019b). Python Machine Learning Third Edition (3nd Edition).
Richards, J. (2009). Remote Sensing with Imaging Radar.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., y Ohno, H. (2005). A crop phenology detection method using time-series modis data. Remote sensing of environment, 96(3-4):366–374.
Shang, Jiali and Liu, Jiangui and Poncos, Valentin and Geng, Xiaoyuan and Qian, Budong and Chen, Qihao and Dong, Taifeng and Macdonald, Dan and Martin, Tim and Kovacs, John and others (2020). Detection of crop seeding and harvest through analysis of time-series sentinel-1 interferometric sar data. Remote Sensing, 12(10):1551.
Shimada, M. (2018). Imaging from Spaceborne and Airborne SARs, Calibration, and Applications. SAR Remote Sensing. CRC Press.
Singh, S. y Gupta, P. (2014). Comparative study id3, cart and c4. 5 decision tree algorithm: a survey. International Journal of Advanced Information Science and Technology (IJAIST), 27(27):97–103.
Szigarski, C., Jagdhuber, T., Baur, M., Thiel, C., Parrens, M., Wigneron, J.-P., Piles, M., y Entekhabi, D. (2018). Analysis of the radar vegetation index and potential improvements. Remote Sensing, 10(11):1776.
Touzi, R., Lopes, A., niquel, J., y Vachon, P. (1999). Coherence estimation for sar imagery. IEEE Transactions on Geoscience and Remote Sensing, 37(1):135–149.
Waldini, H., Ash Shidiq, I., Rokhmatuloh, R., y Supriatna, S. (2021). Rice crop phenology model to monitor rice planting and harvesting time using remote sensing approach. E3S Web of Conferences, 232:03020.
Waske, B., Benediktsson, J. A., y Sveinsson, J. R. (2012). Random forest classification of remote sensing data. Signal and Image Processing for Remote Sensing, p. 365.
Wibowo, A and Shidiq, IPA and others (2020). Spatial analysis of rice phenology using sentinel-1 and sentinel-2 in karawang regency. En IOP Conference Series: Earth and Environmental Science, volumen 500, p. 012033. IOP Publishing.
Woodhouse, I. H. (2017). Introduction to microwave remote sensing. CRC press.
Yamaguchi, Y. (2020). Polarimetric SAR Imaging: Theory and Applications. SAR Remote Sensing. CRC Press.
Yang, H., Pan, B., Li, N., Wang, W., Zhang, J., y Zhang, X. (2021). A systematic method for spatio-temporal phenology estimation of paddy rice using time series sentinel-1 images. Remote Sensing of Environment, 259:112394.
Ye, S., Pontius, R. G., y Rakshit, R. (2018). A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 141.
Zhang, M. y Lin, H. (2019). Object-based rice mapping using time-series and phenological data. Advances in Space Research, 63(1):190–202.
Zheng, H., Cheng, T., Yao, X., Deng, X., Tian, Y., Cao, W., y Zhu, Y. (2016). Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Research, 198:131–139.
Zhu, J., Wen, J., y Zhang, Y. (2013). A new algorithm for sar image despeckling using an enhanced lee filter and median filter. En 2013 6th International congress on image and signal processing (CISP), volumen 1, pp. 224–228. IEEE.
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., y Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4):8–36.
