Dr. David J. Meyer Profile

Dr. David J. Meyer

at US Geological Survey

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Publications (3)

Proceedings Article | 5 October 2007 Paper

Cross-calibration of the Terra MODIS, Landsat 7 ETM+ and EO-1 ALI sensors using near-simultaneous surface observation over the Railroad Valley Playa, Nevada, test site

Gyanesh Chander, Amit Angal, Taeyoung Jason Choi, David Meyer, Xiaoxiong Jack Xiong, Philippe Teillet

Proceedings Volume 6677, 66770Y (2007) https://doi.org/10.1117/12.734292

KEYWORDS: MODIS, Sensors, Alternate lighting of surfaces, Reflectivity, Calibration, Landsat, Image sensors, Spatial resolution, Imaging systems, Earth observing sensors

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Proceedings Article | 22 December 2006 Paper

Cross-calibration of A.M. constellation for long-term monitoring of land surface processes

David Meyer, Gyanesh Chander

Proceedings Volume 6405, 64050Z (2006) https://doi.org/10.1117/12.694127

KEYWORDS: Calibration, Sensors, MODIS, Reflectivity, Alternate lighting of surfaces, Landsat, Sensor calibration, Bidirectional reflectance transmission function, Earth observing sensors, Vegetation

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Data from multiple sensors must be used together to gain a more complete understanding of land surface processes at a variety of scales. Although higher-level products derived from different sensors (e.g., vegetation cover, albedo, surface temperature) can be validated independently, the degree to which these sensors and their products can be compared to one another is vastly improved if their relative spectroradiometric responses are known. Most often, sensors are directly calibrated to diffuse solar irradiation or vicariously to ground targets. However, space-based targets are not traceable to metrological standards, and vicarious calibrations are expensive and provide a poor sampling of a sensor's full dynamic range. Crosscalibration of two sensors can augment these methods if certain conditions can be met: (1) the spectral responses are similar, (2) the observations are reasonably concurrent (similar atmospheric & solar illumination conditions), (3) errors due to misregistrations of inhomogeneous surfaces can be minimized (including scale differences), and (4) the viewing geometry is similar (or, some reasonable knowledge of surface bi-directional reflectance distribution functions is available). This study extends on a previous study of Terra/MODIS and Landsat/ETM+ cross calibration by including the Terra/ASTER and EO-1/ALI sensors, exploring the impacts of cross-calibrating sensors when conditions described above are met to some degree but not perfectly. Measures for spectral response differences and methods for cross calibrating such sensors are provided in this study. These instruments are cross calibrated using the Railroad Valley playa in Nevada. Best fit linear coefficients (slope and offset) are provided for ALIto- MODIS and ETM+-to-MODIS cross calibrations, and root-mean-squared errors (RMSEs) and correlation coefficients are provided to quantify the uncertainty in these relationships. Due to problems with direct calibration of ASTER data, linear fits were developed between ASTER and ETM+ to assess the impacts of spectral bandpass differences between the two systems. In theory, the linear fits and uncertainties can be used to compare radiance and reflectance products derived from each instrument.

Proceedings Article | 8 September 2006 Paper

Cross-calibration of MODIS with ETM+ and ALI sensors for long-term monitoring of land surface processes

David Meyer, Gyanesh Chander

Proceedings Volume 6296, 62960H (2006) https://doi.org/10.1117/12.683567

KEYWORDS: Calibration, Sensors, MODIS, Alternate lighting of surfaces, Reflectivity, Imaging systems, Landsat, Sensor calibration, Bidirectional reflectance transmission function, Earth observing sensors

Read Abstract +

Increasingly, data from multiple sensors are used to gain a more complete understanding of land surface processes at a variety of scales. Although higher-level products (e.g., vegetation cover, albedo, surface temperature) derived from different sensors can be validated independently, the degree to which these sensors and their products can be compared to one another is vastly improved if their relative spectroradiometric responses are known. Most often, sensors are directly calibrated to diffuse solar irradiation or vicariously to ground targets. However, space-based targets are not traceable to metrological standards, and vicarious calibrations are expensive and provide a poor sampling of a sensor's full dynamic range. Cross-calibration of two sensors can augment these methods if certain conditions can be met: (1) the spectral responses are similar, (2) the observations are reasonably concurrent (similar atmospheric & solar illumination conditions), (3) errors due to misregistrations of inhomogeneous surfaces can be minimized (including scale differences), and (4) the viewing geometry is similar (or, some reasonable knowledge of surface bi-directional reflectance distribution functions is available). This study explores the impacts of cross-calibrating sensors when such conditions are met to some degree but not perfectly. In order to constrain the range of conditions at some level, the analysis is limited to sensors where cross-calibration studies have been conducted (Enhanced Thematic Mapper Plus (ETM+) on Landsat- 7 (L7), Advance Land Imager (ALI) and Hyperion on Earth Observer-1 (EO-1)) and including systems having somewhat dissimilar geometry, spatial resolution & spectral response characteristics but are still part of the so-called "A.M. constellation" (Moderate Resolution Imaging Spectrometer (MODIS) aboard the Terra platform). Measures for spectral response differences and methods for cross calibrating such sensors are provided in this study. These instruments are cross calibrated using the Railroad Valley playa in Nevada. Best fit linear coefficients (slope and offset) are provided for ALI-to-MODIS and ETM+-to-MODIS cross calibrations, and root-mean-squared errors (RMSEs) and correlation coefficients are provided to quantify the uncertainty in these relationships. In theory, the linear fits and uncertainties can be used to compare radiance and reflectance products derived from each instrument.

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