## 1 Introduction  
Locusts are agricultural pests found in many parts of the worldwide [1]. Globally, approximately 50 million km\({}^{2}\) of the total land area is infested by locusts each year, and about one-eighth (1/8) of the world's population is affected by locust plagues [2]. In the last 2500 years, China has suffered from frequent locust plagues; the extent and severity of which have been the highest in the world. Of all locust types, East Asian migratory locusts (AM locusts), which appear suddenly and are migratory in nature, cause the most severe damage [3]. For example, in September 2017, AM locusts wiped out nearly 66 ha of crops in Shandong Province, and in July 2015, an AM locust plague covering an area of nearly 8200 km\({}^{2}\) occurred in the city of Chifeng in the Inner Mongolia autonomous region. Therefore, rapidly and accurately acquiring information concerning the growth conditions, distribution densities, and community features of locusts are important for the timely and effective prevention and control of locust plagues. Conventional locust monitoring systems rely on manual field acquisition of the locust type, instar, and population data obtained from plant protection specialists. This method has numerous shortcomings, including the high cost of data acquisition, low efficiency, limited coverage, inability to monitor some locust-infested areas in the field (e.g., lakes, wetlands, and tidal flats), difficulties in identification (locust instars are generally identified based on the development of their wings; however, third-, fourth-, and fifth-instar locusts do not have notably different wings and thus cannot be effectively distinguished by specialists), and the proneness to identification errors owing to the existence of numerous locust types and morphologies as well as their complex habitats [4]. Additionally, because the locusts can increase rapidly in number and migratory, manual surveys are unable to respond to rapid changes in the locust distribution density and community features. Given these challenges, several researchers have increased their monitoring scales [5; 6]. Specifically, they have shifted from monitoring individual locusts and locust communities on a quadrat scale to monitoring host vegetation and habitats of locusts on a regional scale. These researchers have banked on the advantages of satellite remote-sensing observation technologies (e.g., wide-coverage, high temporal, and spatial resolutions) to make up for the deficiencies of conventional methods [7; 8; 9; 10]; this has resulted in excellent results reported by several researchers [11]. As early as 1986, [PERSON] and [PERSON] [12] proposed combining a locust breeding simulation model and satellite remote-sensing data to extract areas suitable for locust breeding by monitoring changes in the vegetation; they also demonstrated that using this approach could effectively supplement missing ground survey data. [PERSON] et al. [13] applied digital satellite data to detect potential areas of Australian plague locust outbreaks at an early stage in semi-arid regions of Southwest Queensland. [PERSON] et al. [14] produced dynamic greenness maps by developing a colorimetric transformation from SPOT-VEGFATION and moderate resolution imaging spectroradiometer (MODIS) data and used them in day to day monitoring of desert locusts in arid--semi-arid regions. This method was found to be exhibit significant accuracy when employed in summer breeding areas; however, the accuracy decreased when employed in winter breeding areas. Further, researchers noted that the current spatial resolution remains too coarse to resolve complex or fragmented patterns [14]. [PERSON] et al. [15] extracted seasonal characteristics of typical land covers in normalized difference vegetation index, triangular vegetation index, and land surface temperature data from fused Landsat-MODIS surface reflectance imagery data.