Vol. 34 (2024)
Artículos de Investigación

Índices para el monitoreo de cuerpos de agua usando sensores remotos

Rodolfo Hernández-Lozano
Universidad Autónoma del Estado de Hidalgo
Biografía
Numa P. Pavón
Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo
Biografía

Publicado 2024-03-06

Cómo citar

Hernández Lozano, R., & Pavón, N. (2024). Índices para el monitoreo de cuerpos de agua usando sensores remotos. Acta Universitaria, 34, 1–19. https://doi.org/10.15174/au.2024.3814

Resumen

La sobreexplotación de los cuerpos de agua aunado a las sequías y el impacto del cambio climático reducen el agua disponible para actividades humanas, lo cual genera serios problemas económicos y sociales. Por tanto, una tarea imprescindible es el monitoreo del estado de los cuerpos de agua superficiales, y una alternativa rápida, precisa y económica es hacerlo mediante técnicas de teledetección usando sensores remotos satelitales. Estas técnicas ayudan a obtener información a distancia de un determinado objeto situado sobre la superficie terrestre. El objetivo de este estudio fue, mediante el método PRISMA, realizar una revisión de las aplicaciones de los sensores remotos en el monitoreo de cuerpos de agua para dar alternativas de uso de los índices de agua. El índice de agua modificado de diferencia normalizada (MNDWI, por sus siglas en inglés) y el índice de extracción de agua automatizado (AWEI, por sus siglas en inglés) son los más adecuados debido a que son fáciles de construir e interpretar, además de que tienen alta precisión.

Citas

  1. Abbaspour, M., Javid, A. H., Mirbagheri, S. A., Ahmadi Givi, F., & Moghimi, P. (2012). Investigation of lake drying attributed to climate change. International Journal of Environmental Science and Technology, 9, 257-266. https://doi.org/10.1007/s13762-012-0031-0
  2. Ali, D. A., Deininger, K., & Monchuk, D. (2020). Using satellite imagery to assess impacts of soil and water conservation measures: evidence from Ethiopia’s Tana-Beles watershed. Ecological Economics, 169, 106512. https://doi.org/10.1016/j.ecolecon.2019.106512
  3. Ariza, A., Roa, O. J., Serrato, P. K., & León, H. A. (2018). Uso de índices espectrales derivados de sensores remotos para la caracterización geomorfológica en zonas insulares del Caribe colombiano. Perspectiva Geográfica, 23, 105-122. https://doi.org/10.19053/01233769.5863
  4. Arreola-Esquivel, M., Delgadillo-Herrera, M., Toxqui-Quitl, C., & Padilla-Vivanco, A. (2019). Index-based methods for water body extraction in satellite data. Proceedings of Spie, 111372N. https://doi.org/10.1117/12.2529756
  5. Asfaw, W., Haile, A. T., & Rientjes, T. (2020). Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin, Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 89, 102095. https://doi.org/10.1016/j.jag.2020.102095
  6. Asfaw, A., Simane, B., Hassen, A., & Bantider, A. (2018). Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: a case study in Woleka sub-basin. Weather and Climate Extremes, 19, 29–41. https://doi.org/10.1016/j.wace.2017.12.002
  7. Bangira, T., Alfieri, S. M., Menenti, M., & van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351. https://doi.org/10.3390/rs11111351
  8. Benefoh, D. T., Villamor, G. B., van Noordwijk, M., Borgemeister, C., Asante, W. A., & Asubonteng, K. O. (2018). Assessing land-use typologies and change intensities in a structurally complex Ghanaian cocoa landscape. Applied Geography, 99, 109–119. https://doi.org/10.1016/j.apgeog.2018.07.027
