The experimental conditions.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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He currently works as the director at the Institute for Advanced Engineering, in Korea.\r\nHe had participated as a Korean delegate at the preparation workshops on CCS from 2007 to 2008 to reach the G8 Hokkaido Toyako Summit Leaders Declaration. He has concentrated on CO2 capture technologies from 2005, mainly on the precombustion dry sorbent process development. He works as the president in KAWET from 2013 and vice president in Korea DME Association from 2008. 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work in normal everyday situations, it is necessary to discover the principles relevant to establishing and maintaining social interaction between humans and robots. Even if short-term human-robot interaction can be performed by implementing simple behaviors in a robot, it remains difficult to realize long-term social interaction. We have explored the principles underlying natural human-robot communication by development of an android which closely resembles a human being, which is called an android science approach (Ishiguro, 2005).
\n\t\t\t\n\t\t\t\tNass et al. (Nass et al., 1994) demonstrated that the human-computer relationship is fundamentally social and that a person\'s social response toward computers is automatic in social situations. It is inferred from their studies that person\'s interpersonal responses subconsciously expressed toward a robot (in other words, perceptual social illusion (Jacob & Jeannerod, 2005)) underlie the natural communication between the person and the robot. The condition to elicit interpersonal behavior must be related to a mechanism to support natural communication. The android science approach explores the boundary conditions to elicit subconscious interpersonal behavior toward an android from humans by investigating methods to make the android more humanlike.
\n\t\t\tHumanlike body motions are necessary to implement humanlike behavior in an android. There have been several studies on the generation of humanlike motion, including studies on a model to generate human motion trajectories based on a neurocomputational approach (Flash & Hogan, 1985; Uno et al., 1989; Kawato, 1992; Schaal & Sternad, 2001), a study on the control of a manipulator based on a model of motion trajectories of a person\'s arm (Kashima & Isurugi, 1998), and studies on a computer graphics (CG) animated characters, which have shown that noise in the motion makes the character\'s motion more humanlike (Perlin, 1995; Bodenheimer et al., 1999). These studies successfully generated a humanlike motion. However, a humanlike motion specific to communication situations has not been considered. The present study considers the human-like nature of a person\'s motion during interaction with other people.
\n\t\t\tA person generally does not produce exactly identical motion when he/she repeats a behavior with the same intention, as shown in Figure 1(a). In contrast, a robot is able to repeat exactly identical motion with a purpose. A person\'s motion is diverse in that the motion
\n\t\t\tAn example of motion decomposition in a reaching movement.
varies according to noises, mental and physical states, the social situation, and so on, even if the person\'s intention does not change. We endow an android with motion variety in order to make the android behavior more humanlike. If a person consciously or subconsciously attributes a cause of motion variety in an android motion to such things as the android\'s mental states, physical states, and the social situations, the person has more humanlike impression toward the android.
\n\t\t\tWe further consider the variety of human motion. We divide a person\'s motion into the following two components (an example is shown in Figure 1(b)):
\n\t\t\tA motion that satisfies his/her intention.
A motion change that is not relevant to the intention (a variation of the physical properties of the motion (i) such as its trajectory and velocity).
The motion variety described in the above means a variety in the motion change. Motion generation models involving a signal-dependent noise have been proposed in studies related to the variety of the motion change (Todorov & Jordan, 2002; Miyamoto et al., 2004). This noise-based variety cannot be controlled even if the subject consciously attempts to control or suppress this variety. In contrast, motion variety caused by such things as mental strain or hesitation can be consciously controlled. We assume that the motion variety in an intentional motion influences the human-like nature of the android behavior, even if an observed motion change caused by the variety is small.
\n\t\t\tThe present chapter hypothesizes that the motion change that is not relevant to a subject\'s intention and can be consciously controlled influences the humanlike impression towards the subject. In particular, we focus on motion variety in an intentional motion caused by the social relationship between the subject and another person. The present chapter concretely
\n\t\t\tGestures to be modelled. A subject reaches out and touches an object or a person.
takes up the motion of a subject reaching out and touching another person. Even a simple reaching motion of a humanoid robot has not been studied with respect to how the change of its properties due to social situations affects the impression of an observer toward the robot. As an extreme case, the present chapter models the motion difference between two cases in which a subject touches another person or an inanimate object through observing the subject\'s behavior. We then examine how the presence of the motion variety in an android motion influences the impression toward the android. In a psychological experiment, as a third party, participants watch an android touches a person or an object and report their impressions.
\n\t\tThe present chapter hypothesizes that motion variety in an intentional motion independent of uncontrollable noise contributes to the human-like nature of the motion. In order to examine this hypothesis, we model the motion difference caused by the social relationship between two persons in the motion of one reaching out and touching the other. It is, however, difficult to control the social relationship between two persons in an experiment. As the extreme case, we consider the difference between a person-object relationship (Figure 2, top) and an interpersonal relationship (Figure 2, bottom). The person-object relationship is not social, but this chapter considers it as the least social relationship. We then construct a model of the difference in the subject\'s arm movements in these two cases.
