Accurate Radiometric Calibration Research Articles

In-Flight Spectral Response Function Retrieval of a Multispectral Radiometer Based on the Functional Data Analysis Technique

The spectral response function (SRF) is a crucial parameter in multispectral radiometers, and it influences the radiometric calibration accuracy and quantitative application capabilities. The in-flight SRF often has errors due to prelaunch contamination or postlaunch degradation. This study proposes an innovative new method to retrieve SRFs of multispectral radiometers based on intercomparisons with hyperspectral sounders via the functional data analysis (FDA) technique. Under the FDA framework, all variables, including the hyperspectral radiance and SRF, are regarded as functions rather than discrete data by expanding in the Fourier functional basis. The forward convolution equation is processed directly into a functional integration model rather than a normally pointwise summation; this ensures that the unknown quantities are transformed from numerous SRF samples to several function parameters, thus avoiding the ill-posed problem. The proposed algorithm is verified with both simulated and real data from multiple thermal infrared bands of the FY-3 IRAS and FY-4 AGRI using collocations with METOP-B IASI. All these results demonstrate our algorithm's qualitative and quantitative effectiveness for infrared SRF retrieval. Although the demonstrations are particularly relevant to infrared spectra, the algorithm is universal and also applicable to other spectral bands.

Perennial ryegrass biomass retrieval through multispectral UAV data

Frequent biomass measurement is a key activity for optimal perennial ryegrass (Lolium perenne) management in intensive forage-based dairy operations. Due to the necessary high-frequency (i.e., weekly or monthly) pasture monitoring and continuous trend of larger dairy farms, such activity is perceived as an operational bottleneck. Consequently, substantial effort is directed to the development of accurate and automated technological solutions for biomass assessment. The popularization of unmanned aerial vehicles (UAVs) combined with multispectral cameras should allow for an optimal observational system able to deploy machine learning algorithms for near real-time biomass dry-matter (DM) mapping. For successful operation, these systems should deliver radiometrically accurate orthomosaics and robust models able to generalize across different periods. Nevertheless, the accuracy of radiometric calibration and generalization ability of these models is seldom evaluated. Also, such pipelines should require minimum processing power and allow for fast deployment. This study has established a two-year experiment comparing reflectance measurements between a handheld spectrometer and a commercial multispectral UAV camera. Different algorithms based on regression-tree architecture were contrasted regarding accuracy, speed, and model size. Model performances were validated, providing error-metrics for baseline accuracy and temporal validation. The results have shown that the standard procedure for multispectral imagery radiometric calibration is sub-optimal, requiring further post-processing and presenting low correlation with handheld measurements across spectral bands and dates. Nevertheless, after post-calibration, the use of spectral imagery has presented better baseline error than the point-based sensors, respectively displaying an average of 397.3 and 464.2 kg DM/ha when employed alongside the best performing algorithm (i.e., Cubist). When trained and validated across different years, model performance was largely reduced and deemed unfit for operational purposes. The Cubist/M5 family of algorithms have exhibited advantageous characteristics such as compact model structure, allowing for a higher level of model interpretability, while displaying a smaller size and faster deployment than the Random Forest, Boosted, and Bagged Regression Trees algorithms.

Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance

The Advanced Baseline Imager (ABI) is the primary instrument onboard NOAA’s current generation of Geostationary Operational Environment Satellites R-series (GOES-R) satellites, measuring the reflected and emitted energy from the Earth. It consists of 16 channels, 10 in the thermal infrared (IR) and 6 in the solar reflective spectrum. Being the first in the GOES-R series satellites, GOES-16 was launched on November 19, 2016, and became operational as GOES-East at 75.2°W since December 18, 2017. We examine the radiometric calibration accuracy and stability of GOES-16 ABI IR radiance since its first light in January 2017, including the effects of two major updates of the GOES-R Ground Segment processing for the IR channels in October 2017, and June 2018. Using measurements by multiple hyperspectral radiometers from low Earth orbit satellites as references, it is found that, when converted to scene brightness temperature of 300 K, the calibrated ABI IR radiance is accurate within 0.13 K for Ch16 (13.3 μm), within 0.06 K for channel 12 (9.6 μm), and within 0.05 K for the other IR channels. This is an order of magnitude better than the requirement of 1 K. Since June, 2018, the radiometric calibration of GOES-16 ABI IR channels has been temporally stable, spatially uniform within the ABI full-disk field of view, absent of diurnal and seasonal variations, and invariant within various timelines. Other than short term disruptions as noted in the Calibration Event Log, GOES-16 ABI IR Level 1b products since June 19, 2018, is a reliable reference for satellite intercomparison or intercalibration studies.

