{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:17:29Z","timestamp":1772759849243,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T00:00:00Z","timestamp":1590451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004\u20132016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.<\/jats:p>","DOI":"10.3390\/ijgi9060343","type":"journal-article","created":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T05:35:52Z","timestamp":1590557752000},"page":"343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Estimation of Potato Yield Using Satellite Data at a Municipal Level: A Machine Learning Approach"],"prefix":"10.3390","volume":"9","author":[{"given":"Pablo","family":"Salvador","sequence":"first","affiliation":[{"name":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2812-5716","authenticated-orcid":false,"given":"Diego","family":"G\u00f3mez","sequence":"additional","affiliation":[{"name":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-4831","authenticated-orcid":false,"given":"Julia","family":"Sanz","sequence":"additional","affiliation":[{"name":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Luis","family":"Casanova","sequence":"additional","affiliation":[{"name":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.3732\/ajb.1000277","article-title":"Ecogeography of ploidy variation in cultivated potato (Solanum sect. Petota)","volume":"97","author":"Spooner","year":"2010","journal-title":"Am. J. Bot."},{"key":"ref_2","unstructured":"FAO (2019, September 05). International Year of the Potato 2008: New Light on a Hidden Treasure. Available online: http:\/\/www.fao.org\/potato-2008\/en\/events\/book.html."},{"key":"ref_3","unstructured":"Li, P.H. (1985). Potato Physiology, Academic Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1016\/j.scitotenv.2016.08.195","article-title":"Coincidence of variation in potato yield and climate in northern China","volume":"573","author":"Zhao","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11540-014-9265-1","article-title":"Potatoes for Sustainable Global Food Security","volume":"57","author":"Devaux","year":"2014","journal-title":"Potato Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.fcr.2015.06.002","article-title":"Yield levels of potato crops: Recent achievements and future prospects","volume":"182","author":"Haverkorta","year":"2015","journal-title":"Field Crop. Res."},{"key":"ref_7","unstructured":"FAO (2019, September 05). Statistical Databases FAOSTAT. Available online: http:\/\/www.fao.org\/faostat\/en\/#data."},{"key":"ref_8","unstructured":"(2019, September 19). El Sol de Mexico. Available online: https:\/\/www.elsoldemexico.com.mx\/analisis\/importancia-de-la-produccion-de-papa-en-mexico-3433659.html."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.2134\/agronj2005.0260","article-title":"Whole plant photosynthesis, development, and carbon partitioning in potato as a function of temperature","volume":"98","author":"Timlin","year":"2006","journal-title":"Agron. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"747","DOI":"10.2307\/2401901","article-title":"Solar Radiation and Productivity in Tropical Ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Monteith, J.L., William, M.G., Norman, C., Pirie, W., Douglas, G., and Bell, H. (1977). Climate and the efficiency of crop production in Britain Phil. Trans. R. Soc. Lond. B, 281.","DOI":"10.1098\/rstb.1977.0140"},{"key":"ref_12","unstructured":"Smith, H. (1982). Remote sensing of crop growth. Plants and the Daylight Spectrum, Academic Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0167-8809(02)00021-X","article-title":"Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties","volume":"94","author":"Lobell","year":"2003","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_14","unstructured":"Sessa, R., and Dolman, H. (2020, January 13). Terrestrial Essential Climate Variables for Climate Change Assessment, Mitigation and Adaptation (GTOS 52). Available online: http:\/\/www.fao.org\/3\/i0197e\/i0197e.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1890\/1051-0761(1998)008[1003:SDOAOP]2.0.CO;2","article-title":"Scale dependence of absorption of photosynthetically active radiation in terrestrial ecosystems","volume":"8","author":"Asner","year":"1998","journal-title":"Ecol. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1080\/01431169008955130","article-title":"Spectral estimates of the absorbed photosynthetically active radiation and light-use efficiency of a winter wheat crop subjected to nitrogen and water deficiencies","volume":"11","author":"Steinmetz","year":"1991","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"723","DOI":"10.2135\/cropsci2000.403723x","article-title":"Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies","volume":"40","author":"Serrano","year":"2000","journal-title":"Crop Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2134\/agronj1984.00021962007600020029x","article-title":"Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat","volume":"76","author":"Asrar","year":"1984","journal-title":"Agron. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.2134\/agronj1993.00021962008500060024x","article-title":"Errors in Measuring Absorbed Radiation and Computing Crop Radiation Use","volume":"85","author":"Gallo","year":"1993","journal-title":"Effic. Agron. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(93)90113-C","article-title":"On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna","volume":"45","author":"Benedetti","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1080\/01431169608948732","article-title":"Yield estimation for corn and wheat in the Hungarian Great Plain using Landsat MSS data","volume":"17","author":"Hamar","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.eja.2006.10.007","article-title":"A simple model of regional wheat yield based on NDVI data","volume":"26","author":"Moriondo","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1017\/aae.2019.5","article-title":"Predicting soybean yield with NDVI using a flexible fourier transform model","volume":"51","author":"Chang","year":"2019","journal-title":"J. Agric. Appl. Econ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/01431169308904332","article-title":"The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction","volume":"14","author":"Quarmby","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World Map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Z."},{"key":"ref_27","unstructured":"(2019, June 05). INEGI. Available online: https:\/\/www.inegi.org.mx\/temas\/usosuelo\/default.html#Herramientas."