Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.agrformet.2026.111221 |
| Titel (primär) | Combining sun-induced chlorophyll fluorescence and seasonal climate forecast for 8-day dynamic in-season crop yield prediction |
| Autor | Lu, C.; Leng, G.; Han, L.; Yu, L.; Qiu, J.; Yao, L.; Tu, H.; Liao, X.; Huang, S.; Peng, J.
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| Quelle | Agricultural and Forest Meteorology |
| Erscheinungsjahr | 2026 |
| Department | RS |
| Band/Volume | 385 |
| Seite von | art. 111221 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 |
| Keywords | Solar-induced chlorophyll fluorescence; Reflectance-based vegetation indices; Climate forecast In-season maize yield forecast; Random forest; Northeast China |
| Abstract | Satellite-based solar-induced chlorophyll fluorescence (SIF) is useful for crop yield prediction due to its close relationship with photosynthesis. While previous studies have primarily focused on using SIF for end-of-season yield estimation, its potential for in-season yield prediction remains largely under-explored. Here, we develop a machine learning-based in-season maize yield forecasting system, which dynamically assimilates SIF and observed climate and makes predictions at an 8-day step using seasonal climate forecasts (SCF). A case study is performed in Northeast China (NEC), which produces 40 % of the country’s total maize production. Based on 12 numerical experiments, we found that reliable yield forecasts can be achieved two months before the harvest (tasseling–maturity) in NEC, with an average relative bias of <2.5 %. Compared to traditional vegetation indices, combining SIF with SCF exhibits a better yield prediction performance except for the medium growth stage. The added value of SIF is more pronounced in extreme dry and hot years, especially under the early-medium and medium-late growth phases, driven by its ability to capture the absorbed radiation signal. In the early growth stage, SIF’s outperformance is related to its capability in reflecting the photosynthetic rate. This study highlights a valuable framework for in-season yield prediction by combining SIF with SCF, which can be well extended to other crops and regions for timely yield loss risk warnings. |
| Lu, C., Leng, G., Han, L., Yu, L., Qiu, J., Yao, L., Tu, H., Liao, X., Huang, S., Peng, J. (2026): Combining sun-induced chlorophyll fluorescence and seasonal climate forecast for 8-day dynamic in-season crop yield prediction Agric. For. Meteorol. 385 , art. 111221 10.1016/j.agrformet.2026.111221 |
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