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This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield.

This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A. The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression. The snow depth in case of seasonal ice was calculated by using parameters of the cross-calibration of data from the MWRI Tb. The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97. The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI. Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline. Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration, the standard deviations decreased significantly in the range of 1.32 K to 2.57 K, and the correlation coefficients were as high as 99%. An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated. Furthermore, the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz, 23.8 GHz, and 36.5 GHz (V polarization), and at 89 GHz (H/V polarization), and were applied to the snow depths retrieval in the Arctic. The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products. Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths, with the standard deviations ranging from 4.19 cm to 4.80 cm.

Water temperature monitoring is important in many scientific studies. This study compares three models of water temperature sensors (Vemco Minilog II, HOBO TidbiT and HOBO Pendant) to test the impo...

As part of the Copernicus program, Sentinel-2 is the optical imaging mission designed for the operational monitoring of land and coastal areas. It offers a unique combination of global coverage with a wide field of view, a high revisit capability (5 days with two satellites), a high resolution and multi-spectral imagery. CNES, the French Space Agency, was involved in the commissioning of both Sentinel-2 satellites and is currently working in collaboration with ESA on their long-term monitoring. This paper reviews all the techniques used to ensure an absolute calibration of the 13 spectral bands to better than 5% (the target is 3%) at TOA level. After a brief description of the mission and its related radiometric calibration scheme, we show how standard vicarious calibration methods based on acquisitions over natural targets (oceans, deserts, and Antarctica during winter) are used to check and improve the accuracy of the absolute calibration coefficients. Finally, the verification scheme, exploiting photometer in-situ measurements over the La Crau plain in France and Gobabeb in Namibia, is described. The paper concludes with a summary that includes spectral coherence, agreement between the results obtained with various calibration methods and temporal evolution.

Remote sensing is a very good alternative technology for managing natural resources as compared to conventional technologies. This paper highlights the various challenges in UAS sensors. Comparison of IRS-P6 and Land Sat sensors is described from accuracy point of view by covering same areas by both the sensors which gives the performance features of both the sensors. Inter sensor calibration is depicted to realize its importance in applications like precision farming, disaster management, etc. requiring multiple dated satellite images.

The infrared sensor (IRS) is a payload on the HJ-1B satellite and includes a thermal infrared band (B08). In order to obtain radiometric calibration coefficients and evaluate annual change, this study performed an analysis covering its 10-year orbital lifetime (2008&ndash, 2017). The cross-calibration of IRS B08 with MODIS was performed using near-simultaneous images over Lake Qinghai, China. The results reveal that the radiometric response of IRS B08 notably changed during its orbital lifetime from year-to-year. The offsets fluctuated more than the gain. The top-of-atmosphere (TOA) radiance obtained by calibration coefficients in this study was generally in agreement with those obtained by onboard calibrator, within an error range of &plusmn, 4.00% from 2008 to 2012. The percent difference compared with field validation was within 1.63%. The difference between IRS and MODIS radiance over field validation sites was within &plusmn, 5%. Approximately a 1% difference occurred between the TOA temperature of IRS and MODIS. The radiometric response of IRS B08 continuously decreased from 2008 to 2013, whereas it fluctuated from 2014 to 2017. Moreover, the DN fluctuated more when the at-aperture radiance was low, although it was more stable at higher radiance.

Since its launch on September 6, 2008, HJ-1A has been in the orbit for 13 years. The CCD1 sensor on the HJ-1A has four reflected solar bands. Since the calibration frequency is limited to the annual site calibration, cross-calibration is an effective method to improve the calibration frequency. In this paper, we use 420 image pairs of HJ-1A CCD1 and Terra MODIS over the Dunhuang test site for gains calculation, where we take MODIS as the reference sensor. The spectral band adjustment factors (SBAFs) for cross-calibration are then calculated to compensate for the spectral mismatch. The cross-calibration results are also validated by the field calibration results. From 2008 to 2019, a total of six campaigns have been cross-calibrated on the same day. The gain difference between the site calibration and cross-calibration is less than 3%. The long-term cross-calibration results further indicate that due to the adjustment of HJ-1A CCD gain state in October 2009, an abrupt change occurred 405 days after launch. After 12 years of on-orbit operation, the attenuation rate has reached 23.51%, 21.89%, 8.11%, and 13.37%, respectively by the end of 2019 based on the cross-calibration results.

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross-calibration procedure involves (i) correction of the MSI data to account for spectral band differences with OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (BRDF) effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF normalization, standard least-squares linear regression is used to determine the cross-calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross-calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include seasonal changes in solar zenith and azimuth angles, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.

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%.

