Introduction
Permafrost covers large parts of the Earth's surface and is defined as ground
that remains at or below 0 ∘C for at least 2 consecutive years.
The impact of climate change on the Arctic, as well as other permafrost-dominated environments, is thought to be more severe compared to the rest of
the world . Warming and with
that thawing of permafrost impacts multiple environmental processes ranging
from surface and sub-surface hydrology ,
ecological changes to processes like carbon
exchange . Knowledge of the temperature of the
ground, the extent of permafrost and possible changes in its distribution are
therefore crucial for climate modelling and prediction
. So far, the exact extent of permafrost is
unknown and has only been approximated .
A range of approaches exist for modelling mean annual ground temperature
(MAGT) and subsequent permafrost extent determination. They vary in
complexity and accuracy and their parameterization differs depending on the size
of the area of interest and data availability. Different temperature data
sources are employed for the approximation. For example has used
reanalysis records, which are based on spatially interpolated in situ air
temperature data (mean annual air temperature) together with elevation data
(lapse rate), to generate a global map of permafrost probabilities.
Temperature records from reanalyses and land surface temperature from
satellites together with basic vegetation information and satellite-derived
snow properties have been investigated by
for modelling larger areas as satellite data alone do not provide the required
spatially and temporally consistent temperature values. Cloud cover is
problematic when using thermal infrared. Clear-sky bias is an additional
problem . Improved temperature records can be obtained for
the snow-free period from combination with passive microwave data
. Such records need to be, however, complemented with
reanalysis data for the remaining year in case of further analyses that
target MAGT.
An approach for permafrost extent determination without estimation of MAGT
and use of satellite records has only been recently described by
. They have hypothesized that the number of
frozen days per year from passive microwave satellite data (SSM/I – Special
Sensor Microwave Imager) can be used as indicator for permafrost extent. A
30-year record was analysed for trends and compared to the map of
– often referred to as the IPA (International Permafrost
Association) map – and results of a coupled hydrological and biogeochemical model
. A threshold of half a year of frozen days during at
least 2 consecutive years was chosen to delineate possible permafrost areas.
This value was justified with reference to ,
, , and . These studies
have used measures derived from actual temperature records, specifically the
use of mean annual air temperature and the concept of thawing–freezing
degree days. Results showed differences between the model and microwave
product, especially at the start of the record, which could be due to sparse
satellite data during the beginning of the chosen time period
. The comparison with the IPA permafrost map revealed an
overestimation of permafrost extent around 65∘ N. Overall, agreement
regionally differed, especially over non-continuous permafrost.
suggested that the minimum number of frozen days is
200 for new permafrost to develop (in the form of palsas) in the transition
zone of Scandinavia. Additionally, local factors like a low annual mean air
temperature (< 1 ∘C), water-saturated peat, moss patches and low
vegetation have to be present . This number is considerably higher compared to the
selection of . The utility of a single threshold
may, however, be the result of an underlying relationship between MAGT and
number of frozen days.
Surface state information, and with that the calculation of frozen and thawed
days per year, can also be derived from active microwave sensors operating at
various frequencies
. Frequencies
are usually lower than those measured by SSM/I, and especially C-band
(5.3 GHz) scatterometers like the ASCAT (Advanced SCATterometer) sensor
on-board the Metop satellites have already been shown to be applicable for
freeze–thaw information retrieval in permafrost regions by validation with
near-surface soil temperature from borehole records
.
Multi-annual statistics of thaw and freeze-up timing based on these records
have been applied for the retrieval of circumpolar landscape units, for
example .
The number of frozen days can be observed consistently from space. Freezing
degree days, as conventionally used for MAGT retrieval, require spatial and
temporal interpolation and gap filling of available temperature data (both in
situ and from satellites). The use of frozen days would therefore allow a
purely observation-based assessment. This does, however, require the
assumption that changes at the surface (as represented by the satellite at a
certain frequency) are uniformly and linearly related to sub-ground
temperatures. This neglects effects such as insulation by snow as well as
varying soil thermal conductivity. The validity of the approach may therefore
be limited. In cases in which it is applicable, the approach may, however, allow the
estimation of actual ground temperatures and not only extent.
