Comprehensive Spatial-Temporal Dataset for Precision Viticulture in Hot and Arid Climates: Remote Sensing, Soil Chemistry-Physics, Grapevine Physiology and Grape Quality Data (2020-2021)

This dataset provides comprehensive information on grapevine water status and associated environmental factors collected over two consecutive growing seasons (2020-2021) in a Vitis vinifera L. cv. Merlot vineyard in Bakersfield, California, USA. The data includes ground-based measurements of midday stem water potential (Ψstem), leaf gas exchange parameters (carbon assimilation rate AN and stomatal conductance gs), grape composition, soil data, weather conditions, and corresponding Landsat 8 satellite imagery. Measurements were taken from 24 experimental units, each containing 36 vines, designed to align with Landsat 8 pixels. Plant physiological data were collected biweekly, coinciding with satellite overpasses. Weather data, including maximum temperature and minimum relative humidity, were obtained from a nearby California Irrigation Management Information System (CIMIS) station. Landsat 8 imagery with less than 10% cloud cover was acquired for all measurement dates, and various vegetation indices were computed. This dataset is valuable for researchers and practitioners in viticulture, remote sensing, and agricultural sciences, offering opportunities to develop and validate models for predicting grapevine water status using satellite imagery and machine learning techniques. It enables the exploration of spatial-temporal patterns in vineyard water status and the assessment of different cross-validation techniques for machine learning models in agriculture. The dataset's comprehensive nature allows for in-depth analysis of the relationships between grapevine water status, grape composition, environmental conditions, and remotely sensed data.