Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards

Abstract

Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water-use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual-to-reference ET (fRET) based on remotely sensed land-surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface-energy balance model, a multi-scale ET remote-sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote-Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET time-series retrievals from multiple satellite platforms to generate estimates at both the high spatial (30 m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.6 mm day−1 in ET at the daily time step and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multi-annual time scales.

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    The use of trade, firm, or corporation names in this article is for the information and convenience of the reader. Such use does not constitute official endorsement or approval by the US Department of Agriculture or the Agricultural Research Service of any product or service to the exclusion of others that may be suitable.

References

  1. Alfieri JG, Kustas WP, Prueger JH, Hipps LE, Evett SR, Basara JB, Neale CMU, French AN, Colaizzi PD, Agam N, Cosh MH, Chavez JL, Howell TA (2012) On the discrepancy between eddy covariance and lysimetry-based turbulent flux measurements under strongly advective conditions. Adv Water Resour 50:62–78. https://doi.org/10.1016/j.advwatres.2012.07.008

    Article  Google Scholar 

  2. Alfieri JG, Kustas WP, Prueger JH, McKee LG, Hipps LE, Gao F (2018) A multi-year intercomparison of micrometeorological observations at adjacent vineyards in California’s central valley during GRAPEX. Irrig Sci. https://doi.org/10.1007/s00271-018-0599-3

    Article  Google Scholar 

  3. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome

    Google Scholar 

  4. Allen RG, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. J Irrig Drain Eng 133:380–394. https://doi.org/10.1061/(ASCE)0733-9437

    Article  Google Scholar 

  5. Anderson MC, Norman JM, Diak GR, Kustas WP, Mecikalski JR (1997) A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens Environ 60:195–216. https://doi.org/10.1016/S0034-4257(96)00215-5

    Article  Google Scholar 

  6. Anderson MC, Norman JM, Mecikalski JR, Torn RD, Kustas WP, Basara JB (2004) A multi-scale remote sensing model for disaggregating regional fluxes to micrometeorological scales. J Hydrometeorol 5:343–363. https://doi.org/10.1175/1525-7541(2004)005%3C0343:AMRSMF%3E2.0.CO;2

    Article  Google Scholar 

  7. Anderson MC, Norman JM, Kustas WP, Li F, Prueger JH, Mechikalski JR (2007a) A climatological study of evapotranspiration and moisture stress across the continental United States: I. Model formulation. J Geophys Res. https://doi.org/10.1029/2006JD007506

    Article  Google Scholar 

  8. Anderson MC, Norman JM, Mecikalski JR, Otkin PJ, Kustas WP (2007b) A climatological study of evapotranspiration and moisture stress across the continental United States: II. Surface moisture climatology. J Geophys Res. https://doi.org/10.11029/12006JD007507

    Article  Google Scholar 

  9. Anderson MC, Hain CR, Wardlow B, Mecikalski JR, Kustas WP (2011) Evaluation of drought indices beased on thermal remote sensing of evapotranspiration over the continental U.S. J Clim 24:2025–2044. https://doi.org/10.1175/2010JCLI3812.1

    Article  Google Scholar 

  10. Anderson MC, Kustas WP, Alfieri JG, Gao F, Hain C, Prueger JH, Chavex JL (2012) Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens Environ 122:50–65. https://doi.org/10.1016/j.rse.2011.08.025

    Article  Google Scholar 

  11. Anderson MC, Hain CR, Otkin JA, Zhan X, Mo KC, Svoboda M et al (2013) An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. drought monitor classifications. J Hydrometeorol 14:1035–1056. https://doi.org/10.1175/JHM-D-12-0140.1

    Article  Google Scholar 

  12. Anderson MC, Gao F, Knipper KR, Hain CR, Dulaney W, Baldocchi D, Eichelmann E, Hemes K, Yang Y, Medellin-Azuara J, Kustas K (2018) Field-scale assessment of land and water use change over the California Delta using remote sensing. Remote Sens. https://doi.org/10.3390/rs10060889

    Article  Google Scholar 

  13. Azevedo PV, Soares JM, Silva V, Silva BB, Nascimento T (2008) Evapotranspiration of “Superior” grapevines under intermittent irrigation. Agric Water Manag 95:301–308. https://doi.org/10.1016/j.agwat.2007.10.011

    Article  Google Scholar 

  14. Basile B, Marsal J, Mata M, Vallverdú X, Bellvert J, Girona J (2011) Phenological sensitivity of Cabernet Sauvignon to water stress: vine physiology and berry composition. Am J Enol Vitic 62:452–461. https://doi.org/10.5344/ajev.2011.11003