\n\t\t\tIn order to construct the model, we set up the situations shown in Figure 2 and measured the subject\'s arm movements with a motion capture system (MAC 3D System, Motion Analysis Corporation). The task of the subject was to reach out with the right hand and touch a box or a female experimenter in front of the subject. The hand position of the subject was measured by attaching a marker to the back of the hand. The sampling rate was 60 Hz. The subject touched the left shoulder, nose, and forehead of the experimenter and two spots on the box, the heights of which are the same as those of the shoulder and forehead (box low and box high). The subjects were seven male students. Some of the subjects were familiar with the female experimenter and others were not. All subject touched the target in the order of box low, box high, left shoulder, nose, and forehead, once for each target (total of thirty-five trials). The subjects were told to touch the target and return their hand to the initial position.
\n\t\t\tThe analysis is not for the purpose of finding differences in motion common to all subjects because the motion variation is caused by individuality in some cases. It is sufficient to find a feature to differentiate a subject-person relationship (interpersonal case) from a subject-object relationship (impersonal case) within a subject. However, if the feature is not common among the subjects, it may be difficult to obtain a common impression towards an android in the later experiment. Therefore, we attempt to find a feature that is shared by the majority of the subjects.
\n\t\t\tAn example of a subject\'s hand velocity (subject 1).
First, we calculated the absolute value of the hand velocity in order to facilitate the analysis. The trajectory of the hand position was smoothed by a low-pass filter, and the velocity was calculated by forward differences. We investigated the difference in the velocity profiles. As an example, the results for a typical subject are shown in Figure 3. Each plot is the absolute value of the velocity of the hand and is shifted in time so that the times of the first peaks are the same. In each plot, the first bell-shaped curve indicates the reaching out motion, and the second bell-shaped curve indicates the returning motion. The following features were found for each subject.
\n\t\t\tThe velocity profile in the reaching phase forms a unimodal, bell-shaped curve that does not depend on the relationships (interpersonal and impersonal cases).
The velocity profile in the returning phase varies depending on the relationships.
There were no remarkable differences in motion among the subjects that were familiar with the experimenter and the subjects that were not familiar with the experimenter. In order to examine the returning phase in detail, the horizontal and vertical components of the velocity were calculated. Figure 4 and 5 show the absolute value of the horizontal and vertical components, respectively. In all cases, the profile of the vertical component in the returning phase is a single-peak shape. This characteristic is common among all subjects. Moreover,
\n\t\t\tHorizontal velocity of the hand (the horizontal component of the
Vertical velocity of the hand (the vertical component of the
An example of a subject\'s hand velocity (subject 2).
An example of a subject\'s hand velocity (subject 3).
the profile of the horizontal component in the returning phase has a peak before the maximum peak. Other examples of the horizontal and vertical components are shown in Figure 6 and 7. We can also find a similar profile of the horizontal component in these examples.
\n\t\t\tThis characteristic appears 6 times among the 14 trials of the impersonal case and 18 times among the 21 trials of the interpersonal case. In other words, this characteristic appears more often in the interpersonal case than in the impersonal case. Although there is no statistically significant difference between the two cases because the number of the subjects is not sufficient, we focus on this feature in order to differentiate the interpersonal and impersonal cases. Comparing the horizontal and vertical components, the time to start increasing the vertical velocity is always later than the time to start increasing the horizontal velocity. There is a tendency for this time delay to be larger in interpersonal cases than in impersonal cases. These results suggest that, in the interpersonal case, when the subjects returned their hands, they moved their hands horizontally at first and then brought their hands down, whereas, in the impersonal case, subjects brought their hands down from the beginning. It is generally thought that a person moves his/her arm by controlling his/her
\n\t\t\tThe model of variation in returning phase of touching motion.
hand position initially with feedback control and then moves his/her arm in a ballistic trajectory. This difference in motion can be modelled as the difference of desired hand position of feedback control in the space close to another person (Figure 8). To put it more concretely:
\n\t\t\tIn the impersonal case, the desired hand position is set such that the hand can be returned in the fastest path (Figure 8, top).
In the interpersonal case, the desired hand position is set such that the hand can move from the space in proximity to the other person along the fastest path (Figure 8, bottom).
Although this model is specific to the motion for touching another person or a box, it can be taken as a model of human motion variety due to differences in social situation. In the next section, we examine the influence of the model on the impression towards an android.
\n\t\tThe android (called Repliee Q2) used in the experiment is shown in Figure 9. The android is modelled after a Japanese woman, the standing height of which is approximately 160 cm.
\n\t\t\t\tThe skin is composed of a kind of silicone that feels like human skin. The android is driven by pneumatic actuators that give it 42 degrees of freedom from the waist up. The legs and feet are not powered. The android can neither stand up nor move from a chair. The joints driven by the pneumatic actuator has mechanical flexibility in the control thanks to the high compressibility of air. The flexibility of the joints makes for safer interaction, with movements that are generally smoother than those of other similar systems. The complicated dynamics of the air actuator make executing the trajectory tracking control difficult.
\n\t\t\t\t“Repliee Q2” android. The left figure is blurred in order to hide the details.
Android motions generated based on the constructed model.
We implemented the motion variation based on the proposed model in the Repliee Q2 android and investigated the impression toward the android from a third-person viewpoint in psychological experiments. We showed video recordings of the android motions to participants and asked them impressions toward the android. The android motions generated based on the proposed model are shown in Figure 10. The top of the figure shows the motion in which the hand is returned along the fastest path (hereafter, motion M1). The bottom panel shows the motion in which the hand leaves from the space near the other person along the fastest path (hereafter, motion M2). The motion of reaching out was implemented according to the average motion of the subjects\' reaching motions observed in the experiment described in Section 2. It is difficult to implement a quick and smooth motion in the android with a simple feedback control because the joints are driven by flexible pneumatic actuators. In order to avoid this difficulty, we implemented the motions in the android so that the speed of motion is slow. The video stimuli were made by playing videos of the android with slow motion at fast speed.
