Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning

TL;DR

This work shows how the performance of Dynamic Targeting systems can be improved by using supplementary data streamed from geostationary satellites that provide lookahead information up to 35 minutes ahead of time rather than the 1 minute latency from an onboard lookahead sensor, and introduces a hierarchical planning approach.

Abstract

The Dynamic Targeting (DT) mission concept is an approach to satellite observation in which a lookahead sensor gathers information about the upcoming environment and uses this information to intelligently plan observations. Previous work has shown that DT has the potential to increase the science return across applications. However, DT mission concepts must address challenges, such as the limited spatial extent of onboard lookahead data and instrument mobility, data throughput, and onboard computation constraints. In this work, we show how the performance of DT systems can be improved by using supplementary data streamed from geostationary satellites that provide lookahead information up to 35 minutes ahead of time rather than the 1 minute latency from an onboard lookahead sensor. While there is a greater volume of geostationary data, the search space for observation planning explodes exponentially with the size of the horizon. To address this, we introduce a hierarchical planning approach in which the geostationary data is used to plan a long-term observation blueprint in polynomial time, then the onboard lookahead data is leveraged to refine that plan over short-term horizons. We compare the performance of our approach to that of traditional DT planners relying on onboard lookahead data across four different problem instances: three cloud avoidance variations and a storm hunting scenario. We show that our hierarchical planner outperforms the traditional DT planners by up to 41% and examine the features of the scenarios that affect the performance of our approach. We demonstrate that incorporating geostationary satellite data is most effective for dynamic problem instances in which the targets of interest are sparsely distributed throughout the overflight.

Figures (5)

• Figure 1: DT uses environmental information collected by a lookahead sensor to intelligently plan primary sensor observations. Figure adapted from dt-jais.
• Figure 2: Example simulated orbit trajectories for each type of simulation data. The red line represents an orbit trajectory for the MODIS Cloud Mask data, while the blue line represents an orbit trajectory for the IMERG data.
• Figure 3: Examples of the CA (top left), CAPD (top right), CART (bottom left), and SH (bottom right) utility models applied to parts of the MODIS Cloud Mask and IMERG datasets. Axes denote pixels, which have 1 km and 10 km spatial resolutions for MODIS-based and IMERG-based utility models, respectively. All images cover an area of about 260 km $\times$ 260 km. Note that the spatial resolution of the MODIS Cloud Mask dataset is finer than that of the IMERG dataset, which is why the SH utility model example appears coarser.
• Figure 4: A visualization of the primary sensor slewing capability. The right edge of the figure represents where the lookahead sensor is sensing new data. The satellite is traveling towards the right edge of the figure. The primary sensor is current pointed at nadir, which is indicated by the red dot. The yellow area shows the reachable set of observation targets that the primary sensor can be slewed to within one cycle (4 seconds) of slewing time. The turquoise blue area shows the entire primary sensor range, which is determined by the primary sensor's $15\degree$ maximum off-nadir. The purple area represents observation targets that have been sensed by the lookahead sensor but could not be reached during this cycle regardless of the initial position of the primary sensor.
• Figure 5: Average predictive performance of the GOES Clear Sky product, with the MODIS Cloud Mask as ground truth.