Watershed hydrologic models often possess different structures and distinct methods and require dissimilar types of inputs. As spatially-distributed data are becoming widely available, macro-scale modeling plays an increasingly important role in water resources management. However, calibration of a macro-scale grid-based model can be a challenge. The objective of this study is to improve macro-scale hydrologic modeling by joint simulation and cross-calibration of different models. A joint modeling framework was developed, which linked a grid-based hydrologic model (GHM) and the subbasin-based Soil and Water Assessment Tool (SWAT) model. Particularly, a two-step cross-calibration procedure was proposed and implemented: (1) direct calibration of the subbasin-based SWAT model using observed streamflow data; and (2) indirect calibration of the grid-based GHM through the transfer of the well-calibrated SWAT simulations to the GHM. The joint GHM-SWAT modeling framework was applied to the Red River of the North Basin (RRB). The model performance was assessed using the Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS). The results highlighted the feasibility of the proposed cross-calibration strategy in taking advantage of both model structures to analyze the spatial/temporal trends of hydrologic variables. The modeling approaches developed in this study can be applied to other basins for macro-scale climatic-hydrologic modeling.

Purpose The present study aimed to define the errors in SUV and demonstrate the feasibility of SUV harmonization among contemporary PET/CT scanners using a novel National Institute of Standards and Technology (NIST)-traceable 68Ge/68Ga source as the reference standard. Methods We used 68Ge/68Ga dose calibrator and PET sources made with same batch of 68Ge/68Ga embedded in epoxy that is traceable to the NIST standard. Bias in the amount of radioactivity and the radioactive concentrations measured by the dose calibrators and PET/CT scanners, respectively, was determined at five Japanese sites. We adjusted optimal dial setting of the dose calibrators and PET reconstruction parameters to close the actual amount of radioactivity and the radioactive concentration, respectively, of the NIST-traceable 68Ge/68Ga sources to harmonize SUV. Errors in SUV before and after harmonization were then calculated at each site. Results The average bias in the amount of radioactivity and the radioactive concentrations measured by dose calibrator and PET scanner was −4.94% and −12.22%, respectively, before, and −0.14% and −4.81%, respectively, after harmonization. Corresponding averaged errors in SUV measured under clinical conditions were underestimated by 7.66%, but improved by −4.70% under optimal conditions. Conclusion Our proposed method using an NIST-traceable 68Ge/68Ga source identified bias in values obtained using dose calibrators and PET scanners, and reduced SUV variability to within 5% across different models of PET scanners at five sites. Our protocol using a standard source has considerable potential for harmonizing the SUV when contemporary PET scanners are involved in multicenter studies.

This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.

The major global biogeophysical products obtained from remote sensing are usually composites of 8 or 16 days, and are almost always retrieved from a single sensor. Subsequently, the applications of these products are limited in cases requiring higher temporal frequency. With the increasing number of freely available moderate resolution remote sensing datasets, multi-sensor synergy to increase temporal sampling is warranted. However, radiometric consistency of multi-sensor data is not as good as expected; therefore, first the constituency should be evaluated. A new cross-calibration approach of reflective bands for moderate resolution remotely sensed data is proposed in this paper. The new approach uses a time series of MODIS data from both Terra and Aqua to retrieve both the bidirectional reflectance distribution function (BRDF) and aerosol optical depth (AOD) simultaneously over an invariant desert target. The MODIS retrieved BRDF and AOD are then used to cross-calibrate the medium resolution spectral imager (MERSI) and visible and infra-red radiometer (VIRR) onboard FengYun3-A and B satellites (FY3A/B) and advanced very high resolution radiometer (AVHRR) onboard NOAA-16, 17, and 18. The cross-calibration method is validated in two ways: 1) by comparing with the vicarious calibration; and 2) the top of atmosphere (TOA) reflectance before and after calibration, which show that the calibration error of this new approach is consistent within 5%. Compared with most cross-calibration methods, our new method is generic and better consider on BRDF models; furthermore, the well-built BRDF of the calibration site and simultaneously retrieved AOD make the method fully automatic and cost effective for the calibration of most moderate resolution remote sensors, especially Chinese sensors.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 and −7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to ∼ 0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and they are shown to decrease the bias and total error in AOD across the mid-visible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multi-sensor data continuity.

Near-nadir observations by the Multispectral Instrument (MSI) onboard the Sentinel-2 and the Operational Land Imager (OLI) onboard Landsat 8 were collected during two Simultaneous Nadir Overpasses (SNO). Multispectral images with 10, 20, and 30 m resolution from a spatially uniform area in the Saharan desert were acquired for direct comparison of MSI and OLI Top- Of-Atmosphere (TOA) reflectances. This paper presents an initial radiometric cross-calibration of the 8 corresponding spectral bands of the Sentinel-2 MSI and Landsat 8 OLI sensors. With the well-calibrated Landsat 8 OLI as a reference, the comparison indicates that 6 MSI bands are consistent with OLI within 3% in terms of spectral band adjustment factors Bi . The Near-Infra-Red (NIR) and cirrus bands are exceptions. They yield radiometric differences on the order of 8% and 15% respectively. Cross-calibration results show that the radiometric difference of the 7 corresponding bands are consistent to OLI within 1% or better, except on cirrus band. A pixel-by-pixel match between the MSI and OLI observations for different land covers showed that. This initial study suggests that the red-edge band B8A of MSI can be used to replace the NIR band B08 when conducting vegetation monitoring.

