Satellite Images Research Articles

Inverse Synthetic Aperture Radar Imaging of Space Targets Using Wideband Pseudo-Noise Signals with Low Peak-to-Average Power Ratio

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals.

Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China

This study aims to evaluate the effectiveness of various individual machine learning and their ensemble techniques such as Stacking, Voting and Meta-learning in landslide susceptibility assessment taking Poyang, Jiangxi, China as an example. Multi-source geo-environmental data including field surveys, Sentinel-2A/B satellite images, Digital Elevation Models (DEM), and geological and hydrological data were utilized to construct and validate landslide susceptibility models. Results show that the Stacking Classifier outperformed other models, achieving the highest F1 Score of 0.846 and AUC (Area Under ROC Curve) of 0.923, demonstrating its strong predictivity, followed by the Voting Classifier with the F1 Score of 0.829 and AUC of 0.922. Among the individual models, the Multi-Layer Perceptron (MLP) performed best with the F1 Score of 0.828 and AUC of 0.904. Furthermore, the explainable Artificial Intelligence (XAI) technique was applied to better understand the mechanism of classifiers in predicting landslide susceptibility and it suggests a significant correlation between land use, distance to fault, and landslide occurrences. In conclusion, Stacking and Voting hybrid learning models show clear advantages over the individual ones for landslide risk zoning. The results of study may provide technical support for disaster mitigation efforts and future urban planning in areas prone to landslides in Poyang.

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.

Using HawkEye Level-2 Satellite Data for Remote Sensing Tasks in the Presence of Dust Aerosol

This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol stratification and typing maps, and the global forecasting model SILAM. In a comparative analysis of in-situ and satellite measurements of the remote sensing reflectance, an error in the atmospheric correction of HawkEye measurements was found both for a clean atmosphere and in the presence of an absorbing aerosol. It is shown that, on average, the dependence of the atmospheric correction error on wavelength has the form of a power function of the form from λ−3 to λ−9. The largest errors are in the short-wavelength region of the spectrum (412–443 nm) for the dust and dusty marine aerosol domination dates. A comparative analysis of satellite and in situ measurements of the optical characteristics of the atmosphere, namely the AOD and the Ångström parameter, was carried out. It is shown that the aerosol model used by HawkEye underestimates the Angström parameter and, most likely, large errors and outliers in satellite measurements are associated with this.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Cross-Regional Crop Classification Based on Sentinel-2

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Reach scale channel planform dynamics of transboundary River Jaldhaka within Himalayan Foreland Basin

ABSTRACT The slopes of Himalayan piedmont and foothill plain are being modified by the Himalayan origin river systems. In this study, spatiotemporal dynamics of channel planform of braided River Jaldhaka have been estimated using United States Army Map (1956) and landsat satellite images (1980-MSS, 1990-TM, 2000-TM, 2010-ETM+ and 2022-OLI) for the delineation of banklines, channel length, sinuosity index, braiding index, channel widths, identification of partial channel avulsion sites and bankline migration using Digital Shoreline Assessment System (DSAS) toolbar. Linear Regression Rate, Net Bankline Movement and End Point Rate were also calculated. Results revealed that the lower reach is more sinuous (maximum average channel sinuosity 1.65) compared with reach-I (1.29) and II (1.11)). The maximum channel width was in the year 2022 (968.6 m) and minimum average channel width was in 1956 (239.87 m). The maximum BI value (3.55) has been calculated in the sub reach-2 (SR-2, reach-I). Similarly, DSAS output shows maximum average EPR values (6.43 m/year) in lower reach which was most erosion prone bank. Bank facies study showed that non-cohesive bank materials accelerate rapid rate of channel avulsion and bank erosion. The variation of discharge, velocity, presence of vegetation and anthropogenic interventions controls the nature of planform changes of Jaldhaka River.

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

China’s northern Hulun Lake is a significant body of water internationally. The issue of eutrophication has gained prominence in recent years. The achievement of precise chlorophyll-a (Chl-a) monitoring is crucial for safeguarding Hulun Lake’s ecosystem. The machine learning-based remote sensing inversion method has been shown to be effective in capturing the intricate relationship between independent and dependent variables; however, it lacks a priori knowledge and is limited by the quality of remote sensing data sources. The relationship between independent and dependent variables can be more accurately simulated with the use of suitable auxiliary variables. Therefore, three machine learning models—random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost)—were established in this study using meteorological observation parameters as auxiliary variables combined with Sentinel-2 satellite image remote sensing band combinations as independent variables and measured Chl-a data as dependent variables. The estimation effects before and after the fusion of meteorological ground observation data were compared, and the best model was used to estimate the spatial–temporal variation trend of Chl-a in the regional water body. The results show that (1) the addition of meteorological parameters as auxiliary variables improved the precision of the three machine models; the decision coefficient (R2) rose by 7.25%, 5.71%, and 7.20%, respectively, to 0.76, 0.66, and 0.73. (2) The concentration of Chl-a in the lake region was projected from June to October 2019 to October 2021 using the RF optimal estimating model of meteorological fusion. The northeast, southwest, and south of the lake were where the comparatively high concentration values of Chl-a were located, whereas the lake’s center had a generally low concentration of the substance. Chromatically, Chl-a typically peaked in August after initially increasing and then declining. (3) The three rivers that feed into the river have varying levels of water pollution, with chemical oxygen demand (COD) and total nitrogen (TN) pollution being the most severe. This is what primarily caused the higher levels of Chl-a in the northeast, southwest, and south. This study is crucial for the preservation and restoration of Hulun Lake’s natural ecosystem and offers some technical support for the monitoring of the lake’s concentration of Chl-a.