Forward Modeling of Solar Coronal Magnetic-field Measurements Based on a Magnetic-field-induced Transition in Fe x

Abstract It was recently proposed that the intensity ratios of several extreme ultraviolet spectral lines from Fe x ions can be used to measure the solar coronal magnetic field based on magnetic-field-induced transition (MIT) theory. To verify the suitability of this method, we performed forward modeling with a three-dimensional radiation magnetohydrodynamic model of a solar active region. Intensities of several spectral lines from Fe x were synthesized from the model. Based on MIT theory, the intensity ratios of the MIT line Fe x 257 Å to several other Fe x lines were used to derive magnetic-field strengths, which were then compared with the field strengths in the model. We also developed a new method to simultaneously estimate the coronal density and temperature from the Fe x 174/175 and 184/345 Å line ratios. Using these estimates, we demonstrated that the MIT technique can provide reasonably accurate measurements of the coronal magnetic field in both on-disk and off-limb solar observations. Our investigation suggests that a spectrometer that can simultaneously observe the Fe x 174, 175, 184, 257, and 345 Å lines and allow an accurate radiometric calibration for these lines is highly desired to achieve reliable measurements of the coronal magnetic field. We have also evaluated the impact of the uncertainty in the Fe x 3p4 3d 4D5/2 and 4D7/2 energy difference on the magnetic-field measurements.

Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R2) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature (Ts). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and Ts uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

Radiometric Calibration Evaluation for FY3D MERSI-II Thermal Infrared Channels at Lake Qinghai

The absolute radiometric accuracy of the Fengyun 3D advanced Medium Resolution Spectral Imager (FY3D MERSI-II) thermal infrared bands was evaluated using the collected field measurements and atmospheric transfer simulations during 16–22 August 2019 at Lake Qinghai. A thermal infrared radiometer equipped on an unmanned surface vehicle was used to continuously collect the water temperature. Atmospheric conditions, surface emissivity, and aerosol optical depth measured near the field experiment site were adopted by the atmospheric transfer code to calculate the parameters about the influence of atmosphere on long-wave radiation, including the path radiance and the transmittance propagated from land surface to the satellite. The radiometric calibration accuracy analysis suggests that the differences between the simulated brightness temperature and satellite-based brightness temperature are −0.346 K and −0.722 K for channel 24 on 18 and 20 August, respectively, while it reaches −0.460 K and −1.036 K for channel 25 on 18 and 20 August, respectively. The vicarious calibration coefficients were found to be in good agreement with the internal onboard calibration coefficient in channel 24 and 25 of the FY3D MERSI-II according to the validation analysis in selected regions. The thermal infrared bands of the FY3D have a good in-orbit operational status according to our vicarious calibration experiments.

Evaluation of the Radiometric Calibration of FY4A-AGRI Thermal Infrared Data Using Lake Qinghai

The absolute radiometric accuracy of thermal infrared channels of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FengYun-4A (FY4A) satellite was assessed with field experiment data in a vicarious calibration on 18, 2019, and August 20, 2019, in Lake Qinghai. Continuous water temperature records were collected by an unmanned surface vehicle equipped with a radiometer during the experimental period. We proposed a spectral matching method based on atmospheric transfer code to create consistent equivalent spectral radiance, and the results predicted by atmospheric transfer simulation were convolved with instrument response functions to obtain predicted brightness temperature for comparison with satellite-derived temperature. Our results indicated that AGRI had an average temperature bias of 0.12 K with an RMSE of 0.17 and 0.61 K with an RMSE of 0.22 K in band 12 and band 13 on August 18, while these biases decreased to -0.01 K with an RMSE of 0.13 and -0.48 K with an RMSE of 0.17 K on August 20, respectively. This radiometric accuracy indicated that AGRI TIR bands are well functional compared with their preflight requirement of less than 1 K. The uncertainty analysis also suggested that atmospheric conditions would alter the calibration accuracy by influencing the transmittance and path thermal radiance. Meanwhile, cloud cover also requires more attention to select the appropriate concurrent satellite pixels to reduce the possible cloud edge effects. It is important and necessary for FY4A AGRI to conduct more intense automatic observations and obtain more data to improve the accuracy of radiometric calibration and monitor the operational status of satellite instruments.

