{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:02:34Z","timestamp":1790787754989,"version":"4.1.0"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Council of Science and Technology of the state of Tabasco, Mexico (CCYTET)","award":["14080.22-PD"],"award-info":[{"award-number":["14080.22-PD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.<\/jats:p>","DOI":"10.3390\/s22134729","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"4729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine"],"prefix":"10.3390","volume":"22","author":[{"given":"Fernando","family":"Pech-May","sequence":"first","affiliation":[{"name":"Department of Computer Science, Instituto Tecnol\u00f3gico Superior de los R\u00edos, Balanc\u00e1n 86930, Tabasco, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8242-0695","authenticated-orcid":false,"given":"Ra\u00fal","family":"Aquino-Santos","sequence":"additional","affiliation":[{"name":"Faculty of Telematics, University of Colima, 333 University Avenue, Colima 28040, Colima, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"German","family":"Rios-Toledo","sequence":"additional","affiliation":[{"name":"Tecnol\u00f3gico Nacional de M\u00e9xico Campus Tuxtla Guti\u00e9rrez, Tuxtla Guti\u00e9rrez 29050, Chiapas, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Pablo Francisco","family":"Posadas-Dur\u00e1n","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100547","DOI":"10.1016\/j.gfs.2021.100547","article-title":"Systemic risk and food security. Emerging trends and future avenues for research","volume":"29","author":"Alpha","year":"2021","journal-title":"Glob. Food Secur."},{"key":"ref_2","unstructured":"Tim, C. (2021, April 20). La Agricultura en el Siglo XXI: Un Nuevo Paisaje Para la Gente, la Alimentaci\u00f3n y la Naturaleza. Available online: https:\/\/humbertoarmenta.mx\/el-rol-de-la-agricultura-en-el-siglo-xxi\/."},{"key":"ref_3","unstructured":"World Bank (2021, May 15). Food Security. Available online: https:\/\/www.worldbank.org\/en\/topic\/food-security."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yawson, D.O., Mulholland, B.J., Ball, T., Adu, M.O., Mohan, S., and White, P.J. (2017). Effect of Climate and Agricultural Land Use Changes on UK Feed Barley Production and Food Security to the 2050s. Land, 6.","DOI":"10.3390\/land6040074"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.jclepro.2019.02.151","article-title":"The impact of farm size on agricultural sustainability","volume":"220","author":"Ren","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MITP.2020.2986121","article-title":"Artificial Intelligence (AI) in Agriculture","volume":"22","author":"Liu","year":"2020","journal-title":"IT Prof."},{"key":"ref_7","unstructured":"Organizaci\u00f3n de las Naciones Unidas para la Alimentaci\u00f3n y la Agricultura (2020, June 14). M\u00e9xico en Una Mirada. Available online: http:\/\/www.fao.org\/mexico\/fao-en-mexico\/mexico-en-una-mirada\/es\/."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pareeth, S., Karimi, P., Shafiei, M., and De Fraiture, C. (2019). Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach. Remote. Sens., 11.","DOI":"10.3390\/rs11050601"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.geosus.2021.08.004","article-title":"GIScience and remote sensing in natural resource and environmental research: Status quo and future perspectives","volume":"2","author":"Pei","year":"2021","journal-title":"Geogr. Sustain."},{"key":"ref_10","first-page":"102514","article-title":"Integrating remote sensing and geospatial big data for urban land use mapping: A review","volume":"103","author":"Yin","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pantazi, X.E., Moshou, D., and Bochtis, D. (2020). Chapter 2 - Artificial intelligence in agriculture. Intelligent Data Mining and Fusion Systems in Agriculture, Academic Press.","DOI":"10.1016\/B978-0-12-814391-9.00002-9"},{"key":"ref_12","unstructured":"Fath, B. (2019). Remote sensing. Encyclopedia of Ecology, Elsevier. [2nd ed.]."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s40010-017-0428-8","article-title":"Satellite Remote Sensing: Sensors, Applications and Techniques","volume":"87","author":"Roy","year":"2017","journal-title":"Proc. Natl. Acad. Sci., India Sect. A Phys. Sci."},{"key":"ref_14","unstructured":"Kobayashi, A. (2020). Remote Sensing. International Encyclopedia of Human Geography, Elsevier. [2nd ed.]