{"id":"W2623307522","doi":"10.3390/ijgi6060168","title":"Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents","year":2017,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Panchromatic film; Normalized Difference Vegetation Index; Image resolution; Remote sensing; Vegetation (pathology); Multispectral image; Sharpening; Environmental science; Enhanced vegetation index; Canopy; Thematic Mapper; Leaf area index; Vegetation Index; Satellite imagery; Geography; Physics; Optics; Ecology","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":[0.000661433,0.0006795352,0.000296956,0.002032129,0.0001829382,0.0004339003,0.0004138107,0.0004152447,0.0009114359],"category_scores_gemma":[0.0007957841,0.000401754,0.0005856736,0.001574083,0.000251406,0.0007698506,0.000334488,0.0004649504,0.0002901805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001917062,"about_ca_system_score_gemma":0.0002545879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001570889,"about_ca_topic_score_gemma":0.002499658,"domain_scores_codex":[0.999643,0.00004735128,0.00002343589,0.0001279326,0.0001327128,0.00002549494],"domain_scores_gemma":[0.9997732,0.00004549134,0.00005165823,0.00002699602,0.00009314724,0.000009456398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003095318,0.0001613444,0.01625351,0.0008559646,0.000213459,0.0002755043,0.0003468361,0.0378372,0.3651002,0.002879864,0.001920693,0.5738458],"study_design_scores_gemma":[0.00004709605,0.0004023284,0.1399598,0.00008819999,0.0003428918,0.001360578,0.0003084562,0.5234236,0.3153311,0.003646659,0.01488933,0.0001999528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2477365,0.001425727,0.7431391,0.00007298768,0.00004771963,0.0002263661,0.0006374388,0.002088707,0.004625503],"genre_scores_gemma":[0.4703213,0.001168232,0.5247514,0.00005279664,0.00002988215,0.0001594587,0.0008877849,0.0001512559,0.002477859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002032129,"threshold_uncertainty_score":0.003498018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006483482852973365,"score_gpt":0.257612838876635,"score_spread":0.2511293560236617,"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."}}