Results 211 to 220 of about 8,760 (220)
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Journal of Applied Remote Sensing, 2009
Abstract. Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. Crop models require that vegetation characteristics extracted from an image be the correct crop type.
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Abstract. Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. Crop models require that vegetation characteristics extracted from an image be the correct crop type.
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Journal of Applied Remote Sensing, 2007
This paper investigates the approaches of Saharan dust storm detection with the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and presents an index, thermal-infrared dust index, through quantitative analysis of MODIS data for major dust events over the Atlantic Ocean during year 2004-2006. It is found that aerosol optical
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This paper investigates the approaches of Saharan dust storm detection with the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and presents an index, thermal-infrared dust index, through quantitative analysis of MODIS data for major dust events over the Atlantic Ocean during year 2004-2006. It is found that aerosol optical
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Remote Sensing of Environment, 2012
This study assesses the MODIS standard Bidirectional Reflectance Distribution Function (BRDF)/Albedo product, and the daily Direct Broadcast BRDF/Albedo algorithm at tundra locations under large solar zenith angles and high anisotropic diffuse illumination and multiple scattering conditions.
Zhuosen Wang +8 more
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This study assesses the MODIS standard Bidirectional Reflectance Distribution Function (BRDF)/Albedo product, and the daily Direct Broadcast BRDF/Albedo algorithm at tundra locations under large solar zenith angles and high anisotropic diffuse illumination and multiple scattering conditions.
Zhuosen Wang +8 more
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Rangeland Ecology & Management, 2012
Remotely sensed observations of rangelands provide a synoptic view of vegetation condition unavailable from other means. Multiple satellite platforms in operation today (e.g. Landsat, moderate-resolution imaging spectroradiometer [MODIS]) offer opportunities for regional monitoring of rangelands.
Stephen C. Hagen +6 more
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Remotely sensed observations of rangelands provide a synoptic view of vegetation condition unavailable from other means. Multiple satellite platforms in operation today (e.g. Landsat, moderate-resolution imaging spectroradiometer [MODIS]) offer opportunities for regional monitoring of rangelands.
Stephen C. Hagen +6 more
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A snow index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer
Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium, 2002Describes the snow mapping algorithm being developed for use with the Earth Observing System (EOS) MODerate resolution Imaging Spectroradiometer (MODIS). A key component of this snow mapping algorithm is the normalized difference snow index (NDSI). The NDSI employs Landsat Thematic Mapper (TM) visible (0.56 /spl mu/m) and near-infrared (1.65 /spl mu/m)
G.A. Riggs, D.K. Hall, V.V. Salomonson
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Journal of Applied Remote Sensing, 2010
The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is considered a very versatile tool in studying environmental changes. The multi-spectral sensor owns a high revisit period, a large scanning area, plus a handful of other advantages.
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The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is considered a very versatile tool in studying environmental changes. The multi-spectral sensor owns a high revisit period, a large scanning area, plus a handful of other advantages.
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Journal of Applied Remote Sensing, 2009
In the southeastern United States, most wildland fires are of low intensity. A substantial number of these fires cannot be detected by the MODIS contextual algorithm. To improve the accuracy of fire detection for this region, the remote-sensed characteristics of these fires have to be systematically analyzed.
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In the southeastern United States, most wildland fires are of low intensity. A substantial number of these fires cannot be detected by the MODIS contextual algorithm. To improve the accuracy of fire detection for this region, the remote-sensed characteristics of these fires have to be systematically analyzed.
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