Mapping freeze/thaw boundaries with SMMR data

Zuerndorfer, Brian W.; England, Anthony W.; Dobson, M.C.; Ulaby, F. T.

doi:10.1016/0168-1923(90)90106-g

Cited by 57 publications

(31 citation statements)

References 13 publications

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“…Researchers demonstrated that the emissivity at 18 and 36 GHz is different and depends on the ice content and soil texture Jin, Li, and Che 2009b;Dai et al 2012). Zuerndorfer, England, Dobson, et al (1990) and (Zuerndorfer and England 1992) using SMMR data found that a negative SG 18-37 was a good indicator of frozen and thawed grounds. It was demonstrated that the imaginary part of the soil permittivity ε″ decreased faster than the real part ε′ during the soil freezing process and was likely to lead to a decrease in the loss tangent, tan δ = ε″/ε′, which would result in greater photon path lengths in the frozen soil.…”

Section: Theoretical Backgroundmentioning

confidence: 96%

“…Early soil freeze-thaw discrimination algorithms were mainly based on the brightness temperature (T b ) data obtained by the Scanning Multi-channel Microwave Radiometer (SMMR) on board NASA's Nimbus-7 satellite. Zuerndorfer, England, Dobson, et al (1990) and (Zuerndorfer and England 1992) introduced the dual-index algorithm (DIA) by finding that the 18-37 GHz spectral gradient (SG 18-37 ) was a good indicator of frozen and thawed grounds, and the 37 GHz brightness temperature (T b,37 ), which is highly correlated with soil temperature, was used as an additional criterion for soil freeze-thaw discrimination. Once the Special Sensor Microwave Imager (SSM/I) became operational and was used as the source of passive microwave data, Judge et al (1997) adjusted the DIA and used both the vertically polarized SG 18-37 and T b,37 to determine the soil freeze-thaw state.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

Chai

Zhang

et al. 2014

International Journal of Remote Sensing

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This study compared the classification accuracies of three soil freeze-thaw discrimination algorithms in China using Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) passive microwave imagery from 2008. The algorithms used were the dual-index algorithm (DIA), the decision tree algorithm (DTA), and the discriminant function algorithm (DFA). The comparison was conducted based on 0 cm land-surface temperature data from 756 meteorological stations across China by constructing error meta-matrices, and it is divided into two parts. The first part compared the overall classification accuracies from two aspects: temporal variation and spatial distribution. In the second part, the classification accuracies of frozen and thawed soils were evaluated. Results showed that both SSMIS and AMSR-E data can be applied to the DIA, DTA, and DFA algorithms, although they were originally developed from different satellite data sets. However, each of the three algorithms has its own advantages and disadvantages. Possible improvements in the three algorithms for future work are also discussed.

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Section: Theoretical Backgroundmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

Chai

Zhang

et al. 2014

International Journal of Remote Sensing

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“…Here, frozen soil is modeled by an air medium containing spherical ice particles and spherical quartz particles covered with bound water film. Zuerndorfer et al (1990) use Nimbus 7 SMMR data to map daily freeze/thaw patterns in the upper Midwest for the fall of 1984, while Toll et al (1999) use passive-microwave satellite data and simulation modeling to assess seasonally frozen soils for north central US and Canada. More recently, Zhang et al (2003b) use an approach combining a passive microwave remote sensing algorithm and a one-dimensional numerical heat transfer model with phase change to detect the near-surface soil freeze/thaw cycle over snow-free and snow-covered land areas in the contiguous United States.…”

Section: Electromagnetic Properties Of Components Of the Cryosphere 27mentioning

confidence: 99%

Electromagnetic properties of components of the cryosphere

Tedesco

2014

Remote Sensing of the Cryosphere

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“…CC BY 4.0 License. et al, 2012;Lemmetyinen, 2012), detect permafrost zones and estimate depth of soil freezing (Zuerndorfer et al, 1990). They are also used to assess the depth and phenology of ice cover of large freshwater lakes.…”

Section: Introductionmentioning

confidence: 99%

Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data

Tikhonov¹,

Khvostov²,

Romanov³

et al. 2018

Preprint

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Abstract. The paper presents a theoretical analysis of seasonal brightness temperature variations at a number of large freshwater lakes -Baikal, Ladoga, Great Bear Lake (GBL), Great Slave Lake (GSL), and Huron -retrieved from Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) data (1.4 GHz) of the Soil Moisture and Ocean Salinity (SMOS) satellite. The analysis was performed using the model of microwave radiation of plane layered heterogeneous nonisothermal medium. The input 15 parameters for the model were real regional climatological characteristics and glaciological parameters of ice cover of the study lakes. Three distinct seasonal brightness temperature time regions corresponding to different phenological phases of the lake surfaces: complete ice cover, ice melt and deterioration, and open water, were revealed. The paper demonstrates the possibility to determine the beginning of ice cover deterioration from satellite microwave radiometry data. The obtained results can 20 be useful for setting the operating terms of winter crossings and roads on ice, since with the beginning of ice deterioration, these transportation routes across water bodies (rivers, lakes, water reservoirs) become insecure and cannot be used any more.

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Mapping freeze/thaw boundaries with SMMR data

Cited by 57 publications

References 13 publications

Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

Electromagnetic properties of components of the cryosphere

Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data

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