Grapevine production worldwide is adversely impacted by climate change, including limited water availability, low-quality or sudden excess of water, and more frequent, severe, and prolonged heatwaves. As a result, grapevine growers require reliable spatial and temporal information on vine water status to adapt practices. This research evaluates the use of Landsat 8 satellite imagery in conjunction with weather data, and a machine learning algorithm (Gradient Boosting Machine) to predict vine water status in large vineyard blocks. The accuracy of predictions was assessed across both space (mapping) and time (forecast) using block-out and date-out cross-validation techniques. The study was conducted over two consecutive growing seasons on a Vitis vinifera, L. cv. Merlot vineyard in Central California. The ground data included measurements of midday stem water potentials, Ψstem and leaf gas exchange (net assimilation, AN and stomatal conductance, gs). Data acquisition was performed in twenty-four experimental units on the same day of the satellite overpasses. The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. The temporal forecast, assed via date-out cross-validation, proves to be more challenging, although the addition of ground data at one single spatial location improves the date-out performances and allows the NRMSE to reach 6.8 % for Ψstem with R2 of 0.90, 53.4 % for gs with R2 of 0.74, and 25.5 % for AN with R2 of 0.78. The findings from this study have important implications for precision viticulture. They provide an assessment of Landsat 8 imagery, coupled with machine learning, as a means for growers to monitor and forecast vine water status at the field scale. The study highlights the importance of the validation method to ensure the proper use and assessment of machine learning models on agriculture data.

中文翻译：

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通过分块和日期输出交叉验证评估机器学习在 Landsat 8 影像中预测葡萄藤水状态时的时空性能


全球的葡萄藤生产受到气候变化的不利影响，包括水资源有限、水质低下或突然过剩，以及更频繁、更严重和更持久的热浪。因此，葡萄种植者需要有关葡萄藤水分状况的可靠空间和时间信息，以适应实践。本研究评估了 Landsat 8 卫星影像与天气数据以及机器学习算法 （Gradient Boosting Machine） 的使用情况，以预测大片葡萄园的葡萄树水分状况。使用草图和日期输出交叉验证技术在空间（映射）和时间（预测）上评估预测的准确性。该研究是在 Vitis vinifera， L. cv.加利福尼亚州中部的梅洛葡萄园。地面数据包括正午茎水势、Ψ茎和叶气体交换（净同化，AN 和气孔导度，gs）的测量。在卫星立交桥的同一天在 24 个实验单元中进行了数据采集。研究结果表明，机器学习在训练测量日期内准确预测葡萄藤水分状态，误差低（NRMSEΨstem = 2.7 %，NRMSEgs = 16.2 %，NRMSEAN = 11.2 %）和高精度（在所有三个测量的预测中 R2 大于 0.8）通过块状交叉验证进行评估。通过日期交叉验证进行的时间预测被证明更具挑战性，尽管在单个空间位置添加地面数据可以提高日期输出性能，并允许 NRMSE 达到 Ψstem 的 6.8 %，R2 为 0.90 的 Ψstem，gs 为 53.4 %，R2 为 0.74，AN 为 25.5 %，R2 为 0.78。 这项研究的结果对精准葡萄栽培具有重要意义。它们提供对 Landsat 8 影像的评估，并结合机器学习，作为种植者在田间规模监测和预测葡萄藤水分状况的一种手段。该研究强调了验证方法对于确保正确使用和评估农业数据机器学习模型的重要性。