As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m⁻², NRMSD = 64–84%, bias = −0.17–0.89 g m⁻²). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m⁻², NRMSD = 52–73%, bias = 0.03–0.42 g m⁻²) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties.

With increasing use of satellite-derived data in climate and Earth monitoring, the importance of reliable and traceable radiometric and spectral information is key. Due to the difficulties of maintaining instrument calibration post-launch, vicarious calibration sites play a vital part in ensuring the stability and interoperability of satellite sensor data.RadCalNet, the Radiometric Calibration Network established through the Committee on Earth Observation Satellites Working Group on Calibration and Validation (CEOS-WGCV), provides a network of, currently four, instrumented ground reference sites providing users with bottom and top-of-atmosphere (BOA and TOA) reflectance measurements every 30 minutes in 10 nm spectral intervals and for nadir view. (For all sites, more detailed spectral information and off-nadir reflectances can be obtained from site owners). It is a key aspect of RadCalNet that the sites document their traceability to the International System of Units (SI) and that they provide traceable uncertainties associated with individual observations. These documents and uncertainties are peer reviewed by the RadCalNet working group.  Each RadCalNet site provides ground reflectance observations that are propagated to TOA through a centralised processing system. RadCalNet has over 300 active users who value the available information.Gobabeb, in Namibia, is one of these four sites, given the reference GONA. GONA was the first site that was established as a new RadCalNet site (the other sites were pre-existing) and the location was determined from a global survey to find suitable sites, primarily due to spatial uniformity and the probability of suitable atmospheric conditions, such as clear skies. With an automatic radiometric station, this site continuously collects atmospheric data and surface radiance measurements. These are then processed to ground spectral reflectance and provided with uncertainties to the RadCalNet processor which propagates values to TOA.Due to the limitations of the instrument used for autonomous measurements, recent fieldwork has been carried out in this location to acquire additional hyperspectral data to maintain the quality of the site products. In addition, further site characterisation was conducted to prepare a best location for a new site nearby that is being developed under the HYPERNETS project. This paper presents both the RadCalNet site and the results of the recent fieldwork.