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

Geomorphological Structure of Landform Characteristics As A Reference for Development Recommendations in Active Volcanic and Faulting Areas, A Case Study in Kerinci Region, Jambi Province, Indonesia

Kerinci Regency is located on the western to the southwestern part of the capital city of Jambi Province, Indonesia. It has interesting geomorphological sites consisting of the physiography of the Barisan Mountain Zone associated with the Sumatran Fault System known as a volcanic-tectonic complex. Geomorphology has an important role in providing information of the landscape in an area. This paper aims to determine the geomorphological characteristics of the area which can provide information regarding disaster mitigation, the direction of land-use innovation, and infrastructure development strategies. This research uses analyses of morphographic, morphometric, morphogenetic, morphoconservation, and morphochronological aspects. Analysis of satellite images and topographic contours is a method used to determine the characteristics of drainage patterns and geological mapping. A significant result of this research is a geomorphological map of Kerinci that divides the area into several geomorphological units, namely volcanic-denudational, karst, structural, volcanic-structural, structural-denudational, and fluvial morphologies. Based on the geomorphological map, the area has a very high potential hazard consisting of volcanic structural landforms, which are Tanco Isolated Hill (TIH), Kerinci Fault Escarpment Undulation (KFEU), Kerinci Fault Escarpment Volcanic Undulation (KFEVU), structural landforms (Alang Structural Valley (ASV), Kumun Fault Hills (KFH), and Pengasih Fault Undulation (PGFU)). This map can be used to design the Standard Operating Procedures (SOP) for regional development in Kerinci.

Geographical Area Classification on Satellite Images Using CNN Architecture

This paper presents a novel approach for classi- fying geographical areas in satellite imagery using a modified Convolutional Neural Network (CNN) architecture.The archi- tecture enhances feature extraction and classification accuracy by combining specialized layers like fully connected, pooling, and conventional layers. Our modified CNN performs better at accurately classifying a variety of geographic locations, according to our testing results. By efficiently capturing and analyzing complex spatial patterns, the use of customized layers enhances classification results in satellite-based geographical area classifi- cation. Index Terms—Geographical area classification, Convolutional Neural Network (CNN), Satellite imagery, Image classification, Fully connected layers, Pooling layers, Conventional layers

Research on Polar Operational Limit Assessment Risk Indexing System for Ships Operating in Seasonal Sea-Ice Covered Waters

The Polar Operational Limit Assessment Risk Indexing System (POLARIS) has been established as a viable framework for assessing operational capabilities and associated risks in polar waters. Despite its inherent suitability for high-latitude territories, ships navigating through seasonal ice-infested waters at lower latitudes also encounter critical safety, environmental, and economic issues exacerbated by the presence of ice. This necessitates a reliable and adaptable methodology that can serve as a reference for devising effective countermeasures. This study evaluated the use of POLARIS in the intricate ice conditions prevalent in the northern navigable waters (channels and anchorages) within Liaodong Bay of the Bohai Sea, located at relatively low latitudes. Using GF-4 satellite imagery, ice conditions were collected, and the POLARIS methodology was employed to calculate Risk Index Outcome (RIO) values for non-ice-strengthened vessels during the winter season of 2021–2022. The results showed that sectors 3, 4, 5, 7, 9, 10, and 11 within the northern part of Liaodong Bay exhibited a higher risk, with sectors 5 and 10 exhibiting the most significant risk, while sectors 1 and 2 demonstrated relatively lower risk levels. The concurrence of these findings with acknowledged ice patterns and local maritime practices confirms the applicability of the POLARIS methodology in saline, seasonally ice-covered seas. Notably, the combination of POLARIS with high-resolution satellite imagery facilitated a more precise and rapid assessment of ice risk, thereby enhancing situational awareness and informing decision-making processes in maritime operations under icy conditions. In addition, this study provides preliminary evidence that POLARIS is suitable for fine-scale scenarios, in addition to being applicable to sparse-scale scenarios, such as polar waters, especially with high-resolution ice data. At the same time, this study highlights the potential of POLARIS as a disaster prevention strategy and a tool for the maritime industry to address ice challenges.

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.

Do Parties Negotiate After Trespass Litigation?An Empirical Study of Coasean Bargaining

The allocative efficiency outcome predicted by the Coase theorem critically depends on the assumption that, barring high transaction costs, parties will bargain after litigation and misallocated entitlements by courts will be re-allocated through voluntary exchanges. Ward Farnsworth’s 1999 small-scale survey lent credence to the claim that parties do not bargain after litigation because of the endowment effect and the animosity created by litigation. Farnsworth’s sample is small and statistically biased. Yet no other article has tested whether parties in the real world would systematically fail to exchange for behavioral reasons. This paper combines seven different data sources to shed light on this issue. We survey 1511 practicing attorneys, who reported that a substantial minority of their clients settled with the other litigating party after courts had rendered decisions. We also examine over 300 hand-coded Taiwanese cases in which the landowner sued the illicit possessor for building a structure on the plaintiff’s property. Real estate transaction records of the land in dispute show that in 6% of the cases, the landowner registered a sale of property to the possessor after the litigation. Evidence from Google Street View and satellite pictures taken by the Taiwan government suggests that the exchange rate is higher than 6%. Logistic regressions suggest that post-litigation bargaining dynamics are at least partly rational — allocative efficiency and transaction costs (conventionally defined) still matter. To the extent that the pro se status proxies for animosity incurred during litigation, Farnsworth’s thesis is also supported.

Intensity correlation imaging design for geostationary satellite inspection

Recent advances in the reduction of the integration time required of intensity correlation imaging (ICI) have opened the possibility for significant improvements in astronomical imaging. This paper discusses the application of ICI to the fine-