Radiometric Cross Calibration and Validation Using 4 Angle BRDF Model between Landsat 8 and Sentinel 2A (original) (raw)

Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites

European Journal of Remote Sensing

The Sentinel-2A and Landsat-8 satellites carry on-board moderate resolution multispectral imagers for the purpose of documenting the Earth's changing surface. Though they are independently built and managed, users will certainly take advantage of the opportunity to have higher temporal coverage by combining the datasets. Thus it is important for the radiometric and geometric calibration of the MultiSpectral Instrument (MSI) and the Operational Land Imager (OLI) to be compatible. Cross-calibration of MSI to OLI has been accomplished using multiple techniques involving the use of pseudo-invariant calibration sites (PICS) using direct comparisons as well as through use of PICS models predicting top-ofatmosphere reflectance. A team from the University of Arizona is acquiring field data under both instruments for vicarious calibration of the sensors. This paper shows that the work done to date by the Landsat and Sentinel-2 calibration teams has resulted in stable radiometric calibration for each instrument and consistency to~2.5% between the instruments for all the spectral bands that the instruments have in common.

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

Remote Sensing, 2014

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.

An Evaluation of the Use of Atmospheric and BRDF Correction to Standardize Landsat Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2000

Normalizing for atmospheric and land surface bidirectional reflectance distribution function (BRDF) effects is essential in satellite data processing. It is important both for a single scene when the combination of land covers, sun, and view angles create anisotropy and for multiple scenes in which the sun angle changes. As a consequence, it is important for inter-sensor calibration and comparison. Procedures based on physics-based models have been applied successfully with the Moderate Resolution Imaging Spectroradiometer (MODIS) data. For Landsat and other higher resolution data, similar options exist. However, the estimation of BRDF models using internal fitting is not available due to the smaller variation of view and solar angles and infrequent revisits. In this paper, we explore the potential for developing operational procedures to correct Landsat data using coupled physics-based atmospheric and BRDF models. The process was realized using BRDF shape functions derived from MODIS with the MODTRAN 4 radiative transfer model. The atmospheric and BRDF correction algorithm was tested for reflectance factor estimation using Landsat data for two sites with different land covers in Australia. The Landsat reflectance values had a good agreement with ground based spectroradiometer measurements. In addition, overlapping images from adjacent paths in Queensland, Australia, were also used to validate the BRDF correction. The results clearly show that the algorithm can remove most of the BRDF effect without empirical adjustment. The comparison between normalized Landsat and MODIS reflectance factor also shows a good relationship, indicating that cross calibration between the two sensors is achievable.

The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI

Remote Sensing, 2015

This paper presents the vicarious calibration results of Landsat 8 OLI that were obtained using the reflectance-based approach at test sites in Nevada, California, Arizona, and South Dakota, USA. Additional data were obtained using the Radiometric Calibration Test Site, which is a suite of instruments located at Railroad Valley, Nevada, USA. The results for the top-of-atmosphere spectral radiance show an average difference of −2.7, −0.8, 1.5, 2.0, 0.0, 3.6, 5.8, and 0.7% in OLI bands 1-8 as compared to an average of all of the ground-based measurements. The top-of-atmosphere spectral reflectance shows an average difference of 1.6, 1.3, 2.0, 1.9, 0.9, 2.1, 3.1, and 2.1% from the ground-based measurements. Except for OLI band 7, the spectral radiance results are generally within ±5% of the design specifications, and the reflectance results are generally within ±3% of the design specifications. The results from the data collected during the tandem Landsat 7 and 8 flight in March 2013 indicate that ETM+ and OLI agree to each other to within ±2% in similar bands in top-of-atmosphere spectral radiance, and to within ±4% in top-of-atmosphere spectral reflectance. OPEN ACCESS Remote Sens. 2015, 7 601

Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors

This paper provides a summary of the current equations and rescaling factors for converting calibrated Digital Numbers (DNs) to absolute units of at-sensor spectral radiance, Top-Of-Atmosphere (TOA) reflectance, and at-sensor brightness temperature. It tabulates the necessary constants for the Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Land Imager (ALI) sensors. These conversions provide a basis for standardized comparison of data in a single scene or between images acquired on different dates or by different sensors. This paper forms a needed guide for Landsat data users who now have access to the entire Landsat archive at no cost.

Radiometric Calibration of the Landsat MSS Sensor Series

IEEE Transactions on Geoscience and Remote Sensing, 2000

Multispectral remote sensing of the Earth using Landsat sensors was ushered on July 23, 1972, with the launch of Landsat-1. Following that success, four more Landsat satellites were launched, and each of these carried the Multispectral Scanner System (MSS). These five sensors provided the only consistent multispectral space-based imagery of the Earth's surface from 1972 to 1982. This work focuses on developing both a consistent and absolute radiometric calibration of this sensor system. Cross-calibration of the MSS was performed through the use of pseudoinvariant calibration sites (PICSs). Since these sites have been shown to be stable for long periods of time, changes in MSS observations of these sites were attributed to changes in the sensors themselves. In addition, simultaneous data collections were available for some MSS sensor pairs, and these were also used for cross-calibration. Results indicated substantial differences existed between instruments, up to 16%, and these were reduced to 5% or less across all MSS sensors and bands. Lastly, this paper takes the calibration through the final step and places the MSS sensors on an absolute radiometric scale. The methodology used to achieve this was based on simultaneous data collections by the Landsat-5 MSS and Thematic Mapper (TM) instruments. Through analysis of image data from a PICS location and through compensating for the spectral differences between the two instruments, the Landsat-5 MSS sensor was placed on an absolute radiometric scale based on the Landsat-5 TM sensor. Uncertainties associated with this calibration are considered to be less than 5%.