  9. Bhaga, T. D., Dube, T., Shekede, M. D., & Shoko, C. (2020). Impacts of climate variability and drought on surface water resources in sub-Saharan Africa using remote sensing: a review. Remote Sensing, 12(24), 4184. https://doi.org/10.3390/rs12244184
  10. Caballero, M., & Vázquez, G. (2019). Lagos como sensores de cambio climático: el caso de La Alberca de Tacámbaro, Michoacán, México. TIP Revista Especializada en Ciencias Químico-Biológicas, 22, 1-8. https://doi.org/10.22201/fesz.23958723e.2019.0.193
  11. Calvario, G., Hernández, C., Lazkano, E., Sierra, B., Dalmau, O., & Alarcón, T. (2017). Machine learning approach to fuse multiple band for water bodies detection. 3rd International Conference on Computer Science Networks and Information Technology, August 26-27, Montreal, Canada. https://www.researchgate.net/profile/Gabriela-Calvario-2/publication/321151332_MACHINE_LEARNING_APPROACH_TO_FUSE_MULTIPLE_BAND_FOR_WATER_BODIES_DETECTION/links/5a11d120a6fdccc2d79b64db/MACHINE-LEARNING-APPROACH-TO-FUSE-MULTIPLE-BAND-FOR-WATER-BODIES-DETECTION.pdf
  12. Calvario, G., Dalmau, O., Alarcón, T. E., Sierra, B., & Hernández, C. (2018). Selection and fusion of spectral indices to improve water body discrimination. IEEE Access, 6, 72952-72961. https://doi.org/10.1109/ACCESS.2018.2881430
  13. Castilla, J. L. (2016). IPICIM: Módulo clasificador de imágenes ópticas multiespectrales aplicado al área de geociencias [Tesis de posgrado]. Instituto Potosino de Investigación Científica y Tecnológica, A. C. https://ipicyt.repositorioinstitucional.mx/jspui/bitstream/1010/459/3/TMIPICYTC3I62016.pdf
  14. Castillo, M. D. (2003). Morfometría de lagos. Una aplicación a los lagos del Pirineo. Universitat de Barcelona.
  15. Castro-Lazcarro, M., Davydova-Belitskaya, V., & Cárdenas-Tristán, A. (2021). Assessment of climate indices and NDWI analysis in Lerma Chapala basin. Preprints, 2021020067. https://doi.org/10.20944/preprints202102.0067.v1
  16. Chapala, L., Pátzcuaro, L., Cuitzeo, L., & López-Caloca, A. A. (2015). Inpainting restoration for inland waters Mexico ecosystems. 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), Annecy, France. https://doi.org/10.1109/Multi-Temp.2015.7245782
  17. Colditz, R. R., Souza, C. T., Vazquez, B., Wickel, A. J., & Ressl, R. (2018). Analysis of optimal thresholds for identification of open water using MODIS-derived spectral indices for two coastal wetland systems in Mexico. International Journal of Applied Earth Observation and Geoinformation, 70, 13-24. https://doi.org/10.1016/j.jag.2018.03.008
  18. Comisión Nacional del Agua (Conagua). (2018). Estadísticas del agua en México 2018.Conagua. https://files.conagua.gob.mx/conagua/publicaciones/publicaciones/eam2018.pdf
  19. Comisión Nacional del Agua (Conagua). (2019). Más de 66 por ciento de México con algún grado de sequía. https://www.gob.mx/conagua/prensa/mas-de-66-por-ciento-de-mexico-con-algun-grado-de-sequia
  20. Danladi, I. B., Gül, M., & Ateş, E. (2020). Response of the barrier island coastal region of southwestern Nigeria to climate and non-climate forcing. African Journal of Marine Science, 42(1), 43–51. https://doi.org/10.2989/1814232X.2020.1727953
  21. Dávila, J., Díaz, R. E., Navarro, L. A., & Romeo, E. (2018). Las presas de jales en el noroeste del estado de Sonora: una aproximación geográfica mediante percepción remota. Investigaciones Geográficas, (97), 1-18. https://doi.org/10.14350/rig.59624
