Development of geophysical retrieval algorithms for the MIMR

Pulliainen, Jouni; Karna, J.-P.; Hallikainen, M.

doi:10.1109/36.210466

Cited by 66 publications

(26 citation statements)

References 17 publications

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“…(1987), Goodison et al (1993), Pulliainen et al (1993), Grody and Bassist (1996), Foster et al (1997), Armstrong and Brodzik, (2001), Kelly et al (2003) and Foster et al (2005) (Armstrong et al, 2008). For this investigation, brightness temperature differences between the 18 (19) GHz and 37 GHz channels were multiplied by a coefficient related to the average snow grain size to derive SWE (Chang at al., 1987).…”

Section: Passive Microwave Observationsmentioning

confidence: 99%

Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979–2006)

Foster

Hall

Kelly

et al. 2009

Remote Sensing of Environment

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Section: Passive Microwave Observationsmentioning

confidence: 99%

Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979–2006)

Foster

Hall

Kelly

et al. 2009

Remote Sensing of Environment

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“…The specular part of reflectivity in MEMLS was considered to be 0.9 across all frequencies. Downwelling sky brightness temperature was estimated using a 55% fractile atmosphere transmissivity model [32]. Snow density (ρ snow ) was assigned a constant value of 200 kg m −3 , which closely corresponds to the approximate bulk density over the four seasons at the test site [27], and which is also very close to the typical taiga snow density [33].…”

Section: Retrieval Of Effective Correlation Lengthmentioning

confidence: 99%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.

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“…at L-band. The main components of L-MEB are (1) the vegetation module using the t-w approach [see, e.g., Wigneron et al, 1995], (2) a snow emission model developed by Pulliainen et al [1993] and Pulliainen and Hallikainen [2001], and (3) a simple parameterisation of the atmospheric effects (Pellarin et al, submitted manuscript, 2003). The main variables needed to simulate T B are the wg and temperature, the vegetation water content, the soil roughness parameters, the soil type, and the snow mantel characteristics (depth, density, grain size, liquid water content).…”

Section: The L-meb Modelmentioning

confidence: 99%

Global soil moisture retrieval from a synthetic L‐band brightness temperature data set

Pellarin¹

2003

J. Geophys. Res.

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[1] A technique to retrieve surface soil moisture was assessed at the global scale using a synthetic data set of L-band (1.4 GHz) brightness temperatures T B for 2 years, 1987 and 1988. The global T B database consists of half-degree continental pixels and accounts for within-pixel heterogeneity, on the basis of 1 km resolution land cover maps. The retrievals were performed using a three-parameter inversion method applied to the L-band Microwave Emission of Biosphere model (L-MEB). Three land surface variables were retrieved simultaneously from the multiangular and dual-polarization T B data: surface soil moisture wg, vegetation optical depth t, and surface temperature T S . The retrievals were obtained in two T S configurations: T S was either unknown or known with an uncertainty of 2 K. Applying these two assumptions, global maps of the estimated accuracy of the wg retrievals were produced, and the capability of the T B to monitor wg was evaluated. A sensitivity study was carried out in order to analyze the effect of the main parameters that may affect the retrieval accuracy: the fraction cover of open water and forests, frozen soil conditions, and the radiometric noise on T B . These results contribute to the better definition of the potential of the observations from future spaceborne missions such as the Soil Moisture and Ocean Salinity (SMOS) project.

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Development of geophysical retrieval algorithms for the MIMR

Cited by 66 publications

References 17 publications

Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979–2006)

Seasonal snow extent and snow mass in South America using SMMR and SSM/I passive microwave data (1979–2006)

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Global soil moisture retrieval from a synthetic L‐band brightness temperature data set

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