\n\t\t\t\tIn order to examine the influence of the motion variety on the impression toward the android, we prepared three types of android, as shown in Table 1. Three androids reach out and touch persons and inanimate objects in different manners. The android in Condition A (hereafter, android A) touches persons and objects with motion M1. The android in Condition B (hereafter, android B) touches persons and objects with motion M2. The android in Condition C (hereafter, android C) touches objects with motion M1 and persons with motion M2. Conditions A and B are used to examine the impressions with respect to the androids without motion variety, and Condition C is used to examine the impressions with respect to the androids with motion variety. When the target is a person, the android touches the left shoulder of the person sitting in a face-to-face position.
\n\t\t\t\t\n\t\t\t\t\t\t\t | Android A | \n\t\t\t\t\t\t\tAndroid B | \n\t\t\t\t\t\t\tAndroid C | \n\t\t\t\t\t\t
A motion with which the android touches an object | \n\t\t\t\t\t\t\tMotion M1 | \n\t\t\t\t\t\t\tMotion M2 | \n\t\t\t\t\t\t\tMotion M1 | \n\t\t\t\t\t\t
A motion with which the android touches a person | \n\t\t\t\t\t\t\tMotion M1 | \n\t\t\t\t\t\t\tMotion M2 | \n\t\t\t\t\t\t\tMotion M2 | \n\t\t\t\t\t\t
The experimental conditions.
In each condition, a participant was presented six android motions to report the impression. The android touches three objects (a calendar, a video camera, and a small shelf) and three male persons once for each target. The six targets are shown in Figure 11. The video stimulus is synthesized from a video recording of the android motion without the target and a video recording of the only target. The video of each motion is five seconds long. In each condition, six motions were randomly presented to a participant with a constraint in which the motion of touching an object and the motion of touching a person were alternately presented. In order to eliminate memory effect and aftereffect, a blank image was presented for two seconds between the videos, as shown in Figure 12.
\n\t\t\t\tWe designed a questionnaire using a five-point Likert scale with 1 = strongly disagree, 3 = neutral, and 5 = strongly agree. The aim of the questionnaire is to ask the impression of the android\'s human-like nature; therefore, the questionnaire asked how the android is “humanlike.” It is, however, possible that the variation in the arm trajectories does not influence the impression on the human-likeness. We then prepared other six items in the
\n\t\t\t\tThe objects and persons in the video stimuli.
The procedure to present the video stimuli.
questionnaire, which are likely influenced by the variation in the arm trajectories. The items are ``the android is (1) polite, (2) accurate, (3) intellectual, (4) conscientious, (5) friendly, (6) graceful, and (7) humanlike.\'\' The items are listed in random order in order to avoid the order effect, except for “humanlike,” which always appears at the end of the questionnaire, because an answer to the item “humanlike” is likely to influence the responses to the other items.
\n\t\t\tAt first, we compared Androids A and C in order to investigate the influence of the presence of motion variety in the android. The expectation is that the comparison of the impressions of the human-like nature results in the following:
\n\t\t\t\tAndroid C > Android A.
\n\t\t\t\tThe results of questionnaire about impressions towards Androids A and C.
The results of questionnaire about impressions towards Androids A, B, and C.
The participants were twenty-four university students (nineteen males and five females) who were familiar with the Repliee Q2 android. Each participant participated in both conditions, although the order of the conditions was changed randomly. The participant answered the questionnaire after every condition was presented.
\n\t\t\t\tThe average scores of the impressions are shown in Figure 13. A paired t-test revealed significant differences (
Here, we consider the difference between Androids A and C. In Condition C, the android\'s arm trajectory varies according to the android-target relationship, and in Condition A, the android\'s arm trajectory does not vary according to the android-target relationship. Another difference is that, in Condition C, the android shows motion M2, which is not the case in Condition A. In other words, there is a possibility that the presence of motion M2 produced different impressions. In order to show that the different impressions are caused by the difference in social situation, it is necessary to examine the influence of motion M2. Therefore, in the next section, we conducted an additional experiment to assess the android in Condition B.
\n\t\t\tThe participants in this experiment were twelve of twenty-four participants who participated in experiment 1. They were nine males and three females. Each participant was presented Android B and answered the questionnaire about it. The expectation is that the comparison of the impressions of the human-like nature results in the following:
\n\t\t\t\tAndroid C > Android A, Android B.
\n\t\t\t\tThe average scores of the impressions toward Android B are shown in Figure 14 by adding the result to Figure 13. Ryan\'s multiple comparison test revealed a significant difference (
We then considered the influence of the order of stimulus presentation. The participants of the experiment 2 assessed in order of Androids A, C, and B or C, A, and B. Hereinafter, Case O1 and Case O2 indicate the order of ACB and CAB, respectively. A repeated measures two-way ANOVA with a factor of condition order and a factor of android motion was conducted. There were significant interactions at the 5% level for the items of “intellectual”, “friendly”, and “humanlike” and at the 10% level for the item of “conscientious.” It is possible that the effect of the android motion on the impression score depends on the order of conditions.
