## 1 Introduction  
Cotton root rot, also known as Texas root rot, is caused by the soil-borne fungus _Phymatotrichopsis omnivora_. This disease occurs commonly in the southwestern and south central United States [1]. The leaves of the infested plant first turn yellow and then wilt rapidly, followed by plant death in a few days. Plant infection typically starts around the flowering stage and will continue for the rest of the season. Thus, the disease significantly reduces cotton yield and lowers lint quality [2]. Besides, the infested areas are stable across years, but the spatial distribution is not continuous across the field and the shapes of infested areas are irregular [3]. In early 2015, the Topguard Terra fungicide was registered to control the disease [4]. However, this fungicide is very expensive, so it is more cost-effective for farmers to apply the fungicide only to infested areas. Therefore, it is of great significance to map cotton root rot-infested areas for the management of this disease. Remote sensing can monitor the dynamic changes of vegetation, and has been used as a useful tool for monitoring crop growth and identifying crop diseases, such as wheat yellow rust [5], bacterial leaf blight in rice [6], sclerotinia rot disease on celery [7], citrus greening disease [8]. When plants are infested with diseases, plant water content, pigment, and internal structure will change to varying degrees, resulting in changes in spectral reflectance [9]. [PERSON] et al. [10] analyzed the canopy spectrum of Verticillium wilt of cotton and found that the visible light band of 680-760 nm and the near-infrared (NIR) band of 731-1371 nm had a significant response to the infection of Verticillium wilt. [PERSON] et al. [11] quantitatively extracted and analyzed the hyperspectral wavelet characteristics of canopy of wheat stripe rust and powdery mildew. The results showed that the change of wavelet characteristics at 480, 633, and 943 nm could effectively diagnose and distinguish wheat stripe rust and powdery mildew stress. In addition, spectral features based on vegetation indices (VIs) can enhance and highlight some spectral changes, and are often used as features for crop disease monitoring. [PERSON] et al. [12] proposed the red edge disease stress index (REDSI) based on Sentinel-2 imagery, to detect yellow rust at the canopy and regional scales. [PERSON] et al. [13] showed the VIs using any of the bands related to red edge (740 and 750 nm) or NIR (760 and 850 nm), such as the transformed chlorophyll absorption in reflectance index (TCARI), the green normalized difference vegetation index (GNDVI), VIGreen and so on, had robustness in the ability of discriminating laurel wilt disease in avocado. The VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE) were proved to easily identify the banana Fusarium wilt disease [14]. However, spectral bands and VIs show different sensitivities to different crop diseases, so it is necessary to explore which spectral bands and VIs are suitable for the identification of specific diseases. In practice, historical airborne and high-resolution satellite imagery were used to monitor cotton root rot and its changes [15,16], and NDVI proved to be an effective index for identifying root rot [17,18]. However, the availability of airborne imagery is affected by location and time of year and the cost is high. More recently, satellite imaging systems such as Sentinel-2 with fine spatial, spectral, and temporal resolutions have become freely available [19,20]. Additionally, the Sentinel-2 Multispectral Instrument (MSI) has three red-edge bands that are sensitive to the growth status of vegetation, which provide important information for monitoring the physiological status of plants [21,22]. For example, [PERSON] et al. [23] suggested that the red-edge index from Sentinel-2 was suitable for spatiotemporal gross primary productivity (GPP) assessments. [PERSON] et al.