Box sampling: a new spatial sampling method for grapevine macronutrients using Sentinel-1 and Sentinel-2 satellite images

The ability to reduce sampling distance or time is crucial for growers to monitor vineyard nutrients more frequently. Extension specialists often recommend collecting large random samples, but this is frequently overlooked, leading to inaccurate fertilizer recommendations. A novel, one-location square grid area-based sampling method called “box” sampling was developed to capture the overall nutrient distribution within a block, providing guidance for growers on sample collection in vineyards for nutrient monitoring. Box sampling was compared with random and stratified sampling methods at both bloom and veraison for grapevine foliar nitrogen (N%), phosphorus (P%), potassium (K%), magnesium (Mg%), and calcium (Ca%). Box and stratified sampling locations were determined based on Synthetic Aperture Radar (SAR) from Sentinel-1 and Sentinel-2 Normalized Difference Vegetation Index (NDVI) images. SAR and NDVI images were stratified into three variability zones using the k-means + + algorithm. Representative pixels from each zone were sampled using the stratified method, while the junction of these variability zones (30mx30m sampling window) was sampled using the new box method. In 2021 and 2022, these methods were compared against nutrient population parameters in two vineyard blocks. Both methods showed marginal differences in mean, median, and standard deviation, with box sampling consistently capturing a broader range of variations. This was evidenced by the Bhattacharya coefficient, which indicates the overlap between two probability distributions (with values closer to 1 for greater overlap). The coefficient was > 0.80 for N%, P%, and Mg%, and > 0.60 for K% and Ca% at both bloom and veraison. For 14 different commercial vineyards in 2022 and 2023, box sampling accurately captured random nutrient variability for N%, P% and Mg% at both bloom and veraison. However, for K% (at veraison) and Ca% box sampling performed poorly due to high spatial variability. Box sampling reduced the sampling distance and time by 75% compared to random sampling.