\n\t\t\t\tWe divided the twelve participants into participants who participated in Case O1 (seven persons) and participants who participated in Case O2 (five persons) and analyzed their impression scores. The average impression scores obtained in Cases O1 and O2 are shown in Figure 15 and 16, respectively. For each item, three conditions are rearranged in the order of presentation. Two tendencies can be seen in these figures:
\n\t\t\t\tThe scores obtained in the condition right after Condition C are smaller than those in Condition C.
The scores obtained in the condition right after Condition A are larger than those in Condition A.
Ryan\'s multiple comparison test revealed several significant differences at 5% level among Androids A, B, and C as shown in Figure 15 and 16. In particular, as expected, the score of “humanlike” for Android C is significantly larger than that for Android B in Case O1. In Case O2, the participants thought the android which touched anything with the motion M2 was more deliberate and careful than the android which touched anything with the motion M1. Furthermore, it is likely that this careful motion gave an impression that Android B was more humanlike than Android A in Case O2. However, the participants thought that Android C with motion variation was more humanlike than Android B when Android C was presented just after Android B in Case O1. It is possible that the participants think the android with the motion variation is more humanlike than the android with only the careful motion.
\n\t\t\tThe experimental results showed that the variety of the android motion enhances the impression of human-like nature toward the android under the influence of the order of stimulus presentation, although the expected result (i.e., Android C is more humanlike than Androids A and B) was not obtained. In addition, the results showed that motion variety influences impressions such as “conscientious” and “graceful”, which are related to the human-like nature of the android. The number of participants of the experiments was too few to compare the three conditions. The expected effect of the motion variety may be shown by an experiment with a larger number of participants.
\n\t\t\t\tAs an example of motion variety, the present chapter examined the motion variation in which the desired hand position of feedback control varies in two ways when a person returns his/her hand after touching a target. In addition, the social relationship which causes this variation was also designed to be varied in two cases, that is, android-person and android-object relationships. This is a simple example of variety. However, more complicated motion variation can be designed, for example, by changing the causes of the variation. It is inferred that complicated variation has a different influence on the impression, although there are appropriate variations for enhancing the human-like nature. Further investigation is necessary in order to clarify what motion variety makes the android humanlike.
\n\t\t\t\tIn Section 2, we assumed that the variation of the subject\'s arm motion is caused by the social relationship between the subject and the target. However, the subconscious motion variation was not verified to be due to the social situation. There is another possibility, i.e., that the variation is, for example, due to the hardness of the target, such as a hard box or a soft human body. In addition, it was not verified that the participants in Section 3 actually attributed the cause of motion variation to the social situation, although the motion variation conditionally enhanced the impression of the android\'s human-like nature. In other words, it is not clear that the participants think Android C socially behaves like
\n\t\t\t\tImpressions of participants assessed in order of Androids A, C, and B (Case O1).
Impressions of participants assessed in order of Androids C, A, and B (Case O2).
human beings. One possible design of an experiment is to compare with the android which has same motions but different motion variation, that is, the android which touches objects with motion M2 and persons with motion M1 (this manner is opposite to Android C). If this android is less humanlike than Android C, the motion variation which is congruent with that of human subjects shown in Section 2 contributes the human-likeness of the android. However, further investigation is necessary to verify whether the social relationship caused the arm motion variation observed in Section 2 and the different impressions toward the android obtained in Section 3.
\n\t\t\tWe hypothesized that a motion variety that is not related to a subject\'s intention and can be consciously controlled influences the humanlike impression of the subject, and we assumed that this motion variety makes the android more humanlike. In order to verify this hypothesis, we constructed a model of the motion variety through the observation of persons’ motions. We examined the variation in a motion of reaching out and touching another person, which occurred in different social relationships between the subject and the other person (or object). The experimental results showed that the modelled motion variety conditionally influences the impression toward the android.
\n\t\t\tThe results of the present chapter are specific to the android\'s motion of reaching out and touching a person. The present study is a first step in the exploration of the principles for providing natural robot behaviors. The results revealed that a phenomenon whereby motion variety influences the impression towards the actor can be seen at least in certain motions of a very humanlike robot. Based on these results, it is possible to examine which aspects of the robot\'s appearance and motion are affected by this phenomenon. This exploration will help to clarify the principles underlying natural human-robot communication.
\n\t\t\tFrom the viewpoint of the robot motion design, a motion variety model is also useful. Several studies have proposed a method by which to implement humanlike motion in a humanoid robot by copying human motion as measured by a motion capture system to the robot (Riley et al., 2000; Nakaoka et al., 2003; Matsui et al. 2005). In order to make a robot motion more humanlike, it is necessary to implement a humanlike motion variation. However, it is not necessary to copy all human motions. This humanlike motion variation can be automatically generated from an original motion by the motion variety model.
\n\t\tThe android robot Repliee Q2 was developed in collaboration with Kokoro Company, Ltd.
\n\t\tRadar satellite altimetry provides global, frequent, and precise measurements of uniform accuracy of the sea level height related to a desired geodetic reference frame at different time epochs and from various altimeter sensors. Designed in 1969 at the Williamstown Conference on Solid Earth and Ocean Physics [1, 2], the technology was developed through the experimental missions Skylab (see [3]), Geodynamics Experimental Ocean Satellite 3 (GEOS-3, see [4]), and SEAfaring SATellite (SEASAT, see [5]). Since the early 1990s, different altimeter satellite missions provide reliable and solid information on the sea level thus enabling various applications in geodesy, oceanography, glaciology, climate research, atmosphere, wind, waves, biology, and navigation [6, 7]. To this day, more than 80,000 publications discuss or include altimeter data, technology, or products [8].
