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

Authors: White, William - Alex; ALSINA, M. - E & J Gallo Winery; NIETO, H. - Institute De Recerca I Tecnologia Agroalimentaries (IRTA); McKee, Lynn; Gao, Feng; Kustas, William - Bill

Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2018
Publication Date: 8/5/2018
Citation: White, W.A., Alsina, M., Nieto, H., McKee, L.G., Gao, F.N., Kustas, W.P. 2018. Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals. Irrigation Science. https://doi.org/10.1007/s00271-018-0614-8.
DOI: https://doi.org/10.1007/s00271-018-0614-8

Interpretive Summary: Leaf Area Index (LAI; total one-sided leaf area per unit ground surface area) is an important parameter in describing plant canopy processes such as radiation interception, evapotranspiration, and carbon uptake as well as an indicator of crop productivity. In grapevines, methods for accurate and rapid LAI retrieval are needed in plant growth and water use models to determine vine conditions, and provide useful information for growers in their decision-making. Several methods for indirect LAI estimation with the Plant Canopy Analyzer (PCA) were evaluated using destructive (direct) leaf area measurements in 3 split-canopy vineyards and 1 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 4 readings occurring evenly across the interrow space, gave the highest correlation and lowest error compared to the direct measurements of LAI. This indirect method is being used to evaluate both satellite and small unmanned aerial vehicles (UAVs) remote sensing-based LAI retrieval algorithms for vineyards. Refinements to the remote sensing algorithms for vineyard LAI is expected to result in robust estimates at field and landscape scales.

Technical Abstract: 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 3 split-canopy vineyards and 1 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 4 readings occurring evenly across the interrow space, showed high coefficient of determination (R^2) and low relative root mean square error (RRMSE) to directly measured LAI (R^2 = 0.87, RRMSE = 16%). A previously-used method, with the sensor facing down-row, showed lower correlation to direct LAI (R^2 = 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 (UAVs). The utility of ground-based LAI measurements to validate remote sensing products is discussed.