## 1 Introduction
[24] demonstrated that resampled Sentinel-2 sensor data had the potential for discriminating between coffee leaf rust (CLR) infestation severities (healthy, moderate, and severe). [PERSON] et al. [25] pointed out that Sentinel-2A red-edge spectral indices are adequate to discriminate burn severity. [PERSON] et al. [2] assessed the potential of the four Sentinel-2A bands (i.e., blue, green, red, and NIR) with 10 m spatial resolution for cotton root rot detection. Therefore, it is necessary to explore the potential of other spectral bands and VIs of Sentinel-2 in the identification of cotton root rot. Image texture reflects the brightness nature of the image and its spatial arrangement of color, and different objects can be distinguished using different texture features [26]. [PERSON] et al. [27] used thegray-level co-occurrence matrix (GLCM) to extract texture features to identify grains. Texture features are similarly important indicators for crop growth monitoring [28; 29]. When plants are infested by disease, the external characteristics changed significantly, such as morphology and texture. There have been studies using spatial features of images to monitor vegetation diseases. [PERSON] et al. detected round-like crop lesion targets effectively using the improved Hough transform [30]. In addition, some studies show that combining texture features with spectral information have a certain impact on the results of remote sensing image classification [31]. [PERSON] et al. [32] proved that the combination of spectral features and textural features significantly improved the identification accuracy of wheat yellow rust. The use of texture features and the combination of spectral and texture features to identify cotton root rot has not received much attention to date and requires further study. In recent years, many methods and models have been used to select sensitive features to monitor and distinguish crop diseases [33]. The random forest (RF) algorithm, an integrated technology, could handle well large and high dimensional data [34], and is widely used in variable importance ranking. [PERSON] et al. [12] used the RF method to select three important bands in wheat yellow rust detection. [PERSON] et al. [35] proved that the short-wave infrared band is the most important variable to distinguish soybean from two pigweeds using the RF algorithm. Binary logistic regression (BLR) is one of the most commonly used multivariate analysis methods, where the dependent variable is a binary variable that indicates whether an event exists [36]. BLR is a suitable method when the predictor variable has a binary nature, so it has certain potential in identifying crop diseases and insect pests. [PERSON] et al. [14] showed that banana Fusarium will can be identified by VIs extracted from UAV-based multispectral imagery and the BLR method. The overall goal of this study was to explore the potential of spectral indices and textural features of Sentinel-2 imagery and the combination of both for identifying root rot-infested cotton. The specific objectives were to (1) analyze the spectral difference between infested cotton and healthy cotton and identify optimal spectral indices by the RF algorithm; (2) extract the textural features using GLCM from the Sentinel-2 imagery and select optimal textural features using RF; (3) construct BLR models based on different features (spectral indices, textural features, and their combinations) and evaluate their performance for identifying cotton root rot and creating prescription maps.