Back to Search
Start Over
Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite
- Source :
- Big Earth Data, Vol 5, Iss 2, Pp 161-181 (2021)
- Publication Year :
- 2021
- Publisher :
- Taylor & Francis Group, 2021.
-
Abstract
- Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.
Details
- Language :
- English
- ISSN :
- 20964471 and 25745417
- Volume :
- 5
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Big Earth Data
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2301a79f56e34fe5948940cf2b28640f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1080/20964471.2021.1912898