REMOTE SENSINGStefan Kern

Burcu Özsoy

AbstractRemote sensing has revolutionized our picture and knowledge of our planet. The

quality of our daily weather forecast, our knowledge about distribution, seasonal and inter-annual variation of many climate relevant parameters would be much less advanced than it is today without remote sensing. This book chapter elaborates on this motivation and provides a brief physical background and some of the terminology used in remote sensing in Section 2. This is followed in Section 3 by a description of different forms of remote sensing which are ordered after wave type and frequency ranges employed. The classical electromagnetic visible, infrared and microwave frequency ranges are framed by remote sensing using acoustic waves and of the gravity field. This section is followed by a brief description of the main different observation principles in Section 4. Here we distinguish between imaging and/or sounding sensors, altimeters, and profiling sensors and give a number of examples for sensors associated with the described principles in four separate tables. For each of these principles the subsequent Section 5 gives examples for applications in the water, on the ground, in the air, and from space. The application examples in this section as well as in the section where the different forms of remote sensing (Section 3) are given are subjectively selected by the authors who do not claim to have selected the most relevant ones. While we stress the importance of satellite remote sensing for Earth observation one more time in the concluding Section 6 we wish to underline at the same point that this book chapter just gives the tip of a large tabular iceberg of potential remote sensing sensors and applications which are possible and which became reality over the four decades passed.

UZAKTAN ALGILAMA

ÖzetUzaktan algılama, gezegenimizi anlama ve betimleme hususunda köklü değişiklikler

yapmıştır. Uzaktan algılama olmasaydı günlük hava tahminlerinin kalitesi, dağılım hakkındaki bilgilerimiz, iklimle alakalı pek çok parametrenin mevsimsel ve yıl içerisindeki çeşitliliği günümüzdeki kadar gelişmezdi. Çalışma genel hatlarıyla bu bilgiler ışığında değerlendirilmiştir. Bölüm 2’de uzaktan algılamada kullanılan bazı terminolojiler ele alınmış ve kısa bir fiziksel arka plan sunulmuştur. Bölüm 3’te, kullanılan frekans aralıkları ve dalga tiplerinden sonra düzenlenen uzaktan algılamanın farklı formları tanımlanmıştır. Klasik elektromanyetik görünür; kızılötesi ve mikrodalga frekans aralıkları, uzaktan algılamada kullanılan yer çekimi alanı ve akustik dalgalar tarafından şekillendirilmiştir. Bunu, Bölüm 4’teki farklı temel gözlem prensiplerinin kısa açıklaması takip etmiştir. Burada sondaj ve/veya görüntüleme alıcısı, yükseklikölçer, profilleme alıcıları ile dört farklı tabloda tanımlı ilkelerle ilişkili alıcılara yönelik verilen örnekler arasında ayrım yapılmıştır. Bunu takip eden 5. Bölümde bu prensiplerden her biriyle ilgili sudaki, yüzeydeki, havadaki ve uzaydaki uygulamalara yönelik örnekler verilmiştir. Uzaktan algılamanın farklı formlarının yer aldığı 3. Bölümdeki gibi, bu bölümde konuya en uygun olduğu öne sürülmeyen uygulamalar yazar tarafından öznel olarak seçilerek örneklendirilmiştir. 6. ve son bölümde yer kürenin incelenmesi için uzaktan algılamanın önemi bir kez daha belirtilirken, aynı şekilde potansiyel uzaktan algılama alıcılarına ve 40 yılı aşkın bir zamandır gerçekleşen ve gerçekleşmesi mümkün olan uygulamalara yönelik bilgi verilen bu çalışmaya vurgu yapılmak istenmiştir.

1 INTRODUCTION / MOTIVATION

Dr., Hamburg Üniversitesi, İklim Araştırmaları Mükemmeliyet Merkezi, Almanya. Doç. Dr., İstanbul Teknik Üniversitesi, Denizcilik Fakültesi, Deniz Ulaştırma İşletme Mühendisliği.

A) How do we know about decadal changes in the sea surface temperature in the middle of the Pacific Ocean? B) How do we know the thickness of the Antarctic Ice Sheet? C) How did weather forecast models achieve an ultimate improvement in forecast skill? This is a tiny subset of questions which can be answered with “By remote sensing.” The Earth’s climate system is composed of components, e.g. the ocean, the atmosphere, the biosphere, which each comprise a vast volume – a volume which cannot be sensed and monitored by mankind with classical measurement tools. These either measure a physical quantity directly like, for example, a thermometer measuring the temperature, a rain gauge measuring the amount of precipitation falling on a known area, a tide gauge measuring the height of the sea level relative to a reference elevation, a ruler stick measuring depth or thickness or distance.

Of course these are the most direct and most accurate measurements. But how, if we consider question A), can one measure with a thermometer the sea surface temperature in the middle of the Pacific Ocean every day over a period of decades? Concerning question B) we could say that one can go there and determine the altitude relative to the sea level via an air-pressure measurement. But this would be a point measurement and not a distribution. Already for this single measurement one needs an immense logistical effort. Concerning question C) it is fair to say that the advent of satellite remote sensing allowed for the first time to i) get an impression of the areal cloud distribution and to ii) get information about meteorological parameters between the ground observations stations – e.g. particularly over the oceans.

This book chapter tries to shed some light on the physical background of remote sensing and browses briefly through different forms of remote sensing, giving some examples where possible; these focus on Earth observation. The requirement to stay within a certain page limit explains why we cannot go into detail here. An uncountable amount of books is filled explaining remote sensing sensors and tools and how different parameters can be retrieved by means of remote sensing. The reader please sees this chapter as a teaser to get a first glimpse at the vast possibilities remote sensing offers.

2 Some physical backgroundWhat is remote sensing? Remote sensing can be understood as gaining information

about a substance or substance properties without getting into contact with it like, for instance, with a thermometer. Looking out of our eyes is a form of remote sensing. Taking a photo is remote sensing. If this photo is taken with a regular camera one gets a regular picture in the so-called visible or optical range of the electromagnetic spectrum (Figure 1). If this photo is taken with an infraredcamera – like is used, e.g., by surveillance cameras during darkness – one gets a picture of theinfrared temperature. One can of course use other frequencies / wavelengths from the electromagnetic spectrum than the visible and infrared ones and, for instance, take “pictures” with a remote sensing instrumentin the microwave frequency range. The different spectral ranges used for remote sensing are detailed in the 3rd section of this chapter together with a selection of quantities to be obtained.

