Revisit Time Of Satellites Research Articles

A direct insertion technique to assimilate sea surface height into a storm surge model

Natural hazards like hurricanes, cyclones, and typhoons frequently cause major economic and livelihood loss in coastal areas around the world. However, accurately predicting sea surface height and flood inundation extent at the operational level remains a challenge. This work tests the hypothesis that assimilating satellite-like sea surface observations within a storm surge model can improve the estimation of storm surge dynamics for operational forecasting. A synthetic experiment is proposed based on Hurricane Irene, which moved along the United States East Coast in 2011. With a focus on the Chesapeake Bay area, a framework is developed to simulate coastal sea surface height using a storm surge model and merging synthetic satellite observations. To represent the “truth” of the system, a hindcast simulation was run for the Hurricane Irene event. Different satellite revisit times are considered to assess the improvement over the numerical model open-loop (i.e., no data assimilation) simulation. Results indicate that the assimilation of synthetic sea surface height observations has the potential to reduce errors and improve the accuracy in the hydrodynamic model simulations by up to 25% (in terms of correlation coefficient), especially when the satellite has short revisit times (i.e., daily, half-daily and flood tides). Such improvements are consistent regardless of the bias sign in the satellite observations and the location, which is fundamental in an operational setting where errors in satellite observations are often unknown.

Spotting Culex pipiens from satellite: modeling habitat suitability in central Italy using Sentinel-2 and deep learning techniques.

Culex pipiens, an important vector of many vector borne diseases, is a species capable to feeding on a wide variety of hosts and adapting to different environments. To predict the potential distribution of Cx. pipiens in central Italy, this study integrated presence/absence data from a four-year entomological survey (2019-2022) carried out in the Abruzzo and Molise regions, with a datacube of spectral bands acquired by Sentinel-2 satellites, as patches of 224 × 224 pixels of 20 meters spatial resolution around each site and for each satellite revisit time. We investigated three scenarios: the baseline model, which considers the environmental conditions at the time of collection; the multitemporal model, focusing on conditions in the 2 months preceding the collection; and the MultiAdjacency Graph Attention Network (MAGAT) model, which accounts for similarities in temperature and nearby sites using a graph architecture. For the baseline scenario, a deep convolutional neural network (DCNN) analyzed a single multi-band Sentinel-2 image. The DCNN in the multitemporal model extracted temporal patterns from a sequence of 10 multispectral images; the MAGAT model incorporated spatial and climatic relationships among sites through a graph neural network aggregation method. For all models, we also evaluated temporal lags between the multi-band Earth Observation datacube date of acquisition and the mosquito collection, from 0 to 50 days. The study encompassed a total of 2,555 entomological collections, and 108,064 images (patches) at 20 meters spatial resolution. The baseline model achieved an F1 score higher than 75.8% for any temporal lag, which increased up to 81.4% with the multitemporal model. The MAGAT model recorded the highest F1 score of 80.9%. The study confirms the widespread presence of Cx. pipiens throughout the majority of the surveyed area. Utilizing only Sentinel-2 spectral bands, the models effectively capture early in advance the temporal patterns of the mosquito population, offering valuable insights for directing surveillance activities during the vector season. The methodology developed in this study can be scaled up to the national territory and extended to other vectors, in order to support the Ministry of Health in the surveillance and control strategies for the vectors and the diseases they transmit.

Applicability of the InSAR technique for slope monitoring

Interferometry is a technique that can be applied to SAR (Synthetic Aperture Radar) images that allow obtaining Digital Elevation Models, displacement measurements and assistance for monitoring large areas and/or engineering constructions. The objective of this paper is to present aspects related to the technique of Interferometry SAR (InSAR) applied to the monitoring of slopes. To do so, a systematic bibliographical review was used, through research platforms in scientific collections, in order to highlight the functioning and method of operation of InSAR. Besides that, the practical experience of the authors contributed to a critical analysis of the remote sensing technique addressed. The results show theoretical aspects related to the operation of SARs on board satellites, highlighting their characteristics, orbital systems, types of imaging geometry, as well as the principles of interferometric processing of SAR images. Practical applications demonstrate the potential of InSAR with an emphasis on slope monitoring, highlighting its ability to acquire topographic information on a millimeter scale, monitoring the long-term temporal evolution of displacements, the possibility of composing a monitoring system allowing directing the implementation of other instruments for evaluating the in situ conditions and some limitations regarding the time interval (satellite revisit time) for the acquisition of the displacement data.

