Measurements From Space
On average, sea ice covers roughly seven percent of our planet’s oceans. Given the massive area to cover, ship-based and aerial observations can only provide pieces of the “big picture”. Therefore, anyone looking for as complete an overview as possible needs a much higher vantage point: a wide range of Earth observation satellites like the European Space Agency (ESA) satellite CryoSat-2 offer precisely that. Circling the Earth at an altitude of more than 700 kilometres, CryoSat-2 “only” needs 99 minutes to complete an orbit. But the satellite doesn’t simply take pictures like a camera; equipped with a radar altimeter, it can produce precise relief maps of the surfaces of the ocean and sea ice. CryoSat-2 transmits its data to the European Space Operations Centre (ESOC) in Darmstadt. Once at the ESOC, it can be used by researchers around the globe. Thanks to satellites like CryoSat-2, the scientific community always has an up-to-date picture of global sea-ice cover. Since these “eyes in space” orbit the planet several times a day, they can also detect short-term changes in the ice. In this section, we’ll present the most important, sea-ice-relevant Earth observation satellites and describe the high-precision sensors they’re equipped with.
When an object’s properties are measured without any direct contact, it is referred to as remote sensing. Just as the human eye can register light in the visible spectrum, in remote sensing a sensor mounted on a platform (aircraft, satellite, drone, etc.) registers the electromagnetic radiation emitted by a given object. In the case of satellite-based observation, the “object” is the Earth’s surface – in particular, the ocean and the sea ice.
The radiation measured by the sensor is either emitted by the surface itself – for instance, in the case of heat radiation (thermal infrared) or microwave radiation – or comes from another source and is scattered and reflected by the surface. How a given surface emits, scatters or reflects radiation depends on its material, condition and structure. Accordingly, the data gathered by satellite sensors allow us to draw conclusions regarding the properties of the ocean and ice – such as the temperature, ice cover, and how the wind makes the ocean “rougher”.
Fundamentally speaking, a distinction is made between active and passive remote sensing. In passive remote sensing, sensors are used that record naturally occurring radiation. Here, the radiation source is either the sun or the object itself, which emits heat radiation. This is comparable to taking a photograph (i.e., visible radiation) without a flash. In active remote sensing, the system uses its own artificial radiation source, which – like taking a picture with a flash – emits a signal and measures the return signal.
As a rule, larger areas are surveyed using numerous “points” in remote sensing. For example, a point measurement of the temperature represents the mean temperature of a given area, which, depending on the spatial resolutions, can be anywhere from several square metres to several square kilometres across. Taken together, the individual points form an even larger area, which is measured with spatial resolution X. If the remote sensing is repeated at regular intervals, the temporal resolution of the measurements is referred to as the repeat interval.
Detailed investigations of global sea-ice distribution were first made possible by satellite-based remote sensing, which offers both comprehensive spatial coverage and comparatively high temporal resolution. Regular global sea-ice observations, which have been made by a variety of satellites, date back to 1972.
The parts of the electromagnetic spectrum used in satellite-based remote sensing of the ocean and sea ice are
- visible light, with a wavelength of 400 nm – 700 nm
- near infrared (NIR), with a wavelength of ca. 700 nm – 3000 nm (= 3 µm)
- thermal infrared, with a wavelength of ca. 3 – 15 µm
- microwave, with a wavelength of ca. 3 mm – 1 m
In other ranges like UV (200 nm – 400 nm) and long-wave infrared, the atmosphere is not sufficiently transparent for satellites to “see” the Earth’s surface.
Principally speaking, satellite-based ocean and sea-ice observation uses three measuring methods:
- Measuring the incoming solar radiation reflected by the Earth’s surface (i.e., by the ocean or sea ice (visible light and near infrared range)
- Measuring the natural thermal radiation from the Earth’s surface (thermal infrared range and microwave radiation)
- Measuring the signals emitted by an active source and scattered or reflected by the surface. The active source can be e.g. the antenna of a radar system, which emits microwaves, or a laser that emits visible or near-infrared light.