In this paper, radiometric cross-calibration of Ziyuan-3 Satellite was conducted at the homogeneous field test site in August 2015, using GF1 satellite and Landsat-8. The test site was chosen at Dunhuang site, which has been used for vicarious calibration for Chinese Satellite since 1990s. The radiometric cross-calibration of ZY3, GF1 and Landsat-8 was performed by the simultaneously observation of the three satellites on August 15, 2015. In this study, three calibration coefficients from various methods were compared and analyzed. This paper reveals that the cross-calibration of the ZY3 satellite using GF1 PMS sensor is better than using GF1 WFV sensor. The best result is the cross-calibration coefficient using Landsat-8 OLI, which is consistent with the calibration coefficient of the reflectance-based vicarious calibration method in Dunhuang. The validation results show the accuracy of the cross-calibration for ZY3 sensor using GF1 PMS and Landsat-8 OLI is less than 4% and 2%,respectively. This study can supply reference to the radiometric cross-calibration of Chinese satellite senor, and the results suggest that the radiometric cross-calibration coefficients can be useful for maintaining the quantitative application.

The inter-mission cross-calibration is a basic prerequisite for long-term sea level change studies on all spatial scales. Especially, for climate studies the consistent combination of successive missions is essential. This study uses a global multi-mission crossover analysis in order to investigate the performance of the Copernicus Sentinel-3A altimetry mission and its consistency to consecutive missions such as Jason-3. The first 1.5 years of data show an inter-mission bias of 3.6 cm with respect to Jason-3 with a linear trend of 4.0 mm/year. When using Pseudo Low Resolution Mode (PLRM) instead of Delay-Doppler data the bias increases whereas the trend decreases. Given the short time period under investigation these numbers should not be overrated, however, a careful future monitoring is necessary.

The space laboratory “Tiangong-2” (TG-2) was launched on 15 September 2016. The Multi-Angle Polarization Imager (MAI), onboard TG-2, is the first visible and near-infrared multi-angle polarization imager developed in China. MAI can accomplish polarization observation from up to 14 different viewing angles, which is of great significance for relevant research on the microphysical characteristics of clouds and aerosols. Aimed at the problem of a lack of an onboard calibration device for MAI, to correct TG-2’s calibration coefficients in time, the Simultaneous Nadir Overpass (SNO) cross-calibration method is proposed as a reasonable and feasible method for MAI 565- and 670-nm channels. Data from the M4 and M5 channels of the Visible Infrared Imaging Radiometer Suite (VIIRS), with good calibration accuracy, are used as reference data, and a series of matching conditions and a spectral adjustment algorithm are specified. To apply the SNO method, eight groups of examples from December 2016 to February 2017 are selected, and a large number of matching pixel samples covering land, ocean surface, and cloud layers are obtained. The resulting cross-calibration curves between MAI 565-nm and VIIRS M4 channels, and between MAI 670-nm and VIIRS M5 channels, show correlation coefficients of reflectivity of 0.986 and 0.994, mean biases of 2.48% and 5.90%, and RMSEs of 3.56% and 6.95%, respectively. Overall, the cross-calibration method achieved good results, which are of reference significance to correct the laboratory calibration coefficient of MAI. The proposed method effectively solves the problems of MAI’s subsequent on-orbit monitoring and vicarious calibration, and also lays a foundation for later applications of MAI data in research on cloud and aerosol microphysical parameters.

The objective of this paper is to find an empirical hyperspectral absolute calibration model using Libya 4 pseudo invariant calibration site (PICS). The approach involves using the Landsat 8 (L8) Operational Land Imager (OLI) as the reference radiometer and using Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm as a hyperspectral source. This model utilizes data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the Libya 4 PICS. It uses an improved, simple, empirical, hyperspectral Bidirectional Reflectance Distribution function (BRDF) model accounting for four angles: solar zenith and azimuth, and view zenith and azimuth angles. This model can perform absolute calibration in 1 nm spectral resolution by predicting TOA reflectance in all existing spectral bands of the sensors. The resultant model was validated with image data acquired from satellite sensors such as Landsat 7, Sentinel 2A, and Sentinel 2B, Terra MODIS, Aqua MODIS, from their launch date to 2020. These satellite sensors differ in terms of the width of their spectral bandpass, overpass time, off-nadir viewing capabilities, spatial resolution, and temporal revisit time, etc. The result demonstrates the efficacy of the proposed model has an accuracy of the order of 3% with a precision of about 3% for the nadir viewing sensors (with view zenith angle up to 5°) used in the study. For the off-nadir viewing satellites with view zenith angle up to 20°, it can have an estimated accuracy of 6% and precision of 4%.

