Traditional planning algorithms used in satellite constellations and other sensor webs typically assume a static reward grid where observing each ground point provides a constant and typically uniform value. However, remote sensing of dynamic events on Earth’s surface requires a dynamic reward grid. This in turn requires a reactive planning approach that replans as needed when the reward grid changes. In this paper, we present a simple reactive planning strategy that builds upon existing work in the satellite observation planning field, and we examine its ability to observe dynamic events on Earth’s surface with a heterogeneous satellite constellation. We compare the approach to traditional non-reactive planning strategies. We also test the sensitivity of the reactive planning approach to two parameters - the timeliness of the information about events occurring, and the number of observation types required to complete a heterogeneous observation.

Human modification of the landscape affects total suspended solids (TSS) concentrations in water. The quantitative extent of these changes remains poorly understood, partly because of the challenges associated with observing TSS dynamics in inland waters over large scales. While many current missions and sensors provide usable data to estimate inland water quality (e.g. Landsat series, VIIRS, Sentinel-2), future missions present the opportunity to increase transferability and accuracy of TSS estimation. Here we degrade assumed ideal spectral data to evaluate the optimal data quality for TSS retrieval using an optical sensor configuration. We also perform wavelet analysis and a river size distribution analysis to study temporal and spatial data quantity requirements, respectively. We find that while the highest resolution data always gives the best retrieval accuracy, some factors are more essential in TSS estimation than others and can simplify mission design. Specifically, fine hyperspectral resolution is key in improving retrieval accuracy and a finer spatial resolution allows exponentially more river surface area to be observed. A revisit period of approximately 5 days or less best captures TSS pulse events, such as floods. Understanding the optimal mission specifications for observing inland water quality, especially TSS, will assist in developing and proposing future optical satellite missions.

This work explores a new concept of operations for observation of Earth events by a satellite sensor web that is able to reason about its capabilities and plan observations based on detected or requested events on Earth’s surface. An intelligent agile satellite sensor web is shown to produce more than twice the number of observations of a nadir-looking sensor web.

Monitoring of short-lived and highly-dynamic processes and events such as floods or forest fires has gained an increasing interest in Earth Observation, particularly as global climate change is affecting these processes. The observation of such dynamic events is often limited by the response time of human operation of Earth-Observing satellites or UAVs.To address this bottleneck, this paper presents a Modified Asynchronous Consensus Constraint-Based Bundle Algorithm (MACCBBA) for observation task allocation in Sensor Web mission concepts for Earth Observation. This algorithm allows for the decentralized allocation of observation tasks amongst a network of Satellites and UAVs based on recently measured data processed on board, or on messages received from other sensors or from the ground. This algorithm is also capable of reaching a feasible plan in a dynamic communications network such as the ones present in some Sensor Web mission concepts and allows for complex temporal constraints and dependencies between tasks to model the value of near-simultaneous co-observations by complementary or synergistic sensors.

This article explores the tradespace for a constellation of heterogeneous smart satellites intended to measure soil moisture using a combination of L and P band radars, radiometers, and reflectometers. Orbit inclination, repeat cycle, number of satellites, and number of planes were treated as input variables to create a set of architectures for evaluation. Attempting to optimize multiple output variables (cost, average revisit time, maximum revisit time, and percent coverage) results in a complex tradespace with suitable options at various cost caps. Therefore, several cost ranges are examined to find the best constellation for a given cost cap. It was found that a relatively simple constellation of three satellites in one plane offers acceptable performance at a low cost. This preliminary submission shows results for a homogeneous constellation, while the final paper will include satellites with various instrument configurations.

We describe a suite of scalable software methods and frameworks to helps schedule payload operations of a large constellation, with multiple payloads per and across spacecraft, such that the collection of observational data and their downlink, constrained by the constellation constraints (orbital mechanics), resources (e.g., power) and subsystems (e.g., attitude control), results in maximum science value for a selected use case. Constellation topology, spacecraft and ground network characteristics can be imported from design tools or existing constellations and can serve as elements of an operations design tool. Our framework includes a science simulator to inform the scheduler of the predictive value of observations or operational decisions. Autonomous, realtime re-scheduling based on past observations needs improved data assimilation methods within the simulator.