Radiometric Cross-calibration Research Articles

Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images

Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles.

Cross-Calibration between ASTER and MODIS Visible to Near-Infrared Bands for Improvement of ASTER Radiometric Calibration.

Radiometric cross-calibration between the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) has been partially used to derive the ASTER radiometric calibration coefficient (RCC) curve as a function of date on visible to near-infrared bands. However, cross-calibration is not sufficiently accurate, since the effects of the differences in the sensor’s spectral and spatial responses are not fully mitigated. The present study attempts to evaluate radiometric consistency across two sensors using an improved cross-calibration algorithm to address the spectral and spatial effects and derive cross-calibration-based RCCs, which increases the ASTER calibration accuracy. Overall, radiances measured with ASTER bands 1 and 2 are on averages 3.9% and 3.6% greater than the ones measured on the same scene with their MODIS counterparts and ASTER band 3N (nadir) is 0.6% smaller than its MODIS counterpart in current radiance/reflectance products. The percentage root mean squared errors (%RMSEs) between the radiances of two sensors are 3.7, 4.2, and 2.3 for ASTER band 1, 2, and 3N, respectively, which are slightly greater or smaller than the required ASTER radiometric calibration accuracy (4%). The uncertainty of the cross-calibration is analyzed by elaborating the error budget table to evaluate the International System of Units (SI)-traceability of the results. The use of the derived RCCs will allow further reduction of errors in ASTER radiometric calibration and subsequently improve interoperability across sensors for synergistic applications.

Radiometric Cross-Calibration of GF-4 in Multispectral Bands

The GaoFen-4 (GF-4), launched at the end of December 2015, is China’s first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like the charge-coupled device (CCD) onboard HuanJing-1 (HJ) or the wide field of view sensor (WFV) onboard GaoFen-1 (GF-1), GF-4 also has a wide field of view, which provides challenges for cross-calibration with narrow field of view sensors, like the Landsat series. A new technique has been developed and used to calibrate HJ-1/CCD and GF-1/WFV, which is verified viable. The technique has three key steps: (1) calculate the surface using the bi-directional reflectance distribution function (BRDF) characterization of a site, taking advantage of its uniform surface material and natural topographic variation using Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery and digital elevation model (DEM) products; (2) calculate the radiance at the top-of-the atmosphere (TOA) with the simulated surface reflectance using the atmosphere radiant transfer model; and (3) fit the calibration coefficients with the TOA radiance and corresponding Digital Number (DN) values of the image. This study attempts to demonstrate the technique is also feasible to calibrate GF-4 multispectral bands. After fitting the calibration coefficients using the technique, extensive validation is conducted by cross-validation using the image pairs of GF-4/PMS and Landsat-8/OLI with similar transit times and close view zenith. The validation result indicates a higher accuracy and frequency than that given by the China Centre for Resources Satellite Data and Application (CRESDA) using vicarious calibration. The study shows that the new technique is also quite feasible for GF-4 multispectral bands as a routine long-term procedure.

Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters.

Радиометрическая кросс-калибровка коротковолновых каналов многоканального спутникового устройства КА «Электро-Л» № 2 по данным измерений VIIRS КА Suomi NPP

Радиометрическая кросс-калибровка коротковолновых каналов многоканального спутникового устройства КА «Электро-Л» № 2 по данным измерений VIIRS КА Suomi NPP

Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with Landsat-8 OLI

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.

An Investigation of a Novel Cross-Calibration Method of FY-3C/VIRR against NPP/VIIRS in the Dunhuang Test Site

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.

