TY - JOUR
T1 - Using satellite thermal-based evapotranspiration time series for defining management zones and spatial association to local attributes in a Vineyard
AU - Ohana-Levi, Noa
AU - Knipper, Kyle
AU - Kustas, William P.
AU - Anderson, Martha C.
AU - Netzer, Yishai
AU - Gao, Feng
AU - Alsina, Maria del Mar
AU - Sanchez, Luis A.
AU - Karnieli, Arnon
N1 - Funding Information:
vineyard field data and the financial support for the GRAPEX project by a grant from the NASA Applied ReSsociuerncceess–PWroagtrearmRe(sGoruarncteAs PwroagrdraNmN (HG1ra7nAtE A39wI)arsduN pNpoHrt1e7dAEth3e9Id)esvueplpooprmteedn tthoefdtheveeslaotpemllietne-tb oafs ethdeEsaTteplrloited-uct used in this analysis. Additionally, the research was partially funded by the European Union Horizon 2020 Research Horizon 2020 Research and Innovation Programme under grant agreement no. 871128 “European long-term ecosystem, critical zone and socio-ecological systems research infrastructure PLUS.” Acknowledgments: The authors wish to thank the USDA/NRCS and the USGS for making their data available Acknowledgments: The authors wish to thank the USDA/NRCS and the USGS for making their data available for public use. The authors wish to thank the staff of Viticulture, Chemistry and Enology Division of E&J Gallo for public use. The authors wish to thank the staff of Viticulture, Chemistry and Enology Division of E&J Gallo Winery for logistical support of GRAPEX field and research activities and insight to local irrigation practices. In LanadddsiMtioann, atgheismreesneta,raclho nwgowuldithnotht ehaSvieer braeeLnop mosasvibilnee wyaitrhdoustta tfhf,ep crooovpideriantigonac ocfe sMsrt.o Etrhneei v Dinoesyioaordf Paancdifilco Agigstriic al supLpanodrts oMf GanRaAgePmEXenfti,ealldonmge wasiuthr etmheeS nietrarcat iLvoitmieas. vTinheeyaarudt hstoarfsf,wproouvldidianlgsoalcickeesstotoththaenkviNneuyrairtdA agnadmloagnisdtiYcaulval support of GRAPEX field measurement activities. The authors would also like to thank Nurit Agam and Yuval
Funding Information:
Funding provided by E. & J. Gallo Winery made possible the acquisition and processing of the vineyard field data and the financial support for the GRAPEX project by a grant from the NASA Applied Sciences-Water Resources Program (Grant Award NNH17AE39I) supported the development of the satellite-based ET product used in this analysis. Additionally, the research was partially funded by the European Union Horizon 2020 Research and Innovation Programme under grant agreement no. 871128 "European long-term ecosystem, critical zone and socio-ecological systems research infrastructure PLUS". The authors wish to thank the USDA/NRCS and the USGS for making their data available for public use. The authors wish to thank the staff of Viticulture, Chemistry and Enology Division of E&J Gallo Winery for logistical support of GRAPEX field and research activities and insight to local irrigation practices. In addition, this research would not have been possible without the cooperation of Ernie Dosio of Pacific Agri Lands Management, along with the Sierra Loma vineyard staff, providing access to the vineyard and logistical support of GRAPEX field measurement activities. The authors would also like to thank Nurit Agam and Yuval Reuveni for their constructive comments.
Publisher Copyright:
© 2020 by the authors.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - 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.
AB - 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.
KW - Precision irrigation
KW - Random forest
KW - Spatial statistics
KW - Time-series clustering
KW - Vitis vinifera
KW - Water resource management
UR - http://www.scopus.com/inward/record.url?scp=85089541839&partnerID=8YFLogxK
U2 - 10.3390/RS12152436
DO - 10.3390/RS12152436
M3 - Article
AN - SCOPUS:85089541839
VL - 12
JO - Remote Sensing
JF - Remote Sensing
SN - 2072-4292
IS - 15
M1 - 2436
ER -