Radiometric Calibration Errors Research Articles

OC_3S: An optical classification and spectral scoring system for global waters using UV–visible remote sensing reflectance

Classification of water bodies is an effective means of understanding the optical properties of aquatic environments. However, most existing water classification systems are mainly based on the visible domain, and the ultraviolet (UV) wavelength, which has important impacts on the growth of phytoplankton, has received less focus. In this study, we developed a novel water classification and spectral scoring system (denoted OC_3S) using 26,709 global in situ hyperspectral data. These spectral data range from the UV to visible domain (380 nm to 750 nm), covering waters from clear to turbid. OC_3S classified water types into 30 classes, providing detailed information on the inherent optical properties and apparent optical properties of different water classes and quality assurance of remote sensing reflectance (Rrs) spectra for multiple sensor data. The results show that OC_3S can capture high-quality Rrs data with little loss of data volume. For example, the uncertainty of Rrs (443) can be reduced by ∼1.33 % at the cost of 5 % of observations in oligotrophic oceans. In addition, OC_3S performs more stably in turbid water classes compared to the QA system (Wei, J., Lee, Z., & Shang, S., 2016b. A system to measure the data quality of spectral remote‐sensing reflectance of aquatic environments. Journal of Geophysical Research: Oceans, 8189–8207) because the coefficient of variation of OC_3S scores in turbid waters can be reduced by ∼44 % compared to that of QA scores. Moreover, good consistency was observed when OC_3S was applied to multiple current satellite images due to its superior tolerance to potential errors in atmospheric correction and radiometric calibration. Our OC_3S is capable of reducing the uncertainty of satellite products and could be applied to future sensors with UV bands (e.g., PACE).

CALIBRATION OF A LIGHT HEMISPHERICAL RADIANCE FIELD IMAGING SYSTEM

Abstract. A light hemispherical radiance field imaging system based on fish-eye camera was developed for the measurement of the surface incident radiance in an urban environment, which is often affected by radiometric heterogeneity problems. A linear radiometric model and a polynomial fish-eye lens model are used. A temperature-dependent dark level model is proposed to improve the dark correction for high dynamic range photography. This paper describes the calibration procedure for spectral and geometrical radiance field measurements and presents the results of the calibration. The spectral radiometric calibration error is 2.07%, 1.34%, and 0.98% for blue, green, and red bands, respectively. The mean geometrical calibration error is 2.037 pixels.

Preliminary characteristics of measurements from Fengyun-4A Lightning Mapping Imager

ABSTRACT The geostationary satellite-borne lightning measurements provide new and more effective way for lightning detection from space. In December 2016, Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) geostationary satellite was launched, which was the first satellite-borne lightning imager developed in China. Up to now, LMI has been working stably for more than 4 years to implement the continuous and effective lightning detections within China and its surrounding areas. In this study, the preliminary characteristics of measurements from LMI were analysed in detail by using the LMI data between 2018 and 2019. Moreover, the factors that affect the detection capability of LMI were investigated through an inter-comparison between the observations of LMI and International Space Station (ISS) Lightning Imaging Sensor (LIS). The results show that the LMI detection results are generally stable and reliable. The relative spatial distribution, monthly and diurnal variations, and the relative optical radiance of lightning observed by the LMI are consistent with those observed by ISS/LIS. However, the absolute values between the two sensors behave significant differences. Caused by the observation geometry, the detection results of LMI in low-latitude regions are more similar to those of ISS/LIS than in mid-latitude regions. In addition, there are some errors in the lightning radiance observed by LMI, which are possibly affected by the bright cloud background as well as radiometric calibration errors. Through targeted improvements to the onboard and ground algorithms for LMI, its detection capability can be greatly improved in future.