Радиометрическая интеркалибровка коротковолновых каналов многоканального спутникового устройства КА «Метеор-М» № 2 по радиометру AVHRR КА «Metop-A»

Радиометрическая интеркалибровка коротковолновых каналов многоканального спутникового устройства КА «Метеор-М» № 2 по радиометру AVHRR КА «Metop-A»

Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8 OLI images: A solution for large view angle associated problems

Abstract Four wide-field-of-view (WFV) instruments are onboard the Gaofen-1 (or GF-1) satellite, providing a combined swath of ~ 800 km. However, observations with large view angles pose new challenges for radiometric cross-calibration with a simple image-based method when using Landsat-8 Operational Land Imager (OLI) data as a reference. Based on radiative transfer modeling, a novel radiometric cross-calibration method has been proposed in this study to solve large view angle-associated problems. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were used to simulate the top of atmosphere (TOA) signal of the reference and target instruments, and the unequal bidirectional effects were corrected using MODIS bi-directional reflectance distribution function (BRDF) products. Extensive validations with both satellite data and in situ measurements revealed an uncertainty of ~ 8% for the newly produced cross-calibration coefficients when they were used to calibrate TOA reflectance for both close-nadir and off-nadir instruments. The improvements are discernable when compared with the official provided coefficients and that were derived using the image-based cross-calibration method. This study demonstrated not only the usefulness of Landsat-8 OLI data in sensor radiometric calibration but also the impressive accuracy of the MODIS BRDF and aerosol products in radiative transfer simulations. The proposed method can be used in the future to monitor and correct potential radiometric degradations of the GF-1 WFV instruments, and it also can be easily extended to other similar satellite missions to conduct radiometric cross-calibrations.

Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

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.

Radiometric cross-calibration of Tiangong-1 and MODIS infrared bands and applications in monitoring coastal sea surface temperature

Radiometric cross-calibration of Tiangong-1 and MODIS infrared bands and applications in monitoring coastal sea surface temperature

Cross Calibration Over Desert Sites: Description, Methodology, and Operational Implementation

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 Donnees 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 calibrations 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.

Multitemporal Cross-Calibration of the Terra MODIS and Landsat 7 ETM+ Reflective Solar Bands

In recent years, there has been a significant increase in the use of remotely sensed data to address global issues. With the open data policy, the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) sensors have become a critical component of numerous applications. These two sensors have been operational for more than a decade, providing a rich archive of multispectral imagery for analysis of mutitemporal remote sensing data. This paper focuses on evaluating the radiometric calibration agreement between MODIS and ETM+ using the near-simultaneous and cloud-free image pairs over an African pseudo-invariant calibration site, Libya 4. To account for the combined uncertainties in the top-of-atmosphere (TOA) reflectance due to surface and atmospheric bidirectional reflectance distribution function (BRDF), a semiempirical BRDF model was adopted to normalize the TOA reflectance to the same illumination and viewing geometry. In addition, the spectra from the Earth Observing-1 (EO-1) Hyperion were used to compute spectral corrections between the corresponding MODIS and ETM+ spectral bands. As EO-1 Hyperion scenes were not available for all MODIS and ETM+ data pairs, MODerate resolution atmospheric TRANsmission (MODTRAN) 5.0 simulations were also used to adjust for differences due to the presence or lack of absorption features in some of the bands. A MODIS split-window algorithm provides the atmospheric water vapor column abundance during the overpasses for the MODTRAN simulations. Additionally, the column atmospheric water vapor content during the overpass was retrieved using the MODIS precipitable water vapor product. After performing these adjustments, the radiometric cross-calibration of the two sensors was consistent to within 7%. Some drifts in the response of the bands are evident, with MODIS band 3 being the largest of about 6% over 10 years, a change that will be corrected in Collection 6 MODIS processing.

Applications of Spectral Band Adjustment Factors (SBAF) for Cross-Calibration

To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface acquired from multiple spaceborne imaging sensors. However, an integrated global observation framework requires an understanding of how land surface processes are seen differently by various sensors. This is particularly true for sensors acquiring data in spectral bands whose relative spectral responses (RSRs) are not similar and thus may produce different results while observing the same target. The intrinsic offsets between two sensors caused by RSR mismatches can be compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of the two sensors. The motivation of this work comes from the need to compensate the spectral response differences of multispectral sensors in order to provide a more accurate cross-calibration between the sensors. In this paper, radiometric cross-calibration of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors was performed using near-simultaneous observations over the Libya 4 pseudoinvariant calibration site in the visible and near-infrared spectral range. The RSR differences of the analogous ETM+ and MODIS spectral bands provide the opportunity to explore, understand, quantify, and compensate for the measurement differences between these two sensors. The cross-calibration was initially performed by comparing the top-of-atmosphere (TOA) reflectances between the two sensors over their lifetimes. The average percent differences in the long-term trends ranged from -5% to +6%. The RSR compensated ETM+ TOA reflectance (ETM+*) measurements were then found to agree with MODIS TOA reflectance to within 5% for all bands when Earth Observing-1 Hyperion hyperspectral data were used to produce the SBAFs. These differences were later reduced to within 1% for all bands (except band 2) by using Environmental Satellite Scanning Imaging Absorption Spectrometer for Atmospheric Cartography hyperspectral data to produce the SBAFs.