These measurements allow us to distinguish between three main components of the sea-ice surface:
- Open water, thin sea ice in leads and in larger openings in the ice (polynyas), and melt ponds during the summer melting period
- Sea ice with (depending on its age) a varying number of brine pockets and air pockets
- Snow covering the sea ice
The following properties of these components can influence measurements in remote sensing:
- their percentage and distribution with regard to a certain reference area
- their temperature
- their salinity and the distribution of salt in brine pockets
- the crystalline structure of the ice and snow
- the presence of snow and ice layers, as well as the surface roughness (e.g. pressure ridges, saline frost flowers, ice floes)
- the sea-ice movement (drift)
In addition, there are a range of variables that are provided by the remote sensing instrument itself and which can significantly influence readings, such as the frequency (wavelength) of the radiation or the angle of incidence of solar radiation.
Satellites can measure sea ice in the visible and infrared range, as well as the microwave range (radiometers and radars), of the electromagnetic spectrum. Each method has its strengths and weaknesses, and no range of the spectrum allows all components of the sea ice to be measured equally well or under all conditions. As such, when looking for the best sensor for a given application, one must always carefully consider which qualities are most important for the respective research focus. For e.g. macro-scale applications (e.g. daily observation of the entire Arctic), it is often largely irrelevant whether the sensor has a spatial resolution of 3, 6 or 12 km. However, to seamlessly cover such a large area, it is essential that the satellite flies over and scans the target region as frequently as possible.
All remote-sensing-based methods are prone to various sources of uncertainty, which can significantly affect the quality of the end products. Consequently, estimating this uncertainty is an integral aspect of remote sensing of sea ice and often poses a greater challenge than actually deriving the sea-ice parameter at hand.
The main sources of uncertainty include e.g. the sensor-dependent measuring accuracy (“closeness to the actual value”) and measuring precision (“negligible variance when the same target is repeatedly measured”). In addition, sensors can display constant or intermittent offset in their measurement values, which, especially in the latter case (resulting e.g. from sensor wear), can produce detrimental effects like artificial trends, which are difficult to distinguish from natural trends. Moreover, the various assumptions and simplifications in the models that are used to convert the parameters measured by the sensor into geophysical sea-ice variables (e.g. ice cover, ice thickness, snow cover) represent potential sources of error. Further, the environmental conditions and surface properties / heterogeneities are important aspects which can affect measurements to a greater or lesser extent.
While some of these sources of uncertainty and error can be estimated in advance using theoretical approaches, so-called validation experiments are essential for the majority of satellite-based measurements. In this regard, normally a dual strategy is pursued: on the one hand, trying to gain as detailed information as possible on those physical properties most relevant for the respective measuring method by taking readings from the surface of the sea ice itself. Gathering this reference data is most often possible on ship-based expeditions (e.g. with the research icebreaker Polarstern). However, in order to address the resulting scaling gaps between highly local point measurements and macro-scale satellite measurements, most often aeroplanes and helicopters – the second half of the strategy – are the tools of choice. They can often be fitted with sensors similar to those used on satellites, which, thanks to the lower altitude, yields substantially higher spatial resolution. Of course, here, too, there are numerous challenges (e.g. the number of representative validation measurements), as well as new sources of uncertainty to bear in mind. Depending on the age and condition of the respective satellite sensor, several validation campaigns may be required in the course of its service life.
Sea-ice Measuring Methods
The Earth observation satellites currently circling our planet use various measuring methods, each with its own strengths and weaknesses. Passive sensors that measure in the visible spectrum of light can provide data with a high spatial resolution but require an unobstructed view of the Earth’s surface and – of course – the sun. Accordingly, at night and when faced with cloud cover, they’re effectively blind. In contrast, passive microwave sensors can also be used at night, since objects emit microwaves with or without the sun. In addition, clouds absorb virtually no microwave radiation, making them practically transparent for the sensors. However, the disadvantage of microwave measuring methods is the comparatively poor spatial resolution – details like leads in the ice are very difficult to detect. However, when combined, the various measuring methods deliver a highly complete and detailed image of our planet and its sea-ice cover with high temporal resolution; without it, modern environmental and climate research would be virtually impossible.