The microwave radiometer (MWR) on board the SAC-D/Aquarius mission is a Dicke radiometer operating at 23.8 (H-Pol) and 36.5 GHz (H/V-Pol), which can provide ancillary data for the various retrievals to be performed with Aquarius regarding ocean and land applications. In this study, we report calibration results obtained by a land cross-calibration between Windsat and MWR. Moreover, MWR drifts were monitored using vicarious cold methodology. Results were generated for the 2011?2012 period using version V5.0 S of MWR data. MWR and Windsat cross-calibration was carried out over selected homogeneous targets which include tropical and boreal forests, desert, grassland, and the Sahel. As a result, biases were identified and corrections were proposed. Drifts in MWR observations were identified by implementing the vicarious cold method, which is a statistical approach that estimates the coldest value of the brightness temperature (Tb) (over ocean) histogram. Time series of such cold values are closely related to drifts in the instrument. In general, it was observed that MWR drifts tend to stabilize within 1 K after June 2012, when the software of the on-board computer was updated. Fil: Bruscantini, Cintia Alicia. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Maas, Martín Daniel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Karszenbaum, Haydee. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina

Consistency between national wave buoy networks is extremely important for wave climate studies and verification of global operational wave forecasting systems; however, it is insufficiently investigated. The validation of altimeter significant wave heights (SWHs) with the wave buoy networks of China, Europe and the National Data Buoy Center (NDBC) show significant divergence in assessments. This reveals a negative bias and larger root mean square error and scatter index from the Chinese buoy network than from the European and NDBC buoy networks. A remote cross-calibration method is presented using the collocations between altimeters and buoys to match the buoy observations from different networks. The Chinese buoys are found to yield a negative bias of −0.127 m compared to European/NDBC buoy networks. The cross-calibration equation is achieved by regression of the SWHs between the Chinese and European/NDBC buoy networks. The use of this remote cross-calibration significantly reduces the inconsistency between the Chinese and European/NDBC buoys in the validation of SWH from altimeter HY2B.

In space-borne remote sensing, the accuracy of radiometric calibration is a key issue for image quality and further data applications. According to the relationship between GF-4 visible and near-infrared (VNIR) sensor and CE-3 VNIS sensor, an applicable cross radiometric calibration method is proposed here based on the observation data of lunar. The cross calibration generates a set of radiometric calibration coefficients for GF-4 VNIR sensor. Two validation experiments are implemented to verify the authenticity and accuracy of cross calibration coefficients: the proposed approach shows a high consistency than previous coefficients by comparing the retrieved reflectance of three calibration sites on lunar, and gets the accuracy of 5.34 %, 3.42 %, 3.49 % and 5.96 % with blue, green, red and near infrared bands of GF-4 by ground-based experiments.

The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites. The MWI imagery is not sufficiently radiometrically calibrated, and therefore, the cross-calibration is urgently needed to provide high quality ocean color products for MWI observations. We proposed a simple and effective cross-calibration scheme for MWI data using well calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) imagery over aquatic environments. The path radiance of the MWI was estimated using the quasi-synchronized MODIS images as well as the MODIS Rayleigh and aerosol look up tables (LUTs) from SeaWiFS Data Analysis System 7.4 (SeaDAS 7.4). The results showed that the coefficients of determination (R2) of the calibration coefficients were larger than 0.97, with sufficient matched areas to perform cross-calibration for MWI. Compared with the simulated Top of Atmosphere (TOA) radiance using synchronized MODIS images, all errors calculated with the calibration coefficients retrieved in this paper were less than 5.2%, and lower than the lab calibrated coefficients. The Rayleigh-corrected reflectance (ρrc), remote sensing reflectance (Rrs) and total suspended matter (TSM) products of MWI, MODIS and the Geostationary Ocean Color Imager (GOCI) images for Taihu Lake in China were compared. The distribution of ρrc of MWI, MODIS and GOCI agreed well, except for band 667 nm of MODIS, which might have been saturated in relatively turbid waters. Besides, the Rrs used to retrieve TSM among MWI, MODIS and GOCI was also consistent. The root mean square errors (RMSE), mean biases (MB) and mean ratios (MR) between MWI Rrs and MODIS Rrs (or GOCI Rrs) were less than 0.20 sr−1, 5.52% and within 1 ± 0.023, respectively. In addition, the derived TSM from MWI and GOCI also agreed with a R2 of 0.90, MB of 13.75%, MR of 0.97 and RMSE of 9.43 mg/L. Cross-calibration coefficients retrieved in this paper will contribute to quantitative applications of MWI. This method can be extended easily to other similar ocean color satellite missions.

