{"id":"https://openalex.org/W2986339177","doi":"https://doi.org/10.3390/rs11222673","title":"Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series\u2014A Case Study in Zhanjiang, China","display_name":"Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series\u2014A Case Study in Zhanjiang, China","publication_year":2019,"publication_date":"2019-11-15","ids":{"openalex":"https://openalex.org/W2986339177","doi":"https://doi.org/10.3390/rs11222673","mag":"2986339177"},"language":"en","primary_location":{"id":"doi:10.3390/rs11222673","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11222673","pdf_url":"https://www.mdpi.com/2072-4292/11/22/2673/pdf?version=1573814748","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/11/22/2673/pdf?version=1573814748","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101766813","display_name":"Hongwei Zhao","orcid":"https://orcid.org/0000-0002-7088-8848"},"institutions":[{"id":"https://openalex.org/I4210108914","display_name":"Institute of Agricultural Resources and Regional Planning","ror":"https://ror.org/01nrzdp21","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108914","https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210151987","display_name":"Ministry of Agriculture and Rural Affairs","ror":"https://ror.org/05ckt8b96","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Zhao","raw_affiliation_strings":["Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China","Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China","institution_ids":["https://openalex.org/I4210108914"]},{"raw_affiliation_string":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China","institution_ids":["https://openalex.org/I4210151987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008753","display_name":"Zhongxin Chen","orcid":"https://orcid.org/0000-0001-5757-2972"},"institutions":[{"id":"https://openalex.org/I4210108914","display_name":"Institute of Agricultural Resources and Regional Planning","ror":"https://ror.org/01nrzdp21","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108914","https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210151987","display_name":"Ministry of Agriculture and Rural Affairs","ror":"https://ror.org/05ckt8b96","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongxin Chen","raw_affiliation_strings":["Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China","Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China","institution_ids":["https://openalex.org/I4210108914"]},{"raw_affiliation_string":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China","institution_ids":["https://openalex.org/I4210151987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043096476","display_name":"Hao Jiang","orcid":"https://orcid.org/0000-0002-5122-0412"},"institutions":[{"id":"https://openalex.org/I4210147158","display_name":"Guangzhou Institute of Geography","ror":"https://ror.org/03tbxt129","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210147158"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Jiang","raw_affiliation_strings":["Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China","Guangzhou Institute of Geography, Guangzhou 510070, China","Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China","institution_ids":[]},{"raw_affiliation_string":"Guangzhou Institute of Geography, Guangzhou 510070, China","institution_ids":["https://openalex.org/I4210147158"]},{"raw_affiliation_string":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063344744","display_name":"Wenlong Jing","orcid":"https://orcid.org/0000-0001-8021-3943"},"institutions":[{"id":"https://openalex.org/I4210147158","display_name":"Guangzhou Institute of Geography","ror":"https://ror.org/03tbxt129","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210147158"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenlong Jing","raw_affiliation_strings":["Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China","Guangzhou Institute of Geography, Guangzhou 510070, China","Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China","institution_ids":[]},{"raw_affiliation_string":"Guangzhou Institute of Geography, Guangzhou 510070, China","institution_ids":["https://openalex.org/I4210147158"]},{"raw_affiliation_string":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021834892","display_name":"Liang Sun","orcid":"https://orcid.org/0000-0003-2794-8654"},"institutions":[{"id":"https://openalex.org/I4210108914","display_name":"Institute of Agricultural Resources and Regional Planning","ror":"https://ror.org/01nrzdp21","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210108914","https://openalex.org/I4210127390","https://openalex.org/I4210138501","https://openalex.org/I4210151987"]},{"id":"https://openalex.org/I4210151987","display_name":"Ministry of Agriculture and Rural Affairs","ror":"https://ror.org/05ckt8b96","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Sun","raw_affiliation_strings":["Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China","Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China"],"affiliations":[{"raw_affiliation_string":"Institute of Agricultural Resources and Regional Planning, CAAS, Beijing 100081, China","institution_ids":["https://openalex.org/I4210108914"]},{"raw_affiliation_string":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China","institution_ids":["https://openalex.org/I4210151987"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033295969","display_name":"Min Feng","orcid":"https://orcid.org/0000-0001-7456-7534"},"institutions":[{"id":"https://openalex.org/I4210141476","display_name":"Institute of Tibetan Plateau