METHOD OF AIRBORNE SAR RADIATION CALIBRATION BASED ON POINT TARGET

Abstract. Radiation calibration of airborne SAR images is the basis for realizing the quantitative processing of airborne SAR images. Radiation calibration based on point targets is a general and convenient method. However, high precision is still the goal of improving the current point target SAR radiation calibration. For the key technology of high-precision radiation calibration processing for airborne SAR images, this paper uses the integral method and the peak method to calculate the point target radiation response energy, By analysing the radiation characteristics of the SAR image and comparing the methods, the sinc function model is used to fit the corrected antenna pattern. Combined with the radar power equation to solve the calibration constant, the complete point target radiation calibration method and process are established, and the different methods in the key technology are compared and analysed. The results show that the calibration method combined with the energy calculation model of the integral method and the sinc function antenna pattern correction model can achieve higher relative and absolute calibration accuracy. The optimized method can meet and exceed the requirements for radiometric calibration accuracy for high-resolution airborne SAR applications.

Inflight Performance of the TanSat Atmospheric Carbon Dioxide Grating Spectrometer

TanSat was successfully launched on December 22, 2016, and has been acquiring global measurements of CO2 and O2 spectral bands in reflected sunlight since early February 2017. The atmospheric carbon dioxide grating spectrometer (ACGS) is a spaceborne three-band grating hyperspectral spectrometer suite onboard TanSat. The ACGS is designed to measure high-spectral-resolution, coboresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band range from 0.758 to 0.778 $\mu \text{m}$ and the weak and strong absorption bands of carbon dioxide (WCO2 and SCO2) ranging from 1.594 to 1.624 $\mu \text{m}$ and from 2.042 to 2.082 $\mu \text{m}$ , respectively. The spectral resolving power ( $\lambda /\Delta \lambda $ ) of the ACGS is ~19 000, ~12 800 and ~12 250 in the O2 A-band, WCO2 band and SCO2 band, respectively. The inflight radiometric calibration accuracy is better than 5%, which satisfies the required specification. The wavelength calibration accuracy of the O2 A-band is ~0.19 pm, that of the WCO2 band is ~0.27 pm, and that of the SCO2 band is ~4.75 pm, all of which meet the 0.05 full-width at half-maximum (FWHM) requirement. The spectroscopic performance of the ACGS exceeds the mission requirements by a margin. The ACGS has noise levels that are comparable to or smaller than those observed during prelaunch testing, and the noise has remained stable in the three bands during inflight operations. The signal-to-noise ratio (SNR) levels of the three bands meet the specified requirements. As expected, the ACGS radiometric performance in the O2 A, WCO2, and SCO2 bands was fairly good during its first 17 months inflight.

Determination of the NOAA-20 VIIRS screen transmittance functions with both the yaw maneuver and regular on-orbit calibration data.

The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NOAA-20 satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs) through observations of an onboard sunlit solar diffuser (SD). The incident sunlight passes through an attenuation screen (the SD screen) and then scatters off the SD to provide a radiance source for the calibration. The on-orbit change of the SD's bidirectional reflectance distribution function (BRDF), referred to as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM) whose eight detectors alternately observe the Sun through another attenuation screen (the SDSM screen) and the sunlit SD. The products of the SD screen transmittance and the BRDF at the mission start for both the SDSM and RSBs and the SDSM screen effective transmittance were measured prelaunch. Large unrealistic undulations in the retrieved H-factor were seen when using the prelaunch screen functions. To improve the accuracy of the retrieved H-factor, shortly after the satellite launch, 15 yaw maneuvers were performed to further characterize the screens. Although significantly improved, the H-factor derived using the screen functions determined from the yaw maneuver data still has large unrealistic undulations, revealing that the solar azimuth angular step size of the yaw maneuvers is too large. In this paper, we add high-quality regular on-orbit SD calibration data to the yaw maneuver data to further improve the relative product of the SD screen effective transmittance and the BRDF at the mission start for the SDSM and the SDSM screen relative effective transmittance. The H-factor time series derived from the newly determined screen transmittance functions is much smoother than that derived from using only the yaw maneuver data and thus considerably improves the radiometric calibration accuracy.

