Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of the required processing time pose challenges to real-time applications. NDVI prediction emerges as an innovative solution to address these limitations. It allows for proactive decision-making by providing accurate estimates, enabling farmers and land managers to plan essential agronomic activities such as irrigation, fertilization, and pest control, based on anticipated future conditions. This study introduces an Artificial Neural Network (ANN) model incorporating NDVI, Normalized Difference Water Index (NDWI), temperatures, and precipitation as predictive variables. The model employs a novel time series slicing algorithm, Boosting Adaptive Time Series Slicer (BATS), to enhance the input training dataset's variability, presenting the model with a broader range of examples. A 2-Bidirectional Long Short-Term Memory (LSTM) forecasting model was developed to predict future NDVI values over short and medium-term horizons. The study area used to train, test and validate the ANN corresponds to a diverse landscape of cultivated corn fields located in Piemonte (NW-Italy). Results showed that NDVI future estimates were accurate; considering three time horizons for predictions (5, 10, and 15 days) RMSE values resulted to be 0.028, 0.038 and 0.050, respectively. Additionally, ablation tests proved that the most important variable for enhancing the model’s accuracy is the NDWI, and the most useful timesteps are the four most recent ones. To preliminary investigate the capability of the ANN to operate over a wider and different area it was applied over the entire Europe, using the LUCAS dataset as reference map to locate corn fields. Results show RMSE of 0.062, 0.083 and 0.105 for the 5, 10 and 15 days forecasting horizons, respectively. The methodology proposed in this paper can be a possible alternative to more ordinary approaches for NDVI forecasting that nowadays appears to be a fundamental step for a proactive precision agriculture where crop management can be significantly improved. Future developments should explore the use of sequence-to-sequence ANNs to predict the development of multiple spectral indices over multiple crop types simultaneously.

中文翻译：

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使用哨兵 2 图像时间序列通过基于 AI 的方法预测玉米 NDVI

精准农业（PA）利用信息技术、卫星定位数据和遥感彻底改变了作物管理。 PA 应用中的一个关键组成部分是归一化植被指数 (NDVI)，它提供了有关作物活力和健康的宝贵见解。然而，与云层覆盖相关的光学卫星采集的不连续性以及所需处理时间的巨大负载给实时应用带来了挑战。 NDVI 预测作为解决这些限制的创新解决方案而出现。它通过提供准确的估计来实现主动决策，使农民和土地管理者能够根据预期的未来条件规划灌溉、施肥和病虫害防治等基本农艺活动。本研究引入了人工神经网络 (ANN) 模型，将 NDVI、归一化水分指数 (NDWI)、温度和降水作为预测变量。该模型采用了一种新颖的时间序列切片算法——Boosting Adaptive Time Series Slicer (BATS)，来增强输入训练数据集的可变性，为模型提供更广泛的示例。开发了 2-双向长短期记忆 (LSTM) 预测模型来预测短期和中期的未来 NDVI 值。用于训练、测试和验证人工神经网络的研究区域对应于位于皮埃蒙特（意大利西北部）的不同种植玉米田景观。结果表明，NDVI 未来的估计是准确的；考虑三个时间范围的预测（5、10 和 15 天），RMSE 值分别为 0.028、0.038 和 0.050。此外，消融测试证明，提高模型精度的最重要变量是NDWI，最有用的时间步长是最近的四个时间步长。为了初步调查人工神经网络在更广泛和不同区域运行的能力，将其应用于整个欧洲，使用 LUCAS 数据集作为参考地图来定位玉米田。结果显示，5 天、10 天和 15 天的预测范围的 RMSE 分别为 0.062、0.083 和 0.105。本文提出的方法可以作为更普通的 NDVI 预测方法的替代方法，目前 NDVI 预测似乎是主动精准农业的基本步骤，可以显着改善作物管理。未来的发展应探索使用序列到序列的人工神经网络来同时预测多种作物类型的多个光谱指数的发展。