    Article  CAS  Google Scholar 

  15. Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. J Hydrol 212–213:198–212. https://doi.org/10.1016/S0022-1694(98)00253-4

    Article  Google Scholar 

  16. Bellvert J, Marsal J, Mata M, Girona J (2012) Identifying irrigation zones across a 7.5-ha ‘Pinot noir’ vineyard based on the variability of vine water status and multispectral images. Irrig Sci 30:499–509. https://doi.org/10.1007/s00271-012-0380-y

    Article  Google Scholar 

  17. Bellvert J, Marsal J, Girona J, Gonzalex-Dugo V, Fereres E, Ustin S, Zarco-Tejada PJ (2016) Airborne thermal imagery to detect the seasonal evolution of crop water satus in peach, nectarine and Saturn peach orchards. Remote Sens. https://doi.org/10.3390/rs8010039

    Article  Google Scholar 

  18. Berk A, Bernstein LS, Robertson DC (1989) MODTRAN: A moderate resolution model for LOWTRAN 7. GL-TR-89-0122. Air Force Geophysics Lab, Bedford, p 38

    Google Scholar 

  19. Bramley RGV, Hamilton RP (2004) Understanding variability in winegrape production systems. 1. Within vineyard variation in yield over several vintages. Aust J Grape Wine Res 10:32–45. https://doi.org/10.1111/j.1755-0238.2004.tb00006.x

    Article  Google Scholar 

  20. Bramley RGV, Oriffitt APB, Hinze CJ, Pearse B, Hamilton RP (2005) Generating benefits from precision viticulture through selective harvesting. In: Stafford JV (ed), Precision agriculture’05. Proceedings of the 5th European conference on precision agriculture; 9–12 June; Uppsala Sweden (Wageningen Academic Publishers: Wegeningen, The Netherlands) pp. 891–898

  21. California Department of Food and Agriculture and USDA National Agricultural Statistics Service (2016) California agricultural statistics review, 2015–2016. https://www.cdfa.ca.gov/statistics/PDFs/2016Report.pdf. Accessed Aug 2017

  22. California Department of Food and Agriculture and USDA National Agricultural Statistics Service (2017) California grape acreage report 2016 crop. http://www.nass.usda.gov/Statistics_by_State/California/Publications/Grape_Acreage. Accessed Aug 2017

  23. Cammalleri C, Anderson MC, Gao F, Hain C, Kustas WP (2013) A data fusion approach for mapping daily evapotranspiration at field scale. Water Resour Res 49:4672–4686. https://doi.org/10.1002/wrcr.20349

    Article  Google Scholar 

  24. Cammalleri C, Anderson MC, Kustas WP (2014a) Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrol Earth Syst Sci 18:1885:1894. https://doi.org/10.5194/hess-18-1885-2014

    Article  Google Scholar 

  25. Cammalleri C, Anderson MC, Gao F, Hain CR, Kustas WP (2014b) Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agric For Meteorol 186:1–11. https://doi.org/10.1016/j.agrformet.2013.11.001

    Article  Google Scholar 

  26. Cancela JJ, Fandino M, Rey BJ, Rosa R, Pereira LS (2012) Estimating transpiration and soil evaporation of vineyards from the fraction of ground cover and crop height—application to ‘Albarino’ vineyards of Galicia. Acta Hortic 931:227–234. https://doi.org/10.17660/ActaHortic.2012.931.25

    Article  Google Scholar 

  27. Castellvi F, Snyder RL (2010) A new procedure based on surface renewal analysis to estimate sensible heat flux: a case study over grapevines. J Hydrometeorol 11:496–508. https://doi.org/10.1175/2009JHM1151.1

    Article  Google Scholar 

  28. Crago R (1996) Conservation and variability of the evaporative fraction during the daytime. J Hydrol 180:173–194. https://doi.org/10.1016/0022-1694(95)02906-6

    Article  Google Scholar 

  29. Dee DP, Balmaseda M, Balsamo G, Engelen R, Simmons AJ, Thépaut JN (2013) Toward a consistent reanalysis of the climate system. Bull Am Meteorol Soc 95(8):1235–1248. https://doi.org/10.1175/BAMS-D-13-00043.1

    Article  Google Scholar 

  30. Diak GR (2017) Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U.S. Climate Reference Network (USCRN). Remote Sens Environ 195:79–95. https://doi.org/10.1016/j.rse.2017.04.002

