Geology Reference
In-Depth Information
8
Data Sets of Radiative Measurements and Properties
Data sets of radiative parameters from sea ice, snow,
and seawater are needed for the retrieval of ice param-
eters from remote sensing observations. This can be
achieved by using the data in an inverse model to recover
prognostic parameters or in a search scheme to find the
closest point to a given observation in an established
database. The data can also be used to support develop-
ment of forward models that simulate the reflected/emit-
ted radiation from snow‐covered sea ice. In general, the
data sets will serve the purpose of developing a sense for
parameter values from different ice types and therefore an
insight into the processes that trigger those values.
The chapter includes collections of radiometric data
from different ice types and open water with different
surface conditions. The data include direct observations
obtained from satellite sensors, derived parameters from
the observations (e.g., polarization and gradient ratios
from passive microwave observations), and ground meas-
urements from field experiments or laboratory studies
of sea ice. Five parameters are considered in this chapter:
radar backscatter, microwave brightness temperatures, vis-
ible and near‐infrared albedo, microwave emissivity, and
microwave penetration depth. Derived parameters from
radar backscatter and microwave brightness temperature
are also included. Outlines of a few techniques to derive or
compile the parameters are presented.
Different sources are used in compiling the data sets.
The main source is satellite observations, but airborne and
ground measurements using surface‐based scatterometers,
radiometers, or albedometers are also included. For each
data set, the source and its peculiarities (i.e., location,
acquisition time, and conditions under which data were
acquired) are presented in most cases. However, no attempt
has been made to explain discrepancies (if they exist)
between data sets of the same parameter from different
sources. Reasons for discrepancies may include lack of
characterization of ice surface or snow cover during meas-
urements or inaccuracy in the absolute calibration of the
instruments. Regardless of the discrepancies and the pos-
sible sources of error, the data provide at least approxi-
mate values and identify trends of variations of the given
parameters with ice types and surface conditions. This is
particularly important for the use of the data across differ-
ent disciplines, namely using data for a different purpose
than for what it was intentionally gathered. An example is
using representative values of albedo of snow‐covered ice
in climate modeling, a set of backscatter from different ice
types to train a classifier, or microwave penetration depth
in a scheme to retrieve ice thickness.
The data are mostly grouped following the major age‐
based categories of sea ice: namely young ice (YI), first‐year
(FY) ice, and multiyear (MY) ice in addition to open water
(OW). Researchers who work on ice parameter retrieval
algorithms have always hoped to identify remote sensing
observations or derived parameters with values that can
unambiguously discriminate between ice types. This issue
poses a great challenge because for most parameters the
values from different ice types overlap, sometimes heavily.
This is revealed in the data sets presented in this chapter.
The information about the overlap is important because
it might trigger decisions to increase the dimensionality of
the data (by adding more channels or using a multisenor
approach) or bring ancillary data in order to be able to
resolve the ambiguity between different ice types. This
point was realized in the early years of the applications of
SAR data when heavy overlap between backscatter from
different ice types was repeatedly observed. That has been
the main drive behind the recent development of space‐
borne polarimetric SAR systems to increase the dimen-
sionality of the data.
It should be noted that estimation of sea ice type or any
ice parameter thickness from remote sensing observations
