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Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought

文献类型：期刊论文

作者Luo, Yi5; Wang, Huijing5; Cao, Junjun5; Li, Jinxiao4; Tian, Qun3; Leng, Guoyong2; Niyogi, Dev1
刊名PRECISION AGRICULTURE
出版日期2024-05-18
卷号N/A页码:25
关键词Yield forecast IAP-CAS Solar-induced chlorophyll fluorescence Drought Machine learning
ISSN号1385-2256
DOI10.1007/s11119-024-10149-6
英文摘要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 R-2. With the best combination, it can achieve 4 months before maize harvest (with R-2 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 R-2 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.
WOS关键词INDUCED CHLOROPHYLL FLUORESCENCE ; CROP YIELD ; PHOTOSYNTHESIS ; FORECASTS ; MODELS ; SYSTEM ; GROWTH ; WHEAT ; MAPS ; TIME
资助项目National Natural Science Foundation of China ; Institute of Atmospheric Physics, Chinese Academy of Sciences ; U.S. Department of Agriculture ; NDVI
WOS研究方向Agriculture
语种英语
WOS记录号WOS:001226827100001
出版者SPRINGER
资助机构National Natural Science Foundation of China ; Institute of Atmospheric Physics, Chinese Academy of Sciences ; U.S. Department of Agriculture ; NDVI
源URL[http://ir.igsnrr.ac.cn/handle/311030/205577]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Cao, Junjun
作者单位1.Univ Texas Austin, Dept Civil Architecture & Environm Engn, Austin, TX 78712 USA
2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.CMA, Guangzhou Inst Trop & Marine Meteorol, Guangdong Prov Key Lab Reg Numer Weather Predict, Guangzhou 510641, Peoples R China
4.Shanghai Invest Design & Res Inst Co Ltd, Shanghai 200434, Peoples R China
5.Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Yi,Wang, Huijing,Cao, Junjun,et al. Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought[J]. PRECISION AGRICULTURE,2024,N/A:25.
APA Luo, Yi.,Wang, Huijing.,Cao, Junjun.,Li, Jinxiao.,Tian, Qun.,...&Niyogi, Dev.(2024).Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought.PRECISION AGRICULTURE,N/A,25.
MLA Luo, Yi,et al."Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought".PRECISION AGRICULTURE N/A(2024):25.

入库方式： OAI收割

来源：地理科学与资源研究所

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