    Article  Google Scholar 

  31. Fooladmand HR, Sepaskhah AR (2009) A soil water balance model for a rain-fed vineyard in a micro catchment based on dual crop coefficient. Arch Agron Soil Sci 55:67–77. https://doi.org/10.1080/03650340802382215

    Article  Google Scholar 

  32. French AN, Norman JM, Anderson MC (2003) A simple and fast atmospheric correction for spaceborne remote sensing of surface temperature. Remote Sens Environ 87:2–3

    Article  Google Scholar 

  33. Fry JA, Xian G, Jin S, Dewitz JA, Homer CG, Yang L, Barnes CA, Herold ND, Wickham JD (2011) Completion of the 2006 national land cover database for the conterminous United States. Photogramm Eng Remote Sens 77(9):858–864

    Google Scholar 

  34. Gao F, Masek J, Schwaller M, Hall F (2006) On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans Geosci Remote Sens 44:2207–2218. https://doi.org/10.1109/TGRS.2006.872081

    Article  Google Scholar 

  35. Gao F, Morisette JT, Wolfe RE, Ederer G, Pedelty J, Masuoka E, Myneni R, Tan B, Nightingale J (2008) An algorithm to produce temporally and spatially continuous MODIS-LAI time series. IEEE Geosci Remote Sens Lett 5(1):60–64. https://doi.org/10.1109/LGRS.2007.907971

    Article  Google Scholar 

  36. Gao F, Kustas W, Anderson M (2012a) A data mining approach for sharpening thermal satellite imagery over land. Remote Sens 4:3287–3319. https://doi.org/10.3390/rs4113287

    Article  Google Scholar 

  37. Gao F, Anderson MC, Kustas WP, Wang Y (2012b) Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference. J Appl Remote Sci 6(1):063554. https://doi.org/10.1117/1JRS.6.063554

    Article  Google Scholar 

  38. Girona J, Mata M, del Campo J, Arbonés A, Bartra E, Marsal J (2006) The use of midday leaf water potential for scheduling deficit irrigation in vineyards. Irrig Sci 24:115–127. https://doi.org/10.1007/s00271-005-0015-7

    Article  Google Scholar 

  39. Girona J, Marsal J, Mata M, del Campo J, Basile B (2009) Phenological sensitivity to berry growth and composition of Tempranillo grapevines (Vitis vinifera L.) to water stress. Aust J Grape Wine Res 15:268–277. https://doi.org/10.1111/j.1755-0238.2009.00059.x

    Article  CAS  Google Scholar 

  40. Hansen MC, DeFries RS, Townshend JRG, Sohlberg R (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21:1331–1364

    Article  Google Scholar 

  41. Intrigliolo DS, Lakso AN, Piccioni RM (2009) Grapevine cv, ‘Riesling’ water use in the northeastern United States. Irrig Sci 27:253–262. https://doi.org/10.1007/s00271-008-0140-1

    Article  Google Scholar 

  42. Johnson RS, Williams LE, Ayars JE, Trout TJ (2005) Weighing lysimeters aid study of water relations in tree and vine crops. Calif Agric 59:133–136. https://doi.org/10.1007/s00271-008-0124-1

    Article  Google Scholar 

  43. Jönsson P, Eklundh L (2004) TIMESAT – A program for analyzing time-series of satellite sensor data. Comput Geosci 30:833–845

    Article  Google Scholar 

  44. Kustas WP, Norman JM (1999) Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric For Meteorol 94:13–29. https://doi.org/10.1016/SO168-1923(99)00005-2

    Article  Google Scholar 

  45. Kustas WP, Anderson MC, Alfieri JG, Knipper K, Torres-Rua A, Parry CK, Nieto H, Agam N, White A, Gao F, McKee L, Prueger JH, Hipps LE, Los S, Alsina M, Sanchez L, Sams B, Dokoozlian N, McKee M, Jones S, McElrone A, Heitman JL, Howard AM, Post K, Melton F, Hain C (2018) The grape remote sensing atmospheric profile and evapotranspiration eXperiment (GRAPEX). B Am Meteorol Soc 9:1791–1812

    Article  Google Scholar 

  46. Laszlo I, Ciren P, Liu H, Kondragunta S, Tarpley J, Goldberg M (2008) Remote sensing of aerosol and radiation from geostationary satellites. Adv Space Res 41:1882–1893. https://doi.org/10.1016/j.asr.2007.06.047

    Article  Google Scholar 

  47. McNaughton KG, Spriggs TW (1986) A mixed-layer model for regional evaporation. Bound Lay Meteorol 34:243–262. https://doi.org/10.1007/BF00122381

    Article  Google Scholar 

  48. Mecikalski JM, Diak GR, Anderson MC, Norman JM (1999) Estimating fluxes on continental scales using remotely-sensed data in an atmosphere-land exchange model. J Appl Meteorol 38:1352–1369. https://doi.org/10.1175/1520-0450(1999)038%3C1352:EFOCSU%3E2.0.CO;2.

