School of Geography and Environmental Science, University of Southampton, Southampton, UK; Natural Resources Department, College of Agricultural Engineering Sciences, University of Sulaimani, Sulaimani, Kurdistan Region, Iraq.
School of Geography and Environmental Science, University of Southampton, Southampton, UK.
Sci Total Environ. 2023 Apr 15;869:161716. doi: 10.1016/j.scitotenv.2023.161716. Epub 2023 Jan 20.
Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.
低水平的农业生产力与粮食不安全、贫困和其他社会经济压力的持续存在有关。实时以高空间分辨率绘制和监测农业动态和生产对于确保粮食安全和制定政策干预措施至关重要。然而，在一些干旱和半干旱地区，由于输入数据集通常稀缺，并且由于安全挑战而受到限制，因此准确估计产量可能具有挑战性。本研究探讨了 Sentinel-2 卫星传感器衍生数据、地形和气候变量如何在低数据设置下作为协变量，使用统计模型在干旱和半干旱地区的农场水平上准确建模和预测小麦作物产量，以伊拉克苏莱曼尼亚省为例。我们开发了一种协变量选择程序，该程序评估了协变量之间的相关性及其与小麦作物产量的关系。在后一种情况下，使用回归样条研究了潜在的非线性关系。在协变量和作物产量之间不存在实质性的非线性关系以及残差空间自相关的情况下，我们以 10 m 分辨率拟合了贝叶斯多元线性回归模型来模拟和预测作物产量。在所测试的协变量中，我们的结果表明，在农场水平上，作物产量与生长季节内的平均累积 NDVI、平均海拔、季节末、平均最高温度和平均季节初之间存在显著关系。对于样本内预测，我们估计模型的 R 值为 51%，而对于样本外预测，该值为 41%，这两者都表明了合理的预测性能。对于样本外预测，计算得出的均方根误差为 69.80，小于作物产量的标准差 89.23，这进一步表明模型通过降低预测变异性而表现良好。除了作物产量估计外，该模型还以 10 m 分辨率生成了不确定性指标。总体而言，这项研究表明，Sentinel-2 数据可用于在干旱和半干旱地区扩大对作物产量的田间测量。此外，环境协变量可以增强模型的预测能力。该方法可能适用于具有类似环境的其他地区，特别是在冲突地区，以增加农业统计数据的可用性。