Two concepts for remote sensing exist. One is a so-called passive instrument. A passive instrument measures a signal which is sent out oremittedby the target under investigation, or which is caused by reflection of electromagnetic radiation emitted by a natural source, e.g. the sun. The strength of this signal depends primarily on the material properties of the target, which determine its emissivity and its reflectivity.Taking a photo with a regular camera is an example for this kind of remote sensing. The second concept is a so-called active instrument. An active instrument emits electromagnetic radiation by itself – preferably towards the target of investigation. This radiation is reflected or scattered back by the target under investigation. Depending on the viewing angle of the instrument and the backscattering properties of the target a different, smaller fraction of the electromagnetic energy once emitted is measured by the instrument. Radar instruments operated on ships or at airports follow this concept.

For most remote sensing instruments also the material properties along the path (called path properties henceforth) the signal travels between the target and the instrument determine the measured signal. The path properties determine how much of the signal is attenuated on the way between target and instrument, or in other words: how much the signal changes – usually a reduction in its intensity, but it can also be a change in its polarization or frequency. An easy example is: One wants to take a photo of a person at some distance. Another person enters the scene and obscures the person of interest. Taking a photo of that person becomes impossible. The same is true for satellite remote sensing of the Earths’ surface in the visible part of the electromagnetic spectrum: Clouds can obscure the Earths’ surface completely. Hence for satellite remote sensing the path properties are primarily determined by the atmosphere. The same applies for certain ground- and air-borne remote sensing instruments. The amount by which the atmosphere can attenuate a remote sensing signal can be described by the opacity of the atmosphere (see Figure 1). It describes by which degree a signal emitted fromor scattered at the Earths’ surface would be attenuated by the atmosphere for a nadir looking (vertically downward) looking instrument. The attenuation of electromagnetic waves emitted at the ground in the atmosphere is caused by absorption and emission by molecules and by scattering at and absorption by particles like dust or ice and water droplets, e.g. of clouds and precipitation. The attenuation of electromagnetic radiation in the atmosphere depends on the frequency/wave length and the incidence angle used. Not only is the radiation emitted (passive instrument) or scattered back (active instrument) attenuated, but the atmospheric constituents like, e.g. oxygen, ozone, water vapor, methane or CO2, absorb and emit radiation by themselves. Therefore the properties directly along the path between remote sensing instrument and target and outside of this path can contribute to the signal measured by the instrument (see Figure 2).

For remote sensing instruments operating in the water or directly at the ground the path properties are determined by the water properties like temperature, salinity, and concentration of suspended matter or by the soil properties like layers of different minerals, water content, etc. Depending on the target also properties at or close to the ground, e.g. the canopy or a snow cover, might need to be considered.

Finally, to know the material properties of the target under investigation and the path properties is essential for successful remote sensing.

Figure 7: The electromagnetic spectrum for wavelengths ranging from X-rays to Radio waves together with the atmospheric opacity; an opacity of 100% means that a satellite sensor would not receive a signal from the Earth’s surface at the respective wavelength. Satellite remote sensing focuses on the visible, infrared and microwave range; (I): atmospheric window in the infrared range, (II): wavelength range used by passive microwave sensors; (III): wavelength range used by active microwave sensors; (IV): wavelength range used by spectro radio meters such as, e.g. MODIS.

3 Forms of remote sensing: Wave type and frequency rangesThis section aims at describing the wave forms and frequency ranges which are used

typically for Earth observations.3.1.Acoustic

One medium where usage of electromagnetic waves is quite limited is water because of its high conduction. Electromagnetic radiation in the ultraviolet and visible range can penetrate water up to a certain depth and hence can be used for remote sensing of sub-surface water mass properties. In contrast, in the infrared range of the electromagnetic spectrum and for longer wavelengths (Figure 1) penetration of the waves into water is a few millimeters at most and only surface properties can be remotely sensed.

An alternative is the usage of acoustic or sound waves. The acoustic sensors detailed further below are all active instruments. They emit sound waves which are reflected and/or scattered in the medium they travel, e.g. water or air, or at a target, e.g. the sea floor or sea ice. The two-way travel time of the signal can be used for a distance measurement similar to an altimeter. Moving targets cause a Doppler frequency shift which can be used to derive the motion of the target. More details are given in the next section under headings “Ground based” and “In the water”. Note that the application examples given there are non-exhaustive and are just meant to illustrate three potential application areas for acoustic remote sensing.

3.2.Visible/OpticalFollowing Planck’s law for black body radiators the Earth is not emitting any radiation

in the visible and near-infrared part of the electromagnetic spectrum (Figure 1). Therefore, only reflected radiation from the sun can be observed in the visible (or optical) part of the electromagnetic spectrum – unless an active sensor such as ICESat GLAS is used. Consequently, remote sensing in the visible frequency range typically requires daylight conditions. If one is interested in surface properties, then the surface should not be obscured by clouds or fog. In addition dust, haze, water vapor, precipitation particles and smoke reflect / scatter solar radiation. Absorption of solar radiation by air molecules needs also to be taken into account (see Figure 2 a). It depends on the application of a remote sensing sensor in the visible / near infrared range whether a correction for the influence of the above-mentioned constituents of the atmosphere is required or not. Figure 8: Schematic illustration of relevant processes related to remote sensing in the visible frequency range (a) and in the microwave frequency range (b). Image a): (1) reflection of radiation at the surface; (2) reflection and transmission at canopy; (3) reflection at water surface and transmission and scattering in the water; (4) reflection and scattering at dust, haze, water vapor and other atmospheric constituents; (5) reflection at clouds. Image b): (1) upward emission from water surface, Earth surface, from within the surface and from canopy; (2) upward emission from clouds and atmospheric constituents; (3) downward emission from clouds reflected at the surface; (4) downward emission from atmospheric constituents reflected at the surface. Not considered is cosmic background radiation which makes a considerable contribution at lower microwave frequencies such as 1 GHz. Red ellipses mark issues highlighted in Figure 3.