A further issue is the available data for calibration and validation of data
sets spanning the entire Arctic. The map of does provide
zones of permafrost occurrence which correspond to area fraction of
permanently frozen ground. The actual patterns within the non-continuous zone
are unknown; different sources have been used and it represents the state of
the second half of the 20th century. Results need to be therefore treated
with care. The study of is however used in many studies for
evaluation of modelling results e.g.
in.
An alternative is actual ground temperature measurements. Borehole data only
represent, however, point information, with uneven distribution
and they provide measurements at selected depths only. The
MAGT derived from these records is currently
only provided in some cases by the data owners within the Global Terrestrial
Network on Permafrost (GTN-P) database. A practical method which allows the use
of all freely available data is required for circumpolar applications.
The objective of this study is to investigate the applicability of the frozen
day approach based on satellite data for potential permafrost extent
determination as well as MAGT retrieval. Special emphasis is given on
suitability of calibration and validation data, differences among microwave
sensors, and uncertainties with respect to environmental conditions including
snow, land cover and ground ice content. Regional patterns of agreement with
the map of as well as with in situ data are discussed.
Data sets
Satellite records
We used two microwave remote-sensing data sets derived from globally
available records and with similar classification accuracy obtained by
comparison to air temperature data. They were derived from sensors with
different frequencies, acquisition methods (active versus passive) and
timing. The first was derived from the ASCAT sensor on-board the Metop
satellites. The ASCAT sensor is a C-band (5.255 GHz) scatterometer
, providing almost daily coverage of the Earth's
surface. The Equator overpass time is 09:30 local solar time (LST). The surface state information
(freeze–thaw) was derived from the ASCAT sensor as a surface status flag for
a soil moisture product specifically post-processed for high latitudes
. The circumpolar data set covers the years 2007–2013.
It was developed for permafrost monitoring and climate modelling purposes
and covers the area above
50∘ N with a grid spacing of 12.5 km and an up-to-daily temporal
resolution . This includes the parameters frozen and
thawed ground, temporary water (including snow melt), and frozen
water/permanent ice. The surface status information was derived using a
stepwise threshold algorithm based on ASCAT backscatter values and ECMWF
reanalysis data . The accuracy was assessed with in situ
surface air temperature measurements from the global weather station network
and found to be about 82 % overall . Up to 92 %
agreement was found for near-surface temperature measurements from boreholes
of GTN-P located in Siberia.
The second remotely sensed data set used in this study was derived from
global daily (ascending and descending orbit) 37 GHz vertically polarized
brightness temperature observations from calibrated SMMR (Scanning
Multichannel Microwave Radiometer) and SSM/I satellite sensor records by
. There have been a series of these sensors carried on-board
satellites from the Defense Meteorological Satellite Program. They are
passive radiometric systems that measure atmospheric, ocean and terrain
microwave temperature . SSM/I equatorial crossings are 06:00 and
18:00 LST. The freeze–thaw status was analysed
globally from 1979 to 2012 by . The data set has a nominal
resolution of 25 km and covers the Arctic terrestrial drainage basin. The
actual footprint size at 37 GHz is 37 km × 28 km. The threshold
approach produces two classes: frozen and non-frozen. The estimated
classification accuracy is approximately 85 % (morning passes) to
92 % (afternoon passes) compared to in situ surface air temperature
measurements from the global weather station network . This
data set was used by for the assessment of
permafrost extent changes.
In situ records from boreholes
All borehole records above a latitude of 50∘ N with available time
series of ground temperature were retrieved from the Global Terrestrial
Network for Permafrost database . The network collects
measurements of the thermal state of permafrost (TSP) in polar and mountain
regions. A total of 277 borehole sites have temperature data; single sites often
comprise more than one measurement unit or period, which leads to a total sum
of 1062 ground temperature data sets . The depth of most
boreholes is less than 25 m, although the average is 53 m. The most frequent
sensor depth can be found at 5 m. reported that measurement
systems currently in use generally provide an accuracy and precision of
0.1 ∘C or better. The time series are available in hourly, daily or
annual resolution and cover different time periods. The deepest sensor depths of
the used data set vary between 1 and 99 m. In total, 216 boreholes were
considered. There were however inconsistencies in sensor spacing and the MAGT
at zero annual temperature amplitude was not directly measured. Most records
of North America are accompanied with meta-records, which suggest a sensor
depth (closest to zero annual temperature amplitude) for approximation of the
MAGT. But this information is unavailable for the majority of records from
Asia. MAGT values together with the year they represent are available for 64
sites only (24 % of all sites relevant for the analysis period). The
boreholes represent a MAGT range from -15 to 6 ∘C.