In geodesy, satellite altimetry is used to study Earth’s shape and size, sea-level variability, Earth’s gravity field over oceans and its change, tectonic plate motion, bathymetry, natural hazards, and inland water-related occurrences. The data acquired by the satellite altimeters are distributed at different levels of complexity and applicability; from source, non-processed measurements, which must be corrected using various atmospheric and geophysical models and corrections, up to complete products ready to use in different applications. The measurements are distributed with different timeliness, most often in near real-time (e.g., in less than 3 hours after the acquisition).
This book chapter presents the theoretical background of the technology, basic principles and data processing procedures, current trends in technology, and different applications of the technology. The chapter gives an overview of the relevant literature and points towards more specific studies.
This section gives the theoretical background on the altimeter principles and concepts, the development of the technology and the satellite missions, and current advances on altimeter data processing and product deriving.
Conceptually, satellite altimeters measure the distance from the satellite to the sea-level surface, i.e., the range
After applying the corrections to the measurements, the basic equation can be modified to present corrected range
where
The basic principles of the technology integrated with the other related remote sensing systems are shown in Figure 1. The accuracy of determining the satellite altimeter position is critical for the measurements of the range. The accurate position of the satellite is ensured through the precise orbit computations in combination with satellite and ground-based tracking systems. Satellite altimeters are usually equipped with GNSS and DORIS receivers to ensure onboard satellite tracking. Some of the altimeters are additionally equipped with star trackers, which give altitude and position information when GNSS is not available [11]. The ground tracking system is most often based on satellite laser ranging (SLR) tracking methods that provide satellite position from a global network of observation stations.
Satellite altimetry and related observation systems (tide gauge sea level measurements, interferometric synthetic aperture radar (InSAR), GNSS (global navigation satellite system), Doppler Orbitography and Radiopositioning integrated by satellite (DORIS) and satellite laser ranging (SLR)) shown in integrated observation systems of the earth (adapted from [
Besides the on-board navigation devices and retroreflectors for laser tracking, satellites carry microwave radiometers, which usually operate on two or more frequencies. A radiometer is an instrument that measures radiant energy reflected from the oceans and serves to estimate the surface water vapor (see e.g., [12]). The measurements depend on surface winds, ocean and near-ocean air temperature, salinity, foam, and the absorption by water vapor and clouds [7].
As shown in Figure 1, satellite altimeters are measuring ranges relative to the center of the Earth, i.e., to the reference ellipsoid. Satellites are flying in known pre-defined orbits
The analyses of the shapes of signals returned from the sea surface are used for derivation of the Significant Wave Height (SWH) information. SWH is defined as four times the standard deviation of sea surface elevation and it corresponds to the average crest-to-trough height of 1/3 of the highest waves [6]. Therefore, it is often denoted as
Overall, the development of the satellite altimetry can be divided into three phases – (1) experimental, (2) modern, and (3) future phase (following [6, 9]). Figure 2 present the timeline overview of the altimeter satellite missions launched during all three phases along with the origin of the satellite missions and their period of orbit repeating. The modern (current) era can be defined from the launch of the ERS-1 and TOPEX/Poseidon missions in 1991 and 1992 onwards. European ERS-1 was launched on July 17, 1991, into a sun-synchronous polar orbit (Francis, 1984) with three setups of repetitivity: 3-day, 35-day (the most used), and 336-day repeat cycle. The mission lasted till March 2000, exceeding its expected lifespan by far. To support ERS-1, ESA (European Space Agency) developed a satellite-based tracking system within Precision Range and Range-Rate Equipment (PRARE) mission and widespread ground segment that enabled:
calibration of the radar altimeter to 10 cm using the ground-based laser retroreflector,
real-time data acquisition,
data processing and generation of fast-delivery products [13].
Altimeter satellite missions’ timeline overview divided into an experimental era (yellow), modern era (green), and future altimetry era (blue) along with the missions’ orbit reportativity and information about their countries of origin (following and updating from [
Data were disseminated as low-rate fast-delivery products and high-rate products via the Broadband Data Dissemination Network. At the same time, the efforts by NASA (National Aeronautics and Space Administration) and CNES (French National Centre for Space Studies) resulted in TOPEX/Poseidon mission, being the product of 20 years of technological and engineering development [14]. That satellite mission has revolutionized satellite altimetry by introducing the second altimeter frequency (C-band, 5.3 GHz) and the third frequency on the microwave radiometer (18 GHz), which enabled computations of ionospheric delay corrections, and removing of the effects of wind speed on measurements, respectively [14]. The mission provided high measurement precision of measured data with an RMSE (Root Mean Square Error) of 2 cm and orbit accuracy estimated at around 2.5 cm (see [6, 9]). Later improvements of the TOPEX/Poseidon data processing based on its dual-frequency altimeter estimates of sea-surface height resulted in an overall precision expressed with root-sum-of-squares (RMS) of about 4 cm [6], which today is an expected accuracy of altimeter data from different satellite missions and can get up to RMS of 2 cm for open ocean altimetry [9]. The advances in orbit determination were due to the development of the DORIS satellite tracking system. DORIS was developed by CNES to determine the satellite orbits with centimeter accuracy from a network of 60 ground stations settled worldwide [15].