For applications dedicated to Earths’ surface properties such corrections are often mandatory. These corrections are carried out using so-called radiative transfer models. Such models allow quantifying, among others, the attenuation of solar radiation as function of the atmospheric conditions. Whether such a correction is required or not depends on the geographical region. Usually fewer corrections are required in the thinner and cleaner atmospheres of the polar and sub-polar regions as compared to the dust and smog infested regions of the worlds’ megacities. See also the following paragraph “Infrared”.For applications dedicated to sense the atmosphere itself it is in contrast important to know accurately the reflectivity of the background surface, i.e. the Earths’ surface when sensing from a satellite.Examples of remote sensing in the visible / near-infrared range are given further down under headings “Ground based” and “Space-borne”.

3.3.InfraredAgain following Planck’s law for black body radiators the Earth (and most natural

surfaces) shows its maximum electromagnetic emission in the thermal-infrared part of the spectrum (Figure 1). Hence for cloud-free conditions the physical Earth surface temperature can be measured in this electromagnetic range – provided one is using frequencies / wavelengths of the atmospheric window (see Figure 1, (I)).While remote sensing in the infrared frequency range is independent of daylight conditions the same limitations as mentioned above in terms of obscuring the target by clouds or fog and in terms of the atmospheric influence apply.

Often remote sensing instruments are operating at more than one frequency. This can have several advantages. One can combine measurements of two so-called channelsor bandsin the same frequency range to mitigate an unknown atmospheric influence. This is common practice for land and sea surface temperature measurements [e.g. Becker and Li, 1990; Key and Haefliger, 1992; Sobrino et al., 1993]. Such combinations can also mitigate an unwanted influence of the surface temperature itself, for example for sea ice concentration retrieval [e.g. Cavalieri et al., 1984], or are even required to derive a wanted parameter, for example melt pond fraction on sea ice [Rösel et al., 2012]. Finally, satellites often carry a full suite of sensors which are supporting each other in their functionality. Sensors operating in the visible frequency range which need contemporary information about the atmospheric water vapor content for a correction of its influence on the visible frequency range measurement are often combined with a passive microwave sensor (see “Microwave”) which allows retrieving the columnar water vapor content.

3.4.MicrowaveThe microwave electromagnetic range is well suited for space-borne remote

sensingyear round because microwave radiation can penetrate clouds up to a certain frequency (see Figure 1, (II) and (III)) and is independent of daylight. Similar to the

infrared frequency range there are windows in the atmospheric absorption spectrum (or opacity) within which satellite microwave remote sensing of the Earths’ surface is particularly useful whereas in other regions of that spectrum the atmospheric opacity is too large for Earths’ surface remote sensing. In these regions, however, microwave remote sensing can still be carried out; it simply needs to be dedicated to the quantity which causes the increase in the atmospheric opacity. These are, for instance oxygen at around 50 GHz and water vapor at about 22 GHz. Figure 2 b) illustrates schematically the relevant processes involved in microwave remote sensing of the Earth’s surface – here basically passive microwave remote sensing.

Microwave radiation emitted by a medium is determined by the physical temperature and the emissivity of the medium. Unlike in the infrared frequency range where most natural materials have an (infrared) emissivity, which is close to 1, in the microwave frequency range emissivities are often substantially smaller than 1. Whether and to which degree this is the case depends on the frequency and on the material. Sea water, e.g., has a rather low emissivity ranging from 0.25 at about 5 GHz to close to 0.5 at 100 GHz, both at horizontal polarization. In contrast, many land surfaces or wet snow has an emissivity of 0.9 to 0.95 for the mentioned frequencies. Hence, for the same physical temperature, the energy received by a passive microwave sensor is much smaller over sea water than, e.g., over an adjacent land cover. Usually this energy is expressed as a so-called brightness temperature, is expressed in Kelvin, and one speaks of microwave radiometry. The frequency range used for microwave radiometry ranges from 1.4 GHz (SMOS, Table 1) to close to 200 GHz (e.g. MetOp-MHS, Table 1)

The otherapplication area in the microwave frequency range is given by active instruments. RADAR stands for RAdio Detection And Ranging. A RADAR emits microwave radiation in various forms and polarizations and measures the energy which is reflected and/or scattered by the target medium. Instead of measuring the emissive characteristics of the medium one measures the reflective characteristics of the medium.

Imaging active microwave remote sensing is typically carried out at incidence angles between 20° and 60°. Hence, the signal which is reflected from a perfectly flat medium, e.g. a calm sea surface, is very small because almost all radiation impinging on the surface is reflected away from the sensor. In contrast, the signal which is reflected from a rough medium, e.g. a wind-roughened water surface, is high because there are several small facets at the surface which are tilted towards the sensor such that the impinging signal is partly reflected back into the direction of the sensor (see Figure 3 for an illustration).

Figure 9: Schematic illustration of some surface reflection examples. Black arrows denote direction into which a signal is reflected or scattered. Dashed red lines denote viewing direction of the sensor. (1) perfectly flat surface, incidence angle = reflection angle. Perfect for a visible sensor. Imperfect for an active microwave sensor because all energy emitted downwards will be reflected away from the sensor’s viewing direction; (2) signal emitted is composite of many contribution from inside medium arriving at surface under various

angles; (3) rough surface, the finite footprint contains many small surface facets tilted at different angles relative to the incident solar (or sensor) radiation. Only a small fraction (compared to a flat surface) of solar radiation is scattered into the sensor’s viewing direction. Good for an active microwave sensor because a considerable fraction of the radiation emitted downwards is scattered back into the sensor’s viewing direction; (4) signal arriving at a rough surface from one direction within the medium is scattered at the rough surface into multiple different directions.

Profiling / sounding active microwave remote sensing instruments, in contrast, look into one direction only, e.g. vertically downward from a satellite. Instead of looking at an area the signal is binned along the range and the energy received is associated to the respective bins by carefully tracking the travel time of the radar signal. This technology is used, e.g. for ground penetrating radars (see “air-borne”) or rain radar instruments (see “space-borne”). A special type of sensor of this kind are radar altimeters which are designed to precisely measure the distance between the sensor itself and whatever surface they are passing over, e.g. ocean, land, ice sheet (see “space-borne”).

