Vicarious calibration and validation techniques are important tools to ensure the long-term stability and inter-sensor consistency of satellite sensors making observations in the solar-reflective spectral domain. Automated test sites, which have continuous in situ monitoring of both ground reflectance and atmospheric conditions, can greatly increase the match-up possibilities for a wide range of space agency and commercial sensors. The Baotou calibration and validation test site in China provides operational high-accuracy and high-stability vicarious calibration and validation for high spatial resolution solar-reflective remote-sensing sensors. Two sites, given the abbreviations BTCN (an artificial site) and BSCN (a natural sandy site), have been selected as reference sites for the Committee on Earth Observation Satellites radiometric calibration network (RadCalNet). RadCalNet requires sites to provide data in a consistent format but does not specify the required operational conditions for a RadCalNet site. The two Baotou sites are the only sites to date that make spectral measurements for their continuous operation. One of the core principles of RadCalNet is that each site should have a metrologically rigorous uncertainty budget which also describes the site’s traceability to the international system of units, the SI. This paper shows a formalized metrological approach to determining and documenting the uncertainty budget and traceability of a RadCalNet site. This approach follows the Guide to the Expression of Uncertainty in Measurement. The paper describes the uncertainty analysis for bottom-of-atmosphere and top-of-atmosphere reflectance in the spectral region from 400 to 1000 nm for the Baotou sites and gives preliminary results for the uncertainty propagating this to top-of-atmosphere reflectance.

The panchromatic and multispectral (PMS1/PMS2) sensors designed with one panchromatic band and four multispectral bands are two optical cameras on-board the Gao Fen 1 (GF-1) satellite launched on April 26, 2013. The spatial resolution of PMS1/PMS2 are 2 and 8 m of pan and multispectral, respectively. The vicarious calibration of GF-1/PMS was performed by China Centre for Resource Satellite Data and Application, and the calibration coefficients were released once a year. Due to the lack of on-board calibrator, the on-orbit radiometric performance evaluation of the PMS sensors is very important for the further quantitative application of the data. To evaluate the radiometric performance of PMS1 sensor on-board GF-1 satellite, this paper attempts to calibrate PMS1 sensor against the well-calibrated OLI sensor on-board Landsat-8. An instrumented sand site was used, which is located in the national high resolution remote sensing comprehensive calibration site, Baotou, China. The cross-calibration coefficients of PMS1 on October 5, 2015 and August 20, 2016 are calculated and compared with the official calibration coefficients published in 2015 and 2016. The results show that the cross-calibration of PMS sensor using other sensors is necessary, due to the low updating frequency of calibration coefficient for GF-1/PMS sensor. The uncertainty estimation results show that the total uncertainties associated with the cross-calibration of Landsat-8/OLI and GF-1/PMS sensors over a desert site are within 5.5%.

To regularly evaluate the optical payload performance (geometric, radiometric, and spatial resolution) and the data quality for high-resolution airborne and satellite imaging systems, two new permanent targets (the knife-edge target and the fan-shaped target) made of gravel and with the advantages of year-round availability, lower maintenance operations, and a long lifetime were established in the Academy of Opto-Electronics Baotou site in China. The spectral properties of these targets are investigated in this study. Note that the anisotropy factor at 550 nm for the white gravel is approximately 6%, 12.5% 16.5%, 17.5%, 11.5%, and 5% at the principal plane for the observer zenith angle of 60 deg, 50 deg, 40 deg, 30 deg, 20 deg, and 10 deg (backscatter), respectively. The corresponding value for the gray gravel is 20.8%, 24.8%, 29.4%, 23.8%, 13%, and 3.7%, respectively, and 62.8%, 65.7%, 59.2%, 40.3%, 22.3%, and 9.0%, respectively, for the black gravel. The anisotropy of the black gravel is larger than that of the gray and white gravel areas. The nonuniformity of the target reflectivity is within 2.5%. Furthermore, a calibration for the optical payloads onboard the GF-1 satellite is performed with the knife-edge target, and the uncertainty analysis demonstrates that the uncertainty for this calibration is <2.12% when the relative error for the surface reflectance measurement, the aerosol optical depth, and the total column water vapor are approximately 1%, 10%, and 10%, respectively.

Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral and viewing angle differences adequately; the method is applied to the FY-3C/Visible Infrared Radiometer (VIRR), taking the Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) as a reference. The results show that the relative difference between the two sets increases from January to May 2014, and becomes lower for the data on 24 July, 11 September, and 16 September, within approximately 10%. This phenomenon is caused by the updating of the calibration coefficients in the VIRR datasets with results from a vicarious method on June 2014. After performing an approximate estimation of the uncertainty, it is demonstrated that this calibration has a total uncertainty of 5.5%–6.0%, which is mainly from the uncertainty of the Bidirectional Reflectance Distribution Function model.

Accurate inversion of land surface temperature (LST) from remote sensing data is an essential and challenging topic for earth observation applications. This paper successfully retrieves the LST from FY-3C/VIRR data with split-window method. With the simulated data, the algorithm coefficients are acquired with root mean square errors lower than 1.0 K for all subranges when view zenith angle (VZA) <; 30° and the water vapor content (WVC) <; 4.25 g/cm ² , as well as those in which the VZA <; 30° and the LST <; 307.5 K. In addition, a detailed sensitivity analysis is carried out. The analysis result indicates that the total LST uncertainty caused by the standard error of the algorithm, the uncertainties of land surface emissivity and WVC, and the instrument noise would be 1.22 K and 0.94 K for dry and wet atmosphere, respectively. Furthermore, LST retrieval method is applied to the visible and infrared radiometer measurements over the study area covering the geographical latitude of 31.671°N to 44.211°N and longitude of 10.739°W to 1.898°E, and the derived LST is cross-validated with Terra/MODIS LST product. The preliminary validation result shows that the split-window method determines the LST within 2.0 K for vegetation and soil areas.

Optical satellite sensor generally suffers from drifts and biases relative to their pre-launch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transfer radiometric values between satellite sensor via stable target. Since the data quality of GF-4/VNIR data seriously depends on vicarious calibration, which is performed once every year, cross-calibration is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly has similar degradation trend as a cosine function during 2017 and 2018, varying from 0.0002 to 0.00065. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of reference sensors, the uncertainty of MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%-6.64%.