Intercalibration of FY-3D MWTS Against S-NPP ATMS Based on Microwave Radiative Transfer Model

Accurate and stable in-orbit radiometric calibration of a satellite instrument is fundamental to Earth geophysical parameter estimation. This article addresses the intercalibration of the microwave temperature sounder (MWTS) on the Chinese second-generation polar-orbiting meteorological satellite, Fengyun 3D (FY-3D), against the advanced technology microwave sounder (ATMS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. First, ocean and land microwave radiative transfer models (RTM) are constructed by combining the sea and land surface emissivity models and atmospheric absorption model, as well as the intercalibration equations. Then, the MWTS and ATMS observations are resampled into a 1° × 1° regular grid space, and the matching brightness temperatures (TBs) under clear-sky/near clear-sky conditions are collected. Next, the TBs at top-of-atmosphere are simulated using the RTM and the fifth generation of European Centre for Medium-Range Weather Forecast atmospheric reanalysis (ERA5) data. After that, the double differences between FY-3D MWTS and S-NPP ATMS and the theoretical observations in FY-3D MWTS channels are calculated. Finally, the radiometric calibration coefficients of FY-3D MWTS are successfully derived from the observations of S-NPP ATMS by linear fits on the matching TBs. In contrast to the ATMS measurements, FY-3D MWTS observations are generally overestimated, and the in-orbit radiometric calibration errors (mean ± standard deviation at the mean) are 1.83 ± 1.45, 0.45 ± 0.94, 1.87 ± 0.60, −0.20 ± 0.36, −0.02 ± 0.37, 0.19 ± 0.24, 1.69 ± 0.28, 2.25 ± 0.29, 1.97 ± 0.33, 1.74 ± 0.42, 2.84 ± 0.42, 0.07 ± 0.65, and 0.32 ± 1.18 K in FY-3D MWTS channels 1–13, respectively. The results with Hewison's semi-empirical land surface emissivity (LSE) model and the results with LSEs derived from the coincident ATMS observations at 50.3 GHz are consistent. Moreover, the intercalibration results obtained by the RTM in this work also agree well with the results obtained by the community radiative transfer model.

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.

Updated MISR dark water research aerosol retrieval algorithm – Part 1: Coupled 1.1 km ocean surface chlorophyll <i>a</i> retrievals with empirical calibration corrections

Abstract. As aerosol amount and type are key factors in the atmospheric correction required for remote-sensing chlorophyll a concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chlin situ < 1.5 mg m−3, the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov–Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p > 0.1). We also compare MODIS–Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR–MODIS collocations having MODIS Chl < 1.5 mg m−3, MISR and MODIS show very good agreement: r = 0. 96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS–Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

Derivation and validation of the stray light correction algorithm for the thermal infrared sensor onboard Landsat 8

It has been known and documented that the Thermal Infrared Sensor (TIRS) on-board Landsat 8 suffers from a significant stray light problem (Reuter et al., 2015; Montanaro et al., 2014a). The issue appears both as a non-uniform banding artifact across Earth scenes and as a varying absolute radiometric calibration error. A correction algorithm proposed by Montanaro et al. (2015) demonstrated great potential towards removing most of the stray light effects from TIRS image data. It has since been refined and will be implemented operationally into the Landsat Product Generation System in early 2017. The algorithm is trained using near-coincident thermal data (i.e., Terra/MODIS) to develop per-detector functional relationships between incident out-of-field radiance and additional (stray light) signal on the TIRS detectors. Once trained, the functional relationships are used to estimate and remove the stray light signal on a per-detector basis from a scene of interest.The details of the operational stray light correction algorithm are presented here along with validation studies that demonstrate the effectiveness of the algorithm in removing the stray light artifacts over a stressing range of Landsat/TIRS scene conditions. Results show that the magnitude of the banding artifact is reduced by half on average over the current (uncorrected) product and that the absolute radiometric error is reduced to approximately 0.5% in both spectral bands on average (well below the 2% requirement). All studies presented here indicate that the implementation of the stray light algorithm will lead to greatly improved performance of the TIRS instrument, for both spectral bands.