Two Approaches for Inter-Satellite Radiometer Calibrations between TMI and WindSat

This paper presents recent progress in inter-satellite microwave radiometric cross-calibration to eliminate brightness temperature measurement biases between a pair of radiometer channels operating at slightly different frequencies and incidence angles. The motivation of this research is to develop robust analytical cross-calibration techniques for inter-calibration of various satellite radiometer instruments, with the first projected application being the multi-satellite Global Precipitation Measurement (GPM) constellation to be launched in 2013. The significance of this work is that it will allow the formation of consistent multi-decadal time series of geophysical measurements for multiple satellite microwave radiometers that are free of instrumental biases and other long-term changes in radiometric calibration, which will allow researchers to study global climate change. Descriptions are given for two independent calibration techniques: a Taylor series expansion of the oceanic brightness temperature (Tb) spectrum between dissimilar radiometer channels and a non-linear regression among multi-channel Tb measurements. In the first approach, predictions were made of Tb's at a destination frequency from Tb's of a close by source frequency by expansion of the oceanic brightness temperature spectrum in a Taylor series centered at the source frequency. The relationships between Tb's and frequencies were derived from simulations using a radiative transfer model (RTM), which accounts for the total collected emissions from the ocean surface and the atmosphere. Further, earth incidence angle differences between radiometer channels were transformed in a similar manner using the partial derivatives of Tb with incidence angle derived from RTM simulations. In the second approach, we used a prediction algorithm that relies on the correlation between radiometer Tb's at various frequencies and polarizations and which uses a regression on the Tb's and their non-linear transformations developed using an independent radiative transfer model. As a demonstration, near-simultaneous pair-wise ocean Tb comparisons are presented between the TRMM Microwave Imager (TMI), which is not sun synchronous, and the sun-synchronous polar orbiting WindSat, using oceanic Tb observations from 2003-04. The corresponding results between these two inter-satellite calibration techniques are highly correlated, and results demonstrate that fixed channel-by-channel differences, of order 1-2 K exist between TMI and WindSat. These are significant radiometric calibration differences, which can be removed prior to forming joint data sets of geophysical parameter retrievals.

Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain

In order for quantitative applications to make full use of the ever-increasing number of Earth observation satellite systems, data from the various imaging sensors involved must be on a consistent radiometric scale. This paper reports on an investigation of radiometric calibration errors due to differences in spectral response functions between satellite sensors when attempting cross-calibration based on near-simultaneous imaging of common ground targets in analogous spectral bands, a commonly used post-launch calibration methodology. Twenty Earth observation imaging sensors (including coarser and higher spatial resolution sensors) were considered, using the Landsat solar reflective spectral domain as a framework. Scene content was simulated using spectra for four ground target types (Railroad Valley Playa, snow, sand and rangeland), together with various combinations of atmospheric states and illumination geometries. Results were obtained as a function of ground target type, satellite sensor comparison, spectral region, and scene content. Overall, if spectral band difference effects (SBDEs) are not taken into account, the Railroad Valley Playa site is a “good” ground target for cross calibration between most but not all satellite sensors in most but not all spectral regions investigated. “Good” is defined as SBDEs within ± 3%. The other three ground target types considered (snow, sand and rangeland) proved to be more sensitive to uncorrected SBDEs than the RVPN site overall. The spectral characteristics of the scene content (solar irradiance, surface reflectance and atmosphere) are examined in detail to clarify why spectral difference effects arise and why they can be significant when comparing different imaging sensor systems. Atmospheric gas absorption features are identified as being the main source of spectral variability in most spectral regions. The paper concludes with recommendations on spectral data and tools that would facilitate cross-calibration between multiple satellite sensors.

