John R. Schott

Radiometric scene normalization using pseudoinvariant features

Abstract A scene-to-scene radiometric normalization technique has been developed which corrects for atmospheric degradations, illumination effects, and sensor response differences in multitemporal multispectral imagery. The technique is based on the statistical invariance of the reflectance of man-made in-scene elements such as concrete, asphalt, and rooftops. Differences in the grey-level distributions of these invariant objects is assumed to be a linear function and is corrected statistically to perform the normalization. The technique exhibits errors in reflectance of approximately 1% for Landsat TM and high-resolution air photo imagery in all spectral regions studied.

Validation of a web-based atmospheric correction tool for single thermal band instruments

An atmospheric correction tool has been developed on a public access web site for the thermal band of the Landsat-5 and Landsat-7 sensors. The Atmospheric Correction Parameter Calculator uses the National Centers for Environmental Prediction (NCEP) modeled atmospheric global profiles interpolated to a particular date, time and location as input. Using MODTRAN radiative transfer code and a suite of integration algorithms, the site-specific atmospheric transmission, and upwelling and downwelling radiances are derived. These calculated parameters can be applied to single band thermal imagery from Landsat-5 Thematic Mapper (TM) or Landsat-7 Enhanced Thematic Mapper Plus (ETM+) to infer an at-surface kinetic temperature for every pixel in the scene. The derivation of the correction parameters is similar to the methods used by the independent Landsat calibration validation teams at NASA/Jet Propulsion Laboratory and at Rochester Institute of Technology. This paper presents a validation of the Atmospheric Correction Parameter Calculator by comparing the top-of-atmosphere temperatures predicted by the two teams to those predicted by the Calculator. Initial comparisons between the predicted temperatures showed a systematic error of greater then 1.5K in the Calculator results. Modifications to the software have reduced the bias to less then 0.5 ± 0.8K. Though not expected to perform quite as well globally, the tool provides a single integrated method of calculating atmospheric transmission and upwelling and downwelling radiances that have historically been difficult to derive. Even with the uncertainties in the NCEP model, it is expected that the Calculator should predict atmospheric parameters that allow apparent surface temperatures to be derived within ±2K globally, where the surface emissivity is known and the atmosphere is relatively clear. The Calculator is available at http://atmcorr.gsfc.nasa.gov.

Landsat TM and ETM+ thermal band calibration

Landsat-5 has been imaging the Earth since March 1984, and Landsat-7 was added to the series of Landsat instruments in April 1999. The Landsat Project Science Office and the Landsat-7 Image Assessment System have been monitoring the on-board calibration of Landsat-7 since launch. Additionally, two separate university teams have been evaluating the on-board thermal calibration of Landsat-7 through ground-based measurements since launch. Although not monitored as closely over its lifetime, a new effort is currently being made to validate the calibration of Landsat-5. Two university teams are beginning to collect ground truth under Landsat-5, along with using other vicarious calibration methods to go back into the archive to validate the history of the calibration of Landsat-5. This paper considers the calibration efforts for the thermal band, band 6, of both the Landsat-5 and Landsat-7 instruments. Though stable since launch, Landsat-7 had an initial calibration error of about 3 K, and changes were made to correct for this beginning 1 October 2000 for data processed with the National Landsat Archive Production System (NLAPS) and beginning 20 December 2000 for data processed with the Landsat Product Generation System (LPGS). Recent results from Landsat-5 vicarious calibration efforts show an offset of ‐0.7 K over the lifetime of the instrument. This suggests that historical calibration efforts may have been detecting errors in processing systems rather than changes in the instrument. A correction to the Landsat-5 processing has not yet been implemented but will be in the near future.

Application of spectral mixture analysis and image fusion techniques for image sharpening

Abstract Image fusion is used to merge images of different spatial and spectral resolutions to create a high spatial resolution multispectral combination. High spectral resolution allows identification of materials in the scene, while high spatial resolution locates those materials. Spectral mixing is an algorithm that estimates the percentage of each material (called endmembers) within each low resolution multi/hyperspectral pixel. In this article we extend the spectral mixing (unmixing) approach to use one or more higher resolution sharpening images, locating the endmembers to higher spatial accuracy. The approach starts with conventional unmixing to generate fraction images. Constrained optimization techniques spatially locate the endmembers to high resolution. Synthetic image generation (SIG) tools are used to generate test images. SIG controls all the image parameters, making it easier to analyze algorithm performance. The results show spectral mixing material maps can be successfully sharpened, increasing the effective resolution of the hyperspectral images. Accurate fusion algorithms integrate spectral and spatial information into a single image, presenting the most information to an analyst.

Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration

Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m2·sr·μm or −2.1 K and −4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m2·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required.

Calibration of Landsat thermal data and application to water resource studies

The newest in the Landsat series of satellites was launched April 15, 1999. The imagery collected by Landsat is used for a myriad of applications, from coral reef studies to land management. In order to take advantage of Landsat 7 data, the Enhanced Thematic Mapper+ (ETM+) instrument must be calibrated. This study focuses on the immediate postlaunch calibration verification of the Landsat 7 thermal band (Band 6), specifically so that it can be useful in water resource studies. Two years worth of thermal calibration results using a combination of underfiight data and ground truth show the ETM+ to be extremely stable, though the prelaunch calibration produces an offset of 0.261 W/m2 sr I¼m. This paper focuses on the details of the calibration process, including problems faced with ground truth instrumentation. While the technical emphasis in this paper is the calibration of Landsat thermal data, it is presented in the context of the water resource studies for which calibrated thermal data are required. At certain times in the year, water quality in large lakes, particularly the spatial structure of water quality, is driven by temperature of lake waters. During the spring warming, a phenomena called the thermal bar drives the current and sedimentation of large water bodies. A long-term goal of this study is to use thermally driven hydrodynamic models of lake processes to better understand and monitor water quality in large lakes. This paper presents the hydrodynamic model and the relationship between temperature and water quality in the Great Lakes as one example of why high-resolution, well-calibrated data are critical to earth observing. © 2001 Elsevier Science Inc. All rights reserved.

