## 1 Introduction  
Global change has already caused severe co-epidemics in rice, including rice dwarf, rice blast, glume blight, and sheath blight [1; 2]. These threats may result in serious deterioration of grain yield and quality [3; 4; 5]. Traditionally, manual scouting has been the only way to detect and discriminate crop pests and diseases, but these investigations are expensive and time-consuming [6]. Real-time characterization, identification, and classification of different diseases are necessary to mitigate the problems associated with diseases infestations monitoring and pesticide overuse. Satellite-derived multi-spectral imagery is a feasible method for disease detection and assessment [7]. Satellite-based imagery is routinely captured and represents an independent, affordable source of data for large-scale monitoring of crop diseases [8; 9; 10]. This development has made precision field observations possible for large areas, which helps agricultural administrations determine whether to implement price regulation or to provide financial subsidies to damaged regions. Additionally, knowing the spatial extent of damage caused by crop diseases is vital to agricultural insurance companies. In practice, the use of satellite-derived imagery to monitoring co-epidemic diseases at sub-field scales must address two constraints. Firstly, different diseases can cause similar stresses and symptoms (e.g., discoloration, wilting, and rot), which can result in confusion for multiple disease detection using a single-date satellite imagery. Secondly, different objects with similar spectral properties are affected by a mixed pixel effect from moderate resolution sensors [11]. Thus, canopy structural characteristics and the biological effects induced by different diseases often vary at fine spatial scales (<5 m). Furthermore, disease discrimination and mapping using conventional coarse- to moderate-resolution (\(\geq\)15 m) satellites (e.g., Landsat OLI-8, Sentinel-2), are too coarse to capture the effects caused by crop diseases on an agroecological system [12]. One approach to overcome that has recently been developed to overcome such limitations is the use of multi-temporal hyperspectral or multi-spectral satellite data. Hyperspectral data with a moderate spatial resolution are capable of partly overcoming the limitation of mixed pixels, but the high-cost and data redundancy in time-series analysis limits their use in crop stress monitoring. Alternatively, constellations of very high spatial resolution (VHSR) nano-satellites are composed of a series of small, compact sensor units (\(\sim\)10 kg) that are typically cheap and replaceable. These nano-satellites can fundamentally change spatial analysis methods for crop damage mapping [13]. They can also enhance the opportunity for agricultural monitoring and damage assessment. For instance, the pixels at metric resolutions are directly related to the pathological appearance of crop diseases. Additionally, the information gathered on within-field variability in growing conditions and disease infestations is important for precision agriculture. Planet Labs Inc. (www.planet.com) based in San Francisco, United State, operates the largest nano-satellite constellation system, with a satellite flock termed \"PlanetScope\" that collects multi-spectral imagery at a 3-m spatial resolution with four bands, including blue, green, red, and NIR. [PERSON] et al. [14] employed a data mining approach to build a set of rule-based regression models that relate Planet imagery to atmospherically corrected Landsat-8 Normalized Difference Vegetation Index (NDVI), and successfully applied the method for a desert agricultural landscape in Saudi Arabia. [PERSON] et al. [15] used the PlanetScope scenes before and after the earthquake for observation of land surface displacements; [PERSON] et al. [16] assessed and compared the biomass predictor potential of vegetation indices derived from the Sentinel-2, RapidEye, and PlanetScope satellite systems.