{"id":"https://openalex.org/W3132913802","doi":"https://doi.org/10.1109/igarss39084.2020.9324201","title":"Soil Nutrients Prediction Using Remote Sensing Data in Western India: An Evaluation of Machine Learning Models","display_name":"Soil Nutrients Prediction Using Remote Sensing Data in Western India: An Evaluation of Machine Learning Models","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3132913802","doi":"https://doi.org/10.1109/igarss39084.2020.9324201","mag":"3132913802"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9324201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034210161","display_name":"Gunkirat Kaur","orcid":null},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]},{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gunkirat Kaur","raw_affiliation_strings":["IIIT Delhi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT Delhi","institution_ids":["https://openalex.org/I119939252","https://openalex.org/I68891433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067960968","display_name":"Kamal Das","orcid":"https://orcid.org/0000-0002-5012-8972"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kamal Das","raw_affiliation_strings":["IBM Research - India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - India","institution_ids":["https://openalex.org/I4210103279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020415815","display_name":"Jagabondhu Hazra","orcid":"https://orcid.org/0000-0002-6372-0342"},"institutions":[{"id":"https://openalex.org/I4210103279","display_name":"IBM Research - India","ror":"https://ror.org/014wt7r80","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210103279","https://openalex.org/I4210114115"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jagabondhu Hazra","raw_affiliation_strings":["IBM Research - India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - India","institution_ids":["https://openalex.org/I4210103279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3899,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.60299282,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4677","last_page":"4680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9968000054359436,"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/T13058","display_name":"Soil and Land Suitability Analysis","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/environmental-science","display_name":"Environmental science","score":0.6114168763160706},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.602383017539978},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5817920565605164},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.57175213098526},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5390173196792603},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5058085918426514},{"id":"https://openalex.org/keywords/nutrient","display_name":"Nutrient","score":0.49941062927246094},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"Precision agriculture","score":0.42669951915740967},{"id":"https://openalex.org/keywords/soil-science","display_name":"Soil science","score":0.3799828290939331},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.3492403030395508},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3329177796840668},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21222692728042603},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.18163561820983887},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.12980139255523682}],"concepts":[{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.6114168763160706},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.602383017539978},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5817920565605164},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.57175213098526},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5390173196792603},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5058085918426514},{"id":"https://openalex.org/C142796444","wikidata":"https://www.wikidata.org/wiki/Q181394","display_name":"Nutrient","level":2,"score":0.49941062927246094},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.42669951915740967},{"id":"https://openalex.org/C159390177","wikidata":"https://www.wikidata.org/wiki/Q9161265","display_name":"Soil science","level":1,"score":0.3799828290939331},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.3492403030395508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3329177796840668},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21222692728042603},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.18163561820983887},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.12980139255523682},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9324201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.5799999833106995,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1513618424","https://openalex.org/W1554944419","https://openalex.org/W1831050183","https://openalex.org/W1982475907","https://openalex.org/W2049175330","https://openalex.org/W2056435747","https://openalex.org/W2070493638","https://openalex.org/W2080545724","https://openalex.org/W2139709933","https://openalex.org/W2147028563","https://openalex.org/W2171033594","https://openalex.org/W2582794771","https://openalex.org/W2901863668","https://openalex.org/W4399271987","https://openalex.org/W6870084398"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W3135126032","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W2004826645"],"abstract_inverted_index":{"Soil":[0],"nutrient":[1,43],"estimation":[2,111],"can":[3,162],"be":[4],"used":[5,38,51],"as":[6],"a":[7],"key":[8],"input":[9],"to":[10,18,29,39,69,152],"increase":[11],"crop":[12],"yield":[13],"and":[14,27,57,65,76,88,106,114,121,161],"agriculture":[15],"fertilization.":[16],"Due":[17],"the":[19,22,41,148],"shortage":[20],"of":[21,81,112,132],"ground":[23,66],"measured":[24],"spectrum":[25],"technology":[26],"costliness":[28],"obtain":[30],"hyperspectral":[31],"images,":[32],"multispectral":[33],"remote":[34,53],"sensing":[35,54],"data":[36,55,60],"is":[37,138],"explore":[40],"soil":[42,158,164],"content":[44],"estimation.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"have":[50],"optical":[52],"(Landsat-8":[56],"Sentinel-2),":[58],"terrain/climate":[59],"(precipitation,":[61],"radiation,":[62],"slope":[63],"etc.)":[64],"truth":[67],"value":[68],"estimate":[70],"four":[71,86],"nutrients:":[72],"N,":[73],"K,":[74],"P,":[75],"OC":[77],"for":[78,103,110,134],"two":[79],"districts":[80],"Maharashtra,":[82],"India.":[83],"We":[84],"compared":[85],"linear":[87,93],"non-linear":[89],"regression":[90,94,98,104],"models:":[91],"multiple":[92],"(MLR),":[95],"random":[96],"forest":[97],"(RFR),":[99],"support":[100],"vector":[101],"machine":[102],"(SVR)":[105],"gradient":[107],"boosting":[108],"(GB)":[109],"NPK":[113],"OC.":[115],"Comparative":[116],"results":[117],"suggest":[118],"that,":[119],"GB":[120],"RFR":[122],"performed":[123],"better":[124,139],"than":[125],"other":[126],"models":[127],"with":[128,142],"sMAPE":[129],"in":[130],"range":[131],"0.125-0.377":[133],"all":[135],"nutrients,":[136],"which":[137],"or":[140],"comparable":[141],"literature":[143],"reported":[144],"accuracy":[145],"[1].":[146],"Therefore,":[147],"approach":[149],"has":[150],"potential":[151],"generate":[153],"high":[154],"resolution":[155],"(<;":[156],"ha)":[157],"nutrients":[159],"map":[160],"reduce":[163],"sampling":[165],"effort/cost.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