The CERES measured shortwave and longwave fluxes rely on the cloud properties derived using the coincident observations from the accompanying high-resolution MODIS and VIIRS imagers. The calibration consistency is required between MODIS and VIIRS radiances to ensure that the CERES provided cloud property retrievals are temporally consistent. This paper presents multiple approaches of cross-calibrating the spectrally comparable reflective solar bands (RSB) of Aqua-MODIS and NPP-VIIRS, and estimates the radiometric biases for individual band pair. The inter-comparison is performed between the Aqua-MODIS collection 6.1 level 1B and NPP-VIIRS Land PEATE V1 datasets. Radiometric biases up to 3% were estimated between the MODIS and VIIRS radiances for visible bands.

First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high quality images of the Earth’s surface that are used in both civilian and military applications. Extraction of quantitative information (e.g., surface reflectance) from the Landsat image data is only possible through an accurate absolute radiometric calibration. Typically, this calibration has been performed as a radiance-based cross-calibration between sensors. However, to convert radiance to reflectance, an accurate estimate of solar exoatmospheric irradiance is critical; and there are several solar models currently available which estimate exoatmospheric irradiance with varying levels of accuracy. Because of these inconsistencies in solar models, a TOA reflectance-based approach, independent of exoatmospheric irradiance, has been developed to provide a consistent cross-calibration of the Landsat series (from Landsat 8 OLI to Landsat 4 MSS), based on analysis of coincident and near-coincident scene pairs acquired with each sensor. The methodology uses Landsat-8 OLI reflectance measurements as the starting point (reference), as they are estimated with a 3% uncertainty (compared to the 5% uncertainty associated with radiance measurements). A set of radiometric calibration coefficients has been estimated based on the equations presented in this paper, which allows direct conversion of the digital numbers from the image data to TOA reflectance. The results obtained from application of these coefficients show significant improvement in consistency of reflectance measurements among the Landsat sensors.

The objectives of this study are to validate the applicability of a shortwave infrared atmospheric correction model (SWIR-based model) in deriving remote sensing reflectance from turbid Case II waters and to improve the model using an improved SWIR model (ISWIR-based model). In the ISWIR-based model, the aerosol reflectance and aerosol type at near-infrared wavelengths are determined from SWIR wavelengths using a 5 × 5 box-based cross-calibration method initialized in Case I waters. The remote sensing reflectance predicted by these models was compared to remote sensing reflectance measured from three different coastal waters. The results indicate that the ISWIR-based model provides a superior performance in atmospheric correction than the SWIR-based model. Using a SWIR-based model decreases MRE values from the SWIR-based model by 14.21%-42.7%. These findings indicate that the cross-calibration method can remove noise errors in MODIS SWIR data and improves the accuracy of remote sensing reflectance retrievals using MODIS SWIR wavelengths.

The accuracy of ocean surface wind vectors measured by satellite-borne scatterometers depends on measured backscattering coefficient (σ°). Hence, an in-flight calibration of a satellite scatterometer is essential as this is not guaranteed by its prelaunch absolute calibration. The postlaunch calibration of σ° is also required to monitor the time evolution of the accuracy of measured σ °. This is performed using relative calibration over land targets with minor spatiotemporal variation of σ°. A few such targets are the Amazon rainforest, Greenland, Antarctica, etc. In this paper, relative calibration of σ° from the OceanSAT-2 Scatterometer (OSCAT) has been carried out by comparing it with a similar quantity from the Quick Scatterometer (QuikSCAT) for November 2009. The differences between the average σ° of QuikSCAT and that of OSCAT have been calculated globally to check the overall consistency. Over the calibration sites, the differences are within ±0.25 dB. Histograms of differences in ascending/descending passes and fore/aft looks of OSCAT have also been analyzed over the calibration sites. These indicate that look bias in OSCAT σ° is within the range of ±0.5 dB. It is also evident that pass biases, i.e., differences between ascending and descending passes, exist over the Amazon rainforest for both QuikSCAT and OSCAT. This diurnal variation in σ° may go up to 1.25 dB in OSCAT. Further, computations of daily average and standard deviation over the calibration site show that mean OSCAT σ° is consistent with mean QuikSCAT σ°, whereas the standard deviation in OSCAT is marginally higher. Further, time-series analysis of OSCAT σ° shows its temporal stability.

Cross-calibration of high energetic particle data is a primary requirement for the availability of multi-instrument, multi-spacecraft data. It also can provide a method to verify relative reliability of data from single satellite measurement. This paper presents a case study of energetic particles data cross-calibration between FY-3B and NOAA-17. A generally good agreement is acquired in the flux values and distribution trend of 2.5–6.9 MeV protons and 0.3–1.1 MeV electrons between FY-3B and NOAA-17 satellites. It suggests that the data observed by FY-3B is properly cross-calibrated. We can also confirm that energetic particles data observed by FY-3B satellite is available.