  22. Del-Toro-Guerrero, F. J., Daesslé, L. W., Méndez-Alonzo, R., & Kretzschmar, T. (2022). Surface reflectance–derived spectral indices for drought detection: application to the Guadalupe Valley basin, Baja California, Mexico. Land, 11(6), 783. https://doi.org/10.3390/land11060783
  23. El-Asmar, H. M., Hereher, M. E., & El-Kafrawy, S. B. (2013). Surface area change detection of the Burullus Lagoon, North of the Nile Delta, Egypt, using water indices: a remote sensing approach. Egyptian Journal of Remote Sensing and Space Science, 16(1), 119-123. https://doi.org/10.1016/j.ejrs.2013.04.004
  24. Escobar-Flores, J. G., Torres, J., Valdez, R., Álvarez, S., Galina, P., & Sandoval, S. (2017). Detection of waterholes by Vegetation Index in the habitat of bighorn sheep (Ovis Canadensis) in Baja California. PeerJ Preprints, 5, e2999v1. https://doi.org/10.7287/peerj.preprints.2999v1
  25. Escobar-Flores, J. G., Sandoval, S., Valdez, R., Shahriary, E., Torres, J., Alvarez-Cardenas, S., & Gallina-Tessaro, P. (2019). Waterhole detection using a vegetation index in desert bighorn sheep (Ovis canadensis cremnobates) habitat. PLoS One, 14(1), e0211202. https://doi.org/10.1371/journal.pone.0211202
  26. Fernández, J. D., Gallegos, C. A., Padilla, J. A., Barranco, A. I., Vázquez, J. A., & Correa, P. J. (2021). Detección automática de cuerpos de agua del bajío utilizando parámetros morfométricos obtenidos de imágenes satelitales y procesados con redes neuronales. Pistas Educativas, 43(140), 90-104. https://pistaseducativas.celaya.tecnm.mx/index.php/pistas/article/view/2599/2032
  27. Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029
  28. Fujihara, Y., Tanakamaru, H., Tada, A., Ahmed, B. M., & Eltaib, K. A. (2020). Analysis of cropping patterns in Sudan’s Gash Spate Irrigation System using Landsat 8 images. Journal of Arid Environments, 173, 104044. https://doi.org/10.1016/j.jaridenv.2019.104044
  29. Gómez-Palacios, D., Torres, M. A., & Reinoso, E. (2017). Flood mapping through principal component analysis of multitemporal satellite imagery considering the alteration of water spectral properties due to turbidity conditions. Geomatics, Natural Hazards and Risk, 8(2), 607-623. https://doi.org/10.1080/19475705.2016.1250115
  30. Herndon, K., Muench, R., Cherrington, E., & Griffin, R. (2020). An assessment of surface water detection methods for water resource management in the Nigerien Sahel. Sensors, 20(2), 431. https://doi.org/10.3390/s20020431
  31. Intergovernmental Panel on Climate Change (IPCC). (2023). Summary for Policymakers. En H. Lee & J. Romero (eds.), Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1-34). IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647.001
  32. Jeppesen, E., Brucet, S., Naselli-Flores, L., Papastergiadou, E., Stefanidis, K., Noges, T., Noges, P., Attayde, J. L., Zohary, T., Coppens, J., Bucak, T., Fernandes, R., Sousa, F. R., Kernan, M., Søndergaard, M., & Beklioğlu, M. (2015). Ecological impacts of global warming and water abstraction on lakes and reservoirs due to changes in water level and related changes in salinity. Hydrobiologia, 750, 201-227. https://doi.org/10.1007/s10750-014-2169-x
  33. Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J., & Xiao, T. (2014). An automated method for extracting rivers and lakes from landsat imagery. Remote Sensing, 6(6), 5067-5089. https://doi.org/10.3390/rs6065067
