## 4 Discussion  
Remote sensing technology has long been used for detecting and mapping crop diseases [52; 53]. The Sentinel-2 MSI sensor with refined spatial, spectral, and temporal resolutions is considered to have great potential in estimating and monitoring the biophysical state of plants for precision agriculture [54; 55; 56]. The biophysical and biochemical crop features, such as canopy structure, leaf pigment, and water content change when plants are stressed by diseases [57]. These changes will alter crop canopy reflectance that can be remotely sensed. Based on the results of this study, infested cotton had higher spectral reflectance in 497-704 nm, exhibited a downward reflectance trend in 740-865 nm, and had higher reflectance at 1614 and 2202 nm, compared with healthy cotton. This is attributed to the fact that the leaves of root rot-infested plants turn yellow and brown and then wilt rapidly, causing the reduction of chlorophyll content and the loss of plant water content. There were significant differences in spectral reflectance between infested cotton and healthy cotton due to these changes. Generally, compared with the reflectance of healthy plants, the chlorophyll of diseased plants is significantly reduced, and the ability to absorb visible light is reduced, resulting in higher reflectance in the visible regions. The plant structure is seriously damaged, which reduces the spectral reflectance in the NIR region. Therefore, the spectral reflectance of diseased plants is higher in the visible regions and lower in the NIR region [58]. The research by [PERSON] et al. also demonstrated that the cotton leaf water content had a significant negative correlation with the reflectance in 460-514, 605-698, 1412-1818, and 1933-2479 nm wavebands, and a significant positive correlation with the reflectance in the 727-1345 nm band [59]. The spectral indices can highlight the difference between diseased and healthy plants, and at the same time reduce the influence of factors such as soil and atmosphere [32]. In this study, 10 spectral indices related to canopy structure, pigment content, water content, and leaf senescence [5; 12; 13; 24; 42; 60; 61] were extracted. Then, the RF algorithm was used and five optimal spectral indices sensitive to cotton root rot were selected to establish models. The OA of the infested category in the classification map for the spectral model was 92.95%, and the PA was 89.73%, indicating that the spectral indices alone are useful for identifying cotton root rot. [PERSON] et al. [17] demonstrated that NDVI derived from airborne images achieved good results for identifying cotton root rot. Then, we used the spectral model to generate prescription maps, the OA and PA reached 90.83% and 79.50%, respectively. The reason for the decrease in PA was that some small root rot-infested areas were not identified by the spectral model, and the area increased with the buffer was added to these areas. RF, which could handle well large and high dimensional data, has the ability to produce a variable importance ranking. This information is valuable for the user to select variables to build simpler, more readily interpretable models [62]. [PERSON] et al. [12] also used the RF method to obtain the optimal bands for the Sentinel-2 sensor and achieved satisfactory result in wheat yellow rust detection. In this study, 16 texture features were extracted from Sentinel-2A images, and seven sensitive texture features were selected using RF to build model for identifying root rot. The OA and PA of the infested category in the classification map for the texture model were 84.81% and 92.55%, respectively. Clearly, the PA for the infested category was higher than that of the spectral model, because more cotton root rot areas could be identified using the texture information. This is attributed to the fact that diseased cotton not only has obvious changes in spectral characteristics, but also differs from healthy cotton in texture characteristics.