    Article  Google Scholar 

  49. Moran MS (2003) Thermal infrared measurements as an indicator of plant ecosystem health. In: Quattrochi DA, Luval J (eds) Thermal remote sensing in land processes, Taylor and Francis, Routledge, 257–282

    Google Scholar 

  50. Moratiel R, Martinez-Cob A (2012) Evapotranspiration of grapevine trained to a gable trellis system under netting and black plastic mulching. Irrig Sci 30:167–178. https://doi.org/10.1007/s00271-011-0275-3

    Article  Google Scholar 

  51. Netzer Y, Yao C, Shenker M, Bravdo BA, Schwartz A (2009) Water use and the development of seasonal crop coefficients for Superior Seedless grapevines trained to an open-gable trellis system. Irrig Sci 25:161–170. https://doi.org/10.1007/s00271-008-0124-1

    Article  Google Scholar 

  52. Norman JM, Kustas WP, Humes KS (1995) A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperatures. Agric For Meteorol 77:263–293. https://doi.org/10.1029/97WR00704

    Article  Google Scholar 

  53. Norman JM, Anderson MC, Kustas WP, French AN, Mecikalski J, Torn R, Tanner BCW (2003) Remote sensing of surface energy fluxes 101-m pixel resolutions. Water Resour Res. https://doi.org/10.1029/2002WR001775

    Article  Google Scholar 

  54. Ortega-Farias S, Poblete-Echeverria C, Brisson N (2010) Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements. Agric For Meteorol 150:276–286. https://doi.org/10.1016/j.agrformet.2009.11.012

    Article  Google Scholar 

  55. Otkin JA, Anderson MC, Hain CR, Mladenova IE, Basara JB, Svoboda M (2013) Examining rapid onset drought development using the thermal infrared based Evaporative Stress Index. J Hydrometeorol 14:1057–1074. https://doi.org/10.1175/JHM-D-12-0144.1

    Article  Google Scholar 

  56. Otkin JA, Anderson MC, Hain CR, Svoboda M (2014) Examining the relationship between drought development and rapid changes in the Evaporative Stress Index. J Hydrometeorol 15:938–956. https://doi.org/10.1175/JHM-D-13-0110.1

    Article  Google Scholar 

  57. Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  58. Rodriquez JC et al (2010) Water use by perennial crops in the lower Sonora watershed. J Arid Environ 74:603–610. https://doi.org/10.1016/j.jaridenv.2009.11.008

    Article  Google Scholar 

  59. Santanello JA, Friedl MA (2003) Diurnal variation in soil heat flux and net radiation. J Appl Meteorol 42:851–862

    Article  Google Scholar 

  60. Semmens KA, Anderson MC, Kustas WP, Gao F, Alfieri JG, McKee L, Prueger JH, Hain CR, Cammalleri C, Yang Y, Xia T, Sanchez L, Alsina MM, Velez M (2016) Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens Environ 185:155–170. https://doi.org/10.1016/j.rse.2015.10.025

    Article  Google Scholar 

  61. Singleton PL, Maudsley D (1996) Pattern of water extraction by grapevines on two soils in the Waikato, New Zealand. N Z J Crop Hortic Sci 24:415–424. https://doi.org/10.1080/01140671.1996.9513979

    Article  Google Scholar 

  62. Spano D, Snyder RL, Ducec P, Paw UKT (2000) Estimating sensible and latent heat flux densities from grapevine canopies using surface renewal. Agric For Meteorol 104:71–183. https://doi.org/10.1016/S0168-1923(00)00167-2

    Article  Google Scholar 

  63. Su Z (2002) The surface energy balance system (SEBS) for estimation of the turbulent heat fluxes. Hydrol Earth Sci 6:85–99. https://doi.org/10.5194/hess-6-85-2002

    Article  Google Scholar 

  64. Sun L, Gao F, Anderson MC, Kustas WP, Alsina MM, Sanchez S, Sams B, McKee L, Dulaney W, White WA, Alfieri JG, Prueger JH, Melton F, Post K (2017) Daily mapping of 30 m LAI and NDVI for grape yield prediction in. Calif Vineyard 9:1–18. https://doi.org/10.3390/rs9040317

