Cross comparison of eo-1 sensors and other earth resources sensors to landsat-7 etm+ using railroad valley playa

Abstract

The Remote Sensing Group at the University of Arizona has used ground-based test sites for the vicarious calibration of airborne and satellite-based sensors, of which the Railroad Valley Playa in north central Nevada has played a key role. This work presents a cross comparison of five satellite-based sensors that all imaged this playa on July 16, 2001. These sensors include the Advanced Land Imager and Hyperion on the Earth Observer-1 platform, the Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Terra's Moderate Resolution Imaging Spectroradiometer, and Space Imaging's Ikonos. The approach atmospherically corrects the ETM+ data to derive surface reflectance for a 1 km 1 km area of the playa and then uses these reflectances to determine a hyperspectral at-sensor radiance for each of the sensors taking into account the changes in solar zenith angle due to any temporal differences in the overpass times as well as differences in the view angles between the sensors. Results show that all of the sensors agree with ETM+ to within 10% in the solar reflective for bands not affected by atmospheric absorption. ETM+, MODIS, and ALI agree in all bands to better than 4.4% with better agreement in the visible and near infrared. Poorer agreement between Hyperion and other sensors appears to be due partially to poorer signal to noise ratio in the narrowband Hyperion datasets.

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

“…EO-1 Hyperion is the first hyperspectral satellite that operates across the full solar-reflective spectrum with nominal spectral coverage form 0.4-2.5 µm and 10 µnm spectral response functions [22]. Although the applications of Hyperion imagery is limited by the sensor's relatively low signal-to-noise ratio [16,23], spectral mapping results with Hyperion data compare reasonably well with results obtained from airborne hyperspectral sensors, including Hyperspectral Mapper (HyMap) [24] and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) [25,26]. Nevertheless, the availability of hyperspectral data, spaceborne or airborne, is very limited, which prevents time-series analyses of salt pan surfaces.…”

“…The formation of fresh halite crust after significant rainfall and flooding events may superimpose underlying less soluble (e.g., clastic) minerals [110] and hide these (minor) components from surface observations, especially in the central halite dominated, dynamic part of the salt pan. The detection of minor crust components may be also limited by the low signal-to-noise ratio of the Hyperion data [16,23]. However, the spectral properties of the most abundant crust components are represented in the selected …”

Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2

“…Here, we present analyses based on current hyperspectral sensors (EO-1 Hyperion [14]) and multispectral sensors (Landsat 7 ETM+ [15], EO-1 ALI [14] and Landsat 8 OLI [16]), from the National Aeronautics and Space Administration (NASA), which are available free of charge. Although ETM+ is a whisk-broom scanner, we include it in our analysis for the sake of completeness.…”

Cross-Calibration of GF-1/WFV over a Desert Site Using Landsat-8/OLI Imagery and ZY-3/TLC Data

“…Since the launch of Landsat 1 in 1972, the imagery from the Landsat series of satellites has become the longest continuous dataset of reasonable high spatial-resolution imagery for Earth observing, which is widely used for many types of remote sensing applications, such as land surface parameter retrieval, land use and land cover change [1,7], and cross-calibration for other sensors [8][9][10]. The Landsat-8 was launched to continue Landsat's mission of monitoring Earth systems and capturing changes at a relatively high spatial resolution [11,12].…”