An Effectiveness Evaluation Model for Satellite Observation and Data-Downlink Scheduling Considering Weather Uncertainties

Abstract

Low Earth orbit (LEO) satellites play an important role in human space activities, and market demands for commercial uses of LEO satellites have been increasing rapidly in recent years. LEO satellites mainly consist of Earth observation satellites (EOSs), the major commercial applications of which are various sorts of Earth observations, such as map making, crop growth assessment, and disaster surveillance. However, the success rates of observation tasks are influenced considerably by uncertainties in local weather conditions, inadequate sunlight, observation dip angle, and other practical factors. The available time windows (ATWs) suitable for observing given types of targets and for transmitting data back to ground receiver stations are relatively narrow. In order to utilize limited satellite resources efficiently and maximize their commercial benefits, it is necessary to evaluate the overall effectiveness of satellites and planned tasks considering various factors. In this paper, we propose a method for determining the ATWs considering the influence of sunlight angle, elevation angle, and the type of sensor equipped on the satellite. After that, we develop a satellite effectiveness evaluation (SEE) model for satellite observation and data-downlink scheduling (SODS) based on the Availability–Capacity–Profitability (ACP) framework, which is designed to evaluate the overall performance of satellites from the perspective of time resource utilization, the success rate of tasks, and profit return. The effects of weather uncertainties on the tasks’ success are considered in the SEE model, and the model can be applied to support the decision-makers on optimizing and improving task arrangements for EOSs. Finally, a case study is presented to demonstrate the effectiveness of the proposed method and verify the ACP-based SEE model. The obtained ATWs by the proposed method are compared with those by the Systems Tool Kit (STK), and the correctness of the method is thus validated.

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

“…The sensor planner first acquires a list of satellite sensors that can meet the fundamental observation requirements, such as observation space, time, and parameters. Their expected observation performance is evaluated and compared on the basis of their experiences or those aforementioned evaluation approaches [29][30][31], and the evaluated optimum sensor is selected. However, such a pattern ignores the spatiotemporal characteristics of the flood event, and how these characteristics could be considered and formulated is unclear.…”

“…To pre-evaluate the observation capability in a quantitative manner, Chen and Zhang [29] proposed the dynamic observation capability index (DOCI) model to quantify the observation capability of optical satellite sensors, which can be dynamically applied in various emergencies, such as snowfall and oil spills. Liu et al [30], Zhang et al [31], and Chen et al [32] constructed a series of observation-task-oriented indicators, such as time utilization ratio and observed profit ratio, to quantitatively pre-evaluate the sensor observation capability. Jin et al [33] conducted a quantitative evaluation of the Earth observation satellite sensors' multidimensional observation capabilities (e.g., satellite orbit and spatial resolution) to assess their potential in supporting sustainable development goals.…”

Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response

“…Fan et al (2015) proposed a sensor capability representation model to describe typical remote sensor capabilities for soil moisture detection applications. Zhang et al (2019) proposed a model for evaluating the effectiveness of observations and data downlinks for low-orbiting satellites. Hu et al (2019) constructed the observation capability information association model (OCIAM) for the selection of sensors and their combinations and further proposed the sensor observation capability object field (SOCO-Field) to construct sensor associations for a specific emergent geographical environment observation task (GeoTask) (Hu et al, 2020), and Wang et al (2020) introduced the space-ground maximal coverage model with multiple parameters (SGMC-MP) to complete sensor mission planning.…”

A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism

“…Distributed Satellite Systems (DSS) encompassing several interacting spacecraft are leading the way in applications where monolithic satellites have become obsolete in terms of risk, cost, or even performance [ 5 ]. One typical instance is distributed earth observation satellites (DEOS), which involves fleet of satellites to detect the Earth’s surface and lower atmosphere to obtain information [ 6 ]. Though with more satisfied revisit times, coverage of larger areas and higher resolution, DEOS constellations are facing new challenges, the core of which is how to coordinate the mission plans for numbers of satellites that are generally heterogeneous [ 7 ].…”