Spatial information on environmental conditions
The circumpolar permafrost map by depicts the permafrost
extent divided into different classes as well as the ground ice content for the
Northern Hemisphere (20 to 90∘ N). The data set defines permafrost
as frozen ground that remains at or below 0 ∘C for at least 2 years.
Areas are classified as continuous, discontinuous, sporadic or isolated
permafrost with differing ground ice content. The classes correspond to
percent area categories: 90–100, 50–90, 10–50, < 10 % and no
permafrost. These classes were compared separately and as one aggregated
class (excluding areas of no permafrost) to the results of the frozen day
classifications. Zones with specified ground ice content are also supplied
with the permafrost map and used in this study. Classes are high
> 20 %, medium 10–20 % and low < 10 %.
The Global Snow Monitoring for Climate Research (GlobSnow) data set provides
information about snow water equivalent (SWE) and snow extent for the Northern
Hemisphere (25–84∘ N) . The
products are based on SMMR, SSM/I and AMSR-E sensor data in combination with
ground-based measurements . The SWE data
product used in this study has a spatial resolution of 25 km. The SWE values
are provided as daily SWE, weekly aggregated SWE and monthly aggregated SWE.
In this study, we used the monthly aggregated SWE, which provides a maximum
SWE value for each month to determine the maximum SWE for each winter.
The Global Land Cover 2000 Project (GLC 2000) provides global land cover
information at 1 km resolution . The data are mainly based on
Satellite Pour l'Observation de la Terre-4 (SPOT-4) observations, partially
supported by other Earth observing sensors .
We extracted the soil texture for all points from the Harmonized World Soil
Database and soil organic carbon (SOC) from the Northern
Circumpolar Soil Carbon Database (NCSCD) by to
include the soil properties in our analysis. The NCSCD is a polygon-based
digital database compiled from harmonized regional soil classification maps
in which data on soils have been linked to pedon data from the northern
permafrost regions to calculate SOC content and mass. It includes SOC values
for 0–30, 0–100, 0–200 and 0–300 cm.
Methods
Preparation of borehole temperature data
The MAGT is usually calculated at the depth of zero annual amplitude (ZAA)
for permafrost studies. As the availability of data at specific depths is
limited, representing or reaching the depth of ZAA is not possible for all
cases. The MAGT, defined as the temperature at a specific site in this study,
was therefore calculated for each borehole location at the depth of the
minimum MAGT. The minimum MAGT, in a stable climate, would be the same as the
MAGT at the depth of ZAA . Where available, we have also collected
metadata for all GTN-P boreholes regarding MAGT, the year of the
calculation and the depth of the sensor representing MAGT in order to test the
validity of this approach. The MAGT at the coldest sensor depth is referred to as MAGTc in the
following. Only sensors instrumented below a depth of
1 m were used as the MAGT near the surface can be much colder than at a
larger depth. Temperature time series of the different sensors were tested
for gaps and inconsistencies. Only years with complete records were
considered for calibration and validation. The used sites are located within
an area with 150 to 330 frozen days as observed in the satellite
records in order to account for artifacts which can occur due to large
water bodies within the footprint, for example.
Preprocessing of satellite records
Both surface status data sets underwent post-processing before being used in
our permafrost extent and temperature estimation.
The ASCAT surface status flag (SSF) data contain cells with no data for which the
algorithm failed to produce a result. Gaps were filled by surrounding values
(class with majority), ensuring a complete data set. The sum of frozen days
per year for every pixel was determined for both satellite records, according
to the method of . Grid cells in which the number of
frozen days exceeds the number of thawed days during 2 consecutive years
were classified as permafrost. We defined the averaging period with respect
to the water year from 1 September to 31 August as suggested by
.
To explore the dependency of the results on snow melting events, the
permafrost extent estimation from ASCAT data was carried out excluding the
melt days in the count of frozen days (FT) and a second analysis counting the
melt days as frozen days (FM).
