2.1.2. Disease Survey Data Collection

In this field survey, 58 samples (22 healthy, 36 infected) were investigated from 11 May 2018 to 14 May 2018. Considering the pixels size of remote sensing images, uniformly growing wheat samples were randomly selected with a continuous area of 10 × 10 m, and the severity of disease was surveyed. We selected five representative 1 × 1 m plots (located at the four corners and centers of the 10 × 10 m plots), and the average severity of yellow rust for the five plots was used to represent the disease degree for one sample [8]. The center coordinate of each sample was recorded by a differential global positioning system (GPS) sensor (Trimble GeoXH). The field severity survey and disease index calculation of wheat yellow rust referenced the rule of the National Rules for the Investigation and Forecasting of Crop Diseases (GB/T 15795-1995) [12]. The information of wheat growth, disease incidence, and location are recorded in Appendix A Table A1.

### 2.1.3. Remote Sensing Data Collection and Wheat Planting Area Extraction

Sentinel-2A remote sensing images (processing level 1C) on 2 April 2018 (early onset of disease) and 12 May 2018 (disease outbreak stage) were downloaded from the European Space Agency Sentinels Scientific Date Hub (https://scihub.copernicus.eu/) for the study region [23]. The preprocessing of Sentinel-2A images included atmospheric correction, and clipping. Atmospheric correction was performed using the Sen2cor module (version 2.2.1) within the Sentinel-2 Toolbox, and image mosaic and cropping were implemented in the Sentinel Application Platform software (SNAP, 4.0.2) [23]. In addition, the Sentinel-2A multispectral data carried 13 bands that include three different spatial resolution (Figure 1). For subsequent analysis, the spatial resolutions of the 13 bands were resampled to 10 m using the resampling tool in the software. The large-scale crop disease monitoring was based on the extraction of wheat planting area; therefore, we used the decision tree and multi-temporal phenological information methods, as proposed by Zhang et al. and Xu et al., to extract the planting area of wheat [14,27]. Field survey points were used to verify the accuracy of the extracted wheat area, which reached 94%. This result meets the demand of subsequent remote sensing monitoring of crop diseases.