Usually the energy received by an active microwave instrument is related to the energy emitted; the resulting energy ratio is dimensionless and spans over several orders of magnitude. Therefore it is commonly expressed in decibel (dB). One speaks of scatterometry when large-scale imaging systems are used, e.g. QuikSCAT (Table 2) and of a RADAR or Synthetic Aperture Radar (SAR) for smaller-scale, high-resolution instruments (e.g. TerraSAR-X, Table 2). Active microwave sensors used for Earth observation are operating between 1 GHz and 15 GHz.

3.5.Gravity fieldFinally, one form of remote sensing needs to be mentioned which is dedicated to

measure the Earths’ gravity field and which hence helps to better quantify mass changes on our planet. The strength of the Earths’ gravity field can be measured, e.g. as has been done withthe “Gravity Recovery And Climate Experiment” (GRACE) and with the “Gravity field and steady-state Ocean Circulation Explorer” (GOCE). The GRACE mission consists of two space-craft following each other along the same precisely measured orbit. Both space-craft change their position under the gravity forces of primarily the Earth. Their position relative to each other is measured with high precision. In addition identical accelerometers placed at the gravity center of each space-craft measure the non-gravitational acceleration of a test mass. By combining these acceleration measurements, the precise orbit information, and the movement of the two space-craft relative to each other, the

contribution of the Earth’s gravity field can be separated. GRACE data have been assisting in improving the retrieval of ocean mass [e.g. Chambers et al., 2010].

GOCE employs a so-called gradiometer. A gradiometer measures the acceleration differences between test masses of an ensemble of accelerometers. The measured signal is the difference in gravitational acceleration inside the space-craft. This signal reflects the gravitational pull of the Earth’s varying gravity field as caused, e.g. by varying masses of mountains and valleys – at land as well as in the ocean. The gradiometer measurements are complemented with contemporary highly precise measurements of the satellite orbit which in addition needs tokept as stable as possible. GOCEs measurements revolutionized, e.g., our view of the Earth’s gravity field [e.g. Panet et al., 2014]. In contrast to GRACE (see above) only one satellite is required.

4 Different observation principlesThis section aims to describe the different principles of observation or, in other words,

their different ways of sensing1.2.3.4.

4.1.Imager / SounderThe easiest to imagine form is taking a picture. However, on a satellite, this is usually

not realized the same way as it is done by a photo camera but by a scanning sensor. This can either be a mechanically moved antenna which, e.g., spins around its own axis, or with electronically steered antennas. Both passive and active sensors are used here. Examples of the first kind are the SSM/I (Table 1) or the QuikSCAT (Table 2) sensors (see Figure 5 a); examples of the second kind are SAR sensors (Table 2, Figure 5 b).

Figure 10: Schematic illustration of viewing geometry of a sensor.

Figure 11: Illustration of the typical scan geometry of an imaging passive microwave sensor such as SSM/I (a) and of an active microwave sensor like a SAR (b).

Depending on the aperture of the sensors’ antenna, the spatial resolution of the target to be achieved and the technical possibilities (air-borne sensors have different limitations than space-borne sensors) the sensor usually scans row by row of grid cells at the ground across-track (Figure 4, Figure 5 a). The result is a scanned area at the ground following the track of the sensor. One speaks of an imaging sensor and the sensor itself is often called “Imager”. Such sensors often also measure an integral quantity of the atmosphere, e.g. the total column of water vapor, and are then called sounder. The width of the area scanned across track is called swath width (Figure 5). Depending on the sensor the incidence angle along one row scanned is either constant, like e.g. for SSM/I or AMSR2 (Table 1), or it varies across track being zero at nadir, like e.g. for AVHRR, MODIS or METOP-MHS (see Table 1). Such a type of sensor allows covering a large area. When used aboard a polar-orbiting satellite a complete coverage of the Earths’ surface is possible within 2 – 10 days – depending on the swath width which may vary between 400 km and 3000 km.

4.2.AltimeterThe so-called “Altimeter” has a different viewing geometry. On a satellite an altimeter

usually provide measurements at or close to nadir along track and usually no scanning takes place. As an altimeter measures its elevation above the Earths’ surface one gets a series of elevation measurements along track. Measurements from many satellite overpasses are required to obtain meaningful spatial coverage because the footprint size varies from about 10 km for the early radar altimeters on ERS1/2 to about 60 m for ICESat GLAS (Table 3). When used from aboard an aircraft scanning is possible and usually takes place across track providing a relatively narrow swath but high spatial resolution, like e.g. used during the Operation Ice Bridge flight campaigns [e.g. Panzer et al., 2013]. Altimeters are active instruments. Examples of this type instrument are ICESat GLAS [Zwally et al., 2002] and CryoSat-2 SIRAL [Wingham et al., 2006] sensors (Table 3).

4.3.Profiler

For a classical altimeter the target is the Earths’ surface and one tries to minimize contributions from the path between the target and the sensor. Other, similar sensors, called “Profiler” have a range resolution along the viewing direction of the sensor. Instead of concentrating on the signal emitted or reflected at the Earths’ surface one receives the signals from the entire range between the sensor and the Earth’s surface. By means of tracking the time of the received signal and dividing the range in so-called range bins one obtains a profile of measurements. An example of such a profiler is MetOp-IASI(Table 4).

Another form of profiling can be done when satellites are placed into a low Earth observations orbit and are looking tangential through the Earths’ atmosphere. This is called limb sounding. Electromagnetic radiation emitted by a natural source, e.g. the sun, or by satellites, e.g. Global Positioning Satellites (GPS), is bended and delayed in the atmosphere as a function of the refractive index of the atmosphere. The refractive index is a function of air pressure, temperature and humidity. By means of quantifying the amount of bending and delay of an electromagnetic signal profiles of the above-mentioned atmospheric parameters can be retrieved. If the sun is used as a source one speaks of solar okkultation, when GPS satellites and hence microwave radiation is used as a source one speaks of radio okkultation. One example of a satellite sensor for radio okkultation is the MetOp-GRAS sensor (Table 4).

