High-Frequency Mapping of Downward Shortwave Radiation From GOES-R Using Gradient Boosting

Abstract

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at the Earth's surface using the advanced baseline imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite—R Series (GOES-R). The existing GOES-R DSR product (DSRABI) offers hourly temporal resolution and spatial resolution of 0.25°. To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 cloud and moisture imagery product (5 min) and its native spatial resolution of 2 km at nadir. We compared four common ML regression models through the leave-one-out cross-validation algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that gradient boosting regression (GBR) achieves the best performance (R2 = 0.916, RMSE = 88.05 W·m−2) with more efficient computation compared to long short-term memory, which exhibited similar performance. DSR estimates from the GBR model through the ABI live imaging of vegetated ecosystems workflow (DSRALIVE) outperform DSRABI across various temporal resolutions and sky conditions. DSRALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (5-min intervals) with R2 exceeding 0.85 and RMSE = 122 W·m−2. We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.

Keywords

• Ameriflux
• downward shortwave radiation (DSR)
• geostationary satellite
• machine learning (ML)
• SURFRAD