Оценки погрешности бортовой радиометрической калибровки ИК-зондировщика ИКФС-2 по данным сканера SEVIRI

Оценки погрешности бортовой радиометрической калибровки ИК-зондировщика ИКФС-2 по данным сканера SEVIRI

Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts have been observed in the TIRS data which include banding and absolute calibration discrepancies that violate requirements in some scenes. Banding is undesired structure that appears within and between the focal plane array assemblies. In addition, in situ measurements have shown an error in the TIRS absolute radiometric calibration that appears to vary with season and location within the image. The source of these artifacts has been determined to be out-of-field radiance that scatters onto the detectors thereby adding a non-uniform signal across the field-of-view. The magnitude of this extra signal can be approximately 8% or higher (band 11) and is generally twice as large in band 11 as it is in band 10. A series of lunar scans were obtained to gather information on the source of this out-of-field radiance. Analyses of these scans have produced a preliminary map of stray light, or ghost, source locations in the TIRS out-of-field area. This dataset has been used to produce a synthetic TIRS scene that closely reproduces the banding effects seen in actual TIRS imagery. Now that the cause of the banding has been determined, a stray light optics model is in development that will pin-point the cause of the stray light source. Several methods are also being explored to correct for the banding and the absolute calibration error in TIRS imagery.

Amplitude scintillation effects on SAR

Space-based low-frequency synthetic aperture radar (SAR) is affected by the ionosphere, which induces both phase and amplitude fluctuations, known as scintillation, into the radar signal. This paper describes the effect of amplitude scintillation on SAR imagery. The two-way amplitude and intensity probability density functions (pdf) for both monostatic and bistatic SAR are derived from the one-way Nakagami-m distribution. The moments are then used to determine the SAR radiometric calibration error and image contrast from the one-way S 4 index. It is also shown that monostatic SAR experiences an S 4-dependent radar cross-section (RCS) enhancement that is not experienced by bistatic SAR. The anisotropy of the ionospheric irregularities strongly affects the degree to which amplitude scintillation will be visible in SAR imagery. The description of anisotropic effects is reviewed and extended to cover SAR. The variation over the Earth is illustrated, showing that a sun-synchronous satellite will experience the strongest effect near Brazil. Two PALSAR images of the same area of Brazilian rainforest are compared, one of which shows azimuthal streaking, corresponding to an amplitude modulation of ± 1 dB. The one-way S 4 index is determined from this imagery using both the RCS enhancement and image contrast measures of S 4, which produce similar results.

Regional-scale mineral mapping using ASTER VNIR/SWIR data and validation of reflectance and mineral map products using airborne hyperspectral CASI/SASI data

ASTER data have been widely and successfully used in lithological mapping and mineral exploration for decades. The errors due to atmospheric water vapor and the characteristics of the photoelectric sensor could lead to the anomalous characteristics of band 5 and 9 in the ASTER standard reflectivity product. These anomalies could result in the spectroscopic misidentification of minerals. This study proposed a simple method of atmospheric correction for converting radiance-at-sensor to ground reflectance. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error using airborne hyperspectral CASI_SASI data. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, a band combination mapping method was proposed for identifying minerals more quickly and accurately.The results indicate that this method for atmospheric correction of ASTER data produces very good results in the arid and bare areas. It is still unknown whether the method is suitable for humid and rainy areas where atmospheric water vapor varies spatially more than in arid and bare areas. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, the mean error of all reflectance bands decreased from 0.0256 to 0.002, and the standard deviation decreased from 0.04251 to 0.0007. The errors of the 2/1, 5/6 and 9/8 band ratios decreased from 2.38%, 4.102%, and 4.28% to 1.26%, −0.162%, and 0.31%, respectively. The radiometric calibration error of the ASTER band 1–9 data can lead to the overestimation of kaolinite. A band index of 2/1 for retrieving Fe3+ cannot produce a reliable Fe3+ distribution map, and a new index should be developed.