5 Forms of remote sensing: From the water to the ground into spaceThis section aims to provide a few examples for the different forms of remote sensing

sorted by their application medium. Examples are given for remote sensing in the water, on land, and from air- and space-borne platforms. Note that this only is a tiny subset of the tremendous variety of remote sensing instruments.

1.2.3.4.5.5.1.In the water

For Earth observation three applications of acoustic remote sensing sensors are exemplified in this book chapter. The first application is the usage of Acoustic Doppler Current Profiler (ADCP). ADCP sensors emit sound waves into the water. The sound waves are reflected by particles within the water and received by the ADCP. Measurement of sound wave travel time and (Doppler) frequency shift allow to obtain a 3-dimensional view of the ocean current – provided that the ADCP sensors point to at least 3 directions (usually 4 sensors are used). The sound speed in water depends on water density and hence on temperature and salinity. Therefore it is of advantage to know the distribution of these parameters along the profiling direction of the ADCP. Strong gradients in temperature, i.e. presence of a thermocline, or in salinity, i.e. presence of a halocline, limit accuracy of particularly the vertical current component and could decrease the accuracy of the location of the measured horizontal current components relative to the ADCP. ADCP sensors are widely used from moorings at the ocean floor to measure ocean currents [e.g. Fahrbach et al., 2001; Münchow et al., 2006; Rabe et al., 2010]. ADCP sensors are also used to monitor river discharge; here they are mounted as horizontally profiling sensors.

The second application is usage of Upward Looking Sonar (ULS). This can be regarded as an altimeter operating in the water. Here the travel time of sound waves is used to measure the time-varying distance between the sea surface and the ULS sensor. This can be used to measure, for instance, significant wave height [e.g.Visbeck and Fischer, 1995]. In ice-covered waters ULS sensors are used to obtain sea ice draft, which is the part of floating sea ice underneath the sea surface. ULS can be mounted at submarines and autonomous underwater vehicles [e.g. Rothrock and Wensnahan, 2007; Wadhams et al., 2011; Williams et al., 2014] or moored at the sea floor [e.g. Behrendt et al., 2013]. Such sea

ice draft measurements are one important basis for our knowledge about the Arctic sea ice thickness distribution and evolution [e.g. Kwok and Rothrock, 2009; Hansen et al., 2013].

5.2.Ground basedMost ground-based remote sensing instruments are either sounders or profilers. One

classical ground-based instrument is a rain radar, which often also called weather radar,and are operating in the microwave frequency range.Such instruments usually point at a certain angle into the atmosphere, are operated in a circular scan mode around their location and have a range resolution which allows to locating the signals measured in 3-D space. The strength of the radar signal measured is a function of type, concentration, and size of the precipitation particles. Hence a continuously operated horizontally scanning rain radar allows retrieval of location, movement and intensity of precipitation in a certain radius around its location. Apart for usage for local precipitation forecast rain radar data are also used for validation of precipitation estimates from satellite remote sensing as for example from TRMM (see Table 4) [e.g. Bolen and Chandrasekar, 2000].

Closely connected to precipitation is sounding of the altitude of the cloud base above the ground. This can be done with a vertically upward looking laser operating in the visible or near-infrared frequency range; such a sensor is usually called a ceilometer. Depending on frequency and range resolution used cloud base altitudes or concentrations of aerosols / dust layers can be retrieved.

The third application of sound waves (see section “In the water”) is its usage with the so-called SODAR (SOnic Detection And Ranging). Similar to usage of sound waves in water a SODAR emits sound waves into different directions. The sound is reflected by air particles which move with the air speed, i.e. the wind. By measuring the run time and the Doppler shift of the emitted frequency a profile of the wind speed above the SODAR can be obtained. Typical application area is the monitoring of wind speed and air turbulence in lower troposphere (up to 2 km altitude) – e.g. in the vicinity of airports for safer flight navigation or in the context of wind farm planning [e.g. Barthelmie et al., 2003].

5.3.Air-borneSensors operating in the entire electromagnetic frequency range (Figure 1) can be

used from air-borne platforms like helicopters, fixed-wing air-craft or drones. Their usage from air-borne platforms has the advantage that atmospheric corrections are often not required and that less attention needs to be paid to cloud cover; one can simply fly underneath. However, the disadvantage of using air-borne platforms is their high costs, the limited spatial coverage, and the limited radius to be within range of populated areas. These three disadvantages force most air-borne remote sensing applications to be used merely for the evaluation of satellite remote sensing products and development of satellite remote sensing retrieval algorithms, and less for routine surveillance – although this has been done in the past e.g. by national ice services.

One could highlight the following two applications of air-borne sensors. One is the application of electromagnetic (EM) induction sounding to derive total (sea ice plus snow) thickness [Haas et al., 2009]. The measurement principle exploits the fact that the electric conductivity of sea water is an order of magnitude larger than the one of sea ice. The EM sounder emits a primary EM field, which – while flying a low altitude over the ice-covered ocean –generates eddy currents in the sea water underneath the ice. These induce a secondary EM field which strength is measured by the sounder and which is a function of the distance between the sounder and the eddy currents. Contemporary laser altimeter measurements of the distance between the surface and the sensor complete measurement. The combination of both allows estimating the total thickness. Such data support validation of space-borne ice thickness measurements and numerical models [e.g. Lindsay et al., 2013].

The second is the application of RADAR over ice sheets such as that of Greenland. By using a RADAR operating at a frequency as low as 150 MHz penetration of RADAR waves into the freshwater ice of an ice sheet like that of Greenland is several 1000 meters.

Differences in the electric conductivity of the air, the snow, the ice, and the bedrock underneath the ice form gradients in conductivity between these media. The RADAR waves traveling through the ice are reflected preferably in regions with these gradients and less so inbetween. The result is a so-called RADAR echogram which shows layers of higher and lower return of the emitted RADAR energy. Because the RADAR signal is binned along its path into – here – about 5 m wide vertical bins, knowledge of the number of the RADAR bin paired with the travel time in the ice can be used to derive the elevation of the bedrock [Gogineni et al., 2001].