Cross calibration of GF-4 visible and near-infrared sensor with CE-3 VNIS sensor based on lunar

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.

Coupled approach for radiometric calibration and parameter retrieval to improve SPM estimations in turbid inland/coastal waters.

High-precision radiometric calibration (RC) coefficients are required to retrieve reliable water quality parameter products in turbid inland/coastal waters. However, unreliable RC coefficients when satellite sensors lack accurate and in-time RC may lead to pronounced uncertainties in the products through error propagation. To address this issue, a novel approach for estimating water quality parameters, taking suspended particulate matter (SPM) as a case, was proposed by coupling the procedures of RC and SPM model development. The coupled models were established using digital numbers (DNs) from target sensors and "in-situ" SPM measurements from concurrent well-calibrated reference sensors, with the RC coefficients introduced as unknown model parameters. The approach was tested and validated in varied Chinese inland/coastal regions, including the Hongze lake (HL), Taihu lake (TL), and Hangzhou bay (HB). The results show: (1) the DN-based SPM models can achieve a degree of accuracy comparable to reflectance-based SPM models with determination coefficients (R2) of 0.94, 0.92, and 0.72, and root-mean-square errors (RMSE) of 7.02 mg/L, 15.73 mg/L, and 619.2 mg/L for the HL, TL, and HB, respectively, and the biases less than 3% between the derived and official gain RC coefficients; (2) the uncertainty of SPM products increases exponentially as the RC uncertainty increases for exponential reflectance-based SPM models; (3) the DN-based SPM models are less sensitive to the uncertainties of atmospheric correction and RC coefficients, while the reflectance-based models suffer deeply. This study provides encouraging results to the improvement of SPM retrieval using the DN-based models by coupling RC and SPM retrieving processes, especially for sensors without precise RC coefficients.

Based on the GF1 and GF4 radiation calibration analysis

In order to test the in-orbit radiation characteristics of GF-1 and GF-4, and obtain accurate radiometric calibration coefficients, this paper proposes a MODIS and LandSat 8 cross-calibration method based on Dunhuang radiation correction field and a method for comparing the radiance error of GF-1 and GF-4 satellite images based on uniform regions. First, this article introduces the cross-calibration principle and the overview of the uniform target sample area, and compares the band matching and spectral response of the four remote sensors. Then, cross-calibration based on the scenes of the same area at the same time of GF-1, GF-4, MODIS, and LandSat 8 on 28 July 2017. The results show that the calibration coefficient error obtained by this method is within 6%. The error of the calibration coefficient obtained by cross-calibration with LandSat 8 satellite is smaller, which satisfies the basic remote sensing quantitative demand. The GF-1 satellite has high calibration accuracy and is suitable for cross-calibration research of China's independent satellite remote sensors. It is of great significance for the establishment of China's in-orbit calibration system.

Sentinel-2 Level-1 Radiometry Assessment Using Vicarious Methods From DIMITRI Toolbox and Field Measurements From RadCalNet Database

The Sentinel-2A and Sentinel-2B constellation is an Earth observation optical mission developed and operated by the European Space Agency in the frame of the Copernicus program of the European Commission. The novel observation capacity offered by the multispectral instruments (MSI) on-board Sentinel-2 mission provides a massive resource for the Earth observation community. The accurate radiometric calibration is a critical element of the success of the mission; therefore, in-orbit calibration and validation activities are conducted within the Sentinel-2 Mission Performance Centre. The database of the imaging multispectral instrument and tool for radiometric intercomparison and the ground measurements of the radiometric calibration network (RadCalNet) are used to perform the vicarious validation of the radiometry of the Level-1 products delivered to the users. The aims of this validation are to assess the quality of the data product, to monitor the evolution of the radiometry of both instruments, and to ensure that the products meet the mission requirement accuracy. This article outlines the vicarious methods—Rayleigh scattering, desert pseudoinvariant calibration sites, sensor-to-sensor intercalibration, and the ground reflectance-based approach—that are used for the Sentinel-2 radiometry validation. The validation results indicate good performance and stability of both sensors MSI-A and MSI-B and consistency up to ∼1% (∼2% for red-edge bands) between them. The results of the intercomparison with the in situ measurements from RadCalNet illustrate the relevance of such dataset for the vicarious validation activity.

Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

In recent years, the acquisition of high-resolution multi-spectral images by unmanned aerial vehicles (UAV) for quantitative remote sensing research has attracted more and more attention, and radiometric calibration is the premise and key to the quantification of remote sensing information. The traditional empirical linear method independently calibrates each channel, ignoring the correlation between spectral bands. However, the correlation between spectral bands is very valuable information, which becomes more prominent as the number of spectral channels increases. Based on the empirical linear method, this paper introduces the constraint condition of spectral angle, and makes full use of the information of each band for radiometric calibration. The results show that, compared with the empirical linear method, the proposed method can effectively improve the accuracy of radiometric calibration, with the improvement range of Mean Relative Percent Error (MRPE) being more than 3% in the range of visible band and within 1% in the range of near-infrared band. Besides, the method has great advantages in agricultural remote sensing quantitative inversion.

Updated inflight calibration of Hayabusa2's optical navigation camera (ONC) for scientific observations during the cruise phase

The Optical Navigation Cameras (ONC-T, ONC-W1, ONC-W2) onboard Hayabusa2 are also being used for scientific observations of the mission target, C-complex asteroid 162173 Ryugu. Science observations and analyses require rigorous instrument calibrations. In order to meet this requirement, we have conducted extensive inflight observations during the 3.5 years of cruise after the launch of Hayabusa2 on 3 December 2014. In addition to the first inflight calibrations by Suzuki et al. (2018), we conducted an additional series of calibrations, including read-out smear, electronic-interference noise, bias, dark current, hot pixels, sensitivity, linearity, flat-field, and stray light measurements for the ONC. Moreover, the calibrations, especially flat-fields and sensitivities, of ONC-W1 and -W2 are updated for the analysis of the low-altitude (i.e., high-resolution) observations, such as the gravity measurement, touchdowns, and the descents for MASCOT and MINERVA-II payload releases. The radiometric calibration for ONC-T is also updated in this study based on star and Moon observations. Our updated inflight sensitivity measurements suggest the accuracy of the absolute radiometric calibration contains <1.8% error for the ul-, b-, v-, Na-, w-, and x-bands based on star calibration observations and ~5% for the p-band based on lunar calibration observations. The radiance spectra of the Moon, Jupiter, and Saturn from the ONC-T show good agreement with the spacecraft-based observations of the Moon from SP/SELENE and WAC/LROC and with ground-based telescopic observations for Jupiter and Saturn. Our calibration results suggest that the 0.7-μm absorption band typically observed on Ch and Cgh asteroids at the ~3–4% level can be detected with the ONC's signal-to-noise ratio (SNR) of ~2. We also demonstrate a decrease in SNR due to CCD temperature increases caused by radiant heat when the spacecraft is close to the surface, as the SNR is measured to be 150 at a CCD temperature of 20 °C (the worst case scenario). Since Ryugu may possess a significant amount of internal volatiles, a sodium atmosphere around Ryugu is considered to be highly plausible. We evaluated the upper limit of detectability of a sodium atmosphere around Jupiter using the Na-filter as 100 R with 100 images. This implies that the ONC-T can detect a sodium atmosphere of several 10s kR based on a single image set of v- and Na-bands and of several 100 s R based on 100 image sets. Finally, we report the first inflight observation of Ryugu by ONC-T from 1.3 × 106 km away on 26 February 2018. The ONC-T v-band photometric observation displays consistency with ground-based observation, which confirms the capability of ONC-T.

Polarization sensitivity error analysis and measurement of a greenhouse gas monitoring instrument.

Greenhouse gas monitoring instruments (GMI) are spatial heterodyne spectroscopy (SHS) sensors that monitor greenhouse gases (GHG) from space. Due to several kinds of polarization-sensitive optical elements in GMIs, to some extent, the instrument becomes a polarization-sensitive sensor. Its polarization sensitivity will reduce the radiometric accuracy and spectral inversion accuracy of GHG column concentration. Theoretical radiation response models for analyzing the polarization sensitivity of a GMI, which is mainly affected by a scanning mirror beam splitter and diffraction gratings, are presented in this paper. Based on these models and the polarization performance testing, the theoretical and experimental results of the main spectral band of a GMI, covering the wavelength range of 1.568-1.583μm for carbon dioxide (CO2) detection, have been given. The result shows that the linear polarization sensitivity is less than 0.65% and 1.32% in the nadir (45°, 0°) and in the oblique view direction (45±20°, ±31°), respectively, and that it meets the qualification requirement for an absolute radiometric calibration accuracy better than 5%. The absolute radiometric calibration accuracy directly affects the accuracy of GHG concentration retrieval.

Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands: Harrisia pomanensis as a case study

ABSTRACTOrthomosaics derived from consumer grade digital cameras on board unmanned aerial vehicles (UAVs) are increasingly being used for biodiversity monitoring and remote sensing of the environment. To have lasting quantitative value, remotely sensed imagery should be calibrated to physical units of reflectance. Radiometric calibration improves the quality of raw imagery for consistent quantitative analysis and comparison across different calibrated imagery. Moreover, calibrating remotely sensed imagery to units of reflectance improves its usefulness for deriving quantitative biochemical and biophysical metrics. Notwithstanding the existing radiometric calibration procedures for correcting single images, studies on radiometric calibration of UAV-derived orthomosaics remain scarce. In particular, this study presents a cost- and time-efficient radiometric calibration framework for designing calibration targets, checking scene illumination uniformity, converting orthomosaic digital numbers to units of reflectance, and accuracy assessment using in situ mean reflectance measurements (i.e. the average reflectance in a particular waveband). The empirical line method was adopted for the development of radiometric calibration prediction equations using mean reflectance values measured in only one spot within a 97 ha orthomosaic for three wavebands, i.e. red, green and blue of the Sony NEX-7 camera. A scene illumination uniformity check experiment was conducted to establish whether 10 randomly distributed regions within the orthomosaic experienced similar atmospheric and illumination conditions. This methodological framework was tested in a relatively flat terrain semi-arid woodland that is invaded by Harrisia pomanensis (the Midnight Lady). The scene illumination uniformity check results showed that at a 95% confidence interval, the prediction equations developed using mean reflectance values measured from only one spot within the scene can be used to calibrate the entire 97 ha RGB orthomosaic. Furthermore, the radiometric calibration accuracy assessment results showed a correlation coefficient r value of 0.977 (p < 0.01) between measured and estimated reflectance values with an overall root mean square error of 0.063. These findings suggest that given the entire scene being mapped is experiencing similar atmospheric and illumination conditions, then prediction equations developed using mean reflectance values measured in only one spot within the scene can be used to calibrate the entire orthomosaic in semi-arid woodlands. The proposed methodological framework can potentially be tested and adapted for use in large-scale crop mapping and monitoring in precision agriculture, land-use/land-cover classification as well as plant species delimitation, particularly for mapping widespread invasive alien plants such as H. pomanensis.

On-Orbit Radiometric Calibration for a Space-Borne Multi-Camera Mosaic Imaging Sensor

As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated processing of on-orbit absolute radiometric calibration and relative radiometric correction simultaneously between cameras, influencing the accuracy of quantitative applications. Therefore, taking the GaoFen-1 (GF-1) wide-field-of-view (WFV) sensor as an example in this research, an innovative on-orbit radiometric calibration method is proposed to overcome this bottleneck. Firstly, according to the principle of the cross-calibration approach, we retrieve valid MODIS and GF-1 WFV image pairs over the Dunhuang radiometric calibration sites (DRCS) in China by using a set of criteria and extract the radiometric control points (RCPs) connecting in both images. Secondly, the DEM-aided block adjustment of the rational function model is applied to eliminate the geometrical misalignment of GF-1 WFV images at the same orbit. Then, the average digital numbers of spectral and spatial homogeneous surfaces are calculated and chosen as the radiometric tie points (RTPs) extracted from the overlapping region of the adjacent WFV cameras. Thirdly, the radiometric block adjustment (RBA) algorithm is introduced into on-orbit radiometric calibration of the space-borne multi-camera mosaic imaging sensor. Finally, the radiometric calibration coefficients are solved by the least square method. The validation results indicate that our proposed method can acquire high absolute radiometric calibration accuracy and achieve relative radiometric correction between cameras. Compared with the results using the cross-calibration method to calibrate each WFV camera independently, the advantages of RBA are presented. In addition, the uncertainties caused by RCPs’ distribution are discussed, which is beneficial to further optimize the calibration program.

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.