# _remote sensing_
## 4 Discussion

Table 10: Confusion matrix and kappa value for the monitoring results based on KPCA-SVM of the yellow rust in Ningqiang county (China). localized field surveys. Non-invasive methods carried out with the assistance of remote sensing technologies can be fully exploited to assess the current situation of entire regions of interest, evidencing areas under potential risks, and also under different climate change contexts. The mapping of diseased crops based on automatic analysis of remote sensing data could be a valuable support for in situ investigation planning. In fact, the model shown in Section 2.2.2 achieved accurate monitoring of wheat yellow rust, by exploiting the relationship between LAI and LCC and the severity of yellow rust. Such a model, based on proximal sensing hyperspectral data, aims at defining a possible procedure for detecting crop pests and diseases to be applied to hyperspectral satellite images, for which atmospheric and spatial resolution effects should be considered. Satellite imagery allows covering a large area of interest at an affordable cost. The example for the application of remotely sensed images, at different spatial and spectral resolution, for detecting the presence of the Psa disease in kiwifruit trees shows that the accuracy of the classification is mainly depending on the spectral channels rather than on the spatial resolution. In fact, the best results, shown in Table 7, are obtained by using PRISMA hyperspectral images which have 30 m spatial resolution. Of course, the spatial resolution should be compatible with the size of the orchards' field, that is, the spatial resolution of the satellite images exploited here spans from 5 m to 30 m. Further, it should be underlined that the good results obtained by applying a NN based on statistical parameters extracted by vegetation indices could be due to the distribution of kiwi trees. In fact, kiwifruit vines are organized to form a pergola that provides a single plane canopy; therefore, the monitoring is only minimally affected by the characteristics of soil or the presence of grass on the surface around the trees. As matter of the fact, an approach based on NDVI was applied to detect the presence of olive groves affected by Kf [21]. In that case, the selection of the images for carrying out the analysis would have considered the background grass phenology because, in that case, trees are distributed roughly 6 m apart, so that surface cover is visible and can affect the NDVI estimate. This research topic is currently actively growing, achieving new and promising results, in line with climate change scenarios which predict a higher incidence of plant pathogens. In this continuous changing context, further developments should be considered to act timely and prevent the spread of emerging pests. Plant diseases' epidemiology is affected by three factors (the disease triangle): the host plant, the pathogen and the vector, all influenced by environmental conditions. Therefore, further studies are focusing on the vectors and their dynamics, the environmental conditions for their growth and on the detection of the weather patterns that could leverage the development and diffusion of pest outbreaks. Crop disease monitoring from Earth Observation is an important topic in the development of precision agriculture. This paper presents remote sensing monitoring methods and results for wheat yellow rust at multiple scales. Monitoring accuracy appeared first to increase, and then to decrease, with the progression and spread of the disease. The improvement of monitoring accuracy was mainly due to the increase in the proportion of yellow rust spores in the leaf, which provide more spectral change information and can enhance the model's ability to identify the disease. The subsequent decrease in monitoring accuracy may be due to the natural wilting process of leaves brings noise to the spectral features of the disease, and thus reduces the ability of the model to identify yellow rust.