基于多光源的光谱分布可调谐光源系统

A spectrum-tunable integrating sphere light source based on a Light Emitting diode (LED) and basis sources was developed to eliminate the effect of spectral non-matching between calibration source and an on-orbit detecting target on the calibration accuracy of space optical remote sensor in a laboratory. The developed source was used to produce typical ground object spectra and to calibrate space optical remote sensors, which significantly diminishes the absolute radiometric calibration error caused by spectral non-matching mentioned above. Firstly, the affecting mechanism of the spectral non-matching on the absolute radiometric calibration accuracy of space optical remote sensor was analyzed. Then, the design scheme of the spectrum-tunable light source was given according to technical requirements. The scheme was mainly divided into four key issues: the determination of light source and design of spectral matching algorithm, the design of light source drive circuit and control system, the matching of target spectrum and spectral adjustment, and the optical-mechanical design of spectrum-tunable light source. In the end, the performance of calibrated spectrum-tunable light source was tested. The results show that the spectral non-matching errors of equi-energy spectrum and 6000 K blackbody spectrum are 6.37% and 8.76% respectively, the irradiance non-uniformity is 0.53%, the irradiance instability of the spectrum-tunable light source within 30 min is 0.03%, and the radiance angular non-uniformity of the spectrum-tunable light source within horizontally 30 is 0.80%. Moreover, it offers the spectral radiance above 0.07 W/(m2srnm) and the integral radiance in 410-900 nm of 58.3 W/(m2sr). These data all meet the design requirements of spectrum-tunable light sources.

Rigorous determination of stratospheric water vapor trends from MIPAS observations

The trend of stratospheric water vapor as a function of latitude is estimated by the MIPAS measurements by means of a new method that uses the measurement space solution. The method uses all the information provided by the observations avoiding the artifacts introduced by the a priori information and by the interpolation to different vertical grids. The analysis provides very precise values of the trends that, however, are limited by a relatively large systematic error induced by the radiometric calibration error of the instrument. The results show in the five years from 2005 to 2009 a dependence on latitude of the stratospheric (from 37 to 53 km) water vapor trend with a positive value of (0.41 ± 0.16)%yr-1 in the northern hemisphere and less than 0.16%yr-1 in the southern hemisphere.

Correction of Time-Varying Radiometric Errors in TRMM Microwave Imager Calibrated Brightness Temperature Products

This letter presents an empirical correction for a time-varying radiometric calibration error (2.5 K peak to peak) present in the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 1B11 calibrated brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ) data product. The T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> error is modeled as a function of the spacecraft orbit position and the Sun elevation above the orbit plane (solar beta angle) based on the observed systematic differences between the TMI 10-GHz vertically polarized (10-V) channel oceanic T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> 's as compared to a radiative transfer model with collocated numerical weather model environmental parameters. Assuming a slightly emissive reflector, the errors in the other TMI channels are estimated from the 10-GHz error model. This method is evaluated using before- and after-correction comparisons of 1B11 data over the ocean and Amazon, and results show that this method effectively eliminates the time-varying calibration error. This correction technique will be implemented in version 7 of the TRMM 1B11 data product, which is scheduled to be released in 2010.

A Procedure for Radiometric Recalibration of Landsat 5 TM Reflective-Band Data

From the Landsat program's inception in 1972 to the present, the Earth science user community has been benefiting from a historical record of remotely sensed data. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for the L5 TM imagery used the detectors' response to the internal calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time, causing radiometric calibration errors up to 20%. In May 2003, the L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center through the National Landsat Archive Production System (NLAPS) were updated to use a lifetime lookup-table (LUT) gain model to radiometrically calibrate TM data instead of using scene-specific IC gains. Further modification of the gain model was performed in 2007. The L5 TM data processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing level-1 products. The best recalibration results are obtained if the work-order report that was included in the original standard data product delivery is available. However, if users do not have the original work-order report, the IC trends can be used for recalibration. The IC trends were generated using the radiometric gain trends recorded in the NLAPS database. This paper provides the details of the recalibration procedure for the following: 1) data processed using IC where users have the work-order file; 2) data processed using IC where users do not have the work-order file; 3) data processed using prelaunch calibration parameters; and 4) data processed using the previous version of the LUT (e.g., LUT03) that was released before April 2, 2007.