Contemporary measurements of the elevation of the ice sheet surface – either with the RADAR itself, or with a laser altimeter – allowderiving the total ice sheet thickness and hence an estimate of the total ice sheet volume [e.g. Bamber et al., 2001]. The latter can only be derived from space-borne remote sensing instruments if the bedrock topography is known. The frequencies used for the bedrock mapping cannot be used on a satellite because of the disturbing influence of the Ionosphere on the RADAR signal. Hence this application is unique for air-borne platforms.

5.4 Space-borne Figure 12: 20-year (1992-2011) average sea ice concentration (= percentage of a known area covered with sea ice) for the Arctic; a) March, b) September. Image c) illustrates the seasonal development of the monthly mean Arctic sea ice area for 1992 to 2014. Data set available from http://icdc.zmaw.de; data based on ARTIST Sea Ice (ASI) algorithm [Kaleschke et al., 2001].

Applications of space-borne remote sensing are extremely diverse because of the variety of different frequencies and wavelengths, of different viewing geometries, and potential combinations between different sensors viewing the same area within a short time-period of a few minutes. Because of the incredible amount of possibilities (see also Table 1 to Table 4, keeping in mind that this is a subset of satellite remote sensing sensors) we can only briefly highlight one example per observation principle. Note that this is subjective selection and other authors might have chosen different examples.

Earth observations from imagers / sounders are the backbone for today’s knowledge of many parameters relevant to understand the Earth’s climate system such as the sea surface temperature, the columnar water vapor, the cloud cover, or the sea ice area, to mention a few. Often the respective sensors fly on a satellite with a polar or near-polar orbit at an altitude which can allow complete coverage of the entire globe – provided the swath width is large enough. Sensors such as QuikScat (Table 2), MODIS (Table 1) and SSM/I (Table 1) belong to this category. Particularly if these sensors operate from satellites of a long-lasting satellite program such as the Defense Meteorological Satellite Program (DMSP), which satellites carry the SSM/I sensor family then these are the candidate for climate monitoring. Figure 6 exemplifies one potential application – here the sea ice cover of the Arctic – where satellite remote sensing with imagers not just permits to obtain daily maps of the hemispheric sea ice cover but has provided an over 30-year long record of this parameter.

Figure 13: Sea level anomaly [in meter] relative to 1993-2012 (left). Absolute geostrophic velocity zonal component [in meter/second] (right). Both maps are for February 2, 2014. Data from http://www.aviso.oceanobs.com, plots from http://icdc.zmaw.de.

Earth observations from altimeters (Table 3) partly play a similarly important role for our understanding of the Earth’s climate system. As described before, an altimeter measures the distance between the sensor and the Earth’s surface – whatever this is: land, ocean, cryosphere, canopy. Thanks to satellite radar altimetry and laser altimetry much is known in the meantime about mass changes of the Greenland and Antarctic ice sheets [e.g. Griggs and Bamber, 2009; Bolch et al., 2013; Helm et al., 2014; Sandberg-Soerensen et al., 2015] or other icecaps [Moholdt et al., 2010]. Careful observation and monitoring of the ice sheets is particular important for i) improved initialization of ice sheet models and for ii) a better estimate of the current and future role melting ice sheets could play for global sea level rise. This brings us to the potentially most important application of satellite altimetry: the global derivation of the sea surface height, sea surface height anomalies and hence also of ocean surface currents [e.g. Rio and Hernandez, 2000; Ducet et al., 2004; Volkov and Pujol, 2012]. Figure 7 exemplifies the mean sea surface height anomaly relative to 1993-2012 (left) and the zonal component of the geostrophic surface current vector (right) for February 2, 2014 (chosen arbitrarily) derived from satellite radar altimetry – which is daylight and weather independent.

Earth observations from profilers or limb sounders (Table 4) are a key to better understand the vertical distribution of clouds in the atmosphere as well as to get to know vertical profiles of temperature, water vapor, ozone and other atmospheric constituents and contaminants. One of the most important implications of such sensors is the measurement of constituents of and contaminants in the atmosphere and their seasonal and inter-annual variability – particular of, e.g., ozone [Solomon, 1999]. Without sensors such as GOMOS and AURA (Table 4) it would not have been possible to map the distribution and recent temporal evolution of the ozone hole over the Antarctic [e.g. Bertaux et al., 2010;Froidevaux et al., 2008; Seppälä et al., 2004].

Profilers also play an important role in our understanding and observation of the vertical structure of clouds. While sensors such as SSM/I (Table 1) can obtain a columnar total integral of the cloud liquid water content and sensors such as AVHRR (Table 1) can measure the cloud top temperature it needs altimeters such as ICESat (Table 3) [e.g. Naud et al., 2005; Dessler et al., 2006] and profilers such as CALIPSO (Table 4) to obtain a height resolving information about the vertical cloud structure [e.g. Young and Vaughan, 2009; Winker et al., 2009].

1 CONCLUDING REMARKSThis book chapter aims at giving some flavor about remote sensing. It sheds some light

on the physical background. It lists and briefly describes forms and limitations of remote sensing. It further gives examples of the application area and – in form of tables – examples of some relevant satellite missions in Earth observation. These examples are just the tip of the iceberg of remote sensing sensors operating from space. This book chapter is hence written to advertise the variety and immense potential of remote sensing instruments for Earth observations. It is by no means a complete review and is not meant to be one.

In the future, satellite remote sensing will become even more important. While there were just a handful of satellites equipped with remote sensing instruments in the late 1970ties, today there are uncountable satellites in orbit fulfilling Earth observation duties. This was possible with the advent of new technologies but also with the fact that more and more nations are actively contributing to satellite programs – being aware that only with this technology the often vast and remote areas of their countries can be monitored effectively and efficiently.

At the end we would like to high-light the potential which constellations of satellite sensors have in Earth observation. As has been pointed out at the end of the previous section only sounders and profilers are capable to provide height resolved information about the distribution of clouds and aerosols in our atmosphere. However, this type of sensors usually can only cover a limited area at the ground. Hence the optimal way would be to combine them with imaging sensors which provide information about the spatial distribution of what is measured by a profiler. If we talk about aerosols and clouds in the atmosphere, then such observations would need to be carried out as simultaneous as possible because of the high spatiotemporal variability of both aerosol concentration and cloud structure. One solution would be to equip a satellite with a suite of sensors which does all of this. Envisat (Table 2, 4) is one of such satellites. However, at the time of writing, this can only be a compromise because different sensor types on one satellite are often limiting the full measurement capability of the sensors combined. Therefore to date the best solution for this is a constellation of satellites following each other on the same orbit within minutes. This is realized with the so-called A-Train. Here the satellites AQUA (Table 1: MODIS, AMSR), CloudSat (an active microwave instrument), CALIPSO (Table 4), and AURA (Table 4) are following each other within 8 minutes. This gives unprecedented possibility to gather information about the vertical distribution of aerosol and cloud parameters and provides a view which would never be possible with just one satellite sensor alone [e.g. Iwasaki et al., 2010; Stephens and Vane, 2007].

