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18 pages, 4299 KiB

Open AccessArticle

Remote Sensing of Dispersed Oil Pollution in the Ocean—The Role of Chlorophyll Concentration

by Kamila Haule and Włodzimierz Freda

Sensors 2021, 21(10), 3387; https://doi.org/10.3390/s21103387 - 13 May 2021

Cited by 7 | Viewed by 3579

Abstract

In the contrary to surface oil slicks, dispersed oil pollution is not yet detected or monitored on regular basis. The possible range of changes of the local optical properties of seawater caused by the occurrence of dispersed oil, as well as the dependencies [...] Read more.

In the contrary to surface oil slicks, dispersed oil pollution is not yet detected or monitored on regular basis. The possible range of changes of the local optical properties of seawater caused by the occurrence of dispersed oil, as well as the dependencies of changes on various physical and environmental factors, can be estimated using simulation techniques. Two models were combined to examine the influence of oceanic water type on the visibility of dispersed oil: the Monte Carlo radiative transfer model and the Lorenz–Mie model for spherical oil droplets suspended in seawater. Remote sensing reflectance, R_rs, was compared for natural ocean water models representing oligotrophic, mesotrophic and eutrophic environments (characterized by chlorophyll-a concentrations of 0.1, 1 and 10 mg/m³, respectively) and polluted by three different kinds of oils: biodiesel, lubricant oil and crude oil. We found out that dispersed oil usually increases R_rs values for all types of seawater, with the highest effect for the oligotrophic ocean. In the clearest studied waters, the absolute values of R_rs increased 2–6 times after simulated dispersed oil pollution, while R_rs band ratios routinely applied in bio-optical models decreased up to 80%. The color index, CI, was nearly double reduced by dispersed biodiesel BD and lubricant oil CL, but more than doubled by crude oil FL. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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22 pages, 3503 KiB

Open AccessArticle

Simulations of Switchback, Fragmentation and Sunspot Pair in δ-Sunspots during Magnetic Flux Emergence

by Che-Jui Chang and Jean-Fu Kiang

Sensors 2021, 21(2), 586; https://doi.org/10.3390/s21020586 - 15 Jan 2021

Viewed by 2132

Abstract

Strong flares and coronal mass ejections (CMEs), launched from

δ

-sunspots, are the most catastrophic energy-releasing events in the solar system. The formations of

δ

-sunspots and relevant polarity inversion lines (PILs) are crucial for the understanding of flare eruptions and CMEs. In [...] Read more.

Strong flares and coronal mass ejections (CMEs), launched from

δ

-sunspots, are the most catastrophic energy-releasing events in the solar system. The formations of

δ

-sunspots and relevant polarity inversion lines (PILs) are crucial for the understanding of flare eruptions and CMEs. In this work, the kink-stable, spot-spot-type

δ

-sunspots induced by flux emergence are simulated, under different subphotospheric initial conditions of magnetic field strength, radius, twist, and depth. The time evolution of various plasma variables of the

δ

-sunspots are simulated and compared with the observation data, including magnetic bipolar structures, relevant PILs, and temperature. The simulation results show that magnetic polarities display switchbacks at a certain stage and then split into numerous fragments. The simulated fragmentation phenomenon in some

δ

-sunspots may provide leads for future observations in the field. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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21 pages, 10635 KiB

Open AccessArticle

Simulation of Pulse-Echo Radar for Vehicle Control and SLAM

by Girmi Schouten, Wouter Jansen and Jan Steckel

Sensors 2021, 21(2), 523; https://doi.org/10.3390/s21020523 - 13 Jan 2021

Cited by 8 | Viewed by 2971

Abstract

Pulse-echo sensing is the driving principle behind biological echolocation as well as biologically-inspired sonar and radar sensors. In biological echolocation, a single emitter sends a self-generated pulse into the environment which reflects off objects. A fraction of these reflections are captured by two [...] Read more.

