The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery are widely used for crop yield analysis. However, the growth metrics derived from the MODIS NDVI or EVI have so far not been explored and applied to crop yield yet. To the best of our knowledge, this study is the first to design NDVI- and EVI-based crop growth metrics, which biometrically capture the status and trend of crop growth and thus could be more powerful for growth yield management. We developed 19 NDVI- and EVI-based growth metrics, respectively, to monitor crop growth and yield, which is based on a time series of MODIS Terra 16-day 250 m data product from 2000 to 2018. Among the NDVI- and EVI-based vegetation growth metrics (VGM), the maximum (VGMmax), the integrated (VGMinteg), the sum of green-up (VGMsumgrn), the 70 days growth stage (VGM70), 85 days growth stage (VGM85), and 98 days growth stage (VGM98), the sum of 85 days growth stage (VGM85total), and the sum of 98 days growth stage (VGM98total) are mentionable. In this study, we implemented these crop growth metrics for soybean crop yield modeling at Mississippi Delta, Mississippi, USA. Soybean is a major crop cultivated in this region that is consisted of a total of 18 counties with similar agricultural cropping patterns. We observed that NDVI- and EVI-based VGMmax, VGM70, VGM85, VGM98total fitted models best with R-Square about 0.95. Using cross-validation of 80% train and 20% test size, we found NDVI-based VGM85 (e.g., normalized mean prediction error (NMPE) = 0.034) and EVI-based VGMmax (NMPE = 0.033) were the best fit linear yield models for this region. Designing novel crop growth indices based on crop phenological and ecological characteristics, this study further showed NDVI- and EVI-based growth metrics for crop growth monitoring and yield modeling. These growth metrics can be applied to other types of crop monitoring in different climate zones.

从中等分辨率成像光谱仪（MODIS）卫星图像获得的归一化植被指数（NDVI）和增强植被指数（EVI）被广泛用于作物产量分析。但是，到目前为止，尚未探索从MODIS NDVI或EVI得出的生长指标并将其应用于作物产量。据我们所知，这项研究是第一个设计基于NDVI和EVI的作物生长指标的方法，该方法可以生物方式捕获作物生长的状态和趋势，因此对于生长产量管理可能更为有效。我们分别基于2000年至2018年的MODIS Terra 16天250 m数据产品的时间序列，分别开发了19种基于NDVI和EVI的增长指标来监控作物的生长和单产。NDVI-和EVI-基于植被的生长指标（VGM），最大值（VGMmax），集成（VGMinteg），绿色总和（VGMsumgrn），70天增长阶段（VGM70），85天增长阶段（VGM85）和98天增长阶段（VGM98），85天增长阶段的总和（ VGM85total）和98天生长阶段的总和（VGM98total）是可以提及的。在这项研究中，我们在美国密西西比州的密西西比三角洲实施了这些作物生长指标，用于大豆作物产量建模。大豆是该地区主要的农作物，共有18个县，其农业种植方式相似。我们观察到基于NDVI和EVI的VGMmax，VGM70，VGM85，VGM98总体拟合模型的R-Square最好约为0.95。使用80％的训练和20％的测试大小的交叉验证，我们发现基于NDVI的VGM85（例如，标准化平均预测误差（NMPE）= 0.034）和基于EVI的VGMmax（NMPE = 0）。033）是该区域的最佳拟合线性收益率模型。根据作物物候和生态特征设计新颖的作物生长指数，这项研究进一步显示了基于NDVI和EVI的生长指标，用于作物生长监测和产量建模。这些生长指标可以应用于不同气候区域的其他类型的作物监测。