Watching Trade from Space: Nowcasting and Spatial Extrapolation of Port-Level Maritime Trade Using Satellite Imagery

TL;DR

This paper presents a satellite imagery-based method for nowcasting and spatially extrapolating port-level maritime trade, demonstrating accuracy in the US and detecting trade shifts post-2022 sanctions.

Contribution

It introduces a novel approach combining satellite data and port features to measure maritime trade, robust to signal manipulation and effective for detecting trade reorientation.

Findings

Model achieves strong out-of-sample accuracy for US ports.

Percentage changes in trade are reliably estimated.

Detects trade shifts in Russian ports after 2022 sanctions.

Abstract

Satellite data are increasingly used to measure economic activity, yet port-level trade remains largely unmeasured from space. This paper combines synthetic aperture radar imagery, nighttime lights, and port characteristics to measure monthly port-level maritime trade using only publicly available data. The model achieves strong out-of-sample accuracy for U.S. ports, with satellite signals and port attributes playing complementary roles. While absolute levels are difficult to extrapolate beyond the training domain, percentage changes are reliably recovered, as we confirm through a leave-one-region-out exercise and Monte Carlo simulation. Applying the framework to Russian ports after the 2022 sanctions, we detect shifts consistent with trade reorientation toward the Far East. The approach complements AIS-based methods by remaining robust to strategic signal manipulation.