Application of Hybrid-Pol SAR in Oil-Spill Detection

In the application of oil-spill monitoring, the satellite revisit time needs to be as short as possible to identify minor spills before they can cause widespread damage. Simultaneously, it is required to capture a sufficient amount of information about the surface to clearly distinguish between oil-spilled and oil-free sea regions. The Hybrid-polarimetry (hybrid-pol) synthetic aperture radar (SAR) system can be exploited for such capabilities. However, limited hybrid-pol based oil-spill descriptors are reported in the literature in comparison to rich sets of full-polarimetry (full-pol) based descriptors. In this letter, we establish a direct relation between hybrid-pol data and full-pol data under reflection-symmetry condition. Consequently, through the proposed work, the rich sets of full-pol based oil-spill descriptors can be derived directly from the hybrid-pol datasets. For the validation of the proposed work, L-band ALOS PALSAR and UAVSAR datasets acquired over the Gulf of Mexico have been used.

A Novel Multitemporal Deep Fusion Network (MDFN) for Short-Term Multitemporal HR Images Classification

High-resolution (HR) satellite images, due to the technical constraints on spectral and spatial resolutions, usually contain only several broad spectral bands but with a very high spatial resolution. This provides rich spatial details of the objects on the Earth surface, while their spectral discrimination is relatively low. Recently, the increase of the satellite revisit times made it possible to acquire more frequent data coverage for finer classification. In this article, we proposed a novel multitemporal deep fusion network (MDFN) for short-term multitemporal HR images classification. Specifically, a two-branch structure of MDFN is designed, which includes a long short-term memory (LSTM) and a convolutional neural network (CNN). The LSTM branch is mainly used to learn the joint expression of different temporal-spectral features. For the CNN branch, the three-dimensional (3-D) convolution is firstly applied along the temporal and spectral dimensions to jointly learn the temporal-spatial and spectral-spatial information, respectively, and then the 2-D convolution is performed along the spatial dimension to further extract the spatial context information. Finally, features generated from the two different branches are fused to obtain the discriminative high-level semantic information for classification. Experimental results carried on two real multitemporal HR remote sensing datasets demonstrate that the proposed MDFN provides better classification performance over the state-of-the-art methods, and it also shows the potentiality to use short-term multitemporal HR images for more accurate land use/land cover mapping.

SNR-based GNSS reflectometry for coastal sea-level altimetry: results from the first IAG inter-comparison campaign

Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is quickly maturing toward the objective of becoming a viable alternative for operational coastal sea-level (SL) altimetry in a geocentric reference frame. SL has immense societal implications related to climate change. Of particular interest is the exploitation of existing coastal GNSS sites for reflectometry by means of signal-to-noise ratio (SNR) observables. We report results from the first inter-comparison campaign on SNR-based GNSS-R. The goal was to cross-validate retrieval solutions from independent research groups under comparable conditions. This action was an initiative of the International Association of Geodesy working group 4.3.9 (2015–2019 term). Data collected at the Onsala Space Observatory for a 1-year period (2015–2016) were compared to a co-located tide gauge (TG). SNR data for the GPS L1-C/A signal were processed by four groups, in Sweden, Luxembourg/Brazil, Germany, and the UK. Semidiurnal tidal constituents showed good agreement between TG and all GNSS-R groups. SL variations at diurnal and longer periods were also well captured by all series. Most GNSS-R solutions exhibited spurious tones at integer fractions of one sidereal day, the satellite revisit time of the particular GNSS constellation employed (GPS). Band-pass filtering between 3 h and 30 h confirmed that the dominant tidal components were well captured by most GNSS-R solutions. Higher-frequency SL variations (periods < 3 h) are poorly represented by GNSS-R as a consequence of its low temporal resolution. The solution with the worst agreement neglects a correction associated with the rate of change in sea level and uses narrower satellite elevation ranges per retrieval. Overall, there was excellent agreement, with correlation coefficients exceeding 0.9 and RMSE smaller than 5 cm.