Model parameterization for potential mean annual ground temperature retrieval
The relationship between MAGTc and frozen days per
year was further examined for
the retrieval of ground temperature and consecutive determination of
permafrost extent by using the 0 ∘C threshold. Only data inside the
range of 150 to 330 frozen days per year were considered. Additionally, sites
which are located on islands in the high Arctic were excluded with respect to
microwave sensor footprint size. The remaining 168 sites were used to fit the
model. The records from ASCAT as well as SSM/I were split into two parts by
defining a calibration (2009–2011) and a validation period (2007–2008). We
tested linear, logarithmic and polynomial functions on their ability to
describe the relationship between MAGTc and frozen days per year.
We found no significant fit for polynomial functions and a slightly weaker
fit for logarithmic functions compared to a simple linear regression.
Therefore a linear model was applied to the frozen days for the years 2009 to
2011 for the determination of an empirical relationship. The resulting
formula was used to estimate the MAGTc from the day of year data
set for the years 2007 and 2008. The differences between the modelled and in
situ MAGTc were calculated separately for the 2 years in order to
assess the capability of the approach to capture inter-annual variations, to
investigate various environmental impacts (snow water equivalent, land cover
type), and differences between previously defined permafrost zones and
specific regions. The Arctic was split into 14 regions in the latter case
(Fig. ).
The average MAGTc for the entire time period (2007–2011) was
calculated for ASCAT FT and SSM/I results and compared. The standard
deviation was derived in addition.
Map of used GTN-P boreholes with region class. “No data” refers to
sites without publicly available data or sites which failed the selection
criteria. ANS – Alaska Highway transect and North Slope; ArcG – Canadian
High Arctic and Greenland; CR – central Russia; NR – northern Russian Far
East; SR – southern Russian Far East; Sva – Svalbard; Swe – Sweden; WA –
western Alaska; WR – western Russia; YN – Yamalo-Nenets district; Yak –
central and southern Yakutia; cSib – central Siberia; mCa – mainland
Canada. For legend of permafrost extent (line features) see
Fig. .
Frozen day threshold determination for potential permafrost extent
The modelled MAGTc values were also classified for each year in
order to obtain permafrost extent maps (binary maps of values below and above
0 ∘C). Results were compared to two further approaches for threshold
determination. In the study of a threshold of
half a year of frozen days was chosen for the delineation of permafrost extent.
Half a year corresponds to 180 or 182.5 days in climate models
. In the first step, we extended the analysis to 210 frozen
days to test the validity of the suggested threshold. The cross comparison
with the permafrost extent classes considered the information of four
thresholds (180, 190, 200, 210) for the entire study area, which allowed us to
analyse the difference in estimated permafrost extent and the sensitivity of
this approach to the chosen threshold. The results from both active and
passive microwave freeze–thaw data sets were compared with the permafrost map
by . To further evaluate the initial threshold of half a
year, in situ data as well as ASCAT and SSM/I number of frozen days were
extracted for each of the borehole locations. We classified MAGTc>0 derived from a borehole (coldest
sensor) as 0 and MAGTc<=0 as 1 and
the DOY > threshold as 1 and DOY < threshold as 0.
Kendall's tau (τ) analyses were used as an alternative approach to
determine a suitable threshold. The correlation coefficient between the in
situ records and satellite-derived number of frozen days has been examined.
It was chosen as it provides a method to measure the ordinal association with
measured or calculated quantities. In order to determine the most suitable
limit to map permafrost extent, thresholds were varied from 180 to 210 days
in 1-day steps.
Eventually, the classified maps (classified modelled MAGTc and half-year threshold approach) were summed up for each data type for the four
periods to obtain information on inter-annual and spatial variability. ASCAT
FT and SSM/I results were compared by deriving the difference between the
individual sums.
Mean annual ground temperature (MAGT) derived from metadata (at or
close to zero annual amplitude) versus derived MAGTc from the coldest
sensor (for year of MAGT specified in the metadata) based on GTN-P borehole
records. The dotted line represents the linear fit.
(a, c, e) Comparison of number of frozen days (doy – days
per year) from satellite records and mean annual ground temperature for GTN-P
boreholes (at the depth of the coldest sensor, years 2010–2012). The red line
represents the
linear fit. (b, d, f) Box plots of modelled versus mean annual ground
temperature from GTN-P boreholes (at the depth of the coldest sensor, years 2007/2008
and 2008/2009). (a, b) ASCAT excluding snow melt days,
(c, d) ASCAT with snow melt days, and (e, f) SSM/I.