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spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lizarazo Salcedo, Iván Albertob7a911d83f30c19f50a8d2f7b4e94e02Martínez Alayón, Fredy Albertoc022367f9aad079e92efe5e3fc93a06b2023-07-04T21:37:33Z2023-07-04T21:37:33Z2023-01-30https://repositorio.unal.edu.co/handle/unal/84138Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías a color, mapasLa disponibilidad de información oportuna y exacta sobre la fenología del arroz es esencial para diversas actividades de manejo de un cultivo que es esencial para la seguridad agroalimentaria. Aunque la recopilación directa de datos en el campo proporciona información confiable, esa tarea requiere mucho tiempo y trabajo. Como alternativa tecnológica, la teledetección óptica satelital recopila datos de reflectancia cada 5 días. Sin embargo, en ambientes tropicales, la cobertura de nubes obstruye la vista desde arriba. Debido a su capacidad de ver a través de las nubes, las imágenes obtenidas mediante sensores de radar de apertura sintética (SAR) tienen potencial para el monitoreo de las etapas fenológicas del arroz. Este estudio implementa un flujo de trabajo técnico para procesar y analizar datos SAR polarimétricos para el mapeo de la fenología del arroz. Un conjunto multitemporal de imágenes Sentinel-1 de banda C adquiridas en dos zonas de arroz en Colombia se utilizaron para validar el flujo de trabajo. En este estudio, se realizó la clasificación fenológica utilizando índices polarimétricos e interferométricos como variables explicativas. Se obtuvo una buena exactitud de clasificación general para las etapas de fenología del cultivo utilizando polarizaciones VH y VV, junto con el índice DpRVI y una exactitud deficiente para los resultados con la variable coherencia. Esta diferencia en la calidad de los resultados podría deberse a que tanto las polarizaciones como el índice logran describir el crecimiento del cultivo de manera satisfactoria mientras que la coherencia está enfocada en la detección de cambios que no se pudieron caracterizar en coberturas vegetales. Se demuestra la utilidad de las imágenes Sentinel-1 para el monitoreo de la fenología del arroz, así como los desafíos técnicos que deben resolverse para tener éxito con estas imágenes. (Texto tomado de la fuente)Accurate and timely information on rice phenology is crucial for ensuring agrifood security and effective crop management. While direct data collection in the field is reliable, it can be labor-intensive and time-consuming. In tropical environments, cloud cover often obstructs satellite optical remote sensing, which collects reflectance data every 5 days. However, synthetic aperture radar (SAR) sensors have the potential to monitor rice phenology, as they can see through clouds. This study implements a technical workflow to process and analyze polarimetric SAR data for mapping rice phenology. A multi-temporal set of C-band Sentinel-1 images acquired in two rice areas in Colombia were used to validate the workflow. Phenological classification was performed using polarimetric and interferometric indices as explanatory variables. The results show that VH and VV polarizations, together with the DpRVI index, produced good overall classification accuracy for crop phenology stages, while coherence variable had poor accuracy. This difference in the quality of the results could be due to the fact that both polarizations and the index satisfactorily describe crop growth, while coherence is focused on detecting changes that cannot be characterized in vegetation coverages.The usefulness of Sentinel-1 imagery for monitoring rice phenology is demonstrated, along with the technical challenges that need to be resolved for successful use of these images.MaestríaGeoinformación para el uso sostenible de los recursos naturalesxix, 157 páginasapplication/pdfIdentificación del estado fenológico del cultivo del arroz a partir de imágenes de radar