."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, H., Goodchild, M.F., and Annoni, A. (2020). Remote sensing satellites for digital Earth. Manual of Digital Earth, Springer.","DOI":"10.1007\/978-981-32-9915-3"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Oliveira, E.R., Disperati, L., Cenci, L., Gomes Pereira, L., and Alves, F.L. (2019). Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations. Remote. Sens., 11.","DOI":"10.3390\/rs11111305"},{"key":"ref_17","unstructured":"Levin, S.A. (2013). Remote sensing and image processing. Encyclopedia of Biodiversity, Academic Press. [2nd ed.]."},{"key":"ref_18","unstructured":"J\u00f8rgensen, S.E., and Fath, B.D. (2008). Remote sensing. Encyclopedia of Ecology, Academic Press."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Emery, W., and Camps, A. (2017). Chapter 1-The history of satellite remote sensing. Introduction to Satellite Remote Sensing, Elsevier.","DOI":"10.1016\/B978-0-12-809254-5.00001-4"},{"key":"ref_20","unstructured":"Lillesand, T.M. (2006). Remote Sensing and Image Interpretation, John Wiley & Sons, Inc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liang, S. (2018). 1.06 - Copernicus program. Comprehensive Remote Sensing, Elsevier.","DOI":"10.1016\/B978-0-12-409548-9.10365-3"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Louis, J., Pflug, B., Main-Knorn, M., Debaecker, V., Mueller-Wilm, U., Iannone, R.Q., Giuseppe Cadau, E., Boccia, V., and Gascon, F. (August, January 28). Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898540"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote. Sens. Environ."},{"key":"ref_24","unstructured":"Jain, K., Khoshelham, K., Zhu, X., and Tiwari, A. (2019, January 6\u20137). Detection of Water Body Using Very High-Resolution UAV SAR and Sentinel-2 Images. Proceedings of the UASG 2019, International Conference on Unmanned Aerial System in Geomatics, Roorkee, India."},{"key":"ref_25","unstructured":"Huk, M., Maleszka, M., and Szczerbicki, E. (2020). Land Cover Classification Based on Sentinel-2 Satellite Imagery Using Convolutional Neural Network Model: A Case Study in Semarang Area, Indonesia. Intelligent Information and Database Systems: Recent Developments, Springer International Publishing."},{"key":"ref_26","unstructured":"European Space Agency (2020). Sentinel-2\u2014Satellite Description, ESA\u2014Sentinel Online; European Space Agency."},{"key":"ref_27","first-page":"102013","article-title":"Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)","volume":"86","author":"Cici","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","first-page":"70","article-title":"Comparison of different reflectance indices for vegetation analysis using Landsat-TM data","volume":"12","author":"Kumar","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_29","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with Erts","volume":"Volume 351","author":"Rouse","year":"1974","journal-title":"Proceedings of the Third Third Earth Resources Technology Satellite-1 Symposium"},{"key":"ref_30","unstructured":"Samui, P., Kim, D., and Ghosh, C. (2018). Chapter 27\u2014Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model. Integrating Disaster Science and Management, Elsevier."},{"key":"ref_31","unstructured":"Pourghasemi, H.R., and Gokceoglu, C. (2019). 13\u2014Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/S0034-4257(00)00150-4","article-title":"Optical\u2013Biophysical Relationships of Vegetation Spectra without Background Contamination","volume":"74","author":"Gao","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, Q. (2012, January 16\u201318). Enhanced vegetation index using Moderate Resolution Imaging Spectroradiometers. Proceedings of the 2012 5th International Congress on Image and Signal Processing, Chongqing, China.","DOI":"10.1109\/CISP.2012.6470008"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fang, H., and Liang, S. (2014). Leaf Area Index Models. Reference Module in Earth Systems and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-409548-9.09076-X"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_37","unstructured":"Rees, W. (2005). The Remote Sensing Data Book, Cambridge University Press."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mather, P., and Tso, B. (2016). Classification Methods for Remotely Sensed Data, CRC Press.","DOI":"10.1201\/9781420090741"},{"key":"ref_39","unstructured":"Pourghasemi, H.R., and Gokceoglu, C. (2019). 