A reliable groundwater balance assessment is a fundamental tool for any effective resource exploitation plan. Nevertheless, some terms of the balance equation are, generally, very difficult to be estimated, even on average, especially when large and heterogeneous groundwater bodies are considered.

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.

The present paper describes the cross calibration of the Remote Sensing Instrument (RSI) onboard Formosat-2 using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra as a reference, which has been continuously calibrated by means of the onboard and vicarious calibration methods. The data integration system for multi-satellite sensor databases on the Global Earth Observation Grid (GEO Grid) is used in order to find pairs of images acquired by both satellite optical sensors over the radiometric calibration sites on the same days. Radiative transfer simulations that consider different imaging conditions using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code are performed based on the Moderate-resolution Imaging Spectroradiometer (MODIS) atmosphere and land products, and the optimum methodology for this cross calibration is discussed under the assumption of no ground-based measurements. The derived calibration coefficients are normalized by the values of preflight calibration and are compared with the results of in-flight cross calibration by the Centre National d'Etudes Spatiales (CNES) and vicarious calibration described in our previous paper. The average degradations of sensor sensitivity since preflight calibration until 2009 are estimated to be 10% (Band2), 11% (Band3), and 8% (Band4). The derived long-term degradation trends are almost consistent with the results of our vicarious calibration within the precision. The cross-calibration methodology used in the present paper facilitates the in-flight radiometric calibration of satellite optical sensors.

Radiometric calibration is a prerequisite to quantitative remote sensing, and its accuracy has a direct impact on the reliability and accuracy of the quantitative application of remote-sensing data. This article attempts to cross-calibrate the visible and near-infrared channels 1 450–520 nm, 2 520–590 nm, 3 630–690 nm, and 4 770–890 nm of the China–Brazil Earth Resources Satellite CBERS-02B/charge-coupled device CCD camera with channels 3 459–479 nm, 4 545–565 nm, 1 620–670 nm, and 2 841–876 nm of the Terra/Moderate Resolution Imaging Spectroradiometer MODIS, respectively. The radiative transfer modelling RTM method is implemented using the CBERS-02B/CCD and Terra/MODIS measurements at the Dunhuang radiometric calibration site in October 2008. The differences between the cross-calibration and vicarious calibration techniques for channels 1, 2, 3, and 4 are 11.15%, –0.89%, 2.88%, and 11.28%, respectively. Furthermore, the accuracies of the cross-calibration coefficients of CBERS-02B/CCD are analysed, wherein the results indicate that the calibration uncertainty is within 5%.

The Orbiting Carbon Observatory (OCO) by NASA's Jet Propulsion Laboratory (JPL) and Greenhouse gases Observing SATellite (GOSAT) by JAXA were built to provide independent measures of the global distributions of carbon dioxide (CO2) from space. GOSAT achieved a successful orbit on January 23, 2009, and OCO failed its launch attempt on February 24, 2009. Both sensors detect absorptions at the 0.76-?m oxygen band and at the weak and strong CO2 bands at 1.6 and 2.0 ?m, respectively. In order to establish the uncertainties and biases between the respective data products, the OCO and GOSAT teams have planned a number of cross-comparison studies. The first of these, discussed here, is the validation of the prelaunch absolute radiometric calibrations, specified at ±5%. The cross-comparison campaign to validate this OCO approach was performed at NASA's JPL in April 2008. In this paper, the OCO reference detectors and three GOSAT radiometers viewed the OCO sphere and radiometric standard. The overall agreements between the OCO calibration and GOSAT measurement of the OCO integrating sphere were 1.5% at 0.76 ?m, 2.7% ± 1.1% at 1.6 ?m, and 0.2% ± 4.1% at 2.0 ?m. To validate the GOSAT preflight calibration, the cross-calibration experiment continued at JAXA's Tsukuba Space Center in December 2008, where the same radiometers measured the two GOSAT spheres. Agreements are better than 1.8% at 0.76 ?m, 1.6% at 1.6 ?m, and 1.4% at 2 ?m. These studies give confirmation that the flight instruments have been calibrated to within their uncertainty requirements.

The MERTIS (Mercury Thermal Imaging Spectrometer) instrument on the ESA mission BepiColombo will study the surface of Mercury in the wavelength range from 7 to 14 μm. The analysis of the MERTIS data will be posing a number of significant challenges. To facilitate the development of analytic tools, support planned ground based observations and facilitate a cross calibration with other instruments on BepiColombo and MESSENGER, a list of Mercury analogue materials was compiled. This set of analogue materials is based on our current knowledge of the surface composition of Mercury and includes plagioclase and potassium feldspars, low and high Ca pyroxenes, olivine, elemental sulfur, and as an extraterrestrial analogue a lunar highland soil sample. The samples are prepared in four different grain size separates ranging from 0 to 250 μm to cover the expected grain size distribution on the surface of Mercury.

