Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals

  • William A. WhiteEmail author
  • Maria Mar Alsina
  • Héctor Nieto
  • Lynn G. McKee
  • Feng Gao
  • William P. Kustas
Original Paper


Accurate ground-based measurements of leaf area index (LAI) are needed for validation of remote sensing-based retrievals used in models estimating plant water use, stress, carbon assimilation and other land surface processes. Several methods for indirect LAI estimation with the Plant Canopy Analyzer (PCA, LAI-2200C, LI-COR, Lincoln, NE, USA) were evaluated using destructive (direct) leaf area measurements in three split-canopy vineyards and one double-vertical vineyard in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A method with the sensor facing the canopy, and four readings occurring evenly across the interrow space, had a coefficient of determination (R2) of 0.87 and relative root mean square error (RRMSE) of 16%, when compared to direct LAI measurements via destructive sampling. A previously used method, with the sensor facing down-row, showed lower correlation to direct LAI (R2 = 0.75, RRMSE = 33%) and underestimation which was mitigated by removing the outer sensor rings from analysis. A PCA method is recommended for rapid and accurate LAI estimation in split-canopy vineyards, though local calibration may be required. The method was tested within small units of ground surface area, which compliments high-resolution datasets such as those acquired by small unmanned aerial vehicles. The utility of ground-based LAI measurements to validate remote sensing products is discussed.



