**5. Conclusions**

In this study, multispectral satellite imagery (Sentinel-2A) and meteorological data were used to monitor wheat yellow rust disease based on three classification methods (linear discriminant analysis, a support vector machine, and an artificial neural network) on a regional scale. Five meteorological features (sunshine hours in March (SSD\_03), average relative humidity in April and May (RHU\_04, RHU\_05), and average precipitation in April and May (PRE\_04, PRE\_05)) combined with two-stage vegetation indices using the SVM algorithm were found to be optimal for wheat yellow rust monitoring. In addition, the model for yellow rust monitoring base of two-stage vegetation indices significantly outperformed single-stage vegetation index models, with the overall classification accuracy increasing from 63.2% to 78.9%. Moreover, the addition of meteorological data, which is closely related to yellow rust occurrence, increased the accuracy of the two-stage index SVM model to 84.2%. The proposed model is suitable for rapid, large-scale monitoring and forecasting of biotic (bacterial and fungal disease) stress in crops and offers an effective approach for reducing the impacts of crop disease, including the implications for global food security. In the future, we will consider information from multiple sources to develop further comprehensive and reliable crop disease forecasting models.

**Author Contributions:** Q.Z.: field survey, methodology, writing—original draft. H.Y. and W.H.: conceived and designed the experiments; Y.D.: data collection; H.J.: software and processed the data; C.W., D.L., and L.W.: provided advices to improve manuscript; S.C.: modified the structure of the paper and grammar. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by GDAS' Project of Science and Technology Development (2020GDASYL-20200103004), Guangdong Province Agricultural Science and Technology Innovation and Promotion Project (No.2020KJ102), Guangzhou Basic Research Project (202002020076), National special support program for high-level personnel recruitment (Wenjiang Huang).

**Institutional Review Board Statement:** This study not involving humans.

**Informed Consent Statement:** This study not involving humans.

**Data Availability Statement:** Data sharing is not application to this article.

**Conflicts of Interest:** The authors declare no conflict of interest.
