Effective yield forecasting is a key strategy for adaptation when facing food loss to climate variability. Currently, solar-induced chlorophyll fluorescence (SIF) is an emerging remote-sensing index owing to its high relevance to plant photosynthesis, and sensitivity to drought. Despite many studies have focused on drought monitoring and production assessment by SIF, little puts it into practice for in-season yield prediction. In this study, we combined multi-source satellite and meteorological data, especially coupling with subseasonal-to-seasonal (S2S) dynamic atmospheric prediction climate model (IAP-CAS FGOALS-f2), with an addition of SIF, to predict maize yields in the U.S. Corn Belt, based on the developed machine learning dynamical hybrid model (MHCF). By comparison, we found that SIF performed well in the correlation analysis with yield, with average correlations up to 0.719 in August. Then we utilized different algorithms, different models (S2S data for MHCF, climate data for the Benchmark), and different input combinations to train and predict maize yields. All four algorithms using SIF significantly improved prediction performance. S2S + VIs + SIF combination (FGOALS-f2、NDVI、EVI、SIF) can achieve the best performance, while the XGBoost algorithm reached 0.897 of R². With the best combination, it can achieve 4 months before maize harvest (with R² value of 0.85, and RMSE < 13 bu/acre). In 2012, the year had a severe drought, although predictive capability decreased in all the predictions, the models with SIF still maintained robust and improved the prediction (improved R² by 5.92%, and RMSE decreased by 18.08% of XGBoost). According to the study, it can be expected, the combination of MHCF and SIF will play a greater role in subseasonal yield prediction. We also provide an operational proposition of hybrid yield forecasting method to fully integrating climate prediction and machine learning for early notice of crop production losses.

当气候变化导致粮食损失时，有效的产量预测是适应的关键策略。目前，太阳诱导叶绿素荧光（SIF）由于与植物光合作用高度相关且对干旱敏感，是一种新兴的遥感指标。尽管许多研究都集中在 SIF 的干旱监测和产量评估上，但很少将其应用于当季产量预测。在这项研究中，我们结合了多源卫星和气象数据，特别是与次季节到季节（S2S）动态大气预测气候模型（IAP-CAS FGOALS-f2）相结合，并添加了SIF，以预测2019年玉米产量。美国玉米带，基于开发的机器学习动态混合模型（MHCF）。通过比较，我们发现SIF在与收益率的相关性分析中表现良好，8月份平均相关性高达0.719。然后，我们利用不同的算法、不同的模型（MHCF 的 S2S 数据、基准的气候数据）和不同的输入组合来训练和预测玉米产量。使用 SIF 的所有四种算法都显着提高了预测性能。 S2S + VIs + SIF组合（FGOALS-f2、NDVI、EVI、SIF）可以达到最佳性能，而XGBoost算法达到R ² 0.897 。最佳组合可实现玉米提前4个月收获（R ²值为0.85，RMSE < 13 bu/英亩）。 2012年发生严重干旱，尽管所有预测的预测能力均有所下降，但采用SIF的模型仍然保持稳健并改善了预测（XGBoost的R ²提高了5.92%，RMSE降低了18.08%）。研究表明，可以预见，MHCF与SIF的结合将在次季节产量预测中发挥更大的作用。我们还提供了混合产量预测方法的操作建议，以充分整合气候预测和机器学习，以便及早通知作物生产损失。