{"id":"https://openalex.org/W7202416260","doi":null,"title":"SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring","display_name":"SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring","publication_year":2026,"publication_date":"2026-08-11","ids":{"openalex":"https://openalex.org/W7202416260"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.11142","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.11142","pdf_url":"https://arxiv.org/pdf/2608.11142","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.11142","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5146800879","display_name":"Moti Rattan Gupta","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Moti Rattan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005231699","display_name":"Anupam Sobti","orcid":"https://orcid.org/0000-0001-7698-2432","observed_orcids":["https://orcid.org/0000-0001-7698-2432"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sobti, Anupam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9253000020980835,"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":0.9253000020980835,"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":0.033799998462200165,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.012500000186264515,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"synthetic aperture radar","score":0.9319999814033508},{"id":"https://openalex.org/keywords/agricultural-monitoring","display_name":"agricultural monitoring","score":0.843999981880188},{"id":"https://openalex.org/keywords/self-supervised-pretraining","display_name":"self-supervised pre-training","score":0.8289999961853027},{"id":"https://openalex.org/keywords/crop-classification","display_name":"crop classification","score":0.8619999885559082},{"id":"https://openalex.org/keywords/phenology","display_name":"phenology","score":0.6169999837875366},{"id":"https://openalex.org/keywords/mask-wearing","display_name":"masking","score":0.49799999594688416},{"id":"https://openalex.org/keywords/curriculum-learning","display_name":"curriculum learning","score":1.0}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6209999918937683},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5787000060081482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4496999979019165},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.34439998865127563},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C51417038","wikidata":"https://www.wikidata.org/wiki/Q272737","display_name":"Phenology","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.2784000039100647},{"id":"https://openalex.org/C2778135862","wikidata":"https://www.wikidata.org/wiki/Q5416719","display_name":"Event monitoring","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2669999897480011},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C502990516","wikidata":"https://www.wikidata.org/wiki/Q3395383","display_name":"Agricultural land","level":3,"score":0.26269999146461487},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:arXiv.org:2608.11142","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.11142","pdf_url":"https://arxiv.org/pdf/2608.11142","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.11142","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.11142","pdf_url":"https://arxiv.org/pdf/2608.11142","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.6875,"display_name":"Zero hunger"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7202416260.pdf","grobid_xml":"https://content.openalex.org/works/W7202416260.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2626778328","https://openalex.org/W2884822772","https://openalex.org/W3094502228","https://openalex.org/W4393178589","https://openalex.org/W4403998581","https://openalex.org/W4406921704","https://openalex.org/W4408222802","https://openalex.org/W4416052645","https://openalex.org/W4416551717","https://openalex.org/W4416719967","https://openalex.org/W6948349386","https://openalex.org/W7133192139"],"related_works":["https://openalex.org/W4415346651","https://openalex.org/W4367852951","https://openalex.org/W2922412082","https://openalex.org/W4408018279","https://openalex.org/W4402399729","https://openalex.org/W7133535574","https://openalex.org/W1964715579","https://openalex.org/W4377964491","https://openalex.org/W7114917292","https://openalex.org/W6976732232"],"abstract_inverted_index":{"Agricultural":[0],"monitoring":[1,16,36],"faces":[2],"unique":[3],"challenges,":[4],"arising":[5],"from":[6,158],"the":[7,122,139,150,161,188],"landscape's":[8],"complex":[9],"temporal,":[10],"phenological,":[11],"and":[12,44,56,70,82,91,145,180],"climate":[13],"dynamics,":[14],"yet":[15],"them":[17,63],"is":[18],"critical":[19],"for":[20,134,194,199],"ensuring":[21],"food":[22],"security.":[23],"Synthetic":[24],"Aperture":[25],"Radar":[26],"(SAR)":[27],"satellites":[28],"offer":[29],"all-weather":[30],"day-night":[31],"imaging":[32],"capability":[33],"supporting":[34],"key":[35],"tasks":[37,104,142],"including":[38,54],"crop":[39,171],"type":[40,172],"mapping,":[41,90,173],"yield":[42],"prediction":[43],"phenological":[45,156],"event":[46],"detection.":[47],"Existing":[48],"multimodal":[49],"remote":[50],"sensing":[51],"foundation":[52,76],"models":[53,77],"TerraMind":[55],"CopernicusFM":[57],"learn":[58],"SAR":[59,131,182,196],"representations":[60],"by":[61],"grounding":[62],"in":[64],"optical":[65,106,175],"imagery":[66,107,133],"using":[67,129],"joint":[68],"encoding":[69],"contrastive":[71],"learning":[72,125,147],"techniques,":[73],"while":[74],"SAR-specific":[75],"such":[78],"as":[79],"SAR-JEPA,":[80],"SARMAE,":[81],"SAR-W-MixMAE":[83],"primarily":[84],"focus":[85],"on":[86,113,128,170],"target":[87],"detection,":[88],"flood":[89],"land":[92],"cover":[93],"classification":[94],"applications.":[95,136],"Recent":[96],"work":[97],"has":[98,109],"introduced":[99],"phenology":[100],"inspired":[101],"temporal":[102,140],"pretext":[103,141],"with":[105],"which":[108],"shown":[110],"strong":[111],"performance":[112],"agricultural":[114,135,200],"downstream":[115],"tasks.":[116],"In":[117],"this":[118],"work,":[119],"we":[120],"propose":[121],"first":[123],"self-supervised":[124],"pipeline":[126,193],"focused":[127],"only":[130],"intensity":[132,197],"We":[137],"improve":[138],"through":[143],"masking":[144],"curriculum":[146],"to":[148,154],"enhance":[149],"pretraining":[151,195],"pipeline's":[152],"ability":[153],"capture":[155],"features":[157],"SAR.":[159],"On":[160],"SICKLE":[162],"benchmark,":[163],"our":[164,191],"final":[165],"model":[166],"achieves":[167],"84.9%":[168],"IoU":[169],"outperforming":[174],"baselines":[176,183],"(by":[177,184],"15.3":[178],"pt)":[179],"existing":[181],"2.2":[185],"pt),":[186],"demonstrating":[187],"effectiveness":[189],"of":[190],"proposed":[192],"encoders":[198],"monitoring.":[201]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-14T00:00:00"}
