Modeling Phenology Combining Data Assimilation Techniques and Bioclimatic Indices in a Cabernet Sauvignon Vineyard (Vitis vinifera L.) in Central Chile

Phenology is a science that fundamental to crop productivity and especially sensitive environmental changes. In Mediterranean semi-arid climates, vineyard phenology directly affected by changes in temperature rainfall distribution, being highly vulnerable climate change. Due the significant heterogeneity soil, climate, variables, we need fast reliable ways assess large areas. This research aims evaluate performance of phenological data assimilation model (DA-PhenM) compare it with models based on meteorological (W-PhenM) Sentinel-2 NDVI (RS-PhenM). Two W-PhenM approaches were evaluated, one assessing eco- endo-dormancy, as proposed Caffarra Eccel (CaEc) widely used BRIN model, another approach accumulation heat units Parker called Grapevine Flowering Veraison (GFV). The DA-PhenM evaluated corresponds integration between RS-PhenM CaEc (EKF-CaEC) GFV (EKF-GFV). Results show EKF-CaEc EKF-GFV have lower root mean square error (RMSE) values than models. However, number parameters require, performs better because latter has higher Bayesian Index Criterion (BIC) EKF-GFV. Thus, improves both RS-PhenM, which provides novel contribution modeling Vitis vinifera L. cv Cabernet Sauvignon.