Discussion
General issues
The performance of the empirical model for MAGTc (Table )
was partially lower than what can be achieved with the more complex
temperature at the top of permafrost
(TTOP)
model , which considers terrain, snow, land cover
and land surface temperature measurements from satellite data.
reported a model accuracy of 2.5 ∘C for
MAGT. Permafrost temperatures also do not always represent current climate
conditions . This may regionally impact the comparability of
the borehole records with surface observations. Estimates of permafrost
temperatures as well as extent therefore provide a potential distribution
only. Results may however support the identification of regions where
permafrost extent maps, including continuity classes, need to be treated with
care. This includes western and central Siberia. The overall performance of
permafrost extent mapping using number of frozen days is limited but reveals
regional patterns in uncertainties. The extent of permafrost estimated with
the initial threshold is on the order of the actual extent. The error of
commission is, however, relatively large. This problem can be tackled by
adjustment of the threshold and use of different types of satellite
acquisitions (ASCAT versus SSM/I).
The MAGT from boreholes was calculated from the
coldest sensor below 1 m. The depth of these sensors varied from borehole to
borehole, which may impact the empirical model representativity. A high number
of sites was however chosen for calibration, which may weaken the impact. The
evaluation results with MAGT (expected to be at or close to ZAA) in the meta-records for 24 % of the sites support the
assumption that the sensor at the coldest depth can be used for approximation.
Uncertainties introduced by variable sensor spacing can, however, not be
addressed with the available data.
The in all cases (ASCAT and SSM/I, for all permafrost retrieval methods)
higher accounts of the number of frozen days than the previously suggested half-year threshold agrees with field observations by ,
who estimated a minimum number of 200 days for permafrost formation for
Scandinavia. A total of 200 days corresponds to approximately 0.5 ∘C modelled
MAGTc in the case of ASCAT FT. Considering local factors such as
water-saturated peat and organic layer as well as uncertainties in the
retrieval (difference to actual mean temperature), this might still be
sufficient for permafrost formation. Variations in topography within the
footprint also lead to local deviations from days to weeks
. We therefore suggest the consideration of temperature
buffers when such data are applied. Boreholes with in situ MAGTc
below 0 ∘C located in these buffer zones may represent sites at
which
local factors are important. In addition, the role of past climate conditions in present ground temperatures as well as location-specific soil and snow properties need to be considered (see Sect. ).
The classes in correspond to the area fraction of
permanently frozen ground. In the case of the isolated permafrost class it
can be assumed that at least 10 % of ground area is below 0 ∘C, but
the actual mean temperature for a region in these areas (as represented by an
ASCAT or SSM/I cell) can be below or above 0 ∘C depending on local
parameters such as topography and soil properties which impact thermal
conductivity. The latter especially plays a role in occurrence of permafrost
in the transition zone. Data sets of higher spatial resolution would be
required. Relevant measurements from microwave data are only available from
active systems due to technical constraints. Synthetic aperture radar (SAR)
instruments could be used in the case of sufficient sampling intervals
. Current systems and acquisition plans do not however provide
sufficient temporal and spatial sampling .
Regional issues
A threshold higher than the previously suggested half year leads to better
performance of ASCAT than for SSM/I for permafrost extent retrieval,
especially over Scandinavia, western Russia and southern Russian Far East
(Fig. ; region overview in Fig. ). ASCAT
better captures the regional patterns of with the exception of Scandinavia. The actual
temperature amplitude (freezing and thawing degree days) may need to be
considered in this region. The longer snow melt period
(Fig. ) also indicates a certain amount of snow which may
lead to decoupling of air and ground temperatures.
The highest density of boreholes with available data is in the Vorkuta region
in western Russia. This region shows the largest sensitivity to
inclusion–exclusion of snow melting days (Fig. ). Here,
the ASCAT MAGTc results differ by more than 2 ∘C (lower
temperature). This might have an impact on the average deviation derived from
all the borehole records. It is likely larger for the ASCAT FM result than
calculated as most other regions show better agreement.
