We describe the development of the Spaceport Throughput Analysis Resource (STAR), which evaluates Kennedy Space Center spaceport launch throughput. STAR integrates simulation and limited rescheduling, using a constraint programming model to check constraints and reschedule events. The outputs of STAR are launch delays, and the constraint violations leading to those delays, that can be used to inform infrastructure investments to reduce future delays. At STAR's core is a constraint program representing a limited horizon scheduling problem used to identify resource constraint violations, and revise schedules in the presence of unexpected events that disrupt the launch schedule. We describe the design and implementation of STAR using illustrative examples, and describe performance results on use cases showing how increased launch rates stress KSC infrastructure.

We present a novel Mixed Integer Linear Program formulation that produces optimal plans for a constellation of remote sensing satellites. The generalized formulation is applied to an operational NASA constellation to improve wildfire danger prediction. The planner generates integrated data collection and downlink plans for multiple agile satellites with limited storage capacity, minimum energy requirements, and temporal constraints. Observation targets and modes are associated with science rewards. The planner maximizes the aggregate rewards collected for all observations on all satellites. Our generalized model for integrated data collection and downlink uses a novel interval-based abstraction called Data Cycles, without time-indexed variables. Data cycles organize the multitude of observation and downlink opportunities from 1 second granularity into sequences of data collection and downlink intervals. Experiments using large-scale real-world data yield optimal 24-hr plans for an eight satellite constellation, which capture 99% of the ~23,000 available targets and 99.9% of available science rewards.

We develop and verify a space-based, distributed, adaptive intelligent, responsive New Observing System (NOS) to improve wildfire response decisions by monitoring and forecasting fuel flammability and wildfire spread and providing on-demand fire danger and burnt area maps. We use Global Navigation Satellite System Reflectometry (GNSS-R) as the NOS, informed by improvements to existing frameworks - D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) and WRFx (Weather Research and Forecasting Fire Spread Model). Five new products are developed/enhanced using GNSS-R data from CYGNSS (7-sat NASA mission) and Spire Global (commercial fleet) and assimilated into WRFx, which improves existing USGS fire danger and LANDFIRE fuel layers products. These products are expected to inform observation planning and fire management via an observation value framework and a fire forecast reporter that we develop. Adaptive intelligence to dynamically task the observing (satellites) and planning (ground stations) assets, and synchronization between them, is achieved using novel Monte Carlo Tree Search (MCTS) based planner.

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

In this paper we develop a Convolutional Long Short-term memory (ConvLSTM) model, a time series deep learning neural network, to predict soil moisture, with an add-on module of passive microwave (radiometer) soil moisture retrieval using the Tau omega model. We incorporate antecedent observations, landscape properties, and forcing factors such as precipitation, landcover, clay fraction, and brightness temperature in the prediction scheme. A regularization Monte Carlo Dropout layer is added to the network to remove stochasticity and avoid overfitting during the training phase. This dropout layer also provides a Bayesian approximation to quantify uncertainty during forecasting. The model is validated at four Soil Moisture Active Passive (SMAP) Cal/Val locations using performance metrics such as Root Mean Square Error (RMSE) and Bias to evaluate effectiveness of the proposed method. This model is developed as a component of the Science Simulator within the Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions (D-SHIELD) project.

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.

D-SHIELD is a suite of scalable software tools that 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. D-SHIELD will include 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.