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

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 (DSR
_ABI
) 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 (
R
²
= 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 (DSR
_ALIVE
) outperform DSR
_ABI
across various temporal resolutions and sky conditions. DSR
_ALIVE
agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (5-min intervals) with
R
²
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.