Land-Surace-Type Classifcation Using Microwave Brghtness Tempertures From the Special Sensor Microwave/Imager

Christopher M.U. Neale; Marshall J. Mcfarland; Kai Chang

doi:10.1109/36.58970

Land-Surace-Type Classifcation Using Microwave Brghtness Tempertures From the Special Sensor Microwave/Imager

Christopher M.U. Neale, Marshall J. Mcfarland, Kai Chang

Research output: Contribution to journal › Article › peer-review

85 Scopus citations

Abstract

The use of empirical parameter retrieval algorithms over land requires the prior c1assilcation of surface types according to their microwave emission properties. A land-surface-type c1assilcation scheme was developed to be used with the SSM/I algorithm package. The c1assilcation rules were based on statistical analysis of SSM/I brightness temperature combinations from several surfaces, including dense vegetation, rangeland and agricultural soils, deserts, snow, precipitation, surface moisture, etc. A set of independent c1assilcation rules was derived which should result in increased conldence of parameter retrievals.

Original language	English (US)
Pages (from-to)	829-838
Number of pages	10
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	28
Issue number	5
DOIs	https://doi.org/10.1109/36.58970
State	Published - Sep 1990
Externally published	Yes

ASJC Scopus subject areas

Electrical and Electronic Engineering
Earth and Planetary Sciences(all)

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title = "Land-Surace-Type Classifcation Using Microwave Brghtness Tempertures From the Special Sensor Microwave/Imager",

abstract = "The use of empirical parameter retrieval algorithms over land requires the prior c1assilcation of surface types according to their microwave emission properties. A land-surface-type c1assilcation scheme was developed to be used with the SSM/I algorithm package. The c1assilcation rules were based on statistical analysis of SSM/I brightness temperature combinations from several surfaces, including dense vegetation, rangeland and agricultural soils, deserts, snow, precipitation, surface moisture, etc. A set of independent c1assilcation rules was derived which should result in increased conldence of parameter retrievals.",

author = "Neale, {Christopher M.U.} and Mcfarland, {Marshall J.} and Kai Chang",

year = "1990",

month = sep,

doi = "10.1109/36.58970",

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pages = "829--838",

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AU - Chang, Kai

PY - 1990/9

Y1 - 1990/9

N2 - The use of empirical parameter retrieval algorithms over land requires the prior c1assilcation of surface types according to their microwave emission properties. A land-surface-type c1assilcation scheme was developed to be used with the SSM/I algorithm package. The c1assilcation rules were based on statistical analysis of SSM/I brightness temperature combinations from several surfaces, including dense vegetation, rangeland and agricultural soils, deserts, snow, precipitation, surface moisture, etc. A set of independent c1assilcation rules was derived which should result in increased conldence of parameter retrievals.

AB - The use of empirical parameter retrieval algorithms over land requires the prior c1assilcation of surface types according to their microwave emission properties. A land-surface-type c1assilcation scheme was developed to be used with the SSM/I algorithm package. The c1assilcation rules were based on statistical analysis of SSM/I brightness temperature combinations from several surfaces, including dense vegetation, rangeland and agricultural soils, deserts, snow, precipitation, surface moisture, etc. A set of independent c1assilcation rules was derived which should result in increased conldence of parameter retrievals.

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Land-Surace-Type Classifcation Using Microwave Brghtness Tempertures From the Special Sensor Microwave/Imager

Abstract

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