The validation results in the regions central Russia and central
Siberia differ from those of the other areas. SSM/I results suggest between
1 and 2 ∘C lower regionally averaged MAGTc values
(Figs. and ). The majority of
boreholes located in these regions show
MAGTc higher than 0 ∘C (Fig. ). This deviation therefore impacts the
permafrost boundary retrieval based on freeze–thaw records from SSM/I. These
regions are characterized by a longer-than-average period of diurnal thaw and
refreeze cycling during spring melt . Acquisition
timing may therefore play a role in the determination of the length of the
frozen period.
The Greenland and Svalbard sites are expected to have the highest variations
due to the mixture of glaciers, land area and ocean within the ASCAT as well
as SSM/I pixels. There is actually no coverage of the Greenland and several
Canadian High Arctic sites in the SSM/I records.
Performance differences between ASCAT and SSM/I
Results suggest that SSM/I freeze–thaw records are less suitable to derive
actual MAGTc values below 0 ∘C compared to ASCAT
(Fig. ). The thresholds obtained for SSM/I are considerably
lower than for ASCAT, what might be the result of the different wavelength,
the sensing technique (passive or active), overpass timing and classification
methods used to create these data sets. The considerably lower number of
frozen days in regions with low MAGTc might be the result of the
retrieval method (treatment regarding acquisition timing) and sensitivity to
soil state changes. The instruments also differ in wavelength apart from the
fact that one is active and the other passive. ASCAT uses C band with about
5.7 cm and the SSM/I channel used by uses about
0.8 cm. This results in different signal interactions with objects on the
Earth's surface including snow and vegetation. It can be expected that the
C-band signal is less sensitive to interactions, although present. The latter
issue could be addressed by L-band missions with an even lower frequency than
ASCAT such as the SMOS (Soil Moisture and Ocean Salinity) mission or SMAP (Soil
Moisture Active Passive). In general, the role of acquisition timing and
sampling rate needs to be investigated in more detail for permafrost-related
applications for ASCAT as well as SSM/I.
The lower performance of SSM/I might also be attributed to the fact that it
has an even larger footprint (although gridded to 25 km) than ASCAT. The
validation results are therefore not fully comparable between the sensors for
the entire Arctic, only on a regional level. This may also affect the
calibration since the number of available samples is lower for SSM/I. It
especially affects colder sites (Fig. ). In general,
fewer areas are masked in the ASCAT product. This especially applies to lake-rich regions (Fig. ). Findings of
suggest an offset of the state change in the resolution cell due to lakes.
This may lead to lower accuracy in these regions.
The role of environmental conditions
The amount of snow seems to play the most important role for the
applicability of the frozen day approach. One may expect warmer modelled
MAGTc than in situ values in transition zones due to the fact that
boreholes in transition zones often represent isolated patches of permafrost.
However, the opposite is the case (Fig. ). This may
relate to the importance of the insulation effect of snow cover in these
regions, e.g. as known for Scandinavia .
The number of snow melting days is in general highly variable in the Arctic
but the melting period is comparably short
. Snowmelt is expected to delay the soil surface warming due
to latent heat and therefore cools the soils . Latent heat
released due to refreezing of meltwater may have a warming effect after a few
days . also report start of soil thaw
before the end of snowmelt at Utqiaġvik (formerly Barrow). The overall impact of snowmelt is
expected to be dependent on local conditions . Our results
suggest that there is a warming effect with an impact on MAGTc.
Days with melting snow should therefore be treated as unfrozen. This leads to
higher MAGTc (on average 1 ∘C for considered borehole
locations) and better agreement with in situ measurements. Exclusion of the
snowmelt period is also consistent with calculation of thawing and freezing
degree days from air temperature data. The snowmelt period does also count as
unfrozen in this case.
Snow melting days are, however, not mapped for all grid points in the case of the
ASCAT data set (Fig. ). This may depend on snow depth as
well as acquisition timing. The coverage pattern is irregular across the
Arctic with a mix of morning and evening (ascending and descending orbits)
measurements . Usually only evening measurements capture
the melt as diurnal freeze and thaw cycles are common . The
differentiation between frozen and melting days may however be valid in
regions with prolonged melt and high SWE. Areas with melting snow in the
ASCAT data set are common in the high Arctic, in areas with low
MAGTc (Fig. ) and in areas in the transition
zone (such as Scandinavia). This pattern differs from the length of the snowmelt period detected with SeaWinds QuikSCAT, a Ku-band scatterometer
, which provides several measurements per day. The number
of days with snow melt are much lower in the C-band than in the Ku-band product.
