scispace - formally typeset
Topic

Spectral bands

About: Spectral bands is a(n) research topic. Over the lifetime, 5406 publication(s) have been published within this topic receiving 116689 citation(s).

...read more

Papers
More filters

Journal ArticleDOI
Matthew Joseph Griffin, Alain Abergel1, A. Abreu, Peter A. R. Ade2  +186 moreInstitutions (27)
Abstract: The Spectral and Photometric Imaging REceiver (SPIRE), is the Herschel Space Observatory`s submillimetre camera and spectrometer It contains a three-band imaging photometer operating at 250, 350 and 500 mu m, and an imaging Fourier-transform spectrometer (FTS) which covers simultaneously its whole operating range of 194-671 mu m (447-1550 GHz) The SPIRE detectors are arrays of feedhorn-coupled bolometers cooled to 03 K The photometer has a field of view of 4' x 8', observed simultaneously in the three spectral bands Its main operating mode is scan-mapping, whereby the field of view is scanned across the sky to achieve full spatial sampling and to cover large areas if desired The spectrometer has an approximately circular field of view with a diameter of 26' The spectral resolution can be adjusted between 12 and 25 GHz by changing the stroke length of the FTS scan mirror Its main operating mode involves a fixed telescope pointing with multiple scans of the FTS mirror to acquire spectral data For extended source measurements, multiple position offsets are implemented by means of an internal beam steering mirror to achieve the desired spatial sampling and by rastering of the telescope pointing to map areas larger than the field of view The SPIRE instrument consists of a cold focal plane unit located inside the Herschel cryostat and warm electronics units, located on the spacecraft Service Module, for instrument control and data handling Science data are transmitted to Earth with no on-board data compression, and processed by automatic pipelines to produce calibrated science products The in-flight performance of the instrument matches or exceeds predictions based on pre-launch testing and modelling: the photometer sensitivity is comparable to or slightly better than estimated pre-launch, and the spectrometer sensitivity is also better by a factor of 15-2

...read more

2,313 citations


Journal ArticleDOI
Abstract: The Spectral and Photometric Imaging Receiver (SPIRE), is the Herschel Space Observatory`s submillimetre camera and spectrometer. It contains a three-band imaging photometer operating at 250, 350 and 500 microns, and an imaging Fourier Transform Spectrometer (FTS) which covers simultaneously its whole operating range of 194-671 microns (447-1550 GHz). The SPIRE detectors are arrays of feedhorn-coupled bolometers cooled to 0.3 K. The photometer has a field of view of 4' x 8', observed simultaneously in the three spectral bands. Its main operating mode is scan-mapping, whereby the field of view is scanned across the sky to achieve full spatial sampling and to cover large areas if desired. The spectrometer has an approximately circular field of view with a diameter of 2.6'. The spectral resolution can be adjusted between 1.2 and 25 GHz by changing the stroke length of the FTS scan mirror. Its main operating mode involves a fixed telescope pointing with multiple scans of the FTS mirror to acquire spectral data. For extended source measurements, multiple position offsets are implemented by means of an internal beam steering mirror to achieve the desired spatial sampling and by rastering of the telescope pointing to map areas larger than the field of view. The SPIRE instrument consists of a cold focal plane unit located inside the Herschel cryostat and warm electronics units, located on the spacecraft Service Module, for instrument control and data handling. Science data are transmitted to Earth with no on-board data compression, and processed by automatic pipelines to produce calibrated science products. The in-flight performance of the instrument matches or exceeds predictions based on pre-launch testing and modelling: the photometer sensitivity is comparable to or slightly better than estimated pre-launch, and the spectrometer sensitivity is also better by a factor of 1.5-2.

...read more

2,087 citations


Journal ArticleDOI
Pat S. Chavez1Institutions (1)
Abstract: Digital analysis of remotely sensed data has become an important component of many earth-science studies. These data are often processed through a set of preprocessing or “clean-up” routines that includes a correction for atmospheric scattering, often called haze. Various methods to correct or remove the additive haze component have been developed, including the widely used dark-object subtraction technique. A problem with most of these methods is that the haze values for each spectral band are selected independently. This can create problems because atmospheric scattering is highly wavelength-dependent in the visible part of the electromagnetic spectrum and the scattering values are correlated with each other. Therefore, multispectral data such as from the Landsat Thematic Mapper and Multispectral Scanner must be corrected with haze values that are spectral band dependent. An improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value. The improved method normalizes the predicted haze values for the different gain and offset parameters used by the imaging system. Examples of haze value differences between the old and improved methods for Thematic Mapper Bands 1, 2, 3, 4, 5, and 7 are 40.0, 13.0, 12.0, 8.0, 5.0, and 2.0 vs. 40.0, 13.2, 8.9, 4.9, 16.7, and 3.3, respectively, using a relative scattering model of a clear atmosphere. In one Landsat multispectral scanner image the haze value differences for Bands 4, 5, 6, and 7 were 30.0, 50.0, 50.0, and 40.0 for the old method vs. 30.0, 34.4, 43.6, and 6.4 for the new method using a relative scattering model of a hazy atmosphere.

...read more

1,479 citations


Journal ArticleDOI
Abstract: The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies. It will be produced globally at single-pixel resolution; the algorithm uses as many as 14 of the MEDIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MEDIS cloud mask is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options. The MEDIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy-to-follow algorithm paths. The MEDIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear-sky conditions. This paper outlines the MEDIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MEDIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets.

...read more

1,124 citations


Journal ArticleDOI
Abstract: Spectral properties of a wheat canopy with vegetation fraction (VF) from 0% to 100% in visible and near-infrared (NIR) ranges of the spectrum were studied in order to devise a technique for remote estimation of VF. When VF was 60%, the information content of reflectance spectra in visible range can be expressed by only two independent pairs of spectral bands: (1) the blue from 400 to 500 nm and the red near 670 nm; (2) the green around 550 nm and the red edge region near 700 nm. We propose using only the visible range of the spectrum to quantitatively estimate VF. The green (as well as a 700-nm band) and the red (near 670 nm) reflectances were used in developing new indices, which were linearly proportional to wheat VF ranging from 0% to 100%. The Atmospherically Resistant Vegetation Index (ARVI) concept was used to correct

...read more

1,034 citations


11


Network Information
Related Topics (5)
Spectral signature

2.3K papers, 50.1K citations

93% related
VNIR

1.2K papers, 21.5K citations

90% related
Imaging spectrometer

4.2K papers, 82.6K citations

89% related
Hyperspectral imaging

25.6K papers, 433.2K citations

89% related
Multispectral image

13.4K papers, 247.4K citations

89% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20223
2021162
2020251
2019247
2018219
2017199

Top Attributes

Show by:

Topic's top 5 most impactful authors

Xiaoxiong Xiong

88 papers, 2.5K citations

Amit Angal

18 papers, 334 citations

Aisheng Wu

14 papers, 352 citations

Zhipeng Wang

11 papers, 243 citations

Junqiang Sun

9 papers, 448 citations