{"id":"https://openalex.org/W3031263341","doi":"https://doi.org/10.3390/ijgi9060343","title":"Estimation of Potato Yield Using Satellite Data at a Municipal Level: A Machine Learning Approach","display_name":"Estimation of Potato Yield Using Satellite Data at a Municipal Level: A Machine Learning Approach","publication_year":2020,"publication_date":"2020-05-26","ids":{"openalex":"https://openalex.org/W3031263341","doi":"https://doi.org/10.3390/ijgi9060343","mag":"3031263341"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi9060343","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi9060343","pdf_url":"https://www.mdpi.com/2220-9964/9/6/343/pdf?version=1591276532","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/9/6/343/pdf?version=1591276532","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046228763","display_name":"Pablo Salvador","orcid":"https://orcid.org/0000-0002-6520-2389"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Pablo Salvador","raw_affiliation_strings":["Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076594088","display_name":"Diego G\u00f3mez","orcid":"https://orcid.org/0000-0002-2812-5716"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Diego G\u00f3mez","raw_affiliation_strings":["Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"],"raw_orcid":"https://orcid.org/0000-0002-2812-5716","affiliations":[{"raw_affiliation_string":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056168410","display_name":"J. Sanz","orcid":"https://orcid.org/0000-0002-4253-4831"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Julia Sanz","raw_affiliation_strings":["Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"],"raw_orcid":"https://orcid.org/0000-0002-4253-4831","affiliations":[{"raw_affiliation_string":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087288910","display_name":"Jos\u00e9 Luis Casanova Roque","orcid":"https://orcid.org/0000-0002-7435-1175"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jos\u00e9 Luis Casanova","raw_affiliation_strings":["Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Laboratory (LATUV), University of Valladolid, Paseo de Belen 11, 47011 Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046228763"],"corresponding_institution_ids":["https://openalex.org/I108103353"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":3.3765,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.92153059,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"9","issue":"6","first_page":"343","last_page":"343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9754999876022339,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9754999876022339,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11745","display_name":"Potato Plant Research","score":0.9750000238418579,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9100000262260437,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7004903554916382},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.549464762210846},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.507656455039978},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.5034207701683044},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36364755034446716},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35005873441696167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31137901544570923}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7004903554916382},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.549464762210846},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.507656455039978},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.5034207701683044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36364755034446716},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35005873441696167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31137901544570923},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/ijgi9060343","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi9060343","pdf_url":"https://www.mdpi.com/2220-9964/9/6/343/pdf?version=1591276532","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:uvadoc.uva.es:10324/59075","is_oa":true,"landing_page_url":"https://uvadoc.uva.es/handle/10324/59075","pdf_url":"https://uvadoc.uva.es/bitstream/10324/59075/1/Estimation-of-Potato-Yield.pdf","source":{"id":"https://openalex.org/S4306401553","display_name":"UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108103353","host_organization_name":"Universidad de Valladolid","host_organization_lineage":["https://openalex.org/I108103353"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:889dbcf6608f4e66bf3dd972a8c772ac","is_oa":true,"landing_page_url":"https://doaj.org/article/889dbcf6608f4e66bf3dd972a8c772ac","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information, Vol 9, Iss 6, p 343 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2220-9964/9/6/343/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/ijgi9060343","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/ijgi9060343","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi9060343","pdf_url":"https://www.mdpi.com/2220-9964/9/6/343/pdf?version=1591276532","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.6299999952316284,"display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320334116","display_name":"NASA Headquarters","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3031263341.pdf","grobid_xml":"https://content.openalex.org/works/W3031263341.