  34. Jin, C., Xiao, X., Merbold, L., Arneth, A., Veenendaal, E., & Kutsch, W. L. (2013). Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa. Remote Sensing of Environment, 135, 189–201. https://doi.org/10.1016/j.rse.2013.03.033
  35. Kasampalis, D. A., Alexandridis, T. K., Deva, C., Challinor, A., Moshou, D., & Zalidis, G. (2018). Contribution of remote sensing on crop models: a review. Journal of Imaging, 4(4), 52. https://doi.org/10.3390/jimaging4040052
  36. Krinner, G., & Boike, J. (2010). A study of the large-scale climatic effects of a possible disappearance of high-latitude inland water surfaces during the 21st century. Boreal Environment Research, 15, 203-217. https://epic.awi.de/id/eprint/20281/
  37. Landa, R., Magaña, V., & Neri, C. (2008). Agua y clima: elementos para la adaptación al cambio climático. Semarnat. https://www.atmosfera.unam.mx/wp-content/uploads/2017/12/agua-y-clima.pdf
  38. Leal, O. A., Gómez, M. A., Saldaña, M. P., & de la Maza, M. (2019). Tendencias de cambio en los humedales de Cuatro Ciénegas, Coahuila, México. Alter, 20, 57-77. https://static1.squarespace.com/static/552c00efe4b0cdec4ea42d9f/t/5f1a030c0bfb640dc274b3bc/1595540239064/5-ALTER20-tendencias2.pdf
  39. Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., Sun, B., & Zhang, X. (2013). A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI. Remote Sensing, 5(11), 5530–5549. https://doi.org/10.3390/rs5115530
  40. López, P. A., López, E., Martínez, J. A., & Puebla, J. H. (2019). Water bodies detection using supervised learning algorithms. IEEE International Fall Meeting on Communications and Computing (ROC&C), Acapulco, México.https://doi.org/10.1109/ROCC.2019.8873535
  41. Luna, J. D. (2017). Las presas de jales en la zona Noroeste del Estado de Sonora: una aproximación geográfica mediante Percepción Remota. El Colegio de Sonora. https://biblioteca.colson.edu.mx/e-docs/RED/RED001132.pdf
  42. Magaña, V. O., & Neri, C. (2012). Cambio climático y sequías en México. Ciencia-Academia Mexicana de Ciencias, 63(4), 26-35.
  43. https://biblat.unam.mx/es/revista/ciencia-academia-mexicana-de-ciencias/articulo/cambio-climatico-y-sequias-en-mexico
  44. Malahlela, O. E. (2016). Inland waterbody mapping: towards improving discrimination and extraction of inland surface water features. International Journal of Remote Sensing, 37(19), 4574–4589. https://doi.org/10.1080/01431161.2016.1217441
  45. Maldonado, D. (2022). Investigating changes in Mangrove cover and conservation policy in the protected area of Yum Balam, Mexico, 1981-2020. Carleton University. https://repository.library.carleton.ca/concern/etds/j9602172k
  46. Martínez, P. F., Díaz-Delgado, C., & Moeller-Chavez, G. (2019). Seguridad hídrica en México: diagnóstico general y desafíos principales. Ingeniería del Agua, 23(2), 107-121. https://doi.org/10.4995/ia.2019.10502
  47. Masocha, M., Dube, T., Makore, M., Shekede, M. D., & Funani, J. (2018). Surface water bodies mapping in Zimbabwe using landsat 8 OLI multispectral imagery: a comparison of multiple water indices. Physics and Chemistry of the Earth, Parts A/B/C, 106, 63–67. https://doi.org/10.1016/j.pce.2018.05.005
  48. McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
  49. Mekonnen, M. M., & Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity. Science Advances, 2(2), e1500323. https://doi.org/10.1126/sciadv.1500323
  50. Menarguez, M. A. (2015). Global water body mapping from 1984 to 2015 using global high resolution multispectral satellite imagery. University of Oklahoma.