    Article  Google Scholar 

  65. Tang R, Li Z, Sun X (2013) Temporal upscaling of instantaneous evapotranspiration: an intercomparison of four methods using eddy covariance measurements and MODIS data. Remote Sens Environ 138:102–118. https://doi.org/10.1016/j.rse.2013.07.001

    Article  Google Scholar 

  66. Teixeira AH, de C, Bastiaanssen, Bassoi WGM LH (2007) Crop water parameters of irrigated wine and table grapes to supper water productivity analysis in the Sao Francisco river basin, Brazil. Agric Water Manage 94:31–42. https://doi.org/10.1016/j.agwat.2007.08.001

    Article  Google Scholar 

  67. Trambouze W, Bertuzzi P, Voltz M (1998) Comparison of methods for estimating actual evapotranspiration in a row-cropped vineyard. Agric For Meteorol 91:193–208

    Article  Google Scholar 

  68. Twine TE, Kustas WP, Norman JM, Cook DR, Houser P, Meyers TP, Prueger JH, Starks PJ, Wesely ML (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol 103:279–300. https://doi.org/10.1016/S0168-1923(00)00123-4

    Article  Google Scholar 

  69. Van Leeuwen C, Tregoat O, Chone X, Bois B, Pernet D, Gaudillé JP (2009) Vine water status is a key factor in grape ripening and vintage quality for red bordeaux wine. How can it be assessed for vineyard management purposes? J Int des sciences de la vigne et du vin 43:121–143. https://doi.org/10.20870/oeno-one.2009.43.3.798

    Article  Google Scholar 

  70. Whelan BM, McBratney AB, Minasny B (2002) VESPER 1.5 – Spatial prediction software for precision agriculture. In: Robery PC, Rust RH, Larson WE (eds) Precision agriculture, proceedings of the 6th international conference of precision agriculture, ASA/CSSA/SSSA, Madison, Wisconsin

  71. Williams LE, Matthews MA (1990) Grapevines. In: Stewart BA, Nielsen DR (eds) Agronomy monograph #30 irrigation of agricultural crops. ASA-CSSA-SSSA Publishers, Madison, pp 1019–1055

    Google Scholar 

  72. Williams LE, Dokoozlian NK, Wample RL (1994) Grape. In: Shaffer B, Anderson PC (eds) Handbook of environmental physiological of fruit crops. Temperate crops, vol 1. CRC Press, Orlando, pp 83–133

    Google Scholar 

  73. Xia T, Kustas WP, Anderson MC, Alfieri JG, Gao F, McKee L, Prueger JH, Geli HME, Neale CMU, Sanchez L, Alsina MM, Wang Z (2016) Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes. Hydrol Earth Syst Sci 20:1523–1545. https://doi.org/10.5194/hess-20-1523-2016

    Article  Google Scholar 

  74. Yunusa IAM, Walker RR, Guy IR (1997) Partitioning of seasonal evapotranspiration from a commercial furrow-irrigated Sultana vineyard. Irrig Sci 18:45–54. https://doi.org/10.1007/s002710050043

    Article  Google Scholar 

  75. Yunusa IAM, Walker RR, Lu P (2004) Evapotranspiration components form energy balance, sapflow and microlysimetry techniques for an irrigated vineyard in inland Australia. Agric For Meteorol 127:93–107. https://doi.org/10.1016/j.agwat.2011.03.006

    Article  Google Scholar 

  76. Zhang Y, Kang S, Li F, Tong L, Du T (2010) Variation in vineyard evapotranspiration in an arid region of northwest China. Agric For Meteorol 97:1898–1904. https://doi.org/10.1016/j.agwat.2010.06.010

    Article  Google Scholar 

  77. Zhang Y, Kang S, Ward EJ, Ding R, Zhang X, Zheng R (2011) Evapotranspiration components determined by sap flow and microlysimetry techniques of a vineyard in northwest China: dynamics and influence factors. Agric For Meteorol 98:1207–1214. https://doi.org/10.1016/j.agwat.2011.03.006

    Article  Google Scholar 

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Acknowledgements

Authors would like to thank the staff of Viticulture, Chemistry and Enology Division of E&J Gallo Winery for the collection and processing of field data and insight to local irrigation practices. Authors would also like to thank the Borden vineyard staff for logistical support of GRAPEX field and research activities. USDA is an equal opportunity provider and employer.

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Correspondence to Kyle R. Knipper.

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Knipper, K.R., Kustas, W.P., Anderson, M.C. et al. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig Sci 37, 431–449 (2019). https://doi.org/10.1007/s00271-018-0591-y

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