Land-surface-type classification using microwave brightness temperatures from the Special Sensor Microwave/Imager

Neale, Christopher M. U.; McFarland, Marshall J.; Chang, Kunag-An

doi:10.1109/36.58970

Cited by 102 publications

(43 citation statements)

References 5 publications

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“…In essence, two tests are necessary; (1) the possible detection of the scattering signature of rain and (2) the confirmation of its existence over different scattering surfaces. Drawing from a body of literature that has addressed this issue over land (see Neale et al, 1990;Grody, 1991;Hollinger, 1991;Ferraro et al, 1994aFerraro et al, , b, 1998Kniveton et al, 1994;Grody and Basist, 1996), a number of scattering-based tests including frequency-dependent V-and H-polarized TB and V-H TB polarization difference tests are used to differentiate when rain is present or whether a scattering surface itself is giving rise to the scattering signature (see . The complete screening procedure is described in Fig.…”

Section: A12 Screening Over Landmentioning

confidence: 99%

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Mugnai

Smith

Tripoli

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed

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Section: A12 Screening Over Landmentioning

confidence: 99%

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Mugnai

Smith

Tripoli

et al. 2013

Nat. Hazards Earth Syst. Sci.

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“…The emissivity observed at longer wavelengths with a weaker attenuation by the canopy generally represents an effectively thicker layer than those observed at shorter and stronger attenuation wavelengths. Some studies even found that the brightness temperature difference at two microwave wavelengths has certain capability to classify land surface type and to determine forest characteristics [Neale et al 1990;Pulliainen et al 1999;Macelloni et al 2003]. Thus, a new parameter based on the microwave land surface emissivity difference between two wavelengths is introduced to indicate VWC and other vegetation properties of the canopy with a minimal influence of the soil emission underneath vegetation canopy.…”

Section: Microwave Emissivity Difference Vegetation Index (Edvi)mentioning

confidence: 99%

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

Min

Lin

2006

Remote Sensing of Environment

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“…Under no-rain condition, the emissivity of most land surface type increases along with the increasing frequency, thus the TB of 85-GHz channel is always higher than the TB at 37-GHz channel (Neale et al 1998). But under rain condition, the microwave radiation at 85 GHz is much more sensitive to the raindrops and ice particles in the clouds, and the TB of 85 GHz will descend seriously and become lower than the TB at 37-GHz channel.…”

Section: Information From Dt 85v-37vmentioning

confidence: 97%

Rainfall detection over northern Algeria by combining MSG and TRMM data

Ouallouche

Ameur

2014

Appl Water Sci

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In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.

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Land-surface-type classification using microwave brightness temperatures from the Special Sensor Microwave/Imager

Cited by 102 publications

References 5 publications

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

Rainfall detection over northern Algeria by combining MSG and TRMM data

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