Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious calibration is that they can be carried out with traceable and quantifiable accuracy, making them ideal for interconsistency studies of on-orbit sensors. The recent development of automated sites to collect the in situ data has led to an increase in the available number of datasets for sensor calibration. The current work describes the Radiometric Calibration Network (RadCalNet) that is an effort to provide automated surface and atmosphere in situ data as part of a network including multiple sites for the purpose of optical imager radiometric calibration in the visible to shortwave infrared spectral range. The key goals of RadCalNet are to standardize protocols for collecting data, process to top-of-atmosphere reflectance, and provide uncertainty budgets for automated sites traceable to the international system of units. RadCalNet is the result of efforts by the RadCalNet Working Group under the umbrella of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the Infrared Visible Optical Sensors (IVOS). Four radiometric calibration instrumented sites located in the USA, France, China, and Namibia are presented here that were used as initial sites for prototyping and demonstrating RadCalNet. All four sites rely on collection of data for assessing the surface reflectance as well as atmospheric data over that site. The data are converted to top-of-atmosphere reflectance within RadCalNet and provided through a web portal to allow users to either radiometrically calibrate or verify the calibration of their sensors of interest. Top-of-atmosphere reflectance data with associated uncertainties are available at 10 nm intervals over the 400 nm to 1000 nm spectral range at 30 min intervals for a nadir-viewing geometry. An example is shown demonstrating how top-of-atmosphere data from RadCalNet can be used to determine the interconsistency between two sensors.
Radiometric cross calibration of Earth observation sensors is a crucial need to guarantee or quantify the consistency of measurements from different sensors. Twenty desert sites, historically selected, are revisited, and their radiometric profiles are described for the visible to the near-infrared spectral domain. Therefore, acquisitions by various sensors over these desert sites are collected into a dedicated database, Structure d'Accueil des Données d'Etalonnage, defined to manage operational calibrations and the required SI traceability. The cross-calibration method over desert sites is detailed. Surface reflectances are derived from measurements by a reference sensor and spectrally interpolated to derive the surface and then top-of-atmosphere reflectances for spectral bands of the sensor to calibrate. The comparison with reflectances really measured provides an estimation of the cross calibration between the two sensors.
Results illustrate the efficiency of the method for various pairs of sensors among AQUA-Moderate Resolution Imaging Spectroradiometer (MODIS), Environmental Satellite-Medium Resolution Imaging Spectrometer (MERIS), Polarization and Anisotropy of Reflectance for Atmospheric Sciences Couples With Observations From a Lidar (PARASOL)-Polarization and Directionality of the Earth Reflectances (POLDER), and Satellite pour l'Observation de la Terre 5 (SPOT5)-VEGETATION. MERIS and MODIS cali-brations are found to be very consistent, with a discrepancy of 1%, which is close to the accuracy of the method. A larger bias of 3% was identified between VEGETATION-PARASOL on one hand and MERIS-MODIS on the other hand. A good consistency was found between sites, with a standard deviation of 2% for red to near-infrared bands, increasing to 4% and 6% for green and blue bands, respectively. The accuracy of the method, which is close to 1%, may also depend on the spectral bands of both sensor to calibrate and reference sensor (up to 5% in the worst case) and their corresponding geometrical matching.
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