Landsat cross-calibration based on near simultaneous imaging of common ground targets

The paper presents the results of an extended analysis of image data sets acquired during the tandem-orbit configuration in 1999 for the purposes of radiometric cross-calibration of the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM) sensors. Earlier work focused on the tandem pair for the Railroad Valley Playa, Nevada (RVPN) site to tie down the Landsat-5 TM calibration based on the more accurate Landsat-7 ETM+ calibration. This paper describes new results based on as many as eight tandem image pairs. The additional tandem images are of primarily vegetated areas for which little or no ground reference data were available. Increasing the number of tandem pairs yielded results for the Landsat 5 TM gain coefficients within approximately ± 1% of the RVPN-based results in spectral bands 1, 2, 3 and 7, and within − 2% and − 4% of the RVPN-based results for spectral bands 4 and 5, respectively.

Radiometric Cross-calibration of CBERS-02 WFI with MODIS and the Effect on VI

Radiometric Cross-calibration of CBERS-02 WFI with MODIS and the Effect on VI

Absolute radiometric calibration of CBERS-02 IRMSS thermal band

Based on the laboratory calibration before launch of CBERS-02 IRMSS thermal infrared channel, the onboard blackbody calibration, the radiometric crosscalibration against TERRA MODIS corresponding channel and the in-flight field calibration at Lake Qinghai: water surface radiometric calibration test site of China on Aug. 17, 2004 are carried out in this research. When making onboard blackbody calibration of CBERS-02 IRMSS, it is necessary to understand inside structures and light path in IRMSS camera and receive and process calibration blackbody signals at normal and high temperatures. Onboard blackbody calibration results of each detector are very important to relative radiometric calibration of images processing and absolute radiometric calibration of each detector. In-flight field calibration mainly contains field experiments, obtaining synchronous images, spectrum marching and MODTRAN radiative transmission computation. Radiometric cross-calibration of CBERS-02 IRMSS thermal band against TERRA MODIS selected 6 times synchronous images of two sensors passing through Lake Qinghai and Lake Taihu from August to December, 2004 to compute the cross calibration data. Combining the in-flight field calibration and radiometric cross calibration data, the absolute radiometric calibration coefficients are calculated by the linear regression method. This new calibration model can obviously control the radiometric calibration uncertainties aroused by the single point method used in the in-flight calibration of CBERS-02 IRMSS, this kind of sensors cannot receive the cosmic background radiation. In this research, the radiometric calibration coefficients obtained through the linear regression model are 8.53 (gain, unit: DN/W m−2sr−1μm−1) and 44.92 (offset, unit: DN).

Radiometric cross-calibration of the CBERS-02 CCD camera with the TERRA MODIS

For the application of the CCD camera, the most important payload on CBERS-02, the key is to provide long-term stable radiometric calibration coefficients. Although the vicarious calibration had been proved successful, it had its limitations such as test site requirement and unsuitable for historical data. Cross-calibration is one of the alternative methods, but it needs synchro surface spectrum to achieve spectral band matching factors. Our effort is to probe the influences on these factors. Simulations with a lot of surface spectrum showed that the factors changed with the viewing geometry, atmospheric condition and surface targets. However, simulating with the same viewing geometry and atmospheric condition, the spectral band matching factors of the same or similar surface targets, spectrum acquired from different dates and different places would like to be consistent to each other within 1%–5%. Thus, the synchro measurement data can be substituted by the same or similar target from other source. Based on this method, using the MODIS as the reference, the cross-calibration was performed for CCD camera. The research demonstrated that the traditional method with single calibration site was inappropriate for CCD camera, since the offsets for its four spectral bands were not zeros. With four calibration sites, these offsets were obtained. And the camera was detected to degrade with dates based on four times of cross-calibrations.