Sentinel-1Identification of the phenological state of rice cultivation from Sentinel-1 radar imagesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotá - Ciencias Agrarias - Maestría en GeomáticaFacultad de Ciencias AgrariasBogotá,ColombiaUniversidad Nacional de Colombia - Sede BogotáAnderson, F., Ixmuca, A., Herndon, K. E., y Kucera, L. M. (2019). Sar handbook: Background. En Global Forest Observations Initiative (GFOI) Plenary 2019.Argenti, F., Lapini, A., Bianchi, T., y Alparone, L. (2013). A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geoscience and remote sensing magazine, 1(3):6– 35.Benes, R. y Riha, K. (2012). Medical image denoising by improved kuan filter. Advances in Electrical and Electronic Engineering, 10(1):43–49.Bhattacharya, A., Rao, Y. S., y Mandal, D. (2021). Radar remote sensing for crop biophysical parameter estimation. Springer.Bhogapurapu, N., Dey, S., Bhattacharya, A., Mandal, D., Lopez-Sanchez, J. M., McNairn, H., López-Martı́nez, C., y Rao, Y. S. (2021). Dual-polarimetric descriptors from sentinel-1 grd sar data for crop growth assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 178:20–35.Bourbigot, M., Johnsen, H., Piantanida, R., y Hajduch, G. (2016). Sentinel-1 Product Definition.Braun, A. (2019). Radar satellite imagery for humanitarian response. Bridging the gap between technology and application, 10.Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.Bruce, P., Bruce, A., y Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python. O’Reilly Media.Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., y Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. En ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122.CCRS (2019). Remote sensing tutorials. Technical report, Canada Centre for Remote Sensing.Chen, K.-S. (2016). Principles of synthetic aperture radar imaging: a system simulation approach, volumen 2. CRC Press.Chen, X. (2017). Spatiotemporal processes of plant phenology: simulation and prediction. Springer.Cihlar, J. (2000). Land cover mapping of large areas from satellites: status and research priorities. International journal of remote sensing, 21(6-7):1093–1114.Cloude, S. (2009). Polarisation: applications in remote sensing. OUP Oxford.Congalton, R. G. y Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data Principles and Practices, Second Edition, volumen 1.Congalton, R. G. y Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data Principles and Practices, Third Edition, volumen 1.Counce, P. A., Keisling, T. C., y Mitchell, A. J. (2000). A uniform, objectives, and adaptive system for expressing rice development. Crop Sci., 40(2):436–443.DANE (2018). Manual de uso del marco geoestadı́stico nacional en el proceso estadı́stico.DANE y FEDEARROZ (2017). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.DANE y FEDEARROZ (2018). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.DANE y FEDEARROZ (2019). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadı́sticas - Federación Nacional de Arroceros.DANE y FEDEARROZ (2020). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.DANE y FEDEARROZ (2021). Boletín técnico encuesta nacional de arroz mecanizado. Technical report, Departamento Nacional de estadísticas - Federación Nacional de Arroceros.DANE, D. (2014). Censo nacional agropecuario 2014.Davidson, A., Fisette, T., McNairn, H., y Daneshfar, B. (2014). Handbook on remote sensing for agricultural statistics. Número August.Dey, S., Bhogapurapu, N., Bhattacharya, A., Mandal, D., Lopez-Sanchez, J. M., McNairn, H., y Frery, A. C. (2021). Rice phenology mapping using novel target characterization parameters from polarimetric sar data. International Journal of Remote Sensing, 42(14):5515–5539.Dong, J. y Xiao, X. (2016). Evolution of regional to global paddy rice mapping methods: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 119:214–227.Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., y Moore, B. (2016). Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote Sensing of Environment, 185:142–154.EIU (2021). Global economics world commodity forecasts. Technical report, The Economist Intelligence Unit.ESA (2007). ASAR Product Handbook. European Space Agency.ESA (2012). ESA’s radar observatory mission for GMES operational services, volumen 1.ESA (2013a). Sentinel-1 sar technical guide. Último acceso: 16 de NOVIEMBRE de 2021.ESA (2013b). Sentinel-1 user guides. Último acceso: 16 de NOVIEMBRE de 2021.European Space Agency (2013). Sentinel-1 User Handbook, Issue 1 Revision 2. (1):1–64.FAO (2018). Fao rice market monitor (rmm). Technical report, Food and Agriculture Organization of the United Nations.FEDEARROZ-FNA, editor (2014). Manejo integrado del cultivo de arroz. FEDERACIÓN NACIONAL DE ARROCEROS FONDO NACIONAL DEL ARROZ, Área Técnica, Bogotá D. C., Colombia.FEDEARROZ-FNA, editor (2017). IV CENSO NACIONAL ARROCERO 2016. FEDERACIÓN NACIONAL DE ARROCEROS FONDO NACIONAL DEL ARROZ, División de Investigaciones Económicas, Bogotá D. C., Colombia.Filipponi, F. (2019). Sentinel-1 grd preprocessing workflow. En Multidisciplinary Digital Publishing Institute Proceedings, volumen 18, p. 11.Garcés, G. y Medina, J. (2017). LA FISIOLOGÍA DEL CULTIVO DEL ARROZ EN EL PROGRAMA AMTEC. Technical report, FEDEARROZ - Fondo Nacional del Arroz, Bogotá.Garcı́a, S. A. y Martı́nez, L. J. (2010). Método para identificación de cultivos de arroz (Oryza sativa L.) con base en imágenes de satélite. Agronomı́a Colombiana., 28(2):281–290.Gareth, J., Daniela, W., Trevor, H., y Robert, T. (2013). An introduction to statistical learning: with applications in R. Spinger.Girón, Girón, R. y Hernández, Torrez, F. (2017). Identificación de cultivos de arroz empleando imágenes Sentinel-1. Análisis Geográficos, 53:57–67.Gislason, P. O., Benediktsson, J. A., y Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern recognition letters, 27(4):294–300.Gobernación-Tolima, editor (2019). PLAN DEPARTAMENTAL DE EXTENSIÓN AGROPECUARIA-PDEA. Gobernación del Tolima, Ibagué, Colombia.GSARS, editor (2017). Handbook on Remote Sensing for Agricultural Statistics. Global Strategy to improve Agricultural and Rural Statistics (GSARS), Roma - Italia.Hajduch, G., Bourbigot, M., Johnsen, H., y Piantanida, R. (2021). Sentinel-1 Product Specification.Harrison, B., Jupp, D., Coppa, I., Grant, I., Hudson, D., Phinn, S., Smith, C., Mueller, N., Forster, B., y Lewis, M. (2016). Earth Observation: Data, Processing and Applications. data-basics and acquisition. volume 1A. Australia and New Zealand CRC for Spatial Information.Harrison, B., Jupp, D., Coppa, I., Grant, I., Hudson, D., Phinn, S., Smith, C., Mueller, N., Forster, B., y Lewis, M. (2017). Earth Observation: Data, Processing and Applications. data-image interpretation. volume 1B. Australia and New Zealand CRC for Spatial Information.IGAC (2005). Interpretación visual de imágenes de sensores remotos y su aplicación en levantamientos de cobertura y uso de la tierra. Instituto Geográfico Agustı́n Codazzi. Centro de Investigación y Desarrollo de Información Geográfica.Jarvis, A., Rubiano Mejı́a, J. E., Nelson, A., Farrow, A., y Mulligan, M. (2004). Practical use of srtm data in the tropics: Comparisons with digital elevation models generated cartographic data.Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.Jensen, J. R. (2015). Introductory digital image processing: a remote sensing perspective.Jin, Y.-Q. y Xu, F. (2013). Polarimetric scattering and SAR information retrieval. John Wiley & Sons.Khabbazan, S., Vermunt, P., Steele-Dunne, S., Ratering Arntz, L., Marinetti, C., van der Valk, D., Iannini, L., Molijn, R., Westerdijk, K., y van der Sande, C. (2019). Crop monitoring using sentinel-1 data: A case study from the netherlands. Remote Sensing, 11(16):1887.Kim, Y. y van Zyl, J. J. (2009). A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing, 47(8):2519– 2527.Kordi, F. y Yousefi, H. (2022). Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images. Remote Sensing Applications: Society and Environment, 27:100812.Krishnakumar, V., Monserrat, O., Crosetto, M., y Crippa, B. (2018). Atmospheric phase delay in sentinel sar interferometry.Lee, J.-S., Jurkevich, L., Dewaele, P., Wambacq, P., y Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote sensing reviews, 8(4):313–340.Lopez-Sanchez, J. M., Cloude, S. R., y Ballester-Berman, J. D. (2011). Rice phenology monitoring by means of sar polarimetry at x-band. IEEE Transactions on Geoscience and Remote Sensing, 50(7):2695–2709.Lopez-Sanchez, J. M., Vicente-Guijalba, F., Ballester-Berman, J. D., y Cloude, S. R. (2014). Influence of incidence angle on the coherent copolar polarimetric response of rice at x-band. IEEE Geoscience and Remote Sensing Letters, 12(2):249–253.Luo, Y. y Flett, D. (2018). Sentinel-1 data border noise removal and seamless synthetic aperture radar mosaic generation. En Multidisciplinary Digital Publishing Institute Proceedings, volumen 2, p. 330.Ma, L., Li, M., Ma, X., g, L., Du, P., y Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130:277–293.Ma, L., Li, M., Ma, X., g, L., Du, P., y Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130:277–293.Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., McNairn, H., y Rao, Y. S. (2020a). Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 sar data. Remote Sensing of Environment, 247:111954.Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y. S., y Frery, A. C. (2020b). A radar vegetation index for crop monitoring using compact polarimetric sar data. IEEE Transactions on Geoscience and Remote Sensing, 58(9):6321–6335.Mandal, D., Vaka, D. S., Bhogapurapu, N. R., Vanama, V., Kumar, V., Rao, Y. S., y Bhattacharya, A. (2019). Sentinel-1 slc preprocessing workflow for polarimetric applications: A generic practice for generating dual-pol covariance matrix elements in snap s-1 toolbox.Maxwell, A. E. y Warner, T. A. (2020). Thematic classification accuracy assessment with inherently uncertain boundaries: An argument for center-weighted accuracy assessment metrics. Remote Sensing, 12(12).Meier, U. (1997). Growth stages of mono-and dicotyledonous plants. Blackwell Wissenschafts-Verlag.Mestre-Quereda, A., Lopez-Sanchez, J. M., Vicente-Guijalba, F., Jacob, A. W., y Engdahl, M. E. (2020). Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:4070–4084.Mosleh, M. K., Hassan, Q. K., y Chowdhury, E. H. (2015). Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors, 15(1):769–791.Nwanganga, F. y Chapple, M. (2020). Practical Machine Learning in R, volumen 148.Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., y Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148:42–57.Ouchi, K. (2013). Recent trend and advance of synthetic aperture radar with selected topics. Remote Sensing, 5(2):716–807.Palakuru, M., Khadar Babu, S., y Chaube, N. R. (2021). Exploring potential of c band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity. Environmental Science and Pollution Research, 28:42001–42013.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., y Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.Pérez, N. y Ortı́z, U. (2009). Imágenes Aster en la discriminación de áreas de uso agrı́cola en Colombia. Rev. Fac. Nac. Agron., 62(1):4923–4935.Piantanida, R., Miranda, N., y Hadjduch, G. (2017). Thermal denoising of products generated by the s-1 ipf. Sentinel-1 Mission Perform. Centre, Ramonville-Saint-Agne, France, Tech. Rep. MPC-0392.Pohl, C. y Van Genderen, J. (2016). Remote sensing image fusion: A practical guide. Crc Press.Qiu, B., Li, W., Tang, Z., Chen, C., y Qi, W. (2015). Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecological Indicators, 56:79–86.Quiñones, J. (2011). Clasificación automática de los estados de desarrollo del arroz a partir de imágenes de RADARSAT-2. Tesis de máster, Universidad Nacional de Colombia.Raschka, S. y Mirjalili, V. (2019a). