{"id":"W3082590915","doi":"10.1016/j.isprsjprs.2020.08.003","title":"Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data","year":2020,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":137,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency","keywords":"Leaf area index; Mean squared error; Remote sensing; Environmental science; Red edge; Multispectral image; Biomass (ecology); Canopy; Coefficient of determination; Crop; Mathematics; Vegetation (pathology); Agronomy; Statistics; Geography; Hyperspectral imaging; Forestry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002937109,0.0005632323,0.0003210295,0.001376634,0.0002781813,0.0003632154,0.0003213891,0.0003280209,0.0008999801],"category_scores_gemma":[0.0006000289,0.0002975837,0.0004972071,0.001067462,0.00008253461,0.0007491351,0.0002325502,0.0001812231,0.0004756225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004266491,"about_ca_system_score_gemma":0.0004235822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01892803,"about_ca_topic_score_gemma":0.03764611,"domain_scores_codex":[0.9999138,0.000009782025,0.000006116078,0.00002852511,0.00002367418,0.00001804813],"domain_scores_gemma":[0.9997745,0.0000669107,0.0000263335,0.0000238089,0.0000927307,0.000015691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004340918,0.0002715928,0.560564,0.0001937738,0.000392283,0.000395247,0.0001882063,0.1405903,0.114555,0.0005902353,0.00175831,0.1800669],"study_design_scores_gemma":[0.00006622909,0.0001040629,0.4580834,0.00002457204,0.0001589267,0.0001138301,0.0002329374,0.5270273,0.01229107,0.0006890668,0.001165957,0.00004269828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842994,0.0001266247,0.01289395,0.00002984667,0.00001165962,0.0000169974,0.001195202,0.0002415452,0.00118479],"genre_scores_gemma":[0.9697554,0.00009949759,0.02755143,0.00001153287,0.000004944923,0.00001839278,0.001995339,0.00003620373,0.0005272767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01892803,"threshold_uncertainty_score":0.03763568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03654260219640426,"score_gpt":0.2578545796938386,"score_spread":0.2213119774974344,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}