Research","ror":"https://ror.org/03zn6c508","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210141476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Feng","raw_affiliation_strings":["Institute of Tibetan Plateau Research, CAS, Beijing 100101, China"],"affiliations":[{"raw_affiliation_string":"Institute of Tibetan Plateau Research, CAS, Beijing 100101, China","institution_ids":["https://openalex.org/I4210141476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5043096476"],"corresponding_institution_ids":["https://openalex.org/I4210147158"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":9.6804,"has_fulltext":true,"cited_by_count":134,"citation_normalized_percentile":{"value":0.98565605,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"11","issue":"22","first_page":"2673","last_page":"2673"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9998000264167786,"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":0.9998000264167786,"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/T10616","display_name":"Smart Agriculture and AI","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant 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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7486488819122314},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7369045615196228},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6965160369873047},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6231685876846313},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5802767872810364},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5530492067337036},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5369529724121094},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4560830295085907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4355486333370209},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40221118927001953},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37332502007484436},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3325260877609253},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08072075247764587}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7486488819122314},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369045615196228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6965160369873047},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6231685876846313},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5802767872810364},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5530492067337036},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5369529724121094},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4560830295085907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4355486333370209},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40221118927001953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37332502007484436},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3325260877609253},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08072075247764587}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs11222673","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11222673","pdf_url":"https://www.mdpi.com/2072-4292/11/22/2673/pdf?version=1573814748","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:13b0551c7b7e45c2aaaf28fa68497c7b","is_oa":true,"landing_page_url":"https://doaj.org/article/13b0551c7b7e45c2aaaf28fa68497c7b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 11, Iss 22, p 2673 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/11/22/2673/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs11222673","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":"Remote Sensing; Volume 11; Issue 22; Pages: 2673","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs11222673","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs11222673","pdf_url":"https://www.mdpi.com/2072-4292/11/22/2673/pdf?version=1573814748","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.6499999761581421,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2986339177.pdf","grobid_xml":"https://content.openalex.org/works/W2986339177.grobid-xml"},"referenced_works_count":70,"referenced_works":["https://openalex.org/W119403003","https://openalex.org/W159663733","https://openalex.org/W561088580","https://openalex.org/W581956982","https://openalex.org/W1521436688","https://openalex.org/W1523879065","https://openalex.org/W1815076433","https://openalex.org/W1994490949","https://openalex.org/W1999009363","https://openalex.org/W2005156666","https://openalex.org/W2019768584","https://openalex.org/W2033603874","https://openalex.org/W2034394311","https://openalex.org/W2044863747","https://openalex.org/W2056435747","https://openalex.org/W2064675550","https://openalex.org/W2068094410","https://openalex.org/W2081924573","https://openalex.org/W2083097964","https://openalex.org/W2086231373","https://openalex.org/W2114527611","https://openalex.org/W2117294245","https://openalex.org/W2118037698","https://openalex.org/W2120339295","https://openalex.org/W2120586739","https://openalex.org/W2126226085","https://openalex.org/W2138973222","https://openalex.org/W2150355110","https://openalex.org/W2158982637","https://openalex.org/W2167615167","https://openalex.org/W2187127046","https://openalex.org/W2288074780","https://openalex.org/W2293634267","https://openalex.org/W2342893289","https://openalex.org/W2551393996","https://openalex.org/W2587031013","https://openalex.org/W2604086375","https://openalex.org/W2610947800","https://openalex.org/W2737391801","https://openalex.org/W2761898896","https://openalex.org/W2764034829","https://openalex.org/W2766696621","https://openalex.org/W2782522152","https://openalex.org/W2783323081","https://openalex.org/W2786038065","https