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W811749278","https://openalex.org/W838762844","https://openalex.org/W1831050183","https://openalex.org/W1963836745","https://openalex.org/W1979636379","https://openalex.org/W1980426198","https://openalex.org/W1985987493","https://openalex.org/W1992939357","https://openalex.org/W1999428071","https://openalex.org/W2017746108","https://openalex.org/W2036706101","https://openalex.org/W2043981082","https://openalex.org/W2047299675","https://openalex.org/W2050076538","https://openalex.org/W2055522016","https://openalex.org/W2070559755","https://openalex.org/W2071540364","https://openalex.org/W2080344679","https://openalex.org/W2090474171","https://openalex.org/W2091597654","https://openalex.org/W2092219385","https://openalex.org/W2092722122","https://openalex.org/W2097960263","https://openalex.org/W2099507093","https://openalex.org/W2106343913","https://openalex.org/W2116226960","https://openalex.org/W2121297889","https://openalex.org/W2127170577","https://openalex.org/W2135844178","https://openalex.org/W2154700052","https://openalex.org/W2165588550","https://openalex.org/W2168647819","https://openalex.org/W2168703761","https://openalex.org/W2171395380","https://openalex.org/W2179286260","https://openalex.org/W2325074942","https://openalex.org/W2508456100","https://openalex.org/W2792871608","https://openalex.org/W2801490189","https://openalex.org/W2805142011","https://openalex.org/W2809715095","https://openalex.org/W2883854589","https://openalex.org/W2893259731","https://openalex.org/W2898856156","https://openalex.org/W2911964244","https://openalex.org/W3022727620","https://openalex.org/W4239510810","https://openalex.org/W6911097187"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2102148524","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W1980100242","https://openalex.org/W2530420969","https://openalex.org/W2051187167"],"abstract_inverted_index":{"Crop":[0],"growth":[1],"modeling":[2],"and":[3,51,83,97,104,143,172,221],"yield":[4,20,206,210],"forecasting":[5],"are":[6],"essential":[7],"to":[8,61,224],"improve":[9],"food":[10],"security":[11],"policies":[12],"worldwide.":[13],"To":[14],"estimate":[15],"potato":[16,205],"(Solanum":[17],"tubersum":[18],"L.)":[19],"over":[21],"Mexico":[22],"at":[23,228],"a":[24,229],"municipal":[25,230],"level,":[26],"we":[27],"used":[28,60,100,159],"meteorological":[29],"data":[30,95],"provided":[31],"by":[32,39,122,207],"the":[33,40,48,63,102,117,123,127,133,138,148,151,156,160,165,184,189,209,212,215,225],"ERA5":[34],"(ECMWF":[35],"Re-Analysis)":[36],"dataset":[37],"developed":[38],"Copernicus":[41],"Climate":[42],"Change":[43],"Service,":[44],"satellite":[45],"imagery":[46],"from":[47,132],"TERRA":[49],"platform,":[50],"field":[52],"information.":[53],"Five":[54],"different":[55],"machine":[56,70,75,80],"learning":[57],"algorithms":[58],"were":[59,91,120],"build":[62],"models:":[64],"random":[65],"forest":[66],"(rf),":[67],"support":[68,73,78],"vector":[69,74,79],"linear":[71,85],"(svmL),":[72],"polynomial":[76],"(svmP),":[77],"radial":[81],"(svmR),":[82],"general":[84,216],"model":[86,154,195],"(glm).":[87],"The":[88],"optimized":[89],"models":[90,202],"tested":[92],"using":[93,130],"independent":[94],"(2017":[96],"2018)":[98],"not":[99,192],"in":[101,126],"training":[103],"optimization":[105],"phase":[106],"(2004\u20132016).":[107],"In":[108],"terms":[109],"of":[110,137,164,183,211,218],"percent":[111],"root":[112],"mean":[113],"squared":[114],"error":[115],"(%RMSE),":[116],"best":[118,152],"results":[119,177,198],"obtained":[121],"rf":[124],"algorithm":[125],"winter":[128],"cycle":[129,139,166],"variables":[131,168,182],"first":[134,161],"three":[135],"months":[136,163,187],"(R2":[140,169],"=":[141,145,170,174],"0.757":[142],"%RMSE":[144,173],"18.9).":[146],"For":[147],"summer":[149],"cycle,":[150],"performing":[153],"was":[155],"svmP":[157],"which":[158],"five":[162],"as":[167],"0.858":[171],"14.9).":[175],"Our":[176],"indicated":[178],"that":[179,200],"adding":[180],"predictor":[181],"last":[185],"two":[186],"before":[188],"harvest":[190],"did":[191],"significantly":[193],"improved":[194],"performances.":[196],"These":[197],"demonstrate":[199],"our":[201],"can":[203],"predict":[204],"analyzing":[208],"previous":[213],"year,":[214],"conditions":[217],"NDVI,":[219],"meteorology,":[220],"information":[222],"related":[223],"irrigation":[226],"system":[227],"level.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2020-06-05T00:00:00"}