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.Raschka, S. y Mirjalili, V. (2019b). Python Machine Learning Third Edition (3nd Edition).Richards, J. (2009). Remote Sensing with Imaging Radar.Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., y Ohno, H. (2005). A crop phenology detection method using time-series modis data. Remote sensing of environment, 96(3-4):366–374.Shang, Jiali and Liu, Jiangui and Poncos, Valentin and Geng, Xiaoyuan and Qian, Budong and Chen, Qihao and Dong, Taifeng and Macdonald, Dan and Martin, Tim and Kovacs, John and others (2020). Detection of crop seeding and harvest through analysis of time-series sentinel-1 interferometric sar data. Remote Sensing, 12(10):1551.Shimada, M. (2018). Imaging from Spaceborne and Airborne SARs, Calibration, and Applications. SAR Remote Sensing. CRC Press.Singh, S. y Gupta, P. (2014). Comparative study id3, cart and c4. 5 decision tree algorithm: a survey. International Journal of Advanced Information Science and Technology (IJAIST), 27(27):97–103.Szigarski, C., Jagdhuber, T., Baur, M., Thiel, C., Parrens, M., Wigneron, J.-P., Piles, M., y Entekhabi, D. (2018). Analysis of the radar vegetation index and potential improvements. Remote Sensing, 10(11):1776.Touzi, R., Lopes, A., niquel, J., y Vachon, P. (1999). Coherence estimation for sar imagery. IEEE Transactions on Geoscience and Remote Sensing, 37(1):135–149.Waldini, H., Ash Shidiq, I., Rokhmatuloh, R., y Supriatna, S. (2021). Rice crop phenology model to monitor rice planting and harvesting time using remote sensing approach. E3S Web of Conferences, 232:03020.Waske, B., Benediktsson, J. A., y Sveinsson, J. R. (2012). Random forest classification of remote sensing data. Signal and Image Processing for Remote Sensing, p. 365.Wibowo, A and Shidiq, IPA and others (2020). Spatial analysis of rice phenology using sentinel-1 and sentinel-2 in karawang regency. En IOP Conference Series: Earth and Environmental Science, volumen 500, p. 012033. IOP Publishing.Woodhouse, I. H. (2017). Introduction to microwave remote sensing. CRC press.Yamaguchi, Y. (2020). Polarimetric SAR Imaging: Theory and Applications. SAR Remote Sensing. CRC Press.Yang, H., Pan, B., Li, N., Wang, W., Zhang, J., y Zhang, X. (2021). A systematic method for spatio-temporal phenology estimation of paddy rice using time series sentinel-1 images. Remote Sensing of Environment, 259:112394.Ye, S., Pontius, R. G., y Rakshit, R. (2018). A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 141.Zhang, M. y Lin, H. (2019). Object-based rice mapping using time-series and phenological data. Advances in Space Research, 63(1):190–202.Zheng, H., Cheng, T., Yao, X., Deng, X., Tian, Y., Cao, W., y Zhu, Y. (2016). Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Research, 198:131–139.Zhu, J., Wen, J., y Zhang, Y. (2013). A new algorithm for sar image despeckling using an enhanced lee filter and median filter. En 2013 6th International congress on image and signal processing (CISP), volumen 1, pp. 224–228. IEEE.Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., y Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4):8–36.Recursos naturalesFenologíaNatural resourcesPhenologyRadar de apertura sintética (SAR)Imágenes Sentinel-1Fenología del arrozDatos polarimétricosExactitud de clasificaciónLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84138/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL11232581.2023.pdf11232581.2023.pdfTesis de Maestría en Geomáticaapplication/pdf26028868https://repositorio.unal.edu.co/bitstream/unal/84138/2/11232581.2023.pdf4e4e46251cb53e25c6de0f15258be35dMD52THUMBNAIL11232581.2023.pdf.jpg11232581.2023.pdf.jpgGenerated Thumbnailimage/jpeg4933https://repositorio.unal.edu.co/bitstream/unal/84138/3/11232581.2023.pdf.jpge0cc5aa1f81d6788f48c1ec36619c636MD53unal/84138oai:repositorio.unal.edu.co:unal/841382023-08-09 23:04:40.429Repositorio Institucional Universidad Nacional de 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