This paper reports on an investigation of radiometric calibration errors due to differences in spectral response functions between satellite sensors when attempting cross-calibration based on near-simultaneous imaging of common ground targets in analogous spectral bands. Five Earth observation sensors on three satellite platforms were included on the basis of their overpass times being within 45 minutes of each other on the same day (Landsat-7 ETM+; EO-1 ALI; Terra MODIS; Terra ASTER; Terra MISR). The simulation study encompassed spectral band difference effects (SBDE) on cross-calibration between all combinations of the sensors considered, using the Landsat solar reflective spectral domain as a framework. Scene content was simulated using ground target spectra for the calibration test sites at Railroad Valley Playa, Nevada and Niobrara Grassland, Nebraska. Results were obtained as a function of calibration test site, satellite sensor, and spectral region. Overall, in the absence of corrections for SBDE, the Railroad Valley Playa site is a "good" to "very good" ground target for cross-calibration between most but not all satellite sensors considered in most but not all spectral regions investigated. "Good" and "very good" are defined as SBDEs within +/- 3 % and +/- 1 %, respectively. Without SBDE corrections, the Niobrara test site is only "good" for cross-calibration between certain sensor combinations in some spectral regions. The paper includes recommendations for spectral data and tools that would facilitate cross-calibration between multiple satellite sensors.
This paper describes work towards building an integrated Earth sensing capability and focuses on the demonstration of a prototype in-situ sensorweb in remote operation in support of flood forecasting. A five-node sensorweb was deployed in the Roseau River Sub-Basin of the Red River Watershed in Manitoba, Canada in September 2002 and remained there throughout the flood season until the end of June 2003. The sensorweb operated autonomously, with soil moisture
measurements and standard meteorological parameters accessed remotely via land line and/or satellite from the Integrated Earth Sensing Workstation (IESW) at the Canada Centre for Remote Sensing (CCRS) in Ottawa. Independent soil moisture data were acquired from actual grab samples and field-portable sensors on the days of RADARSAT and
Envisat Synthetic Aperture Radar (SAR) data acquisitions. The in-situ data were used to help generate spatial soil moisture estimates from the remotely sensed SAR data for use in a hydrological model for flood forecasting.
In this study, we analyzed for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Railroad Valley Playa, Nevada (RVPN) test site. We evaluated multi-sensor and multi-scale image data acquired for the RVPN, including four SPOT HRV images acquired in 1997 and 1998, five NOAA AVHRR images acquired in 1999, and one Landsat TM image acquired in 1998. The results show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric spatial uniformity and temporal stability of the RVPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site.
This paper describes work towards building an integrated Earth sensing capability, in particular the demonstration of a prototype in-situ sensorweb in autonomous remote operation in the context of soil moisture monitoring. A five-node prototype sensorweb was deployed and tested at Bratt's Lake Station in Saskatchewan. The sensorweb operated autonomously and standard meterological parameters and soil moisture measurements were accessed remotely via satellite from the Integrated Earth Sensing Workstation (IESW) at the Canada Centre for Remote Sensing in Ottawa. The paper reports on the prototype sensorweb deployment in general and on soil moisture measurements in particular.
A single data set of spatially extensive hyperspectral imagery is used to carry out vicarious calibrations for multiple Earth observation sensors. Results are presented based on a data acquisition campaign at the newell County rangeland test site in Alberta in October 1998, which included ground-based measurements, satellite imagery, and airborne casi hyperspectral data. This paper present new calibration monitoring obtained for NOAA-14 AVHRR, OrbView-2 SeaWiFS, SPOT-4 VGT, Landsat-5 TM, and SPOT-2 HRV.
In-flight absolute radiometric calibration is critical for multi-temporal and multi-sensor data comparisons. In the case of vicarious calibration of optical sensors based on ground-level measurements, the test site must be well characterized in spatial, radiometric, spectral, and temporal domains. Remotely sensed data acquired at other wavelengths can contribute to a baseline understanding of ground targets and provide insight into the usefulness of such targets for in-flight calibration of optical sensors. With these considerations in mind, multi-temporal ERS-1 SAR data have been obtained for White Sands, New Mexico, and Lunar Lake and Railroad Valley playas in Nevada. This paper reports on an initial examination of these SAR image data sets and the significant pattern changes observed in the scenes. It is concluded that surface roughness, soil moisture and run-off are major factors giving rise to the observed scene characteristics.
Surface reflectance retrieval from imaging spectrometer data has become important for quantitative information extraction in many application areas. In order to calculate surface reflectance from remotely measured radiance, radiative transfer codes play an important role for removal of the scattering and gaseous absorption effects of the atmosphere. The present study evaluates surface reflectances retrieved from airborne visible/infrared imaging spectrometer (AVIRIS) data using three radiative transfer codes: modified 5S (M5S), 6S, and MODTRAN2. Comparisons of the retrieved surface reflectance with ground-based reflectance were made for different target types such as asphalt, gravel, grass/soil mixture (soccer field), and water (Sooke Lake). The results indicate that the estimation of the atmospheric water vapor content is important for an accurate surface reflectance retrieval regardless of the radiative transfer code used. For the present atmospheric conditions, a difference of 0.1 in aerosol optical depth had little impact on the retrieved surface reflectance. The performance of MODTRAN2 is superior in the gas absorption regions compared to M5S and 6S.
A procedure is formulated to investigate the sensitivity of surface reflectances retrieved from satellite sensor data to uncertainties in aerosol optical properties. Aerosol optical characteristics encompassed in the study include the Junge parameter (i.e., spectral dependence), the imaginary part of the refractive index (i.e., aerosol absorption), and the aerosol optical depth. Key results for a wavelength of 0.550 micrometers are presented graphically in terms of accuracy requirements on the aerosol property under consideration for a 5 uncertainty in predicted surface reflectance.
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