Reflectance-based calibration and validation of the landsat satellite archive

2016

REFLECTANCE-BASED CALIBRATION AND VALIDATION OF THE LANDSAT SATELLITE ARCHIVE SANDEEP KUMAR CHITTIMALLI 2016 The primary objective of this project was to consistently calibrate the entire Landsat series to a common reflectance scale by performing cross-calibration corrections from Landsat-8 OLI to Landsat1 MSS. A consistent radiance-based calibration was already performed from Landsat-8 OLI through Landsat-1 MSS using bright targets and dark targets. The MSS radiance-based calibration results showed an uncertainty of about ±5%. Typically to convert from radiance to reflectance a solar model is used. Unfortunately, there are numerous solar models, all with various levels of accuracies. It was also seen that there is a data format inconsistency for different types of MSS data that impact the radiometric uncertainty of the products when compared to Landsat-8 OLI data. One of the advances Landsat-8 OLI has over to earlier missions is a solar model independent reflectance calibration. He...

Radiometric cross-calibration of KOMPSAT-3 with Landsat-8

Earth Resources and Environmental Remote Sensing/GIS Applications VI, 2015

In this study, Cross calibration was conducted at the Libya 4 PICS site on 2015 using Landsat-8 and KOMPSAT-3A. Ideally a cross calibration should be calculated for each individual scene pair because on any given date the TOA spectral profile is influenced by sun and satellite view geometry and the atmospheric conditions. However, using the near-simultaneous images minimizes this effect because the sensors are viewing the same atmosphere. For the cross calibration, the calibration coefficient was calculated by comparing the at sensor spectral radiance for the same location calculated using the Landsat-8 calibration parameters in metadata and the DN of KOMPSAT-3A for the regions of interest (ROI). Cross calibration can be conducted because the satellite sensors used for overpass have a similar bandwidth. However, not all satellites have the same color filter transmittance and sensor reactivity, even though the purpose is to observe the visible bands. Therefore, the differences in the RSR should be corrected. For the cross-calibration, a calibration coefficient was calculated using the TOA radiance and KOMPSAT-3 DN of the Landsat-8 OLI overpassed at the Libya 4 Site, As a result, the accuracy of the calibration coefficient at the site was assumed to be ± 1.0%. In terms of the results, the radiometric calibration coefficients suggested here are thought to be useful for maintaining the optical quality of the KOMPSAT-3A.

Radiometric calibration and stability of the Landsat-8 Operational Land Imager (OLI)

Earth Observing Systems XX, 2015

Landsat-8 and its two Earth imaging sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have been operating on-orbit for 2 ½ years. The OLI radiometric calibration, which is monitored using on-board lamps, on-board solar diffusers, the moon and vicarious calibration techniques has been stable to within 1% over this period of time. The Coastal Aerosol band, band 1, shows the largest change at about 1% over the period; all other bands have shown no significant trend. OLI bands 1-4 show small discontinuities in response (+0.1% to 0.2%) beginning about 7 months after launch and continuing for about 1 month associated with a power cycling of the instrument, though the origin of the recovery is unclear. To date these small changes have not been compensated for, but this will change with a reprocessing campaign that is currently scheduled for Fall 2015. The calibration parameter files (each typically covering a 3 month period) will be updated for these observed gain changes. A fitted response to an adjusted average of the lamps, solar and lunar results will represent the trend, sampled at the rate of one value per CPF.

Cross-calibration of Landsat-7's visible-near-infrared bands with Terra-MODIS over dark waters

Ocean Sensing and Monitoring IV, 2012

Since its launch, the Enhanced Thematic Mapper plus (ETM+) onboard Landsat-7 has been continuously monitored via different calibration techniques to ensure it maintains the science requirements for demanding application areas. The majority of its applications, including agriculture and forestry, require a robust calibration for medium to high reflective targets. However, when imaging water resources, then the question becomes whether the calibration coefficients are valid for the dark end of the sensor's responsivity curve. Motivated by the Landsat Data Continuity Mission (LDCM) and its potential for providing long-term, robust water studies, in this effort, the calibration status of ETM+ visible bands are examined using a cross-calibration technique. The well-calibrated Terra-MODIS scenes (collection 5) of the past decade over relatively optically stable waters were chosen to evaluate ETM+ stability. The cross-comparison showed that the calibration instability of ETM+ reflective bands lie well within its radiometric uncertainty. The slight calibration differences were found to be less than 1.6%, 0.93%, and 3.2% for the blue, green and the red bands obtained for the period when the MODIS had been radiometrically stable. The NIR band of ETM+, however, exhibits, on average, 4.8% higher signals than those of MODIS. The error budget analysis for the calibration differences showed that 1.2%, 1.6%, 3.5%, and 6.5% errors are associated with the Visible-Near-Infrared (VNIR) bands, respectively. Using the results from the calibration study combined with simulations, it was demonstrated that ETM+ underestimates the retrieved diffuse surface reflectance (R d) in the blue and the green bands as much as 12.2% and 4.4%, respectively, while the red band is overestimated 37%, on average, when studying slightly/moderately turbid. The uncertainties in the retrieval of R d were applied in a water constituent retrieval framework where a physics-based model was utilized to obtain concentrations of chlorophyll-a (CHL) and total suspended solids (TSS) from a Landsat-7 dataset in slightly/moderately turbid waters. It was found that the calibration-induced errors in the retrieval of R d yield, on average, 20% error in retrieval of these components. The results indicate that although ETM+ is well calibrated, its calibration status should be quantified rigorously for water studies when physics-based methods are employed for the removal of atmospheric effects. The over-water characterization of Landsat satellites will become more crucial when the LDCM becomes operational due to its increased capabilities for water resource studies.