This thesis presents a multi-faceted approach to improve grapevine management through novel, data-driven methodologies aimed at optimizing nutrient sampling, quantifying cropload using physiological traits and tracking in-season cluster closure. A key focus of the study was to evaluate a new nutrient sampling method—termed “box sampling”— developed using remote sensing data from synthetic aperture radar (SAR) and Normalized Difference Vegetation Index (NDVI) imagery. Compared to traditional random and stratified methods, box sampling reduced sampling time and distance by up to 75%, while capturing broader nutrient variability of macro-nutrients across 14 vineyards in New York, Washington, and California. It performed particularly well for nitrogen (N%), Phosphorus (P%), and Magnesium (Mg%), but exhibited limitations for potassium (K%) and calcium (Ca%) due to their high spatial variability. Despite some constraints, box sampling offers a scalable, efficient solution for regional nutrient monitoring.The thesis also describes the use of physiological tools such as chlorophyll fluorescence (Fs) and solar-induced fluorescence (SIF) to study the effects of source–sink relationships, particularly crop load. It is often defined as fruit-to-leaf area ratio (FTLR) or proxied by yield to pruning weights ratios called RAVAZ index. A higher FTLR typically indicates increased photosynthesis, suggesting that space-based SIF proxies could map in-season FTLR variation for crop quality optimization. Results demonstrated that both Fs and SIF responded more strongly and consistently to FTLR after veraison. While early-season relationships were weak due to low signal-to-noise ratios of SIF, post-veraison canopy SIF correlated more reliably with FTLR, pruning weights and yield than reflectance-based indices like NDVI. The thesis also introduces a novel computer vision pipeline to quantify and monitor cluster closure (CC)—a key morphological stage in grapevine development—with high accuracy (<2% error). Using mobile phone imagery processed through a Pyramid Scene Parsing Network (PSPNet) and Otsu’s thresholding, the method effectively captured the timing and progression of CC across three cultivars. The progression curve revealed an asymptotic trend, enabling the proposal of a consistent phenological marker for CC based on when this curve plateaus. This continuous %CC metric lays the groundwork for better understanding of disease susceptibility, cluster compactness, and quality control in viticulture.