://openalex.org/W2791592925","https://openalex.org/W2803187616","https://openalex.org/W2803507394","https://openalex.org/W2886493749","https://openalex.org/W2890713182","https://openalex.org/W2890942070","https://openalex.org/W2897656581","https://openalex.org/W2900420505","https://openalex.org/W2903282641","https://openalex.org/W2911964244","https://openalex.org/W2919115771","https://openalex.org/W2938102982","https://openalex.org/W2964199361","https://openalex.org/W2988111912","https://openalex.org/W3104839310","https://openalex.org/W4237764598","https://openalex.org/W4255808146","https://openalex.org/W4255949318","https://openalex.org/W6618372016","https://openalex.org/W6631361901","https://openalex.org/W6649054543","https://openalex.org/W6674304311","https://openalex.org/W6679082163","https://openalex.org/W6733018535","https://openalex.org/W6754228586"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560","https://openalex.org/W2969450769"],"abstract_inverted_index":{"Timely":[0],"and":[1,7,28,123,182,197,214,239,282,323,347,367],"accurate":[2],"estimation":[3],"of":[4,9,73,88,146,235,258,285,293,319,358,361,394,422],"the":[5,144,176,202,219,236,249,270,291,299,314,320,324,329,348,356,370,375,392,395,398,403],"area":[6,27],"distribution":[8],"crops":[10],"is":[11,65,413],"vital":[12],"for":[13,24,138,178,184,264,385,407,419],"food":[14],"security.":[15],"Optical":[16],"remote":[17,50],"sensing":[18,51],"has":[19],"been":[20,131,154],"a":[21,79,84,241],"key":[22],"technique":[23],"acquiring":[25],"crop":[26,103,159,266,409],"conditions":[29],"on":[30,201],"regional":[31],"to":[32,40,95,100,133,143,166,174,207,229,268,415],"global":[33],"scales,":[34],"but":[35],"great":[36],"challenges":[37],"arise":[38],"due":[39,142],"frequent":[41],"cloudy":[42,425],"days":[43],"in":[44,157,280,328,424],"southern":[45],"China.":[46,288],"This":[47,411],"makes":[48,92],"optical":[49],"images":[52],"usually":[53],"unavailable.":[54],"Synthetic":[55],"aperture":[56],"radar":[57],"(SAR)":[58],"could":[59,379],"bridge":[60],"this":[61,162,294],"gap":[62],"since":[63],"it":[64,93],"less":[66,155],"affected":[67],"by":[68],"clouds.":[69],"The":[70,305],"recent":[71],"availability":[72],"Sentinel-1A":[74],"(S1A)":[75],"SAR":[76],"imagery":[77],"with":[78,169,211,244,338,402],"12-day":[80],"revisit":[81],"period":[82],"at":[83,218,251],"high":[85],"spatial":[86],"resolution":[87],"about":[89],"10":[90],"m":[91],"possible":[94],"fully":[96],"utilize":[97],"phenological":[98],"information":[99],"improve":[101],"early":[102,158,408,420],"classification.":[104,160,410],"In":[105,161],"deep":[106,150,344,399],"learning":[107,151,345,400],"methods,":[108],"one-dimensional":[109],"convolutional":[110],"neural":[111,119],"networks":[112,120],"(1D":[113],"CNNs),":[114],"long":[115],"short-term":[116],"memory":[117],"recurrent":[118,125],"(LSTM":[121],"RNNs),":[122],"gated":[124],"unit":[126],"RNNs":[127,199,331],"(GRU":[128],"RNNs)":[129],"have":[130,153],"shown":[132],"efficiently":[134],"extract":[135],"temporal":[136],"features":[137],"classification":[139,172,227,242,405],"tasks.":[140],"However,":[141],"complexity":[145],"training,":[147],"these":[148],"three":[149,209,343],"methods":[152,346],"used":[156],"work,":[163],"we":[164,191,223,296],"attempted":[165],"combine":[167],"them":[168],"an":[170,225,381],"incremental":[171,226,404],"method":[173,412],"avoid":[175],"need":[177],"training":[179],"optimal":[180,212,271],"architectures":[181,213],"hyper-parameters":[183],"data":[185],"from":[186],"each":[187,231,252,259,265],"time":[188,205,221,253,260,272,332],"series.":[189],"First,":[190],"trained":[192],"1D":[193,311,371],"CNNs,":[194],"LSTM":[195],"RNNs,":[196],"GRU":[198,330],"based":[200],"full":[203],"images\u2019":[204],"series":[206,273,333],"attain":[208],"classifiers":[210],"hyper-parameters.":[215],"Then,":[216],"starting":[217],"first":[220],"point,":[222],"performed":[224],"process":[228],"train":[230],"classifier":[232,373],"using":[233],"all":[234,245,342],"previous":[237],"data,":[238],"obtained":[240],"network":[243],"parameter":[246],"values":[247],"(including":[248],"hyper-parameters)":[250],"point.":[254],"Finally,":[255],"test":[256],"accuracies":[257],"point":[261],"were":[262,307],"assessed":[263],"type":[267],"determine":[269],"length.":[274],"A":[275],"case":[276],"study":[277],"was":[278,334,374],"conducted":[279],"Suixi":[281],"Leizhou":[283],"counties":[284],"Zhanjiang":[286],"City,":[287],"To":[289],"verify":[290],"effectiveness":[292,393],"method,":[295],"also":[297],"implemented":[298],"classic":[300],"random":[301],"forest":[302],"(RF)":[303],"approach.":[304],"results":[306,390],"as":[308],"follows:":[309],"(i)":[310],"CNNs":[312],"achieved":[313,350],"highest":[315,325],"Kappa":[316],"coefficient":[317],"(0.942)":[318],"four":[321],"classifiers,":[322],"value":[326],"(0.934)":[327],"attained":[335],"earlier":[336],"than":[337],"other":[339],"classifiers;":[340],"(ii)":[341],"RF":[349],"F":[351],"measures":[352],"above":[353,383],"0.900":[354,384],"before":[355,387],"end":[357],"growth":[359],"seasons":[360],"banana,":[362],"eucalyptus,":[363],"second-season":[364],"paddy":[365],"rice,":[366],"sugarcane;":[368],"while,":[369],"CNN":[372],"only":[376],"one":[377],"that":[378],"obtain":[380],"F-measure":[382],"pineapple":[386],"harvest.":[388],"All":[389],"indicated":[391],"solution":[396],"combining":[397],"models":[401],"approach":[406],"expected":[414],"provide":[416],"new":[417],"perspectives":[418],"mapping":[421],"croplands":[423],"areas.":[426]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":26},{"year":2020,"cited_by_count":9}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
