Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

中文翻译：

---

OCO-2和MODIS-EVI的太阳诱导叶绿素荧光监测美国中西部大豆和玉米产量空间变化的能力

卫星太阳诱导的叶绿素荧光（SIF）已成为监测植物生长条件和生产力的有前途的工具。但是，与广泛使用的卫星植被指数（VI）（例如，中分辨率成像光谱仪（MODIS）的增强植被指数（EVI））相比，卫星SIF数据在区域范围内预测作物产量的能力仍不清楚。 。此外，几乎没有尝试验证新的轨道碳观测站2（OCO-2）卫星的SIF产品是否可以用于区域玉米和大豆的产量估算。使用深度神经网络（DNN）方法，本研究调查了OCO-2 SIF，MODIS EVI和气候数据估算美国玉米带县县级玉米和大豆产量的能力。生长高峰期的月均SIF和最大SIF和MODIS EVI与玉米和大豆单产显示相似的相关性。以SIF作为预测因子的DNN能够很好地估算玉米和大豆的单产，但表现不及基于MODIS EVI和基于气候变量的DNN。基于SIF和MODIS EVI的DNN的性能随农作物的区域优势而变化，而基于气候的DNN的空间变异性较小。SIF数据可以为MODIS EVI和气候变量提供有用的补充信息，以改善作物产量的估算。在高峰生长季节（6月至8月），MODIS EVI和气候预测因子（例如VPD和温度）在预测美国中西部12个州的玉米和大豆产量中起着重要作用 结果突出了将卫星和气候来源的数据相结合对作物产量估算的好处。此外，这项研究表明，尽管模型性能略有改善，但仍可能在作物产量预测中添加SIF，这可能是由于现有SIF产品的局限性所致。这项研究的框架可以结合不同类型的遥感数据（例如OCO-2 SIF和MODIS EVI）和气候数据，应用于不同地区和其他类型的农作物，以利用深度学习进行农作物产量的预测。