Without remote sensing our daily weather forecast, our knowledge about distribution, seasonal and inter-annual variation of many climate relevant parameters would be much less advanced than it is today. Satellite missions dedicated for Earth observation need to be continued in a sustainable manner to maintain and further enhance today’s skill in weather prediction and to extend time-series of climate relevant parameters needed to assess the climate state of our planet.

ANNEXESTable 1: Overview about selected passive satellite remote sensing instruments. This list is non-exclusive; please check https://directory.eoportal.org/web/eoportal/satellite-missions/. Pol. = polarization; H = horizontal, V = vertical; Res. = resolution; Inc. angle = incidence angle; n.a. = not applicable.Full Name Acronym Frequency

[GHz] or wavelength

Swath width [km]

Res. [km] Inc. angle [deg]

Pol. Primary application areas

Special Sensor Microwave

SSM/I (SSMIS)

19.4, 22.2, 37.0, 85.5 (91.7)

1394 15 to 50 53 H, V sea ice fraction & motion;

/ Imager (Sounder)

SSMIS: + 150.0, 183.3 + O2)

(1707) (to 150) snow parameters; ocean surface wind speed & vertical fluxes; clouds; precipitation; water vapor

Advanced Micro-wave Scanning Radiometer

AMSR-E; AMSR2

6.9, 10.7, 18.7, 23.8, 36.5, 89.0

1450 5 to 50 55 H, V As SSM/I & SSMIS plus: soil moisture; sea surface temperature

Soil Moisture and Ocean Salinity

SMOS 1.4 900 30 to 50 0 to 55 full soil moisture; ocean surface salinity; sea ice thickness

Advanced Very High Resolution Radiometer

AVHRR (on NOAA &MetOp)

630 nm to 12 µm;

4 to 6 bands

2900 1.1 x 1.1 to 2.3 x 6.2

0 to 55 n.a. (Sea) surface temperature; cloud parameters; radiation; snow cover;land cover type;vegetation;albedo

Microwave Humidity Sounder

MHS (onMetOp)

89.0, 157.0, 183.3, 190.3

2134 17.6 x 15.9 to 27.1 x 52.8

0 to 49 H, V Atmospheric water vapor; surface emissivity

Moderate Resolution Imaging Spectro-radiometer

MODIS (on EOS-AQUA & TERRA)

405 nm to 14.4 µm;

36 bands

2330 0.25 to 1.0 0 to 55 n.a. As AVHRR + ocean parameters; chlorophyll-a concentration & more cloud parameters

Medium Resolution imaging Spectrometer

MERIS (on Envisat)

390 nm to 1040 nm;

15 bands

1150 0.3 to 1.2 0 to 34 n.a. Land cover; vegetation; ocean parameters; chlorophyll-a concentrati

on

Landsat Thematic Mapper / Multi-spectral Scanner

Landsat-TM /MSS

480 nm to 11.5 µm;

7 to 8 bands

185 0.015 to 0.08

0 to 7 n.a. Land cover; vegetation

Spinning Enhanced visible and infrared imager

SEVIRI (on MeteoSat-2)

400 nm to 14.4 µm;

12 bands

n.a., global 5 at nadir 0 to 78 n.a. Global cloud cover and type every 15 minutes

TRMM Microwave Imager

TMI (on TRMM)

10.7; 19.4; 21.3; 37.0; 85.5

780 5 to 45 53 H, V Vertically integrated rainfall distribution

Clouds and the Earth’s Radiant Energy System

CERES (on TRMM & TERRA)

0.3 - 5 µm; 8 - 12 µm; 0.3 - 120µm

n.a., global 20 at nadir 0 to 78 n.a. Global radiation, short and long wave

Multi-spectral Imager

MSI (on Copernicus Sentinel-2)

400 nm to 2400 nm;

12 bands

290 0.01 to 0.06

0 to 10 n.a. As MERIS & Landsat

1)Global coverage until 2003; 2) Operational until 2012; 3) Operational until 1999; 4) Operational until 2014

Table 2: Overview about selected active imaging satellite remote sensing instruments. These have all been operating in the microwave frequency range. The list is non-exclusive; please check https://directory.eoportal.org/web/eoportal/satellite-missions/.Pol. = polarization; HH = horizontal on transmit, horizontal on receive, VV = accordingly, full: HH, VV, HV = horizontal on transmit, vertical on receive, VH = accordingly; Res. = resolution;Inc. angle = incidence angle; n.a. = not applicable. Note that first the fine resolution and then the coarse resolution sensors are listed.

Full Name Acronym Frequency [GHz]

Swath width [km]

Res. [km]

Inc. angle(s) [degree]

Pol. Primary application areas

European Remote Sensing Satellite -Synthetic Aperture Radar1)

ERS1 - / ERS2 -SAR

5.3 100 0.03 20 to 26 VV Ocean surface wind speed; currents; fronts; significant wave height; wave spectra; oil

spill detection; sea ice type & drift; glacier outline & movement; ground subsidence, snow cover, other land covers

Environmental Satellite -Advanced SAR2)

Envisat - ASAR

5.3 100 to 400

0.03 to 1.0

15 to 45 HH, VV

As ERS1/2 SAR

Sentinel-1 SAR n.a. 5.4 80 to 400

0.005 to 0.1

19 to 47 full As ERS1/2 SAR

Radarsat-1 / -2 SAR

n.a. 5.3 / 5.4 18 to 500

0.003 to 0.1

10 to 60 HH / full

As ERS1/2 SAR

TerraSAR-X n.a. 9.6 5 to 200

0.001 to 0.04

15 to 60 full As ERS1/2 SAR

COSMO-SkyMed n.a. 9.6 10 to 100

0.001 to 0.1

16 to 51 HH, VV

As ERS1/2 SAR

ALOS-1 / -2 PALSAR

n.a. 1.3 20 to 350

0.003 to 0.1

8 to 70 full As ERS1/2 SAR, more focus on land cover

European Scatterometer on ERS1/2 1)

