Larry Leigh

The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI

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.

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.

Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) for the Landsat 8 platform was designed and built at NASA Goddard Space Flight Center (GSFC). TIRS data will extend the data record for thermal observations from the heritage Landsat sensors, dating back to the launch of Landsat 4 in 1982. The two-band (10.9 and 12.0 μm) pushbroom sensor with a 185 km-wide swath uses a staggered arrangement of quantum well infrared photodetector (QWIPs) arrays. The required spatial resolution is 100 m for TIRS, with the assessment of crop moisture and water resources being science drivers for that resolution. The evaluation of spatial resolution typically relies on a straight knife-edge technique to determine the spatial edge response of a detector system, and such an approach was implemented for TIRS. Flexibility in the ground calibration equipment used for TIRS thermal-vacuum chamber testing also made possible an alternate strategy that implemented a circular target moved in precise sub-pixel increments across the detectors to derive the edge response. On-orbit, coastline targets were developed to evaluate the spatial response performance. Multiple targets were identified that produced similar results to one another. Even though there may be a slight bias in the point spread function (PSF)/modulation transfer function (MTF) estimates towards poorer performance using this approach, it does have the ability to track relative changes for monitoring long-term instrument status. The results for both pre- and post-launch response analysis show general good agreement and consistency with edge slope along-track values of 0.53 and 0.58 pre- and post-launch and across-track values 0f 0.59 and 0.55 pre- and post-launch.

Recent surface reflectance measurement campaigns with emphasis on best practices, SI traceability and uncertainty estimation

A significant problem facing the optical satellite calibration community is limited knowledge of the uncertainties associated with fundamental measurements, such as surface reflectance, used to derive satellite radiometric calibration estimates. In addition, it is difficult to compare the capabilities of calibration teams around the globe, which leads to differences in the estimated calibration of optical satellite sensors. This paper reports on two recent field campaigns that were designed to isolate common uncertainties within and across calibration groups, particularly with respect to ground-based surface reflectance measurements. Initial results from these efforts suggest the uncertainties can be as low as 1.5% to 2.5%. In addition, methods for improving the cross-comparison of calibration teams are suggested that can potentially reduce the differences in the calibration estimates of optical satellite sensors.

First in-Flight Radiometric Calibration of MUX and WFI on-Board CBERS-4

Brazil and China have a long-term joint space based sensor program called China-Brazil Earth Resources Satellite (CBERS). The most recent satellite of this program (CBERS-4) was successfully launched on 7 December 2014. This work describes a complete procedure, along with the associated uncertainties, used to calculate the in-flight absolute calibration coefficients for the sensors Multispectral Camera (MUX) and Wide-Field Imager (WFI) on-board CBERS-4. Two absolute radiometric calibration techniques were applied: (i) reflectance-based approach and (ii) cross-calibration method. A specific site at Algodones Dunes region located in the southwestern portion of the United States of America was used as a reference surface. Radiometric ground and atmospheric measurements were carried out on 9 March 2015, when CBERS-4 passed over the region. In addition, a cross-calibration between both MUX and WFI on-board CBERS-4 and the Operational Land Imager (OLI) on-board Landsat-8 was performed using the Libya-4 Pseudo Invariant Calibration Site. The gain coefficients are now available: 1.68, 1.62, 1.59 and 1.42 for MUX and 0.379, 0.498, 0.360 and 0.351 for WFI spectral bands blue, green, red and NIR, respectively, in units of (W/(m2·sr·μm))/DN. These coefficients were determined with uncertainties lower than 3.5%. As a validation of these radiometric coefficients, cross-calibration was also undertaken. An evaluation of radiometric consistency was performed between the two instruments (MUX and WFI) on-board CBERS-4 and with the well calibrated Landsat-7 ETM+. Results show that the reflectance values match in all the analogous spectral bands within the specified calibration uncertainties.

