Evaluating Earth-Observing Satellite Sampling Effectiveness Using Kullback-Leibler Divergence

This work presents an objective, repeatable, automatic, and fast methodology for assessing the representativeness of geophysical variables sampled by Earth-observing satellites. The primary goal is to identify and mitigate potential sampling biases attributed to orbit selection during pre-Phase A mission studies. This methodology supports current incubation activities for a future Planetary Boundary Layer observing system by incorporating a sampling effectiveness measure into a broader architectural study. The study evaluates the effectiveness of 20 satellite configurations for observing convective storm activity in the Southwestern U.S. during the North American Monsoon (NAM) season. The primary design variables are the number of satellites, orbit type (sun-synchronous or inclined), and Local Time of Ascending Node (LTAN). Using Kullback-Leibler (KL) divergence to assess observational representativeness and Kernel Density Estimation (KDE) to estimate probability density functions, the study quantifies the discrepancy between observed and ground truth storm features. Results indicate that a two-satellite sun-synchronous system with an 8:00 PM LTAN, achieved the lowest KL divergence, signifying the most representative observation of storm clusters. In contrast, single-satellite configurations, particularly those with late-night LTANs (e.g., 12:00 AM), demonstrated significantly higher KL divergence. The study concludes that dual-satellite configurations in sun-synchronous orbits with evening LTANs outperform single-satellite and inclined configurations in capturing representative convective storm activity. Keywords: Earth-Observing Satellites; Sampling Effectiveness; Kullback-Leibler Divergence; Observational Representativeness; Monsoon