{"id":"https://openalex.org/W2897112385","doi":"https://doi.org/10.1109/agro-geoinformatics.2018.8476034","title":"Spectral Response of Soil Organic Matter by Principal Component Analysis","display_name":"Spectral Response of Soil Organic Matter by Principal Component Analysis","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2897112385","doi":"https://doi.org/10.1109/agro-geoinformatics.2018.8476034","mag":"2897112385"},"language":"en","primary_location":{"id":"doi:10.1109/agro-geoinformatics.2018.8476034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics.2018.8476034","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics)","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/A5026697566","display_name":"Xiaohe Gu","orcid":"https://orcid.org/0000-0002-7102-1939"},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaohe Gu","raw_affiliation_strings":["Beijing Research Center for Information Technology in Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067406261","display_name":"Meiyan Shu","orcid":"https://orcid.org/0000-0002-1519-5520"},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiyan Shu","raw_affiliation_strings":["Beijing Research Center for Information Technology in Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044554280","display_name":"Guiiun Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiiun Yang","raw_affiliation_strings":["Beijing Research Center for Information Technology in Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070651286","display_name":"Xingang Xu","orcid":"https://orcid.org/0000-0002-8473-5631"},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingang Xu","raw_affiliation_strings":["Beijing Research Center for Information Technology in Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000200794","display_name":"Xiaoyu Song","orcid":"https://orcid.org/0000-0003-0294-5705"},"institutions":[{"id":"https://openalex.org/I4210156423","display_name":"National Engineering Research Center for Information Technology in Agriculture","ror":"https://ror.org/04c3j3t84","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210156423"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Song","raw_affiliation_strings":["Beijing Research Center for Information Technology in Agriculture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Research Center for Information Technology in Agriculture, Beijing, China","institution_ids":["https://openalex.org/I4210156423"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5026697566"],"corresponding_institution_ids":["https://openalex.org/I4210156423"],"apc_list":null,"apc_paid":null,"fwci":0.1585,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46816887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9775999784469604,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8466449975967407},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7929257750511169},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5872898101806641},{"id":"https://openalex.org/keywords/soil-organic-matter","display_name":"Soil organic matter","score":0.5281738638877869},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5015847682952881},{"id":"https://openalex.org/keywords/soil-test","display_name":"Soil test","score":0.448896199464798},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44112420082092285},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4226610064506531},{"id":"https://openalex.org/keywords/coefficient-of-determination","display_name":"Coefficient of determination","score":0.41797590255737305},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41459932923316956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.398085355758667},{"id":"https://openalex.org/keywords/soil-science","display_name":"Soil science","score":0.38297832012176514},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37175896763801575},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35534989833831787},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22964569926261902},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1646091341972351},{"id":"https://openalex.org/keywords/soil-water","display_name":"Soil water","score":0.1524437963962555}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8466449975967407},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7929257750511169},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5872898101806641},{"id":"https://openalex.org/C182124840","wikidata":"https://www.wikidata.org/wiki/Q1154759","display_name":"Soil organic matter","level":3,"score":0.5281738638877869},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5015847682952881},{"id":"https://openalex.org/C50516716","wikidata":"https://www.wikidata.org/wiki/Q877107","display_name":"Soil test","level":3,"score":0.448896199464798},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44112420082092285},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4226610064506531},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.41797590255737305},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41459932923316956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.398085355758667},{"id":"https://openalex.org/C159390177","wikidata":"https://www.wikidata.org/wiki/Q9161265","display_name":"Soil