Pulse-echo sensing is the driving principle behind biological echolocation as well as biologically-inspired sonar and radar sensors. In biological echolocation, a single emitter sends a self-generated pulse into the environment which reflects off objects. A fraction of these reflections are captured by two receivers as echoes, from which information about the objects, such as their position in 3D space, can be deduced by means of timing, intensity and spectral analysis. This is opposed to frequency-modulated continuous-wave radar, which analyses the shift in frequency of the returning signal to determine distance, and requires an array of antenna to obtain directional information. In this work, we present a novel simulator which can generate synthetic pulse-echo measurements for a simulated sensor in a virtual environment. The simulation is implemented by replicating the relevant physical processes underlying the pulse-echo sensing modality, while achieving high performance at update rates above 50

Hz

. The system is built to perform design space exploration of sensor hardware and software, with the goals of rapid prototyping and preliminary safety testing in mind. We demonstrate the validity of the simulator by replicating real-world experiments from previous work. In the first case, a subsumption architecture vehicle controller is set to navigate an unknown environment using the virtual sensor. We see the same trajectory pattern emerge in the simulated environment rebuilt from the real experiment, as well as similar activation times for the high-priority behaviors (±1.9%), and low-priority behaviors (±0.2%). In a second experiment, the simulated signals are used as input to a biologically-inspired direct simultaneous mapping and localization (SLAM) algorithm. Using only path integration, 83% of the positional errors are larger than 10

m

, while for the SLAM algorithm 95% of the errors are smaller than

3.2

m

. Additionally, we perform design space exploration using the simulator. By creating a synthetic radiation pattern with increased spatiospectral variance, we are able to reduce the average localization error of the system by 11%. From these results, we conclude that the simulation is sufficiently accurate to be of use in developing vehicle controllers and SLAM algorithms for pulse-echo radar sensors. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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21 pages, 11819 KiB

Open AccessArticle

SimTalk: Simulation of IoT Applications

by Yun-Wei Lin, Yi-Bing Lin and Tai-Hsiang Yen

Sensors 2020, 20(9), 2563; https://doi.org/10.3390/s20092563 - 30 Apr 2020

Cited by 12 | Viewed by 7521

Abstract

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an [...] Read more.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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15 pages, 1624 KiB

Open AccessArticle

Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

by Yangyang Zhang, Jian Yang, Xiuguo Liu, Lin Du, Shuo Shi, Jia Sun and Biwu Chen

Sensors 2020, 20(9), 2460; https://doi.org/10.3390/s20092460 - 26 Apr 2020

Cited by 20 | Viewed by 3267

Abstract

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed [...] Read more.

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m²/m²) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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16 pages, 7105 KiB

Open AccessArticle

Best Water Vapor Information Layer of Himawari-8-Based Water Vapor Bands over East Asia

by You Wu, Feng Zhang, Kun Wu, Min Min, Wenwen Li and Renqiang Liu

Sensors 2020, 20(8), 2394; https://doi.org/10.3390/s20082394 - 23 Apr 2020

Cited by 6 | Viewed by 3906

Abstract

The best water vapor information layer (BWIL), based on Himawari-8 water vapor bands over a typical region of East Asia, is investigated with the U.S. standard atmospheric profile and European Centre for Medium-Range Weather Forecasts Re-Analysis-interim (ERA-interim) dataset. The sensitivity tests reveal that [...] Read more.

The best water vapor information layer (BWIL), based on Himawari-8 water vapor bands over a typical region of East Asia, is investigated with the U.S. standard atmospheric profile and European Centre for Medium-Range Weather Forecasts Re-Analysis-interim (ERA-interim) dataset. The sensitivity tests reveal that the height of the BWIL is connected heavily to the amount of water vapor in the atmosphere, and to the satellite zenith angle. According to the temporal and spatial distribution analysis of BWIL, there are two basic features of BWIL. First, it lifts from January to July gradually and descends from July to October in the whole region. Second, it is higher over sea than land. These characteristics may stem from the transport of water vapor by monsoon and the concentration of water vapor in different areas. With multiple water vapor absorption IR bands, Himawari-8 can present water vapor information at multiple pressure layers. The water vapor content of ERA-interim in July 2016 is assessed as an example. By comparing the brightness temperatures from satellite observation and simulation under clear sky conditions, the ERA-interim reanalysis dataset may underestimate the amount of water vapor at pressure layers higher than 280 hPa and overestimate the water vapor quantity at pressure layers from 394 to 328 hPa, yet perform well at 320~260 hPa during this month. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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17 pages, 2714 KiB

Open AccessArticle

Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

by Tien-Heng Hsieh and Jean-Fu Kiang

Sensors 2020, 20(6), 1734; https://doi.org/10.3390/s20061734 - 20 Mar 2020

Cited by 69 | Viewed by 6555

Abstract

Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture [...] Read more.

Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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24 pages, 6667 KiB

Open AccessArticle

Comparative Study on Planetary Magnetosphere in the Solar System

by Ching-Ming Lai and Jean-Fu Kiang

Sensors 2020, 20(6), 1673; https://doi.org/10.3390/s20061673 - 17 Mar 2020

Cited by 1 | Viewed by 3405

Abstract

The magnetospheric responses to solar wind of Mercury, Earth, Jupiter and Uranus are compared via magnetohydrodynamic (MHD) simulations. The tilt angle of each planetary field and the polarity of solar wind are also considered. Magnetic reconnection is illustrated and explicated with the interaction [...] Read more.

The magnetospheric responses to solar wind of Mercury, Earth, Jupiter and Uranus are compared via magnetohydrodynamic (MHD) simulations. The tilt angle of each planetary field and the polarity of solar wind are also considered. Magnetic reconnection is illustrated and explicated with the interaction between the magnetic field distributions of the solar wind and the magnetosphere. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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Other

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10 pages, 1775 KiB

Open AccessLetter

A Systematic Sensitivity Study on Surface Pixel Shifts in High Spatial Resolution Satellite Images Resulting from Atmospheric Refraction in the Sensor to Surface Ray Path

by Bo-Cai Gao, Evan Ward, Jeffrey Bowles and Adam Yingling

Sensors 2020, 20(23), 6874; https://doi.org/10.3390/s20236874 - 1 Dec 2020

Cited by 2 | Viewed by 1726

Abstract

When viewing Earth’s surfaces from a low Earth orbiting (LEO) satellite platform with an optical sensor, the upward light propagation path from the ground to the satellite is affected by atmospheric refraction. For imaging sensors with a spatial resolution of about one km [...] Read more.

When viewing Earth’s surfaces from a low Earth orbiting (LEO) satellite platform with an optical sensor, the upward light propagation path from the ground to the satellite is affected by atmospheric refraction. For imaging sensors with a spatial resolution of about one km on the ground, atmospheric refraction is typically neglected during geo-registration of the satellite images. However, for high spatial resolution imaging systems with surface pixel sizes of approximately one meter or finer, the neglect of atmospheric refraction effects can typically introduce errors of a few meters in the spatially registered images. The atmospheric refraction effects need to be properly taken into consideration during the spatial registration of high spatial resolution satellite images. We have found that, with minor modifications, the ray tracing models implemented inside the LOWTRAN series of atmospheric radiative transfer codes developed in the 1970s and 1980s, in particular LOWTRAN7 in late 1980s, can be used for modeling the pixel displacement resulting from atmospheric refraction for satellite observations. The LOWTRAN series models were originally designed for calculating atmospheric transmittances and radiances for radiation going through long paths of the Earth’s atmosphere. In the ray tracing portions of the codes, a spherical model atmosphere from the ground to 100 km is finely divided into about 30 thin atmospheric layers. The refraction angles for ray paths between consecutive layer boundaries are accurately calculated. We make a new use of the refraction angles calculated by the LOWTRAN7 code to study the surface pixel shift resulting from atmospheric refraction for satellite observations. In this letter, we report the modeling results on surface pixel displacements for different satellite altitudes and downward view zenith angles, several atmospheric temperature and pressure profiles, a few surface elevations, and wavelength dependencies from blue (450 nm) to near-IR (865 nm). These results can have reference values for researchers to estimate refraction-induced pixel displacements in their high spatial resolution satellite images. The results may also potentially help in designing spacecraft algorithms for accurate instrument pointing and mission tasking to automatically capture short-lived science events. Full article

(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)

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