Abstract
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.
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Acknowledgements
The authors acknowledge the datasets that support our work are as follows. IAP-CAS FGOALS-f2 S2S dynamical atmospheric prediction outputs were provided by Institute of Atmospheric Physics, Chinese Academy of Sciences. Maize yield data were obtained from U.S. Department of Agriculture. NDVI and EVI data were derived from MOD13A2. Historical observational climate variables were obtained from TerraClimate. SIF data were obtained from GOSIF based on OCO-2. Also thank ArcGIS software provided by ESRI; Python software provided by the Python Software Foundation.
Funding
This research was supported by the National Natural Science Foundation of China (Project No. 41701111), the Fundamental Research Funds for the Central Universities (Project No. CCNU22JC022), the Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences (Project No. 2022KFJJ01), the Guangdong Meteorological Service Science and Technology Research Project (Project No. GRMC2021M01), and the Guangdong Basic and Applied Basic Research Foundation (Project No. 2023A1515240029).
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Yi Luo: Conceptualization, Methodology, Formal analysis, Writing—original draft, Writing—review and editing. Huijing Wang: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing—original draft, Writing—review and editing, Visualization. Junjun Cao: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing original draft, Writing—review and editing, Supervision. Jinxiao Li: Conceptualization, Methodology, Writing—review and editing. Qun Tian: Conceptualization, Methodology, Writing—review and editing. Guoyong Leng: Conceptualization, Methodology, Writing—review and editing. Dev Niyogi: Conceptualization, Methodology, Writing—review and editing.
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The averages of correlations between maize yield in the growing season and Satellite, Climate, and S2S data
SPEI trends (mean in multiyear, in 2012) in March to October, 2012
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Luo, Y., Wang, H., Cao, J. et al. Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought. Precision Agric (2024). https://doi.org/10.1007/s11119-024-10149-6
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DOI: https://doi.org/10.1007/s11119-024-10149-6