This could be attributed to the lower sensitivity of the C band to melting
processes and the limited temporal sampling. In addition, the QuikSCAT
results reported by represent periods of freezing
and thawing which can contain breaks (with frozen conditions) of up to 10
days.
Land cover (Fig. ) plays a role when comparing performance
of SSM/I versus ASCAT. Boreholes, which fall into the water class according to
GLC2000, are located close to coasts. The coarser-resolution SSM/I data sets
are
more affected here than ASCAT. Deviations are therefore larger for SSM/I.
Modelled results are on average warmer in all cases for the class “shrubs”.
This mostly represents the tundra biome and is also the largest sample. It
overlaps with continuous permafrost, which shows a similar bias
(Fig. ). The difference is larger in the case of SSM/I, which
might relate to the fact that the variation in below-zero MAGTc is
not well reflected in the SSM/I-derived number of frozen days, and also fewer
samples have been available for colder sites due to masking
(Fig. ).
An influence of soil organic carbon on deviations between the modelled
temperature and in situ measurements cannot be clearly exemplified
(Fig. ). This might be partially influenced by spatial
inconsistencies and the nature of the used database . SOC
is represented by areal averages only, but it is the most detailed data set
available to date. The tendency for warmer modelled temperature in the case
of sites with more than 10 kg m-2 of SOC agrees,
however, with the expected effect of organic soils on ground thermal regime
e.g.. Sites with sandy loam and loamy sand seem to differ
between ASCAT and SSM/I results. They represent different regions across
central Siberia and western Russia, but the sample size is much smaller than
for the other categories. This, however, agrees with the observed regional
patterns for differences between the methods.
Conclusions
Conventional approaches for spatially continuous mapping of permafrost
temperatures require gap filling or spatial interpolation. This applies to
the
use of in situ temperature measurements as well as to satellite-derived land
surface temperature (thermal and passive microwave). The direct comparison of
microwave-satellite-derived surface status (frozen–unfrozen), rather than
actual temperatures, to borehole temperatures revealed the potential of such
information for ground temperature estimation. C-band backscatter-based
records performed better than surface status derived from passive microwave
brightness temperature. The relationship between MAGT at the coldest sensor depth
with SSM/I-derived surface status is comparably weak, especially for lower
temperatures. ASCAT can capture variations over the full MAGT range
investigated. The C-band scatterometer record can therefore provide a purely
observational estimate of MAGT. This refers to temperatures at the coldest sensor
depth and not necessarily zero annual temperature range, due to limitations
of the in situ data records. It could be, however, shown that MAGT at the coldest
sensor depth can be used as a substitute for actual MAGT for validation and
calibration purposes.
Our study also points to the role of snow status (dry or melting) in the
temperature of the soil beneath and subsequent impact on MAGTc. A
linear empirical model performed best when days with melting snow were
excluded. The overall RMSE was 2.2 ∘C with ASCAT but the modelled
temperature deviated on average by less than 1 ∘C in footprints
without glaciers and a mix of land and water. Especially regions with large
variations in frozen days among the years and/or among the three
different analyses (ASCAT with snow melting days and without, the SSM/I
records) need to be further investigated with respect to the representativity
of the borehole records and derived temperatures (e.g. western
Siberia). They mostly correspond to the permafrost transition zones. The
validity of the coarse-resolution microwave satellite records for the point
locations needs to be confirmed by using higher-spatial-resolution
synthetic aperture radar (SAR) records, for example. More detailed analyses of the impact
of melting snow conditions is also required in order to clarify
underlying processes. Results also exemplify the role of organic material in
thermal conductivity which is not accounted for with the application of a
global empirical relationship.
In addition, the suitability of surface state information from satellite data
for permafrost extent estimation could be confirmed, but differences among
the tested methods and data sets were also evident. Agreement was high within
the continuous permafrost zone (as defined in ), except
for mountain ranges. Deviations in transition areas were largest in central
Siberia and areas with high snow depth. This underlines the importance of
snow and suggests that advanced models should be applied in the areas of the
mountain ranges in central Asia, including southern and central Yakutia and
Mongolia. These regional differences should be considered in
interpretation of especially long-term trends.