Improving the Accuracy of Seasonal Crop Coefficients in Grapevine from Sentinel-2 Data

Le résumé fourni par la source

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (Kc) is a dimensionless factor that converts reference evapotranspiration (ET0) into actual crop evapotranspiration (ETc) and is widely used for irrigation scheduling. The Kc reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m2) relative to vine canopy size (~2 m2). This study aimed to improve grapevine Kc predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the Kc prediction models. Unmixing improved the prediction of seasonal Kc values in Shiraz (R2 of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R2 = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R2 = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of Kc values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture.

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