Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak Normalized Difference Vegetation Index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the Southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We developed open-source tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance.

翻译：天气条件可显著改变作物和牧场状态，进而影响全球个体的收入与粮食安全。基于卫星的遥感技术为区域和全球尺度的植被与气候变量监测提供了有效手段。由卫星观测衍生的年度峰值归一化植被指数（NDVI）与作物发育、牧场生物量及植被生长密切相关。尽管已有多种机器学习方法被开发用于短时间范围（如提前一个月）的NDVI预测，但长期预测方法（如提前一年的植被状况预测）尚属空白。为填补这一空白，我们开发了一种两阶段机器学习模型，以美国西南部四角地区为试验场，对高分辨率网格的提前一年峰值NDVI进行预测。在第一阶段，我们识别了包括降水量和最大蒸汽压亏缺在内的关键气候属性，并构建了能够捕捉气候属性与NDVI关系的广义并行高斯过程模型。在第二阶段，我们利用至少早于NDVI预测月份一年的历史数据对这些气候属性进行预测，进而将其作为输入以预测每个空间网格的峰值NDVI。我们开发的开源工具在整体NDVI和网格级NDVI的年度预测中均优于现有方法，可为农户和牧场主提供可提前一年制定行动计划的有效信息。