We 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 during GRAPEX IOPs. This project would not have been possible without the cooperation of vineyard staff and managers including Ernie Dosio, Joe Larranaga, Jose Botello, and Amanpreek Virk, for logistical support of GRAPEX field and research activities. USDA is an equal opportunity provider and employer.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Anderson MC, Neale CMU, Li F et al (2004) Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens Environ 92:447–464. CrossRefGoogle Scholar
  2. Arnó J, Escolà A, Vallès JM et al (2013) Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precis Agric 14:290–306. CrossRefGoogle Scholar
  3. Bergqvist J, Dokoozlian N, Ebisuda N (2001) Sunlight exposure and temperature effects on berry growth and composition of Cabernet Sauvignon and Grenache in the central San Joaquin Valley of California. Am J Enol Vitic 52:1–7Google Scholar
  4. Bréda NJJ (2003) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J Exp Bot 54:2403–2417. CrossRefPubMedGoogle Scholar
  5. Clevers JGPW, Kooistra L, van den Brande MMM (2017) Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens 9:405. CrossRefGoogle Scholar
  6. Costanza P, Tisseyre B, Hunter JJ, Deloire A (2004) Shoot development and non-destructive determination of grapevine (Vitis vinifera L.) leaf area. S Afr J Enol Vitic 25:43–47. CrossRefGoogle Scholar
  7. Delegido J, Verrelst J, Alonso L, Moreno J (2011) Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors (Switzerland) 11:7063–7081. CrossRefGoogle Scholar
  8. Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2016) Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation. Renew Sustain Energy Rev 56:246–260. CrossRefGoogle Scholar
  9. Döring J, Stoll M, Kauer R et al (2014) Indirect estimation of leaf area index in VSP-trained grapevines using plant area index. Am J Enol Vitic 65:153–158. CrossRefGoogle Scholar
  10. Fuentes S, Poblete-Echeverría C, Ortega-Farias S et al (2014) Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods. Aust J Grape Wine Res 20:465–473. CrossRefGoogle Scholar
  11. Gao F, Anderson MC, Kustas WP, Wang Y (2012) Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference. J Appl Remote Sens 6:63554. CrossRefGoogle Scholar
  12. Garrigues S, Shabanov NV, Swanson K et al (2008) Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands. Agric For Meteorol 148:1193–1209. CrossRefGoogle Scholar
  13. Gower ST, Kucharik CJ, Norman JM (1999) Direct and indirect estimation of leaf area index, f(APAR), and net primary production of terrestrial ecosystems. Remote Sens Environ 70:29–51. CrossRefGoogle Scholar
  14. Grantz D, Williams L (1993) An empirical protocol for indirect measurement of leaf area index in grape (Vitis vinifera L.). HortScience 28:777–779Google Scholar
  15. Herrmann I, Pimstein A, Karnieli A et al (2011) LAI assessment of wheat and potato crops by VENµS and Sentinel-2 bands. Remote Sens Environ 115:2141–2151. CrossRefGoogle Scholar
  16. Hicks SK, Lascano RJ (1995) Estimating of leaf area index for cotton canopies using the LI-COR LAI 2000 plant canopy analyzer. Agron J 87:458–464CrossRefGoogle Scholar
  17. Johnson LF (2003) Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard. Aust J Grape Wine Res 9:96–101. CrossRefGoogle Scholar
  18. Johnson LF, Pierce LL (2004) Indirect measurement of leaf area index in California North Coast vineyards. HortScience 39:236–238Google Scholar
  19. Knipper KR, Kustas WP, Anderson MC et al (2018) Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig Sci. CrossRefGoogle Scholar
  20. Kobayashi H, Ryu Y, Baldocchi DD et al (2013) On the correct estimation of gap fraction: how to remove scattered radiation in gap fraction measurements. Agric For Meteorol 174–175:170–183. CrossRefGoogle Scholar
  21. Kustas WP, Anderson MC (2009) Advances in thermal infrared remote sensing for land surface modeling. Agric For Meteorol 149:2071–2081. CrossRefGoogle Scholar
  22. Kustas WP, Anderson MC, Alfieri JG et al (2018) The grape remote sensing atmospheric profile and evapotranspiration experiment (GRAPEX). Bull Am Meteorol Soc 99:1791–1812. CrossRefGoogle Scholar
  23. Lang ARG, Xiang Y (1986) Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agric For Meteorol 37:229–243. CrossRefGoogle Scholar
  24. LI-COR (2016) LAI-2200C Plant Canopy Analyzer instruction manual. LI-COR, Lincoln, NE, USAGoogle Scholar
  25. López-Lozano R, Casterad MA (2013) Comparison of different protocols for indirect measurement of leaf area index with ceptometers in vertically trained vineyards. Aust J Grape Wine Res 19:116–122. CrossRefGoogle Scholar
  26. López-Lozano R, Baret F, García de Cortázar-Atauri I et al (2009) Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies: the case of vineyards. Agric For Meteorol 149:1307–1316. CrossRefGoogle Scholar
  27. Myneni RB, Hoffman S, Knyazikhin Y et al (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231CrossRefGoogle Scholar
  28. Nieto H, Kustas WP, Torres-Rúa A et al (2018) Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrig Sci. CrossRefGoogle Scholar
  29. Ollat N, Fermaud M, Tandonnet JP, Neveux M (1998) Evaluation of an indirect method for leaf area index determination in the vineyard: combined effects of cultivar, year and training system. Vitis 37:73–78Google Scholar
  30. Orlando F, Movedi E, Coduto D et al (2016) Estimating leaf area index (LAI) in vineyards using the pocketLAI smart-app. Sensors (Switzerland) 16:1–12. CrossRefGoogle Scholar
  31. Ryu Y, Nilson T, Kobayashi H et al (2010) On the correct estimation of leaf area index: does it reveal information on clumping effects? Agric For Meteorol 150:463–472. CrossRefGoogle Scholar
  32. Semmens KA, Anderson MC, Kustas WP et al (2016) Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens Environ 185:155–170. CrossRefGoogle Scholar
  33. Sommer K, Lang A (1994) Comparative analysis of two indirect methods of measuring leaf area index as applied to minimal and spur pruned grape vines. Aust J Plant Physiol 21:197. CrossRefGoogle Scholar
  34. Sun L, Gao F, Anderson MC et al (2017) Daily mapping of 30 m LAI and NDVI for grape yield prediction in California vineyards. Remote Sens 9:317. CrossRefGoogle Scholar
  35. Watson DJ (1947) Comparative physiological studies on the growth of field crops. Ann Bot 41:41–76. CrossRefGoogle Scholar
  36. Weiss M, Baret F, Smith GJ et al (2004) Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agric For Meteorol 121:37–53. CrossRefGoogle Scholar
  37. Welles JM, Norman JM (1991) Instrument for indirect measurement of canopy architecture. Agron J 83:818. CrossRefGoogle Scholar
  38. Williams LE, Ayars JE (2005) Grapevine water use and the crop coefficient are linear functions of the shaded area measured beneath the canopy. Agric For Meteorol 132:201–211. CrossRefGoogle Scholar
  39. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82. CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2018

Authors and Affiliations

  • William A. White
    • 1
    Email author
  • Maria Mar Alsina
    • 2
  • Héctor Nieto
    • 3
  • Lynn G. McKee
    • 1
  • Feng Gao
    • 1
  • William P. Kustas
    • 1
  1. 1.US Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing LaboratoryBeltsvilleUSA
  2. 2.E & J Gallo Winery Viticulture ResearchModestoUSA
  3. 3.Efficient Use of Water in Agriculture ProgramIRTA, Research and Technology Food and AgricultureLleidaSpain

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