# _remote sensing_
## 2 Materials and Methods
### Crop Diseases Detection Methods
#### 2.2.2 Wheat Yellow Rust Detection  
To develop efficient wheat yellow rust monitoring models, some field experiments were carried out to acquire remote sensing observation and field survey data of wheat yellow rust (_Puccinia striiformis f.sp. tritici_), which is one of the three major wheat rust diseases.  
At the leaf scale, a series of in situ observations were conducted at the Scientific Research and Experimental Station of Chinese Academy of Agricultural Science (39\({}^{\circ}\)30\({}^{\prime}\)40\({}^{\prime\prime}\) N, 116\({}^{\circ}\)36\({}^{\prime}\)20\({}^{\prime\prime}\) E) in Langfang, Hebei province, China. The images with spectral information of wheat leaves (Figure 4a) were obtained by using a visible and near-infrared (VNIR) hyperspectral imager (Headwall VNIR imagining sensor, Headwall Photonics, Inc., Bolton, MA, USA). The wavelet features that have significant correlation with the severity of wheat yellow rust were extracted, and two monitoring models utilizing linear discriminant analysis (WRSF-LDA) and support vector machine (WRSF-SVM), respectively, were developed using the wavelet features as input (Figure 4b).  
At the canopy scale, the field experiment was designed to acquire hyperspectral UAV images in the development of yellow rust. The experiment was also conducted at the Scientific Research and Experimental Station of Chinese Academy of Agricultural Science. The UAV used in the experiment was an M600 Pro aircraft of Daijang Innovations (DJI), and a Cubert S185 FireIEYE SE hyperspectral imaging camera was used (Figure 5a). Based on UAV hyperspectral images and field survey data, wavelet features and vegetation indices which were sensitive to yellow rust were extracted (Figure 5b). A kernel principal component analysis-based support vector machine model (KPCA-SVM) was proposed to monitor yellow rust by using the above features and indices. This model used kernel function to transform the original nonlinear feature space into a linearly separable space, and then find the occurrence area of wheat yellow rust.  
Figure 3: Infected and not-infected digitalized blocks in the Te Puke region (**A**) and in the Takaka region (New Zealand) (**B**).  
At the regional scale, satellite image data, meteorological data and field survey data were integrated to analyze the relationship between wheat yellow rust and the environment or the host, and then to develop a model for wheat yellow rust monitoring. For this study a satellite-ground synchronous field experiment was carried out in Ningqiang, Shaanxi province, China (Figure 6). The LAI and leaf chlorophyll content (LCC) of wheat infected by wheat yellow rust were measured using a LAI-2200 (Li-Cor Biosciences Inc., Lincoln, NE, USA) and a Dualex (FORCE-A, Inc., Orsay, France). The severity of wheat yellow rust of field samples was calculated based on the rules for monitoring and forecasting wheat yellow rust suggested by the National Plant Protection Department of China (Chinese Standard: GBT 15795-2011). Based on the experimental data and Sentinel-2 images acquired at the same time with the experiment, a wheat yellow rust monitoring model using fast  
Figure 4: Acquisition of hyperspectral images of leaves (**a**) and the method and result of wheat yellow rust identification (**b**).  
Figure 5: Acquisition of UAV hyperspectral images (**a**), and extraction of wavelet features and results of wheat yellow rust monitoring (**b**).  
Fourier transform technology was proposed. This model achieved accurate monitoring of wheat yellow rust through expressing the relationship between LAI and LCC and the severity of yellow rust.