标题: Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States
作者: Wang, YM (Wang, Yumiao); Zhang, Z (Zhang, Zhou); Feng, LW (Feng, Luwei); Du, QY (Du, Qingyun); Runge, T (Runge, Troy)
来源出版物: REMOTE SENSING 卷: 12 期: 8 文献号: 1232 DOI: 10.3390/rs12081232 出版年: APR 2 2020
摘要: Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R-2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R-2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.
作者关键词: Winter wheat; yield prediction; machine learning; multi-source data; CONUS
地址: [Wang, Yumiao; Zhang, Zhou; Feng, Luwei; Runge, Troy] Univ Wisconsin, Biol Syst Engn, Madison, WI 53706 USA.
[Wang, Yumiao; Feng, Luwei; Du, Qingyun] Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Peoples R China.
[Du, Qingyun] Wuhan Univ, Key Lab GIS, Minist Educ, Wuhan 430079, Peoples R China.
[Du, Qingyun] Wuhan Univ, Key Lab Digital Mapping & Land Informat Applicat, Natl Adm Surveying Mapping & Geoinformat, Wuhan 430079, Peoples R China.
[Du, Qingyun] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Peoples R China.
通讯作者地址: Zhang, Z (通讯作者)，Univ Wisconsin, Biol Syst Engn, Madison, WI 53706 USA.
电子邮件地址: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org; email@example.com; firstname.lastname@example.org
版权所有 © 武汉大学资源与环境科学学院