The wide field of view (WFV) is an optical imaging sensor on-board the Gao Fen 1 (GF-1). The WFV lacks an on-board calibrator, so on-orbit radiometric calibration is required. Zhong et al. proposed a method for cross-calibrating the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) that can be applied to the GF-1/WFV. However, the accuracy is limited because of the wider radiometric dynamic range and the higher spatial resolution of the GF-1/WFV. Therefore, Landsat-8 Operational Land Imager (OLI) imagery with a radiometric resolution similar to that of the GF-1/WFV and DEM extracted from ZY-3 three-line array panchromatic camera (TLC) with a higher spatial resolution were used. A calibration site with uniform surface material and a natural topographic variation was selected, and a model of this site’s bidirectional reflectance distribution function (BRDF) was developed. The model has excellent agreement with the real situation, as shown by the comparison of the simulations to the actual OLI surface reflectance. Then, the model was used to calibrate the WFV. Compared with the TOA reflectance from synchronized Landsat-8/OLI images, all errors calculated with the calibration coefficients retrieved in this paper are less than 5%, much less than the errors calculated with the calibration coefficients given by the China Centre for Resource Satellite Data and Application (CRESDA).

The Hyperspectral Imager Suite (HISUI) is a future hyper-spectral and multispectral sensor developed by Japanese Ministry of Economy, Trade, and Industry (METI). Radiometric calibration of satellite sensor system is very important for the higher level products with high quality. This paper mainly shows the vicarious calibration and cross-calibration for assessment of HISUI radiometric performance in HISUI Calibration Working Group.

Cross calibration is critical to provide consistent measurements from multiple sensors. The intrinsic varieties between the Gao Fen (GF)-1 Wide Field of View (WFV) and Terra MODIS are caused by the relative spectral responses, sensor and solar zenith angle, azimuth angle. The sensor and solar observation conditions combined with the typical reflectance spectrum over Dunhuang test site were used to calculate the spectral band adjustment factors (SBAFs). A total of 32 image pairs between WFV and MODIS over Dunhuang site are available considering the cloud contamination. The SBAFs have the similar changing tendency in both 4 bands. According to the SBAFs, the gain of WFV can be calculated. TOA radiance products of MODIS and WFV are compared which shows that the accuracy of cross calibration coefficients is depending on the SBAF.

Any institution wishing to perform an internal cross calibration of its diagnostic dosemeters should first quantify the uncertainty associated with this to demonstrate that it remains appropriate for the measurements being undertaken.An uncertainty budget for internal cross calibration that covers a range of locally used dosemeters has been derived using the methodology of the International Atomic Energy Agency. The specific internal cross calibration protocol requirements necessary for this uncertainty budget to be valid are discussed.The final quantified uncertainty is 5.31%; this is dominated by the 5% uncertainty associated with the calibration of the reference instrument. The next largest contributions are from differences in temperature and pressure and dosemeter energy dependence.It has been demonstrated that with careful adherence to a well designed internal cross calibration protocol, dosemeters can be calibrated in-house against a calibrated reference dosemeter with very little increase in the associated calibration uncertainty.

Nearly three years of Envisat altimetric observations over ocean are available in Geophysical Data Record (GDR) products. The quality assessment of these data is routinely performed at the CLS Space Oceanography Division in the frame of the CNES Segment Sol Altimétrie et Orbitographie (SSALTO) and ESA French Processing and Archiving Center (F-PAC) activities. This paper presents the main results in terms of Envisat data quality: verification of data availability and validity, monitoring of the most relevant altimeter (ocean1 retracking) and radiometer parameters, assessment of the Envisat altimeter system performances. This includes a cross-calibration analysis of Envisat data with Jason-1, ERS-2 and T/P. Envisat data show good general quality. A good orbit quality and a low level of noise allow Envisat to reach the high level of accuracy of other precise missions such as T/P and Jason-1. Some issues raised in this paper, as the gravity induced orbit errors, will be solved in the next version of GDR products. Some others, as the Envisat Mean Sea Level in the first year, still need further investigation.

This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during an underfly event on 29–30 March 2013. The other approach is based on using time series of image statistics acquired by these two sensors over the Libya 4 pseudo invariant calibration site (PICS) (+28.55°N, +23.39°E). Analyses from these approaches show that the reflectance calibration of OLI is generally within ±3% of the ETM+ radiance calibration for all the reflective bands from visible to short wave infrared regions when the ChKur solar spectrum is used to convert the ETM+ radiance to reflectance. Similar results are obtained comparing the OLI radiance calibration directly with the ETM+ radiance calibration and the results in these two different physical units (radiance and reflectance) agree to within ±2% for all the analogous bands. These results will also be useful to tie all the Landsat heritage sensors from Landsat 1 MultiSpectral Scanner (MSS) through Landsat-8 OLI to a consistent radiometric scale.

