Using satellite thermal-based evapotranspiration time series for defining management zones and spatial association to local attributes in a vineyard

Authors: OHANA-LEVI, N. - Ben Gurion University Of Negev; Knipper, Kyle; Kustas, William - Bill; Anderson, Martha; NETZER, Y. - Collaborator; Gao, Feng; DEL MAR ALSINA, M. - E & J Gallo Winery; SANCHEZ, L. - E & J Gallo Winery; KARNELI, A. - Ben Gurion University Of Negev

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/24/2020
Publication Date: 7/29/2020
Citation: Ohana-Levi, N., Knipper, K.R., Kustas, W.P., Anderson, M.C., Netzer, Y., Gao, F.N., del Mar Alsina, M., Sanchez, L., Karneli, A. 2020. Using satellite thermal-based evapotranspiration time series for defining management zones and spatial association to local attributes in a vineyard. Remote Sensing. 12(15):2436. https://doi.org/10.3390/rs12152436.
DOI: https://doi.org/10.3390/rs12152436

Interpretive Summary: Precision irrigation for agricultural activities is a key factor for managing yield and quality, minimizing the use of water resources, and maximizing crop production. Irrigation practices generally relate to the timing, amounts, and spatial distribution of water input. Therefore, both spatial and temporal information about various field characteristics is required to enable precision irrigation applications. Such a modeling framework is proposed to delineate a vineyard into uniform management zones according to their evapotranspiration (ET) or water use patterns derived from remote sensing. The modeling approach 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 is applicable for other cases in both agricultural systems and environmental modeling.

Technical 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 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. Time-series decomposition was used to deconstruct the time-series of each pixel into three components: mean seasonal ET, long-term trend, and remainder. 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. Spatial similarities of the TSC maps were compared using a statistic (V-measure) that quantifies spatial association. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, aspect, 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 check for similarities of yield levels between the two clusters. The trend and mean seasonal TSC maps had a strong spatial association (V=0.7). The RF model showed high error matrix-based prediction accuracy levels ranging between 89.34 and 94.3%. For all models, the most important predictor was soil type, followed by elevation. The yield levels corresponding to the two clusters in the trend and mean seasonal 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 for other cases in both agricultural systems and environmental modeling.