  51. Mozafari, M., Hosseini, Z., Fijani, E., Eskandari, R., Siahpoush, S., & Ghader, F. (2022). Effects of climate change and human activity on lake drying in Bakhtegan Basin, southwest Iran. Sustainable Water Resources Management, 8(109). https://doi.org/10.1007/s40899-022-00707-z
  52. Mullen, C., & Muller, M. F. (2020). Assessing historic water extents in rapidly changing lakes: a hybrid remote sensing classification approach. Hydrology and Earth System Sciences Discussions, 1-17. https://doi.org/10.5194/hess-2020-198
  53. Murray, H., & Khaki, M. (2021). Analysis of Surface Water Areal changes using Remote Sensing Data. Advances in Environmental and Engineering Research, 2(3), 019. https://doi.org/10.21926/aeer.210301z
  54. Nadeem, A. A., Zha, Y., Shi, L., Ali, S., Wang, X., Zafar, Z., Afzal, Z., & Tariq, M. A. U. R. (2023). Spatial downscaling and gap-filling of SMAP soil moisture to high resolution using MODIS surface variables and machine learning approaches over ShanDian river basin, China. Remote Sensing, 15(3), 812. https://doi.org/10.3390/rs15030812
  55. Ndehedehe, C. E., Ferreira, V. G., Onojeghuo, A. O., Agutu, N. O., Emengini, E., & Getirana, A. (2020). Influence of global climate on freshwater changes in Africa’s largest endorheic basin using multi-scaled indicators. Science of the Total Environment, 737, 139643. https://doi.org/10.1016/j.scitotenv.2020.139643
  56. Noyola-Medrano, C., & Martínez-Sías, V. A. (2017). Assessing the progress of desertification of the southern edge of Chihuahuan Desert: a case study of San Luis Potosi Plateau. Journal of Geographical Sciences, 27(4), 420-438. https://doi.org/10.1007/s11442-017-1385-5
  57. Orimoloye, I. R., Ololade, O. O., Mazinyo, S. P., Kalumba, A. M., Ekundayo, O. Y., Busayo, E. T., Akinsanola, A. A., & Nel, W. (2019). Spatial assessment of drought severity in Cape Town area, South Africa. Heliyon, 5(7), e02148. https://doi.org/10.1016/j.heliyon.2019.e02148
  58. Ortega, D., Cruz, J. D. L., & Castellano, H. (2018). Peligro, vulnerabilidad y riesgo por sequía en el contexto del cambio climático en México. Instituto Mexicano de Tecnología del Agua (IMTA).
  59. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., Moher, D., Yepes-Nuñez, J. J., Urrútia, G., Romero-García, M., & Alonso-Fernández, S. (2021). Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. https://doi.org/10.1016/j.recesp.2021.06.016
  60. Pech, F., Sánchez, J. V., & Sánchez, H. (2020). Análisis de zonas de cultivo y cuerpos de agua mediante el cálculo de índices radiométricos con imágenes Sentinel-2. Lámpsakos, (24), 48-59. https://doi.org/10.21501/21454086.3601
  61. Pricope, N. G., Mapes, K. L., & Woodward, K. D. (2019). Remote sensing of human-environment interactions in global change research: a review of advances, challenges and future directions. Remote Sensing, 11(23), 2783. https://doi.org/10.3390/rs11232783
  62. Rico, E., Chicote, A., González, M. E., & Montes, C. (1995). Batimetría y análisis morfométrico del lago de Arreo (N. España). Limnetica, 11(1), 55-58. https://repositorio.uam.es/bitstream/handle/10486/13666/64234_L11a055_Batimetria_lago_Arreo.pdf?sequence=1
  63. Rojas-Villalobos, H. L. (2019). A water informatics approach to exploring the hydrological systems of basins with limited information; the case of the Bustillos Lagoon, Chihuahua, Mexico [Tesis doctoral]. New Mexico State University. https://zenodo.org/records/4302787