Improvement of AVNIR-2 Radiometric Calibration by Comparison of Cross-Calibration and Onboard Lamp Calibration

The Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) on-orbit radiometric performance has been improved through the comparison of cross-calibration and onboard lamp calibration. We proposed a new cross-calibration scheme which uses top-of-atmosphere reflectance functions of satellite zenith angle at temporally and spatially stable ground sites. Each function is made from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution data over 16 days, which includes AVNIR-2 observation dates. The results showed that the radiances of AVNIR-2 bands 1 (463 nm), 2 (560 nm), and 3 (652 nm) agreed with the radiances of Aqua and Terra MODIS within 3% accuracy (standard deviation of 2%). AVNIR-2 band 4 (821 nm) had a difference of about 7% (AVNIR-2< MODIS) due to water-vapor absorption which could explain more than half of the 7%. Using many samples from this scheme, we found dependences of radiometric calibration errors within the field of view (FOV) and for different gain modes. The lamp calibration system onboard AVNIR-2 unveiled these dependences over the FOV and time. Furthermore, for the gain mode, consistent results could be retrieved using the cross-calibration scheme. The retrieved radiometric correction factors (over the FOV and gain modes) have been applied to the Japan Aerospace Exploration Agency AVNIR-2 processing scheme. The subsequent validation of the correction showed, for polar snow areas, an improved radiometric performance over the entire FOV.

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ∼10 mm before correction to ∼5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.

An empirical approach to ocean color data: Reducing bias and the need for post-launch radiometric re-calibration

A new empirical approach is developed for ocean color remote sensing. Called the Empirical Satellite Radiance-In situ Data (ESRID) algorithm, the approach uses relationships between satellite water-leaving radiances and in situ data after full processing, i.e., at Level-3, to improve estimates of surface variables while relaxing requirements on post-launch radiometric re-calibration. The approach is evaluated using SeaWiFS chlorophyll, which is the longest time series of the most widely used ocean color geophysical product. The results suggest that ESRID 1) drastically reduces the bias of ocean chlorophyll estimates, most impressively in coastal regions, 2) modestly improves the uncertainty, and 3) reduces the sensitivity of global annual median chlorophyll to changes in radiometric re-calibration. Simulated calibration errors of 1% or less produce small changes in global median chlorophyll (< 2.7%). In contrast, the standard NASA algorithm set is highly sensitive to radiometric calibration: similar 1% calibration errors produce changes in global median chlorophyll up to nearly 25%. We show that 0.1% radiometric calibration error (about 1% in water-leaving radiance) is needed to prevent radiometric calibration errors from changing global annual median chlorophyll estimates more than the maximum interannual variability observed in the SeaWiFS 9-year record (± 3%), using the NASA standard method. This is much more stringent than the goal for SeaWiFS of 5% uncertainty for water leaving radiance. The results suggest that ESRID, combined with a field sampling program, can improve the quality and reliability of ocean color data, while promoting a unified description of ocean biology from satellite and in situ platforms. Although the results here are focused on chlorophyll, in principle the approach described by ESRID can be applied to any surface variable potentially observable by visible remote sensing.

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.

The Intercomparison of Top-of-Atmosphere Reflectivity Measured by MERIS and SCIAMACHY in the Spectral Range of 443–865 nm

This letter is aimed at better understanding of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) reflectance radiometric calibration errors using the Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT. Earlier investigations showed that the SCIAMACHY calibration error can reach 20% in the visible bands, which prevents aerosol retrievals using the SCIAMACHY data. Recent improvements of the SCIAMACHY calibration are discussed. It is found that the differences in reflectances for the wavelengths 443, 560, 665, 754, and 865 nm between MERIS and improved Processor 6 SCIAMACHY data are close to the MERIS radiometric calibration error, which is below 4%