SMOS prototype algorithm for detecting autumn soil freezing

Rautiainen, Kimmo; Parkkinen, Tiina; Lemmetyinen, Juha; Schwank, Mike; Wiesmann, Andreas; Ikonen, Jaakko; Derksen, Chris; Davydov, S. P.; Davydova, A. I.; Boike, Julia; Langer, Moritz; Drusch, Matthias; Pulliainen, Jouni

doi:10.1016/j.rse.2016.01.012

Cited by 127 publications

(83 citation statements)

References 40 publications

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“…As a consequence, emerging microwave remote sensing techniques have focused on obtaining global-scale information on parameters, such as snow cover [3][4][5], vegetation optical depth [6,7], ground freeze/thaw states [8][9][10], and soil moisture [11][12][13]. The availability of these recently observable state parameters improves forecasts of climate scenarios and the optimization of corresponding mitigation strategies.…”

Section: Introductionmentioning

confidence: 99%

Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects

Schwank

Naderpour

2018

Remote Sensing

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Ground permittivity and snow density retrievals for the "snow-free period", "cold winter period", and "early spring period" are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of "geophysical noise" (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated "geophysical noise" noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = "H", "V", and "HV", where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = "V" are predominantly least prone to the investigated "geophysical noise". The presented experimental results indicate that retrievals match in-situ observations best for the "snow-free period" and the "cold winter period" when "geophysical noise" is at minimum.

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Section: Introductionmentioning

confidence: 99%

Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects

Schwank

Naderpour

2018

Remote Sensing

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“…Transitional periods play a special role in the Arctic ecosystem; they can last several weeks, and changes in their duration and intensity have been found to have significant impact on terrestrial carbon exchange and changes in ecosystem productivity [1,2]. The freezing and thawing process has also been linked to hydrological processes like surface runoff [3], geotechnical properties of soil [4], biogeochemical properties of thermokarst while circumpolar datasets are derived from coarse resolution data from both active sensors like the Advanced SCATterometer (ASCAT) (C-band, 25-km spatial resolution; e.g., [20,42]) and passive sensors like SMOS (L-band, 35-50-km spatial resolution; e.g., [17,43]). The Soil Moisture Active Passive (SMAP) mission was expected to solve this problem [44], but its failure has left this gap open.…”

Section: Introductionmentioning

confidence: 99%

Surface State across Scales; Temporal and Spatial Patterns in Land Surface Freeze/Thaw Dynamics

Bergstedt

Bartsch

2017

Geosciences

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Abstract:Freezing and thawing of the land surface affects ecosystem and hydrological processes, the geotechnical properties of soil and slope stability. Currently, available datasets on land surface state lack either sufficient temporal or spatial resolution to adequately characterize the complexity of freeze/thaw transition period dynamics. Surface state changes can be detected using microwave remote sensing methods. Data available from scatterometer and Synthetic Aperture Radar (SAR) sensors have been used in the past in regional-to continental-scale approaches to monitor freeze/thaw transitions. This study aims to identify temporal and spatial patterns in freeze/thaw dynamics associated with the issue of differing temporal and spatial resolutions. For this purpose, two datasets representing the timing of freeze/thaw cycles at different resolutions and spatial extents were chosen. The used Advanced SCATterometer (ASCAT) Surface State Product offers daily circumpolar information from 2007-2013 for a 12.5-km grid. The SAR freeze/thaw product offers information of day of thawing and freezing for the years 2005-2010 with a nominal resolution of 500 m and a temporal resolution of up to twice per week. In order to assess the importance of scale when describing temporal and spatial patterns of freeze/thaw processes, the two datasets were compared for spring and autumn periods for the maximum number of overlapping years [2007][2008][2009][2010]. The analysis revealed non-linear landscape specific relationships between the two scales, as well as distinct differences between the results for thawing and re-freezing periods. The results suggest that the integration of globally available high temporal resolution scatterometer data and higher spatial resolution SAR data could be a promising step towards monitoring surface state changes on a seasonal, as well as daily and circumpolar, as well as local scale.

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“…The strong dielectric contrast between ice and water at L-Band passive microwave frequencies (≈1.4 GHz [7]) allows for monitoring of global F/T processes using data from passive microwave L-Band satellite missions. Soil F/T processes have been observed using recent passive L-Band missions including: the National Aeronautics and Space Administration (NASA) Satellite de Aplicaciones Cientificas (SAC-D) Aquarius mission ( [8]; [2011][2012][2013][2014][2015], the European Space Agency Soil Moisture Ocean Salinity (SMOS) mission ( [9]; 2011-present), and the NASA Soil Moisture Active Passive (SMAP) mission ( [10]; 2015-present). However, satellite passive microwave (PMW) observations generally have a coarse spatial resolution at L-Band (nominal resolution of about 40 km) and spatial heterogeneity within PMW pixels limits the development and validation of F/T retrieval algorithms [11].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, a better understanding of the spatial variability in L-Band emissions within a pixel of about 40 km of nominal resolution could help apply the ground-based radiometer observations to satellite-borne applications, but also inform ground-based radiometer campaigns how to optimize the data gathering for satellite-scale algorithm calibration and validation. This issue is true for SM retrieval at L-Band (including algorithm development and calibration/validation e.g., [16]), but can also be applied to other passive microwave based retrievals of snow water equivalent [17,18] and soil freeze/thaw status [3,9].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of L-Band Brightness Temperature during Freeze/Thaw Events over a Prairie Environment

et al. 2017

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Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15-50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014-2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (T B ) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, T B was negatively correlated with soil permittivity (ε G ). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects T B . However, because of the effect of snow on L-Band emission, the correlation between T B and ε G decreased with snow accumulation. When compared to satellite measurements, the average T B of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) T B with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., T B increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms.

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SMOS prototype algorithm for detecting autumn soil freezing

Cited by 127 publications

References 40 publications

Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects

Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects

Surface State across Scales; Temporal and Spatial Patterns in Land Surface Freeze/Thaw Dynamics

Spatial Variability of L-Band Brightness Temperature during Freeze/Thaw Events over a Prairie Environment

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