{"id":"https://openalex.org/W2142476294","doi":"https://doi.org/10.1109/igarss.2004.1370202","title":"Soil parameters retrieval from remotely sensed data: efficiency of neural network and Bayesian approaches","display_name":"Soil parameters retrieval from remotely sensed data: efficiency of neural network and Bayesian approaches","publication_year":2004,"publication_date":"2004-12-23","ids":{"openalex":"https://openalex.org/W2142476294","doi":"https://doi.org/10.1109/igarss.2004.1370202","mag":"2142476294"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2004.1370202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5014002590","display_name":"Claudia Notarnicola","orcid":"https://orcid.org/0000-0003-1968-0125"},"institutions":[{"id":"https://openalex.org/I68618741","display_name":"Polytechnic University of Bari","ror":"https://ror.org/03c44v465","country_code":"IT","type":"education","lineage":["https://openalex.org/I68618741"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"C. Notarnicola","raw_affiliation_strings":["Dipartimento Interateneo di Fisica, Politecnico di Bari, INFM, Bari, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento Interateneo di Fisica, Politecnico di Bari, INFM, Bari, Italy","institution_ids":["https://openalex.org/I68618741"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108515683","display_name":"F. Posa","orcid":null},"institutions":[{"id":"https://openalex.org/I68618741","display_name":"Polytechnic University of Bari","ror":"https://ror.org/03c44v465","country_code":"IT","type":"education","lineage":["https://openalex.org/I68618741"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"F. Posa","raw_affiliation_strings":["Dipartimento Interateneo di Fisica, Politecnico di Bari, INFM, Bari, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento Interateneo di Fisica, Politecnico di Bari, INFM, Bari, Italy","institution_ids":["https://openalex.org/I68618741"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055695039","display_name":"M. Angiulli","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092323","display_name":"Nello Carrara Institute of Applied Physics","ror":"https://ror.org/00dqega85","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210092323","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"M. Angiulli","raw_affiliation_strings":["Dipartimento Interateneo di Fisica, CNR-Istituto di Fisica Applicata N. Carrara-IFAC, Bari, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento Interateneo di Fisica, CNR-Istituto di Fisica Applicata N. Carrara-IFAC, Bari, Italy","institution_ids":["https://openalex.org/I4210092323"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":2,"citation_normalized_percentile":{"value":0.451876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":68,"max":71},"biblio":{"volume":"7","issue":null,"first_page":"4682","last_page":"4685"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":1.0,"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":1.0,"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/T10716","display_name":"Soil and Unsaturated Flow","score":0.9899,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9865,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scatterometer","display_name":"Scatterometer","score":0.5373904}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7314321},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.56944937},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.53837156},{"id":"https://openalex.org/C2776212561","wikidata":"https://www.wikidata.org/wiki/Q905295","display_name":"Scatterometer","level":3,"score":0.5373904},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.48076794},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.46115756},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41974226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41680664},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38798785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30665946},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27490914},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.13309291},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09682509},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2004.1370202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life on land","id":"https://metadata.un.org/sdg/15","score":0.66}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":18,"referenced_works":["https://openalex.org/W148455047","https://openalex.org/W1534053224","https://openalex.org/W1622303687","https://openalex.org/W1970170811","https://openalex.org/W1994674869","https://openalex.org/W2002096058","https://openalex.org/W2016203891","https://openalex.org/W2021166922","https://openalex.org/W2045656233","https://openalex.org/W2094414211","https://openalex.org/W2126087248","https://openalex.org/W2129714594","https://openalex.org/W2135558118","https://openalex.org/W2145846654","https://openalex.org/W2155482699","https://openalex.org/W2167735288","https://openalex.org/W2288429382","https://openalex.org/W2611591252"],"related_works":["https://openalex.org/W3000499464","https://openalex.org/W2585691118","https://openalex.org/W2546488237","https://openalex.org/W2182015688","https://openalex.org/W2097761609","https://openalex.org/W2073340904","https://openalex.org/W2068845235","https://openalex.org/W2067079771","https://openalex.org/W1885310920","https://openalex.org/W1589762804"],"abstract_inverted_index":{"Six":[0],"remote":[1],"sensing":[2],"experiments":[3,60],"are":[4,103,122,147],"analyzed":[5,111],"in":[6,157],"order":[7],"to":[8,193],"study":[9],"the":[10,79,90,94,97,109,115,119,150,154,158,161,166,175,186,195],"feasibility":[11],"of":[12,42,81,100,164,177,197,203],"soil":[13,57,101],"parameters":[14],"extraction":[15],"from":[16,181],"active":[17,50,82,139],"and":[18,34,51,58,69,75,83,96,106,125,141,172],"passive":[19,52,84,145],"microwave":[20],"data.":[21],"The":[22,127,189],"inversion":[23],"process":[24],"has":[25],"been":[26,45],"carried":[27,61],"out":[28,62],"through":[29],"two":[30,133],"methodologies:":[31],"a":[32,35,55,64,87],"Bayesian":[33,88,190],"neural":[36,116,151,167],"network":[37,117,168],"approach.":[38],"Two":[39],"different":[40,73,134],"sets":[41],"data":[43,53,112,140,146],"have":[44],"analyzed:":[46],"one":[47],"experiment":[48],"with":[49,63,199],"on":[54,67,185],"smooth":[56,70],"five":[59],"C-band":[65],"scatterometer":[66],"rough":[68],"soils":[71],"at":[72],"polarizations":[74],"incidence":[76],"angles.":[77],"In":[78,114],"case":[80],"data,":[85,165],"using":[86],"algorithm,":[89],"correlation":[91,120],"coefficients":[92,121,183],"between":[93],"extracted":[95],"measured":[98],"values":[99,176,196],"moisture":[102],"R=0.83,":[104],"R=0.84":[105],"0.72":[107],"for":[108,138,144],"three":[110],"configurations.":[113],"approach,":[118],"R=0.72,":[123],"R=0.83":[124],"0.79.":[126],"best":[128],"performance":[129],"is":[130],"achieved":[131],"when":[132],"frequencies,":[135],"4.6":[136],"GHz":[137,143],"2.5":[142],"employed":[148],"where":[149],"networks":[152],"produce":[153],"lowest":[155],"errors":[156],"estimates.":[159],"For":[160],"second":[162],"group":[163],"makes":[169],"fewer":[170],"mistakes":[171],"overestimates":[173],"only":[174],"epsiv":[178,198],"that":[179],"originated":[180],"backscattering":[182],"acquired":[184],"rougher":[187],"field.":[188],"approach":[191],"tends":[192],"overestimate":[194],"an":[200],"average":[201],"bias":[202],"5%":[204]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2142476294","counts_by_year":[],"updated_date":"2025-04-15T22:05:57.011513","created_date":"2016-06-24"}