At present, several satellites are providing measured altimeter data:
Cryogenic Satellite (CryoSat)-2 designed and built by ESA and launched in 2010,
Haiyang (HY)-2a approved and led by China National Space Administration (CNSA) launched in 2011,
SARAL launched in 2013 as a cooperative mission between the Indian Space Research Organization (ISRO) and CNES,
Sentinel-3 launched in 2015 by ESA and operated by EUMETSAT,
Jason-3 designed in collaboration of the NASA and ESA as the successor of TOPEX/Poseidon and Jason 1/2,
Haiyang (HY)-2b launched as the second in the series of Chinese Haiyang satellites in 2018,
and Sentinel-6 Michael Freilich (previously referred to as Jason CS) launched in late 2020, which continues the EU Copernicus and NASA program and previous TOPEX/Poseidon and Jason 1/2/3 satellite missions.
Sentinel-6 satellite mission is currently in its commissioning phase, i.e., in the calibration/validation phase. Figure 3 presents Sentinel-6 sea-level anomaly derived from ‘Short Time Critical Level 2 Low Resolution’ data, overlaid on a map showing similar products from the other Copernicus altimetry missions: Jason-3, Sentinel-3A, and Sentinel-3B (for details and original research, please see [17]). The background image is a map of sea-level anomalies from satellite altimeter data provided by the Copernicus Marine Environment Monitoring Service for 4 December 2020. The data for this image were taken from the Sentinel-6 products generated on 5 December 2020. Being in its commissioning phase, the measurements obtained by the Sentinel-6 are promising [17].
Early Sentinel-6 measurements validation comparing to Jason-3, sentinel-3A, and sentinel-3B [
The characteristics of previous and current satellite missions are given in Table 1.
Mission | Orbit height (km) | Inclination | Latitude coverage | Equator track distance (km) | Band | Frequency (GHz) |
---|---|---|---|---|---|---|
GEOSAT | 785 | 108° | 72° | 163 | Ku | 13.5 |
ERS-1/2 | 785 | 98° | 81° | 80 | Ku | 13.8 |
TOPEX/POSEIDON Jason-1/2/3 Sentinel-6 | 1336 | 66° | 66° | 315 | Ku/C | 13.6/5.3 |
GFO | 785 | 108° | 72° | 163 | Ku | 13.5 |
Envisat | 785 | 98° | 81° | 163 | Ku/S | 13.6/3.2 |
CryoSat-2 | 717 | 92° | 88° | 7 | Ku | 13.6 |
HY-2A/2B | 964 | 99° | 60° | 90 | Ku/C | 13.6/5.3 |
SARAL/ALTIKA | 800 | 98° | 81° | 90 | Ka | 35 |
An overview of the basic characteristics of satellite altimetry missions.
Surface Water Ocean Topography (SWOT) mission is planned to be launched primarily to enable terrestrial water monitoring. The mission is a joint project of NASA, CNES, the Canadian Space Agency, and the UK Space Agency. It is expected to operate in Ka-band with a 0.86 cm radar wavelength [18].
Pulse-limited altimetry, often referred to as low resolution mode (LRM) altimetry, or traditional altimetry, is limited by the size of the radar surface footprint, i.e., the size of the area illuminated by the radar from the satellite [6]. Depending on the SWH, the radius of the altimeter footprint can range from 1 km up to 7 km (e.g. for Jason missions), which enables high accuracy of the altimetry in open ocean areas, and on the other side, due to the contamination in the reflected radar altimeter signal caused by the land [19], lower accuracy in the coastal and inland areas (see e.g., [14]).
Significant efforts were done to overcome coastal altimetry issues through different projects, e.g., for the Mediterranean Sea projects were conducted such as ALBICOCCA (Altimeter-Based Investigations in Corsica, Capraia and Contiguous Areas), ALTICORE (Altimetry for Coastal Regions), COASTALT (Development of Radar Altimetry Data Processing in the Coastal Zone), SAMOSA (SAR Altimetry Mode Studies and Applications), and the PISTACH (Coastal and Hydrology Altimetry product) [20]. The projects resulted in improvements of the onboard trackers and developments of the waveform retrackers. On-board trackers are devices used for the prediction of surface measurements thus enabling outlier detection and easier surface tracking [21]. The waveform retrackers work on the ground after the waveform data are downloaded from a satellite. The retrackers most often attempt to fit the model or function to the measured waveform to provide as accurate as possible results [21]. The retrackers integrate physical functions (such as the Brown ocean retracker) or empirical functions. Altimeter retrackers are further discussed in [22, 23, 24, 25, 26, 27].
Different retrackers process different satellite mission data for different areas. For instance, ALES (Adaptive Leading Edge Subwaveform) is designed to be applied to Jason 1/2 and Envisat in both open ocean and coastal zones [27], X-TRACK retracker was designed particularly for coastal areas, ALES+ was later designed for the sea ice leads, coastal and inland waters [28], Goddard Space Flight Center (GSFC) designed several retrackers for ice areas [29], etc. Such retrackers nowadays enable utilizing of satellite altimetry in the coastal zones, and inland water areas. All the retracked data is available through the Coastal altimetry community [30].