Evaluation of Two Applications of Spectral Mixing Models to Image Fusion

Abstract Many applications in remote sensing require fusing low-resolution multispectral or hyperspectral images with high-resolution panchromatic images to identify and map materials at high resolution. A number of methods are currently used to produce such hybrid imagery. Until now, these methods have only been evaluated independently, and have not been compared to one another to determine an optimum approach. Two different operations are involved in creating high-resolution material maps. One task is to unmix hyperspectral images into the constituent materials (endmembers). The other task is to fuse low-resolution imagery containing spectral detail with high-resolution image(s) containing fine spatial information. This research performs a quantitative test of three image fusion procedures. The first method is to fuse, then unmix. One sharpens low-resolution multispectral data using a panchromatic image, producing a set of high-resolution multispectral images. These images are then separated into a series of high-resolution endmember maps, locating the materials within the scene. The second method is to unmix, then fuse. In this approach, one first separates the low-resolution multispectral data into a series of material maps using a recently developed adaptive unmixing algorithm. These maps are fused with the panchromatic image in a stage called sharpening, to produce high-resolution material maps. The final method is also an unmix-then-fuse approach. Here, the low-resolution material maps are created using traditional image-wide spectral unmixing methods. The resulting images are fused with the panchromatic image to produce sharpened material maps. In this paper, the three image fusion procedures described above are evaluated for their radiometric and unmixing accuracy. The results show that the adaptive unmixing algorithm is superior to the traditional imagewide methods. Differences between quantitative and visual evaluations indicate an improved error metric must be developed.

Leveraging EO-1 to Evaluate Capability of New Generation of Landsat Sensors for Coastal/Inland Water Studies

Monitoring coastal and inland waters, recognized as case II waters, using the existing Landsat technology is somewhat restricted because of its low signal-to-noise ratio (SNR) and its relatively poor radiometric resolution. The new generation of Landsat, Landsat Data Continuity Mission (LDCM) carrying the Operational Land Imager (OLI), has enhanced features allowing for a more lucid characterization of water constituents in either coastal or inland waters with respect to Landsat-7 (ETM+). This paper applies a physics-based approach to fully examine the potential of OLI in terms of its enhanced features in a water constituent retrieval framework. An EO-1 dataset, including Hyperion and the Advanced Land Imager (ALI), together with nearly coincident ETM+ imagery were atmospherically corrected using a data-driven approach. An in-water radiative transfer model, i.e., Hydrolight, was applied to generate a Look-Up-Table (LUT) of simulated surface reflectances for various combinations of water constituents. Using the Hyperion-derived concentration maps as validation sources, it was found that the simulated OLI imagery is superior to ETM+ on the order of 40%, 20%, and 28% when retrieving the concentrations of chlorophyll-a and total suspended solids (TSS), as well as the absorption of the colored dissolved organic matter (CDOM), respectively. It was also demonstrated that the simulated OLI imagery outperforms the simulated ALI and the recorded ALI datasets in the retrieval of chlorophyll-a and CDOM absorption. It is concluded that the new generation of Landsat enables mapping and monitoring of case II waters with accuracies not achieved with the previous Landsat satellite series.

Remote optical detection of biomass burning using a potassium emission signature

A remotely detectable signature for biomass burning that is specific to flaming combustion is found in the strong emission lines of potassium (K) at 766.5 nm and 769.9 nm. Ground level spectra of a test fire illustrate the high contrast signal provided by K emission. Image data collected at high altitude using the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and analysed for K emission vividly displays the fire fronts of a 1995 fire in Brazil. Sensors for K emission can use silicon detector technology for advantages in high sensitivity, low cost, wide area coverage and fine spatial resolution.

Landsat-5 Thematic Mapper Thermal Band Calibration Update

The Landsat-5 thematic mapper (TM) has been operational since 1984. For much of its life, the calibration of TM has been neglected, but recent efforts are attempting to monitor stability and absolute calibration. This letter focuses on the calibration of the TM thermal band from 1999 to the present. Initial studies in the first two years of the TM mission showed that the thermal band was calibrated within the error in the calibration process (plusmn 0.9 K at 300 K). The calibration was not rigorously monitored again until 1999. While the internal calibrator has behaved as expected, recent vicarious calibration results show a significant offset error of 0.092 W/m2 ldr sr ldr mum or about 0.68 K at 300 K. This offset error was corrected on April 2, 2007 within the U.S. processing system through the modification of a calibration coefficient for all data acquired on or after April 1, 1999. Users can correct their own Level-1 data processed prior to April 2, 2007, by adding 0.092 W/m2 ldr sr ldr mum to their radiance level products. The state of the calibration between 1985 and 1999 is unknown; no changes for data acquired in those years are being recommended here.