{"id":"https://openalex.org/W7202389787","doi":"https://doi.org/10.62411/faith.3048-3719-408","title":"A Hybrid Spatio-Temporal Transformer-LSTM Network for Explainable Crop Yield Prediction Using Multi-Source Remote Sensing in Arid African Regions","display_name":"A Hybrid Spatio-Temporal Transformer-LSTM Network for Explainable Crop Yield Prediction Using Multi-Source Remote Sensing in Arid African Regions","publication_year":2026,"publication_date":"2026-08-13","ids":{"openalex":"https://openalex.org/W7202389787","doi":"https://doi.org/10.62411/faith.3048-3719-408"},"language":"en","primary_location":{"id":"doi:10.62411/faith.3048-3719-408","doi":"https://doi.org/10.62411/faith.3048-3719-408","is_oa":true,"landing_page_url":"https://doi.org/10.62411/faith.3048-3719-408","pdf_url":null,"source":{"id":"https://openalex.org/S4404663256","display_name":"Journal of Future Artificial Intelligence and Technologies","issn_l":"3048-3719","issn":["3048-3719"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Future Artificial Intelligence and Technologies","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.62411/faith.3048-3719-408","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120476772","display_name":"Johnson Bisi Oluwagbemi","orcid":"https://orcid.org/0009-0006-6882-9562","observed_orcids":["https://orcid.org/0009-0006-6882-9562"]},"institutions":[{"id":"https://openalex.org/I3131280167","display_name":"McPherson University","ror":"https://ror.org/03zcb2d36","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131280167"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Johnson Bisi Oluwagbemi","raw_affiliation_strings":["McPherson University"],"raw_orcid":"https://orcid.org/0009-0006-6882-9562","affiliations":[{"raw_affiliation_string":"McPherson University","institution_ids":["https://openalex.org/I3131280167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124968596","display_name":"Olabisi Josh-Falade","orcid":"https://orcid.org/0009-0002-7482-5139","observed_orcids":["https://orcid.org/0009-0002-7482-5139"]},"institutions":[{"id":"https://openalex.org/I3131280167","display_name":"McPherson University","ror":"https://ror.org/03zcb2d36","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131280167"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Olabisi Josh-Falade","raw_affiliation_strings":["McPherson University"],"raw_orcid":"https://orcid.org/0009-0002-7482-5139","affiliations":[{"raw_affiliation_string":"McPherson University","institution_ids":["https://openalex.org/I3131280167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066261587","display_name":"Ayokunle John Tadema","orcid":"https://orcid.org/0000-0001-6334-3850","observed_orcids":["https://orcid.org/0000-0001-6334-3850"]},"institutions":[{"id":"https://openalex.org/I3131280167","display_name":"McPherson University","ror":"https://ror.org/03zcb2d36","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131280167"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Ayokunle John Tadema","raw_affiliation_strings":["McPherson University"],"raw_orcid":"https://orcid.org/0000-0001-6334-3850","affiliations":[{"raw_affiliation_string":"McPherson University","institution_ids":["https://openalex.org/I3131280167"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5118666806","display_name":"Ayobami Emmanuel Mesioye","orcid":"https://orcid.org/0000-0003-0406-5226","observed_orcids":["https://orcid.org/0000-0003-0406-5226"]},"institutions":[{"id":"https://openalex.org/I3131280167","display_name":"McPherson University","ror":"https://ror.org/03zcb2d36","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131280167"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Ayobami Emmanuel Mesioye","raw_affiliation_strings":["McPherson University"],"raw_orcid":"https://orcid.org/0000-0003-0406-5226","affiliations":[{"raw_affiliation_string":"McPherson University","institution_ids":["https://openalex.org/I3131280167"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3131280167"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":"2","first_page":"342","last_page":"365"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.5737000107765198,"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":0.5737000107765198,"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":0.42410001158714294,"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/T10439","display_name":"Climate