**4. Discussion**

Remote sensing data has the characteristics of spatial continuity and rich information, which facilitates the acquisition of crop growth and environmental information, and provided a basis for crop pest monitoring [8,47]. This study explored the potential of spectral VIs and meteorological information related to disease occurrence to monitor wheat yellow rust infestation on a regional-scale.

### *4.1. Performance of Spectral Vegetation Indices in Wheat Yellow Rust Discrimination*

VIs can reflect the biophysical and biochemical change of crops, and can be used for detection and identification of plant diseases [37,48]. Yellow rust primarily infects wheat leaves, causing green fading and deformation of leaf tissue, thereby significantly changing the chlorophyll content and biomass [23]. We selected VIs with highly sensitive yellow rust discrimination during the wheat milking stage based on single- and two-stage remote sensing images. The Sentinel-2 satellite has rich red-edge information that are significant for crop growth status and stress monitoring [22,23]. In particular, REDSI consists of red edges and bands and was proposed by Zheng for monitoring wheat yellow rust, particularly during the filling stage [12,23]. In this study, REDSI and VARIgreen were more important for wheat yellow rust discrimination among the single-stage indices; PSRI1, NREDI1, and

NDVIre1 were most sensitive wheat yellow rust among the two-stage indices. This is primarily related to the destruction of the tissue structure of leaf cells and the decrease of leaves chlorophyll content under yellow rust disease stress, resulting in the shift of the spectrum on the red edge [22,49]. PSRI1 can be used to assess the crop pigment content and status [10]. Moreover, the band combination of nPSRI1, nNREDI1, and nNDVIre1 contains red-edge information that can capture changes in physiological and biochemical parameter, and better eliminate the effects of growth factors compared with single-stage vegetation index models (classification accuracy is 5.2% higher, Table 4) [23]. Here, the optimal model (i.e., that using the two-stage vegetation indices) captured changes caused by yellow rust disease with a classification accuracy of 78.9%.

### *4.2. Performance of Meteorological Data in Wheat Yellow Rust Discrimination*

The propagation, spread, and infection of pathogen spores require suitable environmental conditions (such as, precipitation, humidity, and temperature). Wheat yellow rust disease occurs in high humidity and low-temperature environments. Favorable climate conditions such as warm winters and heavy rainfall in early spring are external causes of wheat yellow rust occurrence and epidemics in Shaanxi Province [26]. In this study, the average relative humidity (RHU\_04, RHU\_05) and average precipitation (PRE\_04, PRE\_05) were sensitive to yellow rust discrimination (Figure 5b). Moreover, the TEM in Shaanxi Province reaches a suitable range for the incidence of wheat yellow rust in April and May. The study area belongs to the winter breeding region of wheat yellow rust in China [50]. That is, the yellow rust pathogen in this area infects wheat during the winter, making WIN less important in the monitoring of wheat yellow rust than RHU and PRE [26]. However, as wheat yellow rust disease is an air-borne bacterium, WIN can provide important information for forecasting. In summary, our results confirm that meteorological information can provide crop disease monitoring, which is consistent with the conclusions of Yuan et al. [8].

### *4.3. Performance of Wheat Yellow Rust Monitoring Classification Algorithms*

Among LDA, SVM, and ANN, the SVM algorithm exhibited the best performance for distinguishing healthy and yellow rust-infested wheat, with a classification accuracy of 73.7–84.2%. The SVM classifier is based on the threshold discriminant rule and maps the samples to appropriate feature space. Some researchers have also shown that the SVM is superior to LDA in remote sensing classification or extraction in plants [40,41,51]. For example, Yue et al. reported that SVM-based models achieve higher classification accuracies than those using LDA in wheat yellow rust monitoring on leaf scale [40].

In terms of classification accuracy, SVM outperformed the ANN classifiers by 5.2–10.5% in different feature spaces. These results differ from those of Raczko et al. who found that ANN performed better than the SVM model [18]. However, ANNs are more difficult to use and optimize, and require many parameters. The number of samples in this study was limited, and ANN requires a large number of parameters to set the initial value of the network topology, weights, and thresholds, thereby making it difficult to optimize the model [18]. Compared with ANNs, the SVM algorithm can solve classification problems for nonlinear and small sample situations, and avoid the neural network structure selection and local minima problem [52]. Overall, considering that monitoring and positioning crop disease on a regional scale are more complicated and challenging than at the canopy and on leaf scales, the classification accuracy achieved in this study (73.7–84.2% based on SVM classifier) is acceptable.

Although the current classification accuracy is lower than that obtained based on airborne hyperspectral images (for example, Zhang et al. used deep convolutional neural network to identify wheat yellow rust based on airborne hyperspectral images with an accuracy of 85.0% [53]), it meets the practical demands of disease monitoring and management. It is majorly based on airborne hyperspectral images, for which fine spectral resolution enables more abundant spectral information to be extracted and analyzed, which may lead to a certain improvement in the accuracy of disease mapping. Many researchers have

used medium- and high-resolution satellite images to monitor crop disease. Chemura et al. used spectral indices to identify coffee leaf rust infection based on Sentinel-2 satellite data, with the discrimination accuracy of 82.5% [22]. Yuan et al. used the crop growth index (GNDVI and VARIred-edge) and environmental characteristics to monitor crop disease and pests based on the Wordview2 and Landsat 8 satellite, and proved that the accuracy (82.0%) of models combining VIs and environmental characteristics are better than those of traditional monitoring models that only rely on spectral information [8]. However, despite the significant potential for crop disease monitoring, we should optimize the parameters of the methods to build more robust and reasonable models under the condition of enough samples, and improve the accuracy of crop disease monitoring for practical applications.

In this study, adding monthly average meteorological data to the remote sensing monitoring of crop diseases, we established an effective remote sensing monitoring model for wheat yellow rust. However, crop disease occurrence is also the result of environmental factors, the amount of pathogen, crop planting landscape patterns, and farmland managemen<sup>t</sup> [4,29]. Therefore, future work should integrate more multi-source data (remote sensing and non-remote sensing data) with well-characterized mechanisms and high stability to further improve crop disease monitoring and forecasting. Moreover, due to the influence of weather and manpower, the sample size of wheat yellow rust in this study was small. In the future, we will collect wheat yellow rust data from large areas in different years to verify and improve the wheat yellow rust monitoring model. Furthermore, we will attempt to effectively utilize the complementary features of meteorological data (for example, various types of meteorological data and 10-day average meteorological data), terrain features, and remote sensing data to establish a collaborative scheme for forecasting crop disease at an early stage.

The rapid and large-scale monitoring of crop disease and pests relieves huge pressure on plant protection personnel. It is a weapon to prevent and control disease, promote healthy development of agriculture, and achieve the goal of sustainable agricultural development. In addition, this will contribute to eradicating hunger, achieving food security, improving nutrition and promoting sustainable agriculture as outlined in the United Nations Sustainable Development Goals.