Logarithmic Model Joint Inversion Method for Coseismic and Postseismic Slip: Application to the 2017 Mw 7.3 Sarpol Zahāb Earthquake, Iran

AbstractInterferometric synthetic aperture radar (InSAR) has become an important technique for studying earthquake cycle deformation. However, due to the limited satellite revisit time, it is often difficult to fully separate coseismic and postseismic slip from InSAR data. Nevertheless, accurately estimating spatiotemporal coseismic and postseismic fault slip distribution models is important for quantifying earthquake slip, understanding the kinematics of seismogenic faulting, and evaluating seismic hazards. Here, we develop a logarithmic model‐based method (LogSIM) for the joint inversion of coseismic and postseismic fault slip using InSAR data from multiple platforms in different orbits. This method considers the nature of early postseismic slip following logarithmic decay. The coseismic slip, the decay time constant, and the decay amplitude of the logarithmic model can be jointly estimated from unwrapped interferograms without the need for InSAR time series calculations, thereby reducing the number of unknown parameters and stabilizing the inversion. The robustness of LogSIM is first validated with synthetic experiments. We then apply LogSIM to invert for the coseismic and postseismic slip models of the 2017 Mw 7.3 Sarpol Zahāb earthquake. We find that the maximum afterslip value and postseismic moment release during the first four months reported in previous studies are underestimated by ~26% and ~31%, respectively, due to the unavailability of the first five days of postseismic deformation. The estimated afterslip distribution spatially overlaps with the aftershocks in the updip region of the fault plane. LogSIM could potentially be extended to integrate different space geodetic data in earthquake cycle deformation modeling.

Near Real-time S-AIS

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

Near Real-time S-AIS

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

Near Real-time S-AIS

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

Three-Dimensional Deformation Retrieval in Geosynchronous SAR by Multiple-Aperture Interferometry Processing: Theory and Performance Analysis

The 3-D deformation retrieval is significant for the accurate evaluation of geologic disasters (e.g., earthquakes and landslides). Multiple-aperture interferometry (MAI) is an effective method to obtain 3-D deformation, combined with the cross-heading tracks synthetic aperture radar (SAR) data. However, because of the limitations of the low earth orbit SAR, a long satellite revisit time, small common areas of the cross-heading tracks data, and the unsatisfied along-track deformation measurement accuracy usually exist in the traditional MAI 3-D deformation retrieval. Geosynchronous SAR (GEO SAR) runs in the geosynchronous orbit, which has the advantages of a large observation area and a short revisit time. This paper focuses on 3-D deformation retrieval by GEO SAR MAI processing. Aiming at the high orbit and the squint looking of GEO SAR, the accurate expressions of the along-track deformation, 3-D deformation, and the errors in GEO SAR MAI processing are given. The distortions and their correction in the MAI interferogram brought by the geometrical difference between the forward- and backward-looking interferograms and the multicycles flat-earth and topographic phases are given. Moreover, an optimal subaperture selection method based on minimum position dilution of precision is proposed. Finally, the effectiveness of the proposed method is validated by simulations and the experiment of BeiDou-2 inclined geosynchronous orbit navigation satellite. The theoretical analysis and the experimental results suggest centimeter-level and even millimeter-level deformation measurement accuracy could be obtained in 3-D by GEO SAR MAI processing.

Weekly Glacier Flow Estimation from Dense Satellite Time Series Using Adapted Optical Flow Technology

Contemporary optical remote sensing satellites or constellations of satellites can acquire imagery at sub-weekly or even daily timescales. Thus, these systems facilitate the potential for within-season velocity estimation of glacier surfaces. State-of-the-art techniques for displacement estimation are based on matching image pairs and are thus constrained by the need of significant displacement and/or preservation of the surface over time. Consequently, such approaches cannot benefit entirely from the increasing satellite revisit times. Here, we explore an approach that is fundamentally different from image correlation or similar techniques and exploits the concept of optical flow. Our goal is to assess if this concept could overcome above current limitations of image matching and thus give new insights in glacier flow dynamics. We implement two different methods of optical flow, and test these on the SPOT5 Take5 dataset over Kronebreen, Svalbard and over Kaskawulsh Glacier, Yukon. For Kaskawulsh Glacier we are able to extract seasonal velocity variation, that temporally coincide with events of increased air temperatures. Furthermore, even for the cloudy dataset of Kronebreen, we were able to extract spatio-temporal trajectories which correlate well with measured GPS flow paths. Because the underlying concept is simple and computationally efficient due to data-reduction, our methodology can easily be used for exploratory regional studies of several glaciers or estimation of small and slow flowing glaciers.

