A Dynamic Observation Capability Index for Quantitatively Pre-Evaluating Diverse Optical Imaging Satellite Sensors

Abstract

Choosing a capable satellite sensor from a mass of homogeneous sensors to meet the requirements of observation tasks in various application scenarios is one of the basic challenges faced by the collaborative observation in an Earth Observation Sensor Web environment. This paper analyzed five main factors affecting the observation capability of optical imaging satellite sensors. This study proposed the concept of dynamic observation capability index (DOCI), which denotes the continuously changing observation performance of diverse sensors in various applications. A higher DOCI demonstrates stronger observation capability. The DOCI model consists of five subcapabilities: spatial-temporal covering capabilities (Coverage), thematic observation capability (Theme), environmental capability (Radiation), attribute capability (SpaceTime), and quality capability (Accuracy). We discussed the assessment methods on the basis of the DOCI model. To verify the proposed DOCI method, seven sensors (AVHRR/3, BGIS-2000, Hyperion, MERSI-1, MODIS, OLI, and SeaWiFS) were used in four different observation task scenarios: normalized difference vegetation index measurement, snow cover monitoring, oil spill detection, and vegetation-type mapping. The results showed that the changes in the observation capability of different sensors in different scenarios can be effectively assessed and modeled using the DOCI index, thus aiding in the scientific pre-evaluation of homogeneous optical sensors. DOCI can also be used as a quantitative, comprehensive, and all-purpose prior assessment method in web-based sensor planning.