One of the most significant recent developments in satellite altimetry technology was the introduction of the Delay-Doppler (DD) or SAR-mode altimetry that enables better observations of the small-scale features (below 50 km) and improved spatial resolution along the satellite track compared to conventional pulse-limited altimeters (see [31]). DD satellite altimeters employ the Doppler effect caused by the movement of the satellite in the along-track direction to improve the spatial resolution in the same direction [31] enabling the data sampling along-track e.g., up to 300 m for Sentinel-3. In other words, the altimeter footprint of the DD altimeters is reduced by an order of magnitude with respect to conventional altimeters – from a few kilometers up to a few hundreds of meters [32]. Hence, DD altimeters, such as those on the CryoSat-2 (SIRAL, SAR Interferometric Radar Altimeter), Sentinel-3 (SRAL, Synthetic Aperture Radar Altimeter), deliver more and/or improved data over the ocean, and, especially, in sea ice areas and coastal areas in general.
The SAR altimetry is based on the coherent processing of multiple echoes (e.g., 64 Ku-band pulses emitted by CryoSat-2 and Sentinel-3) within each altimeter burst (aperture duration of approx. 3.5 ms for CryoSat-2 and Sentinel-3), which enables resolving the reflected signals for along-track cells rather than the large footprints generated by the pulse limited altimeters. That naturally results in an improved resolution in the along-track (azimuth) direction of the satellite with the pulse-limited form that depends on the altimeter footprint maintained in the across-track direction (see [31, 32]).
Figure 4 presents the SAR technology and processing compared to the conventional satellite altimeters. The SAR processing includes counting for the along-track phase shift within each echo obtained from different radar burst, which depends on the geometry of the observation [31]. That produces the multiple echoes gathered at the same ground cell, which allows for the subsequent averaging (i.e., summing coherently) that increases the signal-to-noise ratio, i.e., it results in improved observations of the sea surface.
Comparison of the principles of the conventional and SAR altimetry (adapted from the [
In [33] different studies on satellite radar altimetry pointed out that the SAR altimetry already performs better over the coastal zones than the conventional altimetry (see also [34]). They also emphasized the potential of the SAR technology for applications to inland water monitoring as well as the applications in cryosphere studies, such as measuring the ice sheet elevation change and sea ice freeboard.
Altimeter data are available at different levels of complexity through different platforms and for various purposes. AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic Data) for instance offers gridded and along-track multi-mission altimeter data products (not) corrected for the geophysical effects and for different purposes [35]. Besides, AVISO offers access to Basic Radar Altimetry Toolbox (BRAT) software as well as the tools such as Marine Geospatial Ecology Tools (MGET). On top of that, through the Live Access Server (LAS), AVISO offers on-the-fly data visualization, metadata access, and quick comparisons of the measurements. For geodetic purposes, AVISO’s most valuable products are related to the SSH, often upgraded to show ocean variability or cryosphere changes.
Near-real-time along-track satellite altimeter data are available also through the Jet Propulsion Laboratory (JPL) PO.DAAC Drive system (Physical Oceanography Distributed Active Archive Center). The data are delivered as a map or digital data, focusing on the SSH, wind speed, wave heights, and geostrophic velocity vectors [36].
Different products are also available from Copernicus Marine Service [16], which offers complete studies on sea-related topics. That includes original measurements, sea-level-related maps, and sea-level forecasts.
Finally, all the georeferenced source altimeter measurements and many corrections for the measurements are available through the RADS (Radar Altimeter Database System) [37]. RADS provides harmonized, validated, and cross-calibrated sea level altimeter data for the desired area and period of the observations, and it is probably the best place to start with the altimetry for the geodetic studies. Also, RADS offers data preprocessing and processing steps integrated within the system and available through the additional tools.
A wide variety of satellite altimetry products cover many research fields. In the following section, we focus on presenting the application of altimetry in geodesy.
Sea level change is studied as the global and local phenomena (Figure 5). Today, the global sea-level change is routinely computed from the altimetry for the period from 1992 onwards by AVISO, Commonwealth Scientific and Industrial Research Organization (CSIRO), University of Colorado Boulder (CU), NASA - Goddard Space Flight Center (GSFC), The National Oceanic and Atmospheric Administration (NOAA), and others. All the global research studies agree on the current sea level linear trend of approx. 3.2 mm/yr. although the processing methods could differ slightly. The estimates on the global sea-level change trends from satellite altimetry are regularly reported within the IPCC (The Intergovernmental Panel on Climate Change) reports that provide policymakers with regular scientific assessments on climate change. Several studies reported on the regional and local sea-level change, e.g., [39] consolidated the trends and expected sea-level change globally and for the ocean regions, [40] reported on the projections of the regional sea level for the 21st century, [41, 42] recomputed all satellite altimeter data to get more pronounced sea-level change estimates and a better perspective on the impact of future sea-level rise.
Global Sea level trends (data downloaded from [
The satellite altimetry enabled finer detection of the current acceleration of global and regional sea-level rise. E.g., [38] reported on the climate-change-driven acceleration in sea level rise over the altimeter era, [43] investigated the regional sea-level rise during the altimeter era with previous studies done on uncovering the anthropogenic influence on the sea level rise in some regions [44]. With the climate change acceleration, monitoring of the sea-level change and its variation is going to be even more important. A future perspective on gauging the sea-level change and the needed improvements, both for the satellite observations and the terrestrial (tide gauge and other) measurements, is summarized in [45].
