{"id":"W3177322315","doi":"10.5194/essd-2021-156","title":"Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; National Oceanic and Atmospheric Administration; National Natural Science Foundation of China; U.S. Geological Survey; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing; National Aeronautics and Space Administration","keywords":"Normalized Difference Vegetation Index; Advanced very-high-resolution radiometer; Remote sensing; Environmental science; Moderate-resolution imaging spectroradiometer; Temporal resolution; Sensor fusion; Spectroradiometer; Satellite; Image resolution; Vegetation (pathology); Time series; Meteorology; Computer science; Geography; Climate change; Reflectivity; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006831432,0.0005776371,0.0003155199,0.00148955,0.0002237163,0.0004432835,0.0002913415,0.0002520163,0.0005893234],"category_scores_gemma":[0.0006727881,0.0001611919,0.0006915277,0.001951473,0.0001047499,0.000519698,0.0003637765,0.0002197333,0.0002887947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003587994,"about_ca_system_score_gemma":0.0004304018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007895282,"about_ca_topic_score_gemma":0.008841156,"domain_scores_codex":[0.9997662,0.00002844351,0.00002293778,0.00007850741,0.00007905965,0.00002503086],"domain_scores_gemma":[0.9997908,0.0000243251,0.00003608681,0.00003432594,0.0001003557,0.00001402827],"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":[0.0006016807,0.0004964593,0.1571473,0.0003754733,0.0006369538,0.0007410786,0.0003524575,0.2598862,0.07166383,0.001800453,0.006825792,0.4994723],"study_design_scores_gemma":[0.00006336296,0.0003747051,0.249895,0.00005616743,0.0003607496,0.0001737176,0.0003681069,0.6971776,0.03710314,0.001540218,0.01277628,0.0001109512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8773633,0.0005066684,0.1095802,0.000161699,0.0001666634,0.0001730144,0.007509704,0.001473667,0.003065068],"genre_scores_gemma":[0.9047194,0.0002471621,0.0841519,0.00003231505,0.00003491601,0.00009043742,0.009706046,0.00007367811,0.0009441737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007895282,"threshold_uncertainty_score":0.01569867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006606149659772952,"score_gpt":0.1927348626957652,"score_spread":0.1861287130359923,"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."}}