change impacts on agriculture","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/1102","display_name":"Agronomy and Crop 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"}}],"keywords":[{"id":"https://openalex.org/keywords/crop-yield-prediction","display_name":"crop yield prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/spatiotemporal-transformer","display_name":"spatio-temporal transformer","score":0.75},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.8339999914169312},{"id":"https://openalex.org/keywords/explainable-machine-learning","display_name":"explainable machine learning","score":0.8090000152587891},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.8809999823570251},{"id":"https://openalex.org/keywords/multi-source-remote-sensing","display_name":"multi-source remote sensing","score":0.7590000033378601},{"id":"https://openalex.org/keywords/sentinel-2","display_name":"Sentinel-2","score":0.8939999938011169},{"id":"https://openalex.org/keywords/nitrogen-dioxide","display_name":"nitrogen dioxide","score":0.8949999809265137},{"id":"https://openalex.org/keywords/drought","display_name":"drought","score":0.7519999742507935},{"id":"https://openalex.org/keywords/arid-regions","display_name":"arid regions","score":0.8669999837875366},{"id":"https://openalex.org/keywords/food-security","display_name":"food security","score":0.8429999947547913},{"id":"https://openalex.org/keywords/agricultural-monitoring","display_name":"agricultural monitoring","score":0.5479999780654907},{"id":"https://openalex.org/keywords/cross-regional-generalization","display_name":"cross-regional generalization","score":0.6919999718666077}],"concepts":[{"id":"https://openalex.org/C150772632","wikidata":"https://www.wikidata.org/wiki/Q1330709","display_name":"Arid","level":2,"score":0.5932000279426575},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5303000211715698},{"id":"https://openalex.org/C549605437","wikidata":"https://www.wikidata.org/wiki/Q1229911","display_name":"Food security","level":3,"score":0.4867999851703644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44839999079704285},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.4422000050544739},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.427700012922287},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4025000035762787},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C128383755","wikidata":"https://www.wikidata.org/wiki/Q3816336","display_name":"Agricultural productivity","level":3,"score":0.33730000257492065},{"id":"https://openalex.org/C107826830","wikidata":"https://www.wikidata.org/wiki/Q929380","display_name":"Environmental resource management","level":1,"score":0.32679998874664307},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C39820584","wikidata":"https://www.wikidata.org/wiki/Q125928","display_name":"Climate pattern","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C4646841","wikidata":"https://www.wikidata.org/wiki/Q13996","display_name":"Mediterranean climate","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.62411/faith.3048-3719-408","doi":"https://doi.org/10.62411/faith.3048-3719-408","is_oa":true,"landing_page_url":"https://doi.org/10.62411/faith.3048-3719-408","pdf_url":null,"source":{"id":"https://openalex.org/S4404663256","display_name":"Journal of Future Artificial Intelligence and Technologies","issn_l":"3048-3719","issn":["3048-3719"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Future Artificial Intelligence and Technologies","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.62411/faith.3048-3719-408","doi":"https://doi.org/10.62411/faith.3048-3719-408","is_oa":true,"landing_page_url":"https://doi.org/10.62411/faith.3048-3719-408","pdf_url":null,"source":{"id":"https://openalex.org/S4404663256","display_name":"Journal of Future Artificial Intelligence and Technologies","issn_l":"3048-3719","issn":["3048-3719"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Future Artificial Intelligence and