P.M. Teillet

On the Slope-Aspect Correction of Multispectral Scanner Data

SUMMARYThe effects of topography on the radiometric properties of multispectral scanner (MSS) data are examined in the context of the remote sensing of forests in mountainous regions. The two test areas considered for this study are located in the coastal mountains of British Columbia, one at the Anderson River near Boston Bar and the other at Gun Lake near Bralorne. The predominant forest type at the former site is Douglas fir, whereas forest types at the latter site are primarily lodgepole pine and ponderosa pine. Both regions have rugged topography, with elevations ranging from 330 to 1100 metres above sea level at Anderson River and from 750 to 1300 metres above sea level at Gun Lake.Lambertian and non-Lambertian illumination corrections are formulated, taking into account atmospheric effects as well as topographic variations. Terrain slope and aspect values are determined from a digital elevation model and atmospheric parameters are obtained from a model atmosphere computation for the solar angles an...

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these var...

Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output

Abstract In response to the need for a simple atmospheric correction method and the consequent verification of such a method, an experiment was conducted to acquire a data set suitable for testing atmospheric correction procedures under a variety of atmospheric conditions. Several procedures, including radiative transfer codes (RTCs) with simulated atmospheres, image-based procedures and dark-object subtraction (DOS), were evaluated by comparing surface reflectance factors derived from Landsat Thematic Mapper (TM) digital data with low-altitude, aircraft-based measurements for seven dates over a 1-year period. Acceptable results, approximately ± 0.02 reflectance (1 σ RMS), were achieved based on an RTC with appropriate simulated atmospheres. The DOS technique was the least accurate method and, in fact, produced greater error in estimations of near-IR reflectance than no correction at all. Two hybrid approaches, which combined the image-based nature of DOS with the precision of an RTC, provided sufficient accuracy and simplicity to warrant consideration for use on an operational basis. Though these results were probably site-specific (characterized by relatively low aerosol levels and low humidity), they illustrate the feasibility of simple atmospheric correction methods and the usefulness of a diverse data set for validation of such techniques.

Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection

The radiometric normalization of multitemporal satellite optical images of the same terrain is often necessary for land cover change detection, e.g., relative differences. In previous studies, ground reference data or pseudo-invariant features (PIFs) were used in the radiometric rectification of multitemporal images. Ground reference data are costly and difficult to acquire for most satellite remotely sensed images and the selection of PIFs is generally subjective. In addition, previous research has been focused on radiometric normalization of two images acquired on different dates. The problem of conservation of radiometric resolution in the case of radiometric normalization between more than two images has not been addressed. This article reports on a new procedure for radiometric normalization between multitemporal images of the same area. The selection of PIFs is done statistically. With quality control, principal component analysis (PCA) is used to find linear relationships between multitemporal images of the same area. The satellite images are normalized radiometrically to a common scale tied to the reference radiometric levels. The procedure ensures the conservation of radiometric resolution for the multitemporal images involved. The new procedure is applied to three Landsat-5 Thematic Mapper (TM) images from three different years (August 1986, 1987, and 1991) and of the same area. Quality control measures show that the error in radiometric consistency between the multitemporal images is reduced effectively. The Normalized Difference Vegetation Index (NDVI) is calculated using the radiometrically normalized multitemporal imagery and assessed in the context of land cover change analysis.

Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets

Abstract Early in its mission, the Landsat-7 spacecraft was temporarily placed in a “tandem” orbit very close to that of the Landsat-5 spacecraft in order to facilitate the establishment of sensor calibration continuity between the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM) sensors. The key period for the tandem configuration was June 1–4, 1999, during which hundreds of nearly coincident matching scenes were recorded by both the Landsat-7 ETM+ and, in cooperation with Space Imaging/EOSAT and international ground stations, the Landsat-5 TM as well. The paper presents a methodology for radiometric cross-calibration of the solar reflective spectral bands of the Landsat-7 ETM+ and Landsat-5 TM sensors and results based on analysis of two different tandem image pairs for which ground reference data are available. With the well-calibrated ETM+ as a reference, the tandem-based cross-calibrations for the two image pairs yield TM responsivities that are consistent to each other to within 2% or better depending on the spectral band. Comparisons with independent methods and results obtained by other groups indicate that the tandem-based cross-calibration is within 3% of the independent results on average in spectral bands 1–4 but compares less favorably in bands 5 and 7. The present study indicates that the tandem cross-calibration approach can provide a valuable “contemporary” calibration update for Landsat-5 TM in the visible and near-infrared spectral bands based on the excellent radiometric performance of Landsat-7 ETM+. The methodology also incorporates adjustments for spectral band differences between the two Landsat sensors. Spectral band difference effects are shown to be more dependent on the surface reflectance spectrum than on atmospheric and illumination conditions. A variety of terrestrial surfaces are assessed regarding their suitability for Landsat radiometric cross-calibration in the absence of surface reflectance spectra.

Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions

Abstract Vegetation indices derived from satellite image data have become one o f the primary information sources for monitoring vegetation conditions and mapping land cover change. The most widely used vegetation index in this context is NDVI, the normalized difference vegetation index, which is a function of red and near-infrared spectral bands. Given that the spectral and spatial of imagery in the red and near-infrared vary from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The present study demonstrates the impact of changes in spectral bandwidth and spatial scale on NDVI derived from Airborne VisiblelInfrared Imaging Spectrometer (AVIRIS) data acquired at 20-m resolution over a forested region in southeastern British Columbia. For this purpose, the 10-nm AVIRIS data were spectrally and spatially aggregated in the red and near-infrared to simulate bandwidths from 10 nm to 150 nm for ground resolutions varying from 20 m to 1100 m. Sensor-specific spectral bands and spatial resolutions such as those for SPOT HRV, Landsat TM, NOAA AVHRR, EOS MODIS, and Envisat MERIS were also generated. NDVI values were then calculated using atmospherically corrected surface reflectances for forestry-related targets for the entire simulated band set at the various scales. The results indicate that the NDVI is significantly affected by differences in spectral bandwidth, especially for the red band, and that changes in spatial resolution lead to less peruasive but more land cover specific effects on NDVI. Results for the forested regions also indicate that NDVI is not very sensitive to the location of the near-infrared spectral band, provided that the bandwidth is no wider than 50 nm and the atmospheric correction for water vapor absorption is adequate. If either proviso is relaxed, the wavelength placement of the near-infrared spectral band is more critical, the optimum location being in the 850–880 nm range. Finally, for the same forest targets, some results were also generated for several other vegetation indices that make straightforward use of atmospherically corrected red and near-infrared spectral bands.

Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery

Abstract An apparatus for acquiring bidirectional reflectance-factor data was constructed and used over four surface types. Data sets were obtained over a headed wheat canopy, bare soil having several different roughness conditions, playa (dry lake bed), and gypsum sand. Results are presented in terms of relative bidirectional reflectance factors (BRFs) as a function of view angle at a number of solar zenith angles, nadir BRFs as a function of solar zenith angles, and, for wheat, vegetation indices as related to view and solar zenith angles. The wheat canopy exhibited the largest BRF changes with view angle. BRFs for the red and the near-infrared (NIR) bands measured over wheat did not have the same relationship with view angle. NIR/Red ratios calculated from nadir BRFs changed by nearly a factor of 2 when the solar zenith angle changed from 20° to 50°. BRF versus view angle relationships were similar for soils having smooth and intermediate rough surfaces but were considerably different for the roughest surface. Nadir BRF versus solar-zenith angle relationships were distinctly different for the three soil roughness levels. Of the various surfaces, BRFs for gypsum sand changed the least with view angle (10% at 30°).

Rayleigh optical depth comparisons from various sources

Rayleigh optical depth values obtained from various computations, tabulations, and parameterizations are not always in good agreement. Important differences as large as 3 or 4% can arise depending on the choice of depolarization factor, the formula used for the refractive index of air, and the choice of standard values for columnar and molecular number densities. The fitting equations generally give rise to the largest differences. The use of different standard altitude profiles for atmospheric pressure and temperature causes a variation of 1% or less in Rayleigh optical depth.

Image correction for radiometric effects in remote sensing

Abstract A general classification of radiometric correction methodologies yields new insights into related procedures as they are implemented typically on image analysis systems. Incorrect results can be obtained from the inappropriate application of different types of corrections related to physical phenomena which give rise to radiometric distortions in a scene. The major types of radiometric correction are reviewed as they apply to digital images acquired by satellite sensors in the visible and infrared portions of the spectrum.

A generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data

Abstract The paper describes a new methodology that uses spatially extensive hyperspectral imagery as reference data to carry out vicarious radiometric calibrations for multiple satellite sensors. The methodology has been validated using data from a campaign at the Railroad Valley playa test site in Nevada in June 1998. The proof of concept has been further tested based on data acquisition campaigns at the Newell County rangeland test site in Alberta in August and October 1998. The rangeland test site in the Newell County region of Alberta is tested for its suitability as a calibration test site for satellite sensor systems. All three campaigns included ground-based measurements, satellite imagery, and airborne hyperspectral data. The airborne hyperspectral sensor data were acquired using the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) at Railroad Valley and the Compact Airborne Spectrographic Imager (casi) in all three campaigns. This paper describes the formulation and implementation of the new methodology, and radiometric calibration monitoring results obtained for five different sensors: NOAA-14 AVHRR, OrbView-2 SeaWiFS, SPOT-4 VGT, SPOT-1/2 HRV, and Landsat-5 TM. The results indicate that the nominal on-orbit radiometric calibrations of all the satellite sensors fit within their predicted uncertainties. The combination of both lower-reflectance and higher-reflectance test sites is shown to improve the quality of the calibration monitoring results. In particular, the combined QUASAR monitoring results obtained from the three airborne data acquisition days at the two test sites, encompassing five satellite sensors and a total of 40 spectral band cases, yield a correlation between QUASAR-based and nominal TOA radiances characterized by y=1.026x−1.26, and r2=0.990. Temporal extensions of QUASAR data sets to calibrate satellite sensors imaging the test site one or more days away from the airborne data acquisition day yield mixed results.