Evaluating Modified Atmospheric Correction Methods for Landsat Imagery : Image-Based and Model-Based Calibration Methods

Abstract To increase the accuracy of remotely sensed data for agricultural forecasting, pixel values must be corrected for atmospheric effects and converted to spectral reflectance. The objective of this research was to compare two atmospheric correction methods of Landsat imagery under a range of atmospheric conditions. Ground‐based dark‐object subtraction (GDOS) is an image‐based calibration method that used in situ ground data that the dark‐object subtraction (DOS) method did not use, whereas atmospheric calibration (AC) is a model‐based calibration method that required a standard atmospheric profile refined with the use of in situ atmospheric data. GDOS and AC methods improved the reflectance values and had relationships with measured bands, which were approximately 1 to 1 in all bands. However, the GDOS generally had lower root‐mean‐square errors (RMSE) than AC. Data from this study suggest that at the present time the GDOS method may be more accurate than the AC method.

Overview of the 2015 Algodones Sand Dunes field campaign to support sensor intercalibration

Abstract. Several sites from around the world are being used operationally and are suitable for vicarious calibration of space-borne imaging platforms. However, due to the proximity of these sites (e.g., Libya 4), a rigorous characterization of the landscape is not feasible, limiting their utility for sensor intercalibration efforts. Due to its accessibility and similarities to Libya 4, the Algodones Sand Dunes System in California, USA, was identified as a potentially attractive intercalibration site for space-borne, reflective instruments such as Landsat. In March 2015, a 4-day field campaign was conducted to develop an initial characterization of Algodones with a primary goal of assessing its intercalibration potential. Five organizations from the US and Canada collaborated to collect both active and passive airborne image data, spatial and temporal measurements of spectral bidirectional reflectance distribution function, and in-situ sand samples from several locations across the Algodones system. The collection activities conducted to support the campaign goal is summarized, including a summary of all instrumentation used, the data collected, and the experiments performed in an effort to characterize the Algodones site.

Evaluation of the uncertainty in the spectral band adjustment factor (SBAF) for cross-calibration using Monte Carlo simulation

ABSTRACT Cross-calibration is one of the various methods applied for Earth Observation Satellites sensor calibration. In the cross-calibration procedure, one sensor is calibrated against another sensor, in which the radiometric calibration is better known, via near-simultaneous imaging of a common ground target. One of the most important steps during the cross-calibration is the Spectral Band Adjustment Factor (SBAF) assessment. The SBAF is used to compensate the differences in the spectral responses of the sensors, avoiding large uncertainties in cross-calibration results. The investigation described in this work focussed on the evaluation of the SBAF’s inherent uncertainties using Monte Carlo Simulation method. Basically, the Monte Carlo approach is based on calculating multiple integral by random sampling. The SBAFs were developed for analogous Landsat 8 Operational Land Imager and CBERS 4 Multispectral Camera spectral bands. The Hyperion hyperspectral sensor on-board Earth Observing-1 was utilized to understand the spectral profile of the target and to derive the SBAF. This study was performed over two pseudo invariant calibration sites: Algodones Dunes and Libya-4. The spectral uncertainty of the SBAFs using Monte Carlo was found to be within 0.01–1.79%. The results suggested that the uncertainty of the SBAFs is dependent on the correlation between the input variables: the higher the correlation, the lowest is the SBAF uncertainty.

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

ABSTRACT 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-of-atmosphere 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.

SBAF for cross-calibration of Landsat-8 OLI and Sentinel-2 MSI over North African PICS.

Accurate radiometric cross calibration is critical for guaranteeing the consistency of measurements from different Earth observation sensors, and fully using the combined data in quantitative applications. It becomes even more indispensable with the rapid increase of remote sensing data availability from numerous sensors. The assessment of the Spectral Band Adjustment Factor (SBAF) is a key component of the cross-calibration method. The SBAF compensates for intrinsic differences in sensor response caused by Spectral Response Function (SRF) mismatches. Currently, Sentinel and Landsat data represent the most widely accessible medium spatial resolution multispectral satellite data. Hence, in this study, the SBAF of the Multi-Spectral Imager (MSI) on-board Sentinel-2 and the Operational Land Imager (OLI) on-board Landsat-8 was estimated over pseudo-invariant calibration sites (PICS) located in North Africa. The SBAF depends on the hyperspectral profile of the target and the sensor SRF. Here, the hyperspectral profile was derived from the Hyperion hyperspectral imager on-board the EO-1. Finally, it is important to highlight that an estimate of the SBAF is incomplete unless accompanied with its uncertainty. The uncertainty analysis of the SBAF was implemented using Monte Carlo simulation. The results obtained in this study can be utilized by any user who needs the SBAF of the OLI and MS1 over North Africa Desert sites.