ESCAT 5.3 500 50 18-59 V As QuikSCAT plus soil moisture

QuikSCAT3) n.a. 13.4 1400 to 1800

25 x 6 46, 54 H, V Ocean surface wind speed; sea ice type & motion

OceanSAT-2 4) n.a. 13.5 1400 to 1840

50 49, 58 H, V As QuikSCAT

Advanced Scatterometer on

ASCAT 5.3 2 times 25 to 25 to 65 V As

METOP 500 50 ESCAT

Table 3: Overview about selected altimeters. The list is non-exclusive; please check https://directory.eoportal.org/web/eoportal/satellite-missions/.Range resol. = Range resolution;n.a. = not applicable; FOV = field-of-view1)Global coverage until 2003; 2) Operational until 2012; 3)JASON-1 operational until 2013; 4)

Operational until 2009; 5) To be launched in 2018; 6) To be launched in 2016

Table 4: Overview about selected profilers & limb sounders. The list is non-exclusive; please check

Full Name Acronym Frequency [GHz]

Beamwidth [deg]

FOV Range resol. [m]

Primary application areas

European Remote Sensing Satellite – Radar Altimeter1)

ERS1 - / ERS2 - RA

13.8 (Ku-Band)

1.3 ~ 10 km 0.1 Ice sheets, sea ice, ocean surface, sea level, currents

Environmental Satellite -Advanced Radar Altimeter-22)

Envisat – RA-2

3.2 (S-Band) and 13.6 (Ku-Band)

5.3 (S-Band) and 1.3 (Ku-Band)

~ 10 km 0.05 As ERS1/2 RA

SAR/Interferometric Radar Altimeter-2

SIRAL (on CryoSat-2

13.6 (Ku-Band)

1.0 x 1.2 ~ 15 km circular; depends on mode: 0.25 km x 15 km

0.45 As ERS1/2 RA

Poseidon-2/3 Radar Altimeter3)

Poseidon-2/3 on JASON-1/2

5.3 (C-Band) and 13.6 (Ku-Band)

1.3 (Ku-Band) and 3.4 (C-Band)

~ 30 km ~ 0.5 State of the ocean surface, sea level (no cryospheric application because is not on polar inclination)

Geoscience Laser Altimeter System 4) to be followed by the Advanced Topographic Laser Altimeter System (ATLAS) on board ICESat-25)

GLAS (on board the Ice Cloud and Elevation Satellite ICESat)

532 nm and 1064 nm

375 µrad 66 m every 172 m along track

n.a. Ice sheet mass balance, cloud vertical structure, aerosols, vegetation, sea ice

Altimeter in Ka-Band

AltiKa (on board SARAL)

36.6 GHz (Ka-Band)

n.a. ~ 8 km 0.3 As ERS1/2 RA, supposed to bridge between Envisat and Copernicus Sentinel-3

SAR Radar Altimeter6)

SRAL (on board Copernicus Sentinel-3

5.4 GHz (C-Band) and 13.6 GHz (Ku-Band)

n.a. Depends on mode: 300 m to 800 m to ~ 10 km

n.a. As ERS1/2 RA

https://directory.eoportal.org/web/eoportal/satellite-missions/. Res. = resolution;n.a. = not applicable.Full Name

Acronym Frequency [GHz] or wavelength

Swath width [km]

Res. [km] Altitude range

Primary application areas

Global Ozone Monitoring Experiment by Occultation of Stars

GOMOS (on Envisat)

625 nm to 12.5 µm spread over 4 bands

2052 Horizontal:

25 to 100

Vertical:

1 to 2

n.a. Atmospheric moisture & temperature profiles; columns of ozone, oxygen, Methane and others; cloud parameters; surface temperature

Michelson Interferometer for Passive Atmospheric Sounding1)

MIPAS (on Envisat)

4.15 to 14.6 µm in 1 band

n.a. Horizontal:

30 km Vertical:

3 km

5 km to 150 km

Limb emission sounding instrument: Stratospheric chemistry: e.g.: Ozone, NOx, HNOx, CH4, H2O, N2O; clouds, aerosols

Scanning Imaging Absorption Spectrometer for Atmospheric Cartography1)

SCIAMACHY (on Envisat)

240 to 2380 nm over 8 channels

Limb: 500 Nadir: 1000

Limb:

Horizontal:

100 km

Vertical:

3 km

Nadir:

32 x 16 km

3 km to 100 km

Three modes: Sun/Moon occultation; nadir looking; limb scattering. Chemistry of tropo- & stratosphere, e.g. in addition to MIPAS: CO2, BrO, SO2

Infrared Sounder Atmospheric interferometer

IASI (on MetOp)

3.6 to 15.5 µm over 3 channels

2052 Horizontal:

25 to 100

Vertical:

1 to 2

Surface: 12.5

Entire atmos.

Atmospheric temperature, water vapor, cloud vertical profiles; columnar values of atmospheric chemistry; surface temperature & emissivity

GNSS Receiver for

GRAS (on MetOp)

n.a. ~ 2000 Horizontal:

100 to 300

5 km to 30 km

Atmospheric profiles of temperature,

Atmospheric Sounding

Vertical:

0.3 to 1.5

humidity and pressure

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations

CALIPSO 532 nm and 1064 nm

n.a. ~ 70 m every ~330 m along track;

Vertical:

up to 30 m

532 nm:

0 - 40 km

1064 nm:

0 - 26km

Cloud and aerosol optical profiles

Earth Observing System Microwave Limb Sounder

EOS-MLS (on AURA)

118 GHz to 2500 GHz over 5 channels

Limb & Nadir

Horizontal: 3 x 300 km to 5 x 500 km

Vertical: 1.5 to 3 km

0 to 120 km

Atmospheric chemistry and dynamics from surface to mesosphere

1)Operational until 2012

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