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods (FFMs) are analysed. Several limits affect the ability of the technique to monitor a landslide process, especially during the pre-failure phase (tertiary creep). In this study, two of the major limitations affecting the technique have been explored: (1) the low data sampling frequency and (2) the phase ambiguity constraints. We explored the time series of displacements for 56 monitored landslides inferred from the scientific literature and from different in situ and remote monitoring instruments (i.e., extensometers, inclinometers, distometers, Ground Base InSAR, and total station). Furthermore, four different forecasting techniques have been applied to the monitoring data of the selected landslides. To analyse the reliability of the FFMs based on the InSAR satellite data, the 56 time series have been sampled based on different satellite features, simulating the satellite revisit time and the phase ambiguity constraints. Our analysis shows that the satellite InSAR technique could be successful in monitoring the landslide’s tertiary creep phase and, in some cases, for forecasting the corresponding time of failure using FFMs. However, the low data sampling frequency of the present satellite systems do not capture the necessary detail for the application of FFMs in actual risk management problems or for early warning purposes.

A Novel Technique to Compute the Revisit Time of Satellites and Its Application in Remote Sensing Satellite Optimization Design

This paper proposes a novel technique to compute the revisit time of satellites within repeat ground tracks. Different from the repeat cycle which only depends on the orbit, the revisit time is relevant to the payload of the satellite as well, such as the tilt angle and swath width. The technique is discussed using the Bezout equation and takes the gravitational second zonal harmonic into consideration. The concept of subcycles is defined in a general way and the general concept of “small” offset is replaced by a multiple of the minimum interval on equator when analyzing the revisit time of remote sensing satellites. This technique requires simple calculations with high efficiency. At last, this technique is used to design remote sensing satellites with desired revisit time and minimum tilt angle. When the side-lap, the range of altitude, and desired revisit time are determined, a lot of orbit solutions which meet the mission requirements will be obtained fast. Among all solutions, designers can quickly find out the optimal orbits. Through various case studies, the calculation technique is successfully demonstrated.

Emerging technology monitors ice‐sea interface at outlet glaciers

Recent melting in Greenland and Antarctica has led to concerns about the long‐term stability of these ice sheets and their potential contributions to future sea level rise. Marine‐terminating outlet glaciers play a key role in the dynamics of these ice sheets; recent mass losses are likely related to increased influx of warmer water to the base of outlet glaciers, as evidenced by the fact that changes in ocean currents, calving front retreats, glacial thinning, mass redistribution based on satellite gravity data, and accelerating coastal uplift are roughly concurrent [e.g., Holland et al., 2008; Wouters et al., 2008; Jiang et al., 2010; Straneo et al., 2012; Bevis et al., 2012]. However, collecting quantitative measurements within the dynamic environment of marine outlet glaciers is challenging. Oceanographic measurements are limited in iceberg‐laden fjords. Measuring ice flow speeds near the calving front is similarly challenging; satellite methods lack temporal resolution (satellite revisit times are several days or longer), while GPS gives limited spatial resolution, a problem for assessing changes near the highly variable calving front.

A practical method for computing SAR satellite revisit times: application to RADARSAT‐1 and ENVISAT

Many geoscience applications of space‐borne Synthetic Aperture Radar (SAR) imagery require knowledge of the revisit time of the satellite at a location. In this paper, a practical method is presented for computing the revisit times of SAR satellites at any location on Earth. The method is based on the use of maps that give the number of imaging tracks, i.e. the number of satellite ground tracks from which a location can be imaged. With these maps and some basic equations, the revisit time of a SAR satellite or an arbitrary constellation of SAR satellites can be computed easily. As such, the presented method offers an alternative to computer programs that may not be available to everyone and often are tailored to the use of specific satellites. As an illustration of the method, maps with the number of imaging tracks have been computed for RADARSAT‐1 and ENVISAT and their ease of use is shown by means of an example.