science","level":1,"score":0.38297832012176514},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37175896763801575},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35534989833831787},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22964569926261902},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1646091341972351},{"id":"https://openalex.org/C159750122","wikidata":"https://www.wikidata.org/wiki/Q96621023","display_name":"Soil water","level":2,"score":0.1524437963962555}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/agro-geoinformatics.2018.8476034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics.2018.8476034","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.800000011920929,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1969655639","https://openalex.org/W1970016156","https://openalex.org/W1980707033","https://openalex.org/W1997287413","https://openalex.org/W2004936618","https://openalex.org/W2055142418","https://openalex.org/W2196579671","https://openalex.org/W2226758819","https://openalex.org/W2355921910","https://openalex.org/W2557398211","https://openalex.org/W6687584342","https://openalex.org/W6729950231"],"related_works":["https://openalex.org/W2659933339","https://openalex.org/W2364784002","https://openalex.org/W2755828367","https://openalex.org/W4383721055","https://openalex.org/W2909099826","https://openalex.org/W4214620440","https://openalex.org/W4309845446","https://openalex.org/W3212595790","https://openalex.org/W3194565370","https://openalex.org/W2902336595"],"abstract_inverted_index":{"Soil":[0],"organic":[1],"matter":[2],"(SOM)":[3],"can":[4],"be":[5],"used":[6,126,151,206],"as":[7,176],"an":[8],"indicator":[9],"to":[10,64,127,134,152,207,299],"guide":[11],"fertilization":[12,40],"and":[13,23,34,95,117,185,222,260],"chemical":[14],"input":[15],"management":[16,41],"of":[17,31,68,108,132,139,147,165,179,196,202,211,234,255,272,296],"farmland.":[18],"It":[19,286],"increases":[20],"soil":[21,80],"porosity":[22],"water":[24],"holding":[25],"capacity.":[26],"Monitoring":[27],"the":[28,49,66,83,105,129,136,154,160,209,212,249,253,274,289,294],"spatial":[29],"distribution":[30],"SOM":[32,70,94,133,148,184,203,236,301],"timely":[33],"accurately":[35],"is":[36],"very":[37],"important":[38],"for":[39],"in":[42,58,74,82,143],"precision":[43],"agriculture.":[44],"Imaging":[45],"hyperspectrometer":[46,73,141],"carried":[47],"on":[48],"unmanned":[50],"aerial":[51],"vehicle":[52],"(UAV)":[53],"has":[54],"been":[55],"developed":[56],"rapidly":[57],"recent":[59],"years.":[60],"The":[61,78,90,181,263],"study":[62,84],"aimed":[63],"test":[65],"practicability":[67],"monitoring":[69,144],"by":[71,104],"imaging":[72,140],"a":[75],"small":[76],"scale.":[77],"132":[79],"samples":[81,149,204],"were":[85,102,150,174,189,205],"collected":[86],"from":[87],"three":[88],"regions.":[89],"quantitative":[91],"relationships":[92],"between":[93,183],"spectral":[96,118,177],"reflectivity":[97],"with":[98,191,214,265],"different":[99],"pixel":[100],"sizes":[101],"analyzed":[103,190],"transformation":[106],"method":[107,123,291],"principal":[109,163],"component":[110],"analysis":[111],"(PCA).":[112],"After":[113],"screening":[114],"sensitive":[115],"bands":[116],"parameters,":[119],"partial":[120],"least":[121],"square":[122,225],"(PLS)":[124],"was":[125,279,283],"develop":[128,153],"inversion":[130,213],"models":[131,233,257],"evaluate":[135,208],"optimal":[137],"scale":[138],"application":[142],"SOM.":[145,180],"Two-third":[146],"PCA-PLS":[155,232,256],"models.":[156],"Results":[157,228],"showed":[158,229],"that":[159,230,288],"first":[161,259],"two":[162],"components":[164],"hyperspectral":[166],"image":[167],"could":[168,237,292],"reach":[169],"99.80%":[170],"relative":[171],"information,":[172],"which":[173,197,273],"chosen":[175],"parameters":[178],"correlations":[182],"PCA1":[186],"or":[187],"PCA2":[188],"five":[192],"resampling":[193,250,267],"sizes,":[194],"all":[195,231,241],"reached":[198,269],"above":[199,246],"0.4.":[200],"One-third":[201],"accuracy":[210],"determination":[215],"coefficient":[216],"(R":[217],"<sup":[218,243,276],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[219,244,277],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[220,245,278],")":[221],"root":[223],"mean":[224],"error":[226],"(RMSE).":[227],"predicting":[235],"get":[238],"good":[239],"accuracy,":[240,271],"R":[242,275],"0.3.":[247],"With":[248],"size":[251,268],"increasing,":[252],"accuracies":[254],"increased":[258],"then":[261],"decreased.":[262],"model":[264],"3*3":[266],"highest":[270],"0.3411,":[280],"while":[281],"RMSE":[282],"3.071":[284],"g/kg.":[285],"indicated":[287],"PCA":[290],"make":[293],"best":[295],"hyperspectrum":[297],"information":[298],"monitor":[300],"effectively.":[302]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}