Technologies","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9208999872207642,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"},{"score":0.5351999998092651,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2086627261","https://openalex.org/W2949642792","https://openalex.org/W3045041747","https://openalex.org/W3197143157","https://openalex.org/W4220820740","https://openalex.org/W4220984994","https://openalex.org/W4224308360","https://openalex.org/W4225556963","https://openalex.org/W4226484292","https://openalex.org/W4309686772","https://openalex.org/W4310082067","https://openalex.org/W4313584940","https://openalex.org/W4316690637","https://openalex.org/W4322740724","https://openalex.org/W4364379307","https://openalex.org/W4389417944","https://openalex.org/W4401486635","https://openalex.org/W4404883378","https://openalex.org/W4407184927","https://openalex.org/W4410546438","https://openalex.org/W4412959402","https://openalex.org/W4414795436","https://openalex.org/W4415076261","https://openalex.org/W4415957649"],"related_works":["https://openalex.org/W4415791952","https://openalex.org/W7169779822","https://openalex.org/W4410296638","https://openalex.org/W7129720095","https://openalex.org/W7129588513","https://openalex.org/W7108923604","https://openalex.org/W7154368446","https://openalex.org/W7221056199","https://openalex.org/W4385272276","https://openalex.org/W4381735579"],"abstract_inverted_index":{"The":[0,101],"escalating":[1],"food":[2],"security":[3],"crisis":[4],"in":[5,44,107,163,191,218],"Sub-Saharan":[6],"Africa,":[7],"driven":[8],"by":[9],"population":[10],"growth":[11],"and":[12,19,30,40,51,97,166,202,213],"climate":[13],"volatility,":[14],"necessitates":[15],"robust":[16],"agricultural":[17,63,216],"monitoring":[18],"prediction":[20,209],"systems.":[21],"However,":[22],"existing":[23],"approaches":[24],"often":[25],"rely":[26],"on":[27],"historical":[28,98],"trends":[29],"struggle":[31],"to":[32,139],"capture":[33,114],"the":[34,57,72,141,172,177],"nonlinear":[35],"effects":[36],"of":[37,59,161],"erratic":[38],"rainfall":[39],"thermal":[41],"shocks,":[42],"particularly":[43],"arid":[45],"regions.":[46,194,220],"Furthermore,":[47],"limited":[48],"geographic":[49],"representation":[50],"opaque":[52],"black-box":[53],"algorithms":[54],"can":[55,205],"constrain":[56],"use":[58],"predictive":[60,189],"models":[61],"for":[62],"decision-making.":[64],"To":[65],"address":[66],"these":[67],"limitations,":[68],"this":[69],"study":[70],"proposes":[71],"Hybrid":[73],"Spatio-Temporal":[74],"Transformer-LSTM":[75],"Network":[76],"(HST-TLN),":[77],"which":[78,108],"integrates":[79],"multi-source":[80],"environmental":[81,129,211],"data,":[82,89],"including":[83],"Sentinel-2":[84],"multispectral":[85],"imagery,":[86],"ERA5-Land":[87],"meteorological":[88],"Sentinel-5P":[90],"atmospheric":[91],"nitrogen":[92],"dioxide":[93],"(NO\u2082),":[94],"soil":[95],"variables,":[96],"yield":[99,208],"information.":[100],"architecture":[102],"employs":[103],"a":[104,119],"dual-pathway":[105],"mechanism":[106],"Long":[109],"Short-Term":[110],"Memory":[111],"(LSTM)":[112],"units":[113],"sequential":[115],"temporal":[116,125,200],"dependencies,":[117],"while":[118,144],"Multi-Head":[120],"Self-Attention":[121],"Transformer":[122],"identifies":[123],"important":[124],"patterns":[126],"associated":[127],"with":[128,176],"stress.":[130,183],"An":[131],"Adaptive":[132],"Cross-Attention":[133],"Gating":[134],"Mechanism":[135],"is":[136],"further":[137,186],"used":[138],"integrate":[140],"two":[142],"representations,":[143],"Shapley":[145],"Additive":[146],"Explanations":[147],"(SHAP)":[148],"provides":[149],"interpretable":[150],"feature":[151],"contributions.":[152],"Experimental":[153],"results":[154],"show":[155],"that":[156,198],"HST-TLN":[157],"achieves":[158],"an":[159],"R\u00b2":[160],"0.94":[162],"normal":[164],"years":[165],"0.89":[167],"during":[168],"drought":[169],"years,":[170],"outperforming":[171],"evaluated":[173],"benchmark":[174],"models,":[175],"largest":[178],"improvement":[179],"observed":[180],"under":[181,210],"climatic":[182],"Cross-regional":[184],"evaluation":[185],"demonstrates":[187],"its":[188],"capability":[190],"geographically":[192],"unseen":[193],"These":[195],"findings":[196],"indicate":[197],"combining":[199],"memory":[201],"contextual":[203],"attention":[204],"improve":[206],"crop":[207],"volatility":[212],"support":[214],"data-driven":[215],"planning":[217],"developing":[219]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-08-14T00:00:00"}