Hashimoto et al. (2026) Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles

Identification

• Journal: Earth system science data
• Year: 2026
• Date: 2026-01-15
• Authors: Hirofumi Hashimoto, Weile Wang, Taejin Park, Sepideh Khajehei, Kazuhito Ichii, Andrew Michaelis, Alberto Guzman, Ramakrishna R. Nemani, M. S. Torn, Koong Yi, Ian G. Brosnan
• DOI: 10.5194/essd-18-397-2026

Research Groups

• NASA Ames Research Center, Moffett Field, CA, USA
• Department of Applied Environmental Science, California State University – Monterey Bay, Seaside, CA, USA
• Bay Area Environmental Research Institute, Moffett Field, CA, USA
• Center for Environmental Remote Sensing, Chiba University, Chiba-shi, Chiba, Japan
• Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
• Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL, USA
• Institute for Sustainability, Energy and Environment, University of Illinois Urbana-Champaign, Urbana, IL, USA

Short Summary

This paper introduces the GeoNEX Coincident Ground Observations (GeCGO) dataset and GeoNEXTools, providing user-friendly subsets of high-frequency geostationary satellite data over 1586 ground observation sites across the Americas to facilitate studies of diurnal land-atmosphere interactions and integration with ground and polar-orbiting satellite data.

Objective

• To introduce the GeoNEX Coincident Ground Observations (GeCGO) dataset and the accompanying GeoNEXTools software.
• To demonstrate the applications of GeCGO, including its integration with other satellite data and its use for comparisons with observations from ground-based networks for terrestrial ecosystem studies.

Study Configuration

• Spatial Scale: 10 km × 10 km areas surrounding 1586 network sites across the Americas (primarily within GOES-16 coverage). GeoNEX data are tiled into 6° by 6° with spatial resolutions ranging from 0.005° to 0.02°, corresponding to approximately 500 m to 2 km at nadir.
• Temporal Scale: Geostationary satellite observations are available as frequently as every 5–10 minutes (GeoNEX data typically between 10 and 15 minutes), enabling the study of diurnal dynamics. Time series examples span multiple years (e.g., 2018, 2019, 2020).

Methodology and Data

• Models used:
  • Phase-only correlation (POC) method for georectification.
  • Multi-Angle Implementation of Atmospheric Correction (MAIAC) for surface reflectance retrieval and Aerosol Optical Depth (AOD) estimation.
  • Ziggy automated processing software for science data analysis pipelines.
• Data sources:
  • Geostationary Satellites: GOES-16/17/18 Advanced Baseline Imager (ABI), Himawari-8/9 Advanced Himawari Imager (AHI), Geo-KOMPSAT-2A (GK-2A) Advanced Meteorological Imager (AMI).
  • Ground Observation Networks: GeCGO includes data from 1586 sites across 14 networks in the Americas, predominantly AmeriFlux, AERONET, and PhenoCam.
  • Polar-orbiting Satellites (for comparison/integration): Terra/Aqua MODIS, ECOSTRESS, VIIRS.
  • Ancillary Data: Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM).
  • Software: GeoNEXTools (an open-source R package developed to mimic MODISTools functionality).

Main Results

• The GeCGO dataset successfully provides high-frequency geostationary satellite data subsets (Level-1G Top-Of-Atmosphere reflectance/brightness temperature, Level-2 surface reflectance, land surface temperature, solar radiation, AOD) in user-friendly CSV and JSON formats, compatible with ORNL TESViS Subset data.
• GeoNEXTools facilitates downloading and manipulating GeCGO data, enabling multi-sensor, spatial, and time-series analyses of diurnal variations in biophysical and meteorological variables (e.g., Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), downward shortwave radiation, AOD).
• Comparisons with AmeriFlux data revealed significant relationships between mean annual Gross Primary Production (GPP) and both Top-Of-Atmosphere (TOA) and surface Vegetation Indices (VIs). Surface VIs showed stronger correlations (NDVI: r = 0.83; NIRv: r = 0.81) with annual GPP than TOA VIs (NDVI: r = 0.73; NIRv: r = 0.64).
• Validation against AERONET data indicated that GeCGO Level-2 AOD products generally overestimated AERONET AOD, particularly for low AOD values (<0.3).
• Time-series comparisons with PhenoCam Green Chromatic Coordinate (GCC) demonstrated that GeCGO NDVI effectively tracks seasonal phenology, with observed discrepancies in the early growing season for deciduous sites attributed to changes in leaf traits and canopy structure, while evergreen sites showed good alignment.

Contributions

• Development and documentation of the novel GeoNEX Coincident Ground Observations (GeCGO) dataset, which provides high-frequency geostationary satellite data subsets over 1586 ground observation sites in a standardized, accessible format.
• Creation of GeoNEXTools, an open-source R package, to simplify the download and analysis of GeCGO data, providing functionality similar to the widely used MODISTools.
• Facilitation of synergistic use of geostationary data with ground observations and polar-orbiting satellite data, enabling more accurate studies of diurnal land-atmosphere interactions and ecosystem processes.
• Addressing the challenges of large data volumes and inconsistent formats of raw geostationary satellite data, making it more readily usable for the Earth science community, particularly as polar-orbiting satellite missions like MODIS approach their end.

Funding

• NASA Earth eXchange (NEX)
• NASA’s Earth Science Research from Operational Geostationary Satellite Systems (grant no. NNH19ZDA001N-ESROGSS)
• Japan Society for the Promotion of Science (JSPS), KAKENHI JP22H05004
• JSPS Core-to-Core Program (JPJSCCA20220008)
• US Department of Energy Office of Science, Office of Biological and Environmental Research (contract number DE-AC02-05CH11231) for AmeriFlux Management Project support

Citation

@article{Hashimoto2026Subsets,
  author = {Hashimoto, Hirofumi and Wang, Weile and Park, Taejin and Khajehei, Sepideh and Ichii, Kazuhito and Michaelis, Andrew and Guzman, Alberto and Nemani, Ramakrishna R. and Torn, M. S. and Yi, Koong and Brosnan, Ian G.},
  title = {Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles},
  journal = {Earth system science data},
  year = {2026},
  doi = {10.5194/essd-18-397-2026},
  url = {https://doi.org/10.5194/essd-18-397-2026}
}