18-Prioritization of Effective Factors on Zataria multiflora Habitat Suitability and its Spatial Modeling. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier."},{"key":"ref_40","unstructured":"J\u00f8rgensen, S.E., and Fath, B.D. (2008). Support Vector Machines. Encyclopedia of Ecology, Academic Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","unstructured":"Ranganathan, S., Gribskov, M., Nakai, K., and Sch\u00f6nbach, C. (2019). Decision Trees and Random Forests. Encyclopedia of Bioinformatics and Computational Biology, Academic Press."},{"key":"ref_44","first-page":"e00971","article-title":"Land use\/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms","volume":"22","author":"Ge","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_45","unstructured":"Breiman, L., Friedman, J., Stone, C., and Olshen, R. (1984). Classification and Regression Trees, Taylor & Francis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s12665-018-7498-z","article-title":"Precipitation forecasting using classification and regression trees (CART) model: A comparative study of different approaches","volume":"77","author":"Choubin","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote. Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2017.07.011","article-title":"A mangrove forest map of China in 2015: Analysis of time series Landsat 7\/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform","volume":"131","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tassi, A., Gigante, D., Modica, G., Di Martino, L., and Vizzari, M. (2021). Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote. Sens., 13.","DOI":"10.3390\/rs13122299"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.scitotenv.2019.01.204","article-title":"Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model","volume":"661","author":"Zarei","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_55","first-page":"100287","article-title":"Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms","volume":"17","author":"Ghani","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_56","first-page":"55","article-title":"Clasificaci\u00f3n de usos del suelo a partir de im\u00e1genes Sentinel-2","volume":"2017","author":"Delegido","year":"2017","journal-title":"Rev. Teledetecci\u00f3n"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105583","DOI":"10.1016\/j.compag.2020.105583","article-title":"A novel method for automatic potato mapping using time series of Sentinel-2 images","volume":"175","author":"Ashourloo","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","first-page":"101980","article-title":"Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification","volume":"85","author":"Macintyre","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","first-page":"147","article-title":"Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons","volume":"25","author":"Hudait","year":"2022","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"105194","DOI":"10.1016\/j.compag.2019.105194","article-title":"Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform","volume":"169","author":"Santiago","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","unstructured":"Comisi\u00f3n Nacional para el Conocimiento y Uso de la Biodiversidad (2021, June 13). Mapa Base del Estado de Tabasco. Available online: http:\/\/www.conabio.gob.mx\/informacion\/metadata\/gis\/tabaprgn.xml?_httpcache=yes&_xsl=\/db\/metadata\/xsl\/fgdc_html.xsl&_indent=no&as=.html."},{"key":"ref_63","unstructured":"Brink, H., Richards, J., and Fetherolf, M. (2016). Real-World Machine Learning, Simon and Schuster."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Loukika, K.N., Keesara, V.R., and Sridhar, V. (2021). Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13.","DOI":"10.3390\/su132413758"},{"key":"ref_65","unstructured":"Shetty, S. (2019). Analysis of Machine Learning Classifiers for LULC Classification on Google Earth Engine. [Master\u2019s Thesis, University of Twente]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4729\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:11Z","timestamp":1760139491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134729"],"URL":"https:\/\/doi.org\/10.3390\/s22134729","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}