As part of the Earth Observer 1 (EO-1) Mission, the Advanced Land Imager (ALI) demonstrates a potential technological direction for Landsat Data Continuity Missions. To evaluate ALI's capabilities in this role, a cross-calibration methodology has been developed using image pairs from the Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) and EO-1 (ALI) to verify the radiometric calibration of ALI with respect to the well-calibrated L7 ETM+ sensor. Results have been obtained using two different approaches. The first approach involves calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors. The second approach uses vicarious calibration techniques to compare the predicted top-of-atmosphere radiance derived from ground reference data collected during the overpass to the measured radiance obtained from the sensor. The results indicate that the relative sensor chip assemblies gains agree with the ETM+ visible and near-infrared bands to within 2% and the shortwave infrared bands to within 4%.

The Chinese Ocean Color and Temperature Scanner (COCTS) is the main sensor launched in May of 2002 on the Chinese first ocean color satellite named HY-1A. In this paper, first, the properties of HY-1A and COCTS are introduced briefly. Second, the theory and methodology of cross-calibration for COCTS ocean color bands by using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data are discussed in detail. Third, with the quasi-synchronized SeaWiFS data, we simulate the COCTS radiances at the top of atmosphere (TOA), and then obtain the calibration coefficients for COCTS ocean color bands. The results show that the cross-calibration TOA radiances are consistent with the in situ vicarious calibration TOA radiances at the COCTS ocean color bands. Finally, we compare the COCTS-retrieved water leaving radiances with SeaWiFS-retrieved ones and the results show that the cross-calibration method could provide reasonable accuracy for ocean color measurement.

Given that many operational satellite sensors are not calibrated, while a handful of research sensors are, cross-calibration between the two types of sensor is a cost-effective means of calibration. A new method of sensor cross-calibration is demonstrated here using the Chinese Multi-channel Visible Infrared Scanning radiometer (MVIRS) and the US Moderate Resolution Imaging Spectrometer (MODIS). MVIRS has six channels, equivalent to the current National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and four additional ones for remote sensing of ocean colour and moisture. The MVIRS on-board China's polar-orbiting meteorological satellite (FY-1D) was launched on 15 May 2002 with an earlier overpass time than Terra. The sensor has no on-board calibration assembly. This study attempts to calibrate MVIRS against the well-calibrated MODIS, by taking a series of measures to account for their differences. Clear-sky measurements made from the two sensors in July-O...

China launched its third spaceship SZ-3 in March. 2002 on which the main remote sensor is the Chinese moderate imaging spectra radiometer (CMODIS). In this paper the properties of CMODIS are firstly introduced briefly. Then, the theory and algorithm of cross-calibration for CMODIS ocean color channels by the sea-viewing wide field-of-view sensor (SeaWiFS) data are discussed in detail. The total radiance (TOA) of four quasi-synchronized crossing ocean areas simulated by SeaWiFS and measured by CMODIS are compared and the calibration coefficients are derived from the relationship between them. Finally, the in-situ water leaving radiance data are used to validate the calibration results. The results show that the cross-calibration method could provide reasonable precision for ocean color measurement.

On 2 June 1999 Landsat-5 and Landsat-7 passed over north-central Nebraska collecting Thematic Mapper (TM) data for essentially the same spatial location, with an acquisition time differing by less than 20 min. At the Niobrara Nature Preserve, Nebraska site, two multifilter rotating shadowband radiometers (MFRSRs), a Cimel sunphotometer, a Microtops sunphotometer, and an ASD-FR spectroradiometer were used to take ground-based readings. This dataset offered a unique opportunity to absolutely calibrate both instruments, and also to cross-calibrate TM with Enhanced Thematic Mapper Plus (ETM+) the new thematic mapper aboard Landsat-7. ModTran3 radiative transfer code, based on the Nebraska datasets, was calibrated to match observed atmospheric transmittance and diffuse-to-global ratios, employing ground reflectance values, for each individual overpass. Having replicated those conditions, models were then extrapolated to satellite orbits, and exoatmospheric irradiances calculated. Finally, by obtaining actual i...

Fourteen and half years of vicarious calibration and cross calibration for Terra/ASTER/VNIR (Visible and Near Infrared Radiometer, one of three ASTER mission instruments onboard Terra satellite) are summarized together with an error budget analysis. As the results, it is found that Onboard Calibration (OBC) data derived Radiometric Calibration Coefficient (RCC) is situated within a range of uncertainty of vicarious calibration except the period after approximately 4000 days after launch. In other word, uncertainty of OBC RCC is overlapped with uncertainty of vicarious calibration for about 4000 days since launch. Meanwhile, cross calibration with MODIS, MISR and ETM+ supports vicarious calibration of VNIR. In other word, Cross RCC shows much consistent trend to Vicarious RCC than OBC RCC.