The mean sea surface and its change are one of the bases for vertical height system modeling and implementation. A wide initiative on unifying the vertical height reference systems (for details see [46, 47, 48]) most usually encompasses absolute sea-level modeling from satellite altimetry extended for the tide gauge measurements at the coast (see e.g., [49]) along with the extensive analysis of vertical land movements, GNSS measurements, gravity estimations, etc. For such purposes, further progress in coastal altimetry and altimetry, in general, is crucial.
One of the basic geodetic tasks is determining the Earth’s shape and size. The satellite altimetry gave an insight into the topography of the oceans, which later enabled the reconstruction of the Earth’s gravity field over the oceans through gravity recovery. Gravity recovery stands for the geodetic operations and procedures of fitting the (altimeter) data to a gravity field that allows for the determination of the gravity information at any location [6]. Three standard procedures can be used to compute the gravity field from the altimetry: (1) employing the least-squares collocation on the altimeter measurements with the computed slopes of the sea surfaces along the satellite tracks or (2) along with the computed deflections of the vertical (e.g. [50, 51]), and (3) using the Vening Meinesz formula for the computations of the gravity field from the deflections of the vertical derived from satellite altimetry [52] (Figure 6).
Altimetry-derived global ocean gravity map (data downloaded from [
Today, the global gravity field models are usually derived from gravity satellite mission(s) only or from combined observations (both ground and satellite data). When using combined data, satellite altimetry is most often included in modeling. Such combined models are, e.g., XGM2019e_2159 [54], GAO2012 [55], EIGEN-6C4 [56], and EGM2008 [57]. Models derived from altimetry only are given in, e.g., [53, 58].
Due to the expenses of the traditional bathymetric measuring methods (e.g., weighted lines/poles), the information about the water depths and topography of the seafloor remained mainly unexplored over the open ocean until the utilization of satellite altimetry. Today, with the global and uniform coverage, satellite altimetry is crucial in computations of the global bathymetric models fulfilling the in-situ data gaps.
Predicting the bathymetry from the altimetry relies on the method developed in 1983 by [59], who have shown the potential of such modeling using the Seasat altimetry data. Over the years, the methods were further developed (e.g., [58]). Today most of the bathymetric models integrate the same altimetry-derived bathymetry. Table 2 presents some of the most common global bathymetric models starting from the most recently updated: (1) GEBCO_2019 (The General Bathymetric Chart of the Oceans) [60], (2) SRTM15+ (Shuttle Radar Topography Mission: Global Bathymetry and Topography at 15 arcseconds) [61], (3) EMODnet (European Marine Observation and Data Network) [62], (4) SRTM30_PLUS [63], (5) S&S V19.1 (Smith & Sandwell) [59], (6) DTU10BAT (Technical University of Denmark) [57], and (7) ETOPO1 (National Oceanic and Atmospheric Administration’s dataset) [64, 65].
Name | Year of issue/update | Resolution |
---|---|---|
GEBCO_2019 | 2019 | 15” |
SRTM15 + V2.1 | 2019 | 15” |
EMODnet | 2018 | 1/16” |
SRTM30_PLUS | 2014 | 30” |
S&S V19.1 | 2014 | 1’ |
DTU10BAT | 2010 | 1′-2′ (Equator) |
ETOPO1 | 2008 | 1’ |
Basic details on the most common global bathymetric models derived from satellite altimetry and shipborne data.
Bathymetric models derived from satellite altimetry are not reliable enough for underwater navigation, construction works, or similar, as the errors of the bathymetric estimates sometimes exceeds 100 m but do offer general insight onto the seafloor topography and make the best available bathymetric data for many areas (see e.g., [60, 66]). Figure 7 presents an example of the global bathymetric model.
Altimetry-derived global ocean bathymetric map (data downloaded from [
As mentioned above, the satellite altimeter data for geodetic purposes can be integrated with tide gauges when estimating the sea-level change, with shipborne bathymetry obtained by echo sounders when modeling the bathymetry, and with discrete gravity measurements or satellite gravity when computing Earth’s gravitational field. Furthermore, the satellite altimetry can be used to access the vertical land motion over the coastal area by comparing the sea level change trends from satellite altimetry and from tide gauges where the latter obtain the trend accounted for the vertical land change (e.g., [10, 67, 68]). The altimetry can further be employed in multidiscipline-based early warning systems such as those forecasting the floods [69], or tsunamis [70], and the other climate-related forecasting systems that lead towards the operational oceanography, i.e., to the forecasting system of the sea-related variables such as sea level, temperature, and currents, based on the long-term routine measurements and real-time observations of the oceans and atmosphere (see e.g., [71]).
Satellite altimetry has proven over the years to be a reliable source of the information on the oceans. Many of the applications of the technology are related to the geodetic tasks, out of which some are almost exclusively reserved for geodesy (such as the gravity field modeling), and some are taking a great part in multidisciplinary research (e.g., as in the climate-related studies). The overview given in this book chapter summarized the theoretical basis of the technology, its evolution, and current developments with insight on the availability of different altimetry data and the ready-to-use altimeter products. The chapter could be a good starting point for diving into the geodetic or related research and practical studies on satellite altimetry.
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