},{"key":"ref_28","unstructured":"Hijmans, R.J. (2020, January 13). Diva-Gis. Vsn. 5.0. A Geographic Information System for the Analysis of Species Distribution Data. Available online: http:\/\/www.diva-gis.org\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1080\/01431168508948283","article-title":"Canopy reflectance, photosynthesis, and transpiration","volume":"6","author":"Sellers","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"1039","article-title":"Special issue on EOS AM-1 platform, instruments, and scientific data","volume":"36","author":"Ranson","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","unstructured":"Didan, K., and MOD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set] (2019, September 06). NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MOD13Q1.006."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250 m vegetation index data for crop classificaation in the U.S. Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01431160902897858","article-title":"A comparison of MODIS 250 m EVI and NDVI data for crop mapping: A case study for southwest Kansas","volume":"31","author":"Wardlow","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","unstructured":"(2020, January 13). Copernicus Climate Change Service. Available online: https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-pressure-levels?tab=overview."},{"key":"ref_35","unstructured":"ENVI (1998). ENVI Programmer\u2019s Guide, Research System, Inc."},{"key":"ref_36","unstructured":"IDL (1997). IDL User\u2019s Guide, Research Systems, Inc."},{"key":"ref_37","unstructured":"The R Development core team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1109\/78.650102","article-title":"Comparing support vector machines with Gaussian kernels to radial basis function classifiers","volume":"45","author":"Scholkopf","year":"1997","journal-title":"IEEE Trans. Signal Proc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2307\/2344614","article-title":"Generalized linear models","volume":"135","author":"Nelder","year":"1972","journal-title":"J. R. Stat. Soc. Ser. A (Gen.)"},{"key":"ref_43","first-page":"665","article-title":"Crop Yield Assessment from Remote Sensing. Photogrammetric","volume":"6","author":"Doraiswamy","year":"2003","journal-title":"Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sommer, R., and Paxson, V. (2010, January 16\u201319). Outside the closed world: On using machine learning for network intrusion detection. Proceedings of the 2010 IEEE Symposium on Security and Privacy, Berkeley\/Oakland, CA, USA.","DOI":"10.1109\/SP.2010.25"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.fcr.2015.03.004","article-title":"How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis","volume":"177","author":"Grassini","year":"2015","journal-title":"Field Crop. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0034-4257(96)00248-9","article-title":"Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions","volume":"61","author":"Teillet","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1080\/014311697217819","article-title":"An assessment of AVHRR\/NDVI-ecoclimatological relations in Nebraska, USA","volume":"18","author":"Yang","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1111\/j.1365-2699.2008.01981.x","article-title":"Enrichment of land-cover polygons with eco-climatic information derived from MODIS NDVI imagery","volume":"36","author":"Maselli","year":"2009","journal-title":"J. Biogeogr."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/014311602753474192","article-title":"Relations between AVHRR NDVI and ecoclimatic parameters in China","volume":"23","author":"Li","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.profoo.2016.02.027","article-title":"Extraction of Agricultural Phenological Parameters of Sri Lanka Using MODIS, NDVI Time Series Data","volume":"6","author":"Jayawardhana","year":"2016","journal-title":"Proced. Food Sci."},{"key":"ref_51","unstructured":"(2019, September 29). Conagua. Available online: https:\/\/smn.conagua.gob.mx\/es\/climatologia\/temperaturas-y-lluvias\/resumenes-mensuales-de-temperaturas-y-lluvias."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s41976-018-0006-0","article-title":"Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices","volume":"1","author":"Newton","year":"2018","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1080\/01431160802552744","article-title":"Correlation between potato yield and MODIS-derived vegetation indices","volume":"30","author":"Bala","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.agsy.2018.06.009","article-title":"Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015","volume":"168","author":"Velde","year":"2009","journal-title":"Agric. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.agee.2009.05.017","article-title":"Regional crop yield, water consumption and water use efficiency and their responses to climate change in the North China Plain","volume":"134","author":"Mo","year":"2009","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cliser.2018.06.003","article-title":"Global crop yield forecasting using seasonal climate information from a multi-model ensemble","volume":"11","author":"Iizumi","year":"2018","journal-title":"Clim. Serv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kasampalis, D.A., Alexandridis, T.K., Deva, C., Challinor, A., Moshou, D., and Zalidis, G. (2018). Contribution of Remote Sensing on Crop Models: A Review. J. Imaging, 4.","DOI":"10.3390\/jimaging4040052"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5868","DOI":"10.3390\/rs6065868","article-title":"Investigating the relationship between the inter-annual variability of satellite-derived vegetation phenology and a proxy of biomass production in the Sahel","volume":"6","author":"Meroni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1126\/science.208.4445.670","article-title":"Global Crop Forecasting","volume":"208","author":"MacDonald","year":"1980","journal-title":"Science"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1080\/01431169108929733","article-title":"Uses of satellite data for famine early warning in sub-Saharan Africa","volume":"12","author":"Hutchinson","year":"1991","journal-title":"Int. J. Remote Sens."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/6\/343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:46Z","timestamp":1760175166000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/6\/343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,26]]},"references-count":60,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["ijgi9060343"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9060343","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,26]]}}}