Validating Leaf Area Index (LAI) and canopy cover estimated from satellite imagery products with ground measurements in plum orchards, Western Cape
Sustainable fruit production increasingly depends on precise monitoring of crop performance, where accurate estimation of the Leaf Area Index (LAI) plays a central role in linking canopy structure to productivity. LAI is not only a key indicator of crop development and yield potential but also a critical input in biophysical and climatic models. However, direct measurement of LAI remains laborious, costly, and impractical for large or remote orchards, making remote sensing an attractive alternative for large-scale monitoring. The specific objectives were: (i) To recommend the best technique for measuring LAI and canopy cover with the LAI-2200 Plant Canopy Analyzer in plum orchards, (ii) To analyze LAI and canopy cover changes over time and during different ages of plum orchards, and different cultivars, and (iii) To validate satellite-derived LAI measurements with ground-based measurements. LAI ground data were collected using the LAI-2000 Plant Canopy Analyzer at the Wellington and Robertson plum orchards, with operators shielded using view caps to minimize sensor interference. Satellite data were sourced from FruitLook and Copernicus. Validation employed statistical metrics including the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Willmott’s Index of Agreement (d). Results showed that a single above-canopy reading effectively captured sky reference, while multiple below-canopy measurements improved precision. Measurements taken facing away from the sun at midday or in the afternoon minimized reflectance errors. ANOVA tests revealed no significant LAI and canopy cover differences between the two sites. In Wellington, FruitLook full-bearing orchards (R² = 0.49) performed better than non-bearing orchards (R² = 0.37), while Copernicus achieved R² = 0.40. In Robertson, correlations were lower (R² = 0.32 and R² = 0.23, respectively), though Fortune showed the highest cultivar correlation (R² = 0.42). Copernicus yielded moderate accuracy (RMSE = 3.54; MAE = 3.42). Mesh nets in orchards reduced measurement precision, contributing to weaker correlations. The study highlights that integrating ground-based and high-resolution satellite data, supported by improved algorithms to distinguish canopy and cover crops, can significantly enhance LAI monitoring accuracy. Such improvements will strengthen precision orchard management and contribute to sustainable fruit production.
Fruitlook, Leaf area index, Copernicus, Plum, Crop development
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