  64. Rojas-Villalobos, H., Samani, Z., Brown, C., Alatorre-Cejudo, L., Stringam, B., & Salas-Aguilar, V. (2022). Comparación de estimaciones de modelos de evaporación REEM y EEFlux en cuerpos de agua someros. Caso: laguna de Bustillos, Chihuahua, México. Tecnología y Ciencias del Agua, 13(6), 209-248. https://doi.org/10.24850/j-tyca-13-06-05
  65. Romero, F. S. (2006). La teledetección satelital y los sistemas de protección ambiental. Revista AquaTIC, 24, 13-41. http://revistaaquatic.com/ojs/index.php/aquatic/article/view/212
  66. Saiz-Rodríguez, J. A. (2020). Evaluación de inundaciones e islas de calor urbano para la planificación de espacios verdes urbanos mediante teledetección, caso de estudio: Mexicali, Baja California [Tesis Doctoral]. Universidad Autónoma de Baja California. https://repositorioinstitucional.uabc.mx/entities/publication/78d9e234-bda6-4a00-89d6-5f543854e1ca
  67. Sánchez, J. V., Pech-May, F., Sánchez, H. G., & Magaña-Govea, J. (2021). Mapeo de inundaciones utilizando imágenes satelitales SAR en Google Earth Engine. Research in Computing Science, 150(4), 83-95. https://rcs.cic.ipn.mx/2021_150_4/Mapeo%20de%20inundaciones%20utilizando%20imagenes%20satelitales%20SAR%20en%20Google%20Earth%20Engine.pdf
  68. Sánchez, G. C., Dalmau, O., Alarcón, T. E., Sierra, B., & Hernández, C. (2018). Selection and fusion of spectral indices to improve water body discrimination. IEEE Access, 6, 72952-72961. https://doi.org/10.1109/ACCESS.2018.2881430
  69. Sandoval, S., & Escobar-Flores, J. G. (2020). Changes in water surface area during the past 30 years in a ramsar wetland in Durango, Mexico using landsat data. IEEE International Geoscience and Remote Sensing Symposium, 5093-5095. https://doi.org/10.1109/IGARSS39084.2020.9323537
  70. Sandoval, S., Escobar-Flores, J. G., & Sánchez-Ortíz, E. (2020). Inventario de cuerpos de agua de la Sierra Madre Occidental (México) usando SIG y percepción remota. Investigaciones Geográficas, (102). https://doi.org/10.14350/rig.59975
  71. Sedeño-Díaz, J. E., & López-López, E. (2021). The influence of climate change on river corridors in drylands: the case of the Tehuacán-Cuicatlán biosphere reserve. Frontiers in Environmental Science, 9, 681703. https://doi.org/10.3389/fenvs.2021.681703
  72. Slagter, B., Tsendbazar, N. E., Vollrath, A., & Reiche, J. (2020). Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: a case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation, 86, 102009. https://doi.org/10.1016/j.jag.2019.102009
  73. Soria-Ruiz, J., Fernández-Ordoñez, Y. M., Ambrosio-Ambrosio, J. P., Escalona-Maurice, M. J., Medina-García, G., Sotelo-Ruiz, E. D., & Ramirez-Guzman, M. E. (2022). Flooded extent and depth analysis using optical and SAR remote sensing with machine learning algorithms. Atmosphere, 13(11), 1852. https://doi.org/10.3390/atmos13111852
  74. Tapia-Silva, F. O., & López-Caloca, A. A. (2018). Calculating long-term changes in Lake Chapala’s area and water volume using remote sensing and field data. Journal of Applied Remote Sensing, 12(4), 042805. https://doi.org/10.1117/1.JRS.12.042805
  75. Tassew, B. G., Belete, M. A., & Miegel, K. (2021). Assessment and analysis of morphometric characteristics of Lake Tana sub-basin, Upper Blue Nile Basin, Ethiopia. International Journal of River Basin Management, 21(2), 1-15. https://doi.org/10.1080/15715124.2021.1938091
