Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard

Abstract

A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling.

Optimization of Vineyard Water Management: Challenges, Strategies, and Perspectives

“…This approach showed high prediction accuracy and revealed that the area of the vineyard planted on top of a silt loam soil had better conditions to consume more water. These findings allowed the spatial quantification of evapotranspiration time series at different temporal scales and may benefit within-season decision-making regarding the amounts, timing, and location of irrigation [256].…”

“…Indeed, precision water management in vineyards may benefit from the quantification of within-field spatial variability and temporal patterns of grapevine transpiration [256]. In this sense, a combination of time-series decomposition of evapotranspiration retrievals from multiple satellite platforms and a random forest classification algorithm has been tested in a Pinot noir vineyard in California [256]. This approach showed high prediction accuracy and revealed that the area of the vineyard planted on top of a silt loam soil had better conditions to consume more water.…”

In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a ‘Cabernet Sauvignon’ Vineyard

“…Within-season measurements in each of the MZs allowed investigation of MZ vine vigor and water status dynamics and its effects on yield and wine parameters. Management zones can be static, with no change of their boundaries throughout the season [14,28] or dynamic, with a change of size or class number [42,50,51]. Since our main objective in this study was to delineate MZs for…”

“…Topographic wetness index (TWI), a steady-state hydrologic variable, mainly determined by slope, can be a good parameter for IMZ as it provides an estimate of the water drainage dynamics within the field during the winter and thus affects vine water availability during the spring and summer [12]. In recent studies, TWI was used for flood risk assessment [13] and as an additional variable for IMZ delineation in vineyards [9,14].…”

“…In previous studies, ground based, proximal and airborne thermal imagery was used for irrigation scheduling [22] or for water status assessment [23][24][25] in vineyards. Thermal imaging was also used to estimate in-field spatial variability of water deficit stress [26] and to classify a vineyard into IMZs [14].…”