Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK; Soil and Water Science Department, Faculty of Agriculture, University of Sulaimani, Kurdistan Region, Iraq.
Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK.
Sci Total Environ. 2018 Feb 1;613-614:250-262. doi: 10.1016/j.scitotenv.2017.09.057. Epub 2017 Sep 12.
Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R=0.70 compared to the date of MODIS EVI (Avg R=0.68) and a NPP (Avg R=0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach.
利用遥感数据进行农作物产量估计和预测已经得到了广泛研究，但在干旱和半干旱地区，此类信息通常较为匮乏。在这些地区，气候因素（如降雨量）的年际变化以及人为因素（如内战）给粮食安全带来了重大风险。因此，需要建立一个可操作的农作物产量估计和预测系统，以帮助决策者尽早估计潜在的粮食供应情况。该研究将美国宇航局 MODIS 的数据与官方作物统计数据相结合，开发了一种基于经验回归的模型，用于预测伊拉克冬小麦和大麦的产量。研究中探索了代表作物整个生长季生产力的遥感指数，以找到与作物产量的最佳相关性。测试了三种不同的遥感指数以及与作物物候相关的信息，以预测省级作物产量，并使用“留一年法”验证了其结果。尽管测试了几种方法并进行了广泛的时空分析，但本文描述了使用当前卫星低分辨率数据进行年度作物产量估算的困难。然而，对作物产量进行更精确的估计还是可行的。当前研究的结果表明，最大植被指数（VI）日期可以提供最准确的作物产量预测，其平均 R 值为 0.70，而 MODIS EVI（平均 R 值为 0.68）和 NPP（平均 R 值为 0.66）日期的预测效果则相对较差。当使用 NDVI、EVI 和 NPP 预测冬小麦和大麦的产量并与官方统计数据进行比较时，相对误差范围分别为-20%至 20%、-45%至 28%和-48%至 22%。研究表明，与简单的种植面积相比，遥感指数可以更准确地描述和预测作物产量，而种植面积被视为评估所提出方法的零模型。