  76. Tulbure, M. G., Broich, M., Stehman, S. V., & Kommareddy, A. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment, 178, 142-157. https://doi.org/10.1016/j.rse.2016.02.034
  77. Tottrup, C., Druce, D., Meyer, R. P., Christensen, M., Riffler, M., Dulleck, B., Rastner, P., Jupova K., Sokoup, T., Haag, A., Cordeiro, M. C. R., Martinez, J. M., Franke, J., Schwarz, M., Vanthof, V., Liu, S., Zhou, H., Marzi, D., Rudiyanto, R., Thompson, M., Hiestermann, J., Alemohammad, H., Masse, A., Sannier, C., Wangchuk, S., Schumann, G., Giustarini, L., Hallowes, J., Markert K., & Paganini, M. (2022). Surface water dynamics from space: a round robin intercomparison of using optical and SAR high-resolution satellite observations for regional surface water detection. Remote Sensing, 14(10), 2410. https://doi.org/10.3390/rs14102410
  78. UNESCO-ONU-Agua. (2020). Informe Mundial de las Naciones Unidas sobre el Desarrollo de los Recursos Hídricos 2020: Agua y Cambio Climático. https://es.unesco.org/themes/water-security/wwap/wwdr/2020
  79. Ureta, C., González, E. J., Espinosa, A., Trueba, A., Piñeyro-Nelson, A., & Álvarez-Buylla, E. R. (2020). Maize yield in Mexico under climate change. Agricultural Systems, 177, 102697. https://doi.org/10.1016/j.agsy.2019.102697
  80. Urrútia, G., & Bonfill, X. (2010). Declaración PRISMA: una propuesta para mejorar la publicación de revisiones sistemáticas y metaanálisis. Medicina Clínica, 135(11), 507-511. https://doi.org/doi:10.1016/j.medcli.2010.01.015
  81. Velasco, I., Ochoa, L., & Gutiérrez, C. (2005). Sequía, un problema de perspectiva y gestión. Región y Sociedad, 17(34), 35-71.
  82. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-39252005000300002&lng=es&tlng=
  83. Veneros, J., García, L., Morales, E., Gómez, V., Torres, M., & López, F. (2020). Aplicación de sensores remotos para el análisis de cobertura vegetal y cuerpos de agua. Idesia (Arica), 38(4), 99-107. https://doi.org/10.4067/S0718-34292020000400099
  84. Wang, Z., Liu, J., Li, J., & Zhang, D. D. (2018). Multi-spectral water index (MuWI): a native 10-m multi-spectral water index for accurate water mapping on Sentinel-2. Remote Sensing, 10(10), 1643. https://doi.org/10.3390/rs10101643
  85. Wickel, B. (A. J.)., Colditz, R., Ressl, R., Kucharski, J., & Salinas-Rodríguez, S. (2020). Monitoring Hydroperiod and Hydropatterns of coastal wetland systems in Mexico using Landsat time series. EGU General Assembly 2020, EGU2020-12991. https://doi.org/10.5194/egusphere-egu2020-12991
  86. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033. https://doi.org/10.1080/01431160600589179
  87. Yudha, I. S. (2023). Detection of changes in water surface area in Limboto Lake using landsat data from 1990 to 2020. IOP Conference Series: Earth and Environmental Science, 1127(1), 012021. https://doi.org/10.1088/1755-1315/1127/1/012021
  88. Yue, L., Li, B., Zhu, S., Yuan, Q., & Shen, H. (2023). A fully automatic and high-accuracy surface water mapping framework on Google Earth Engine using Landsat time-series. International Journal of Digital Earth, 16(1), 210-233. https://doi.org/10.1080/17538947.2023.2166606
  89. Zamora-Rivas, D. (2019). Space-temporal study in the protected natural area of Xochimilco Lake with remote sensing in the period 1987-2016. In Proceedings of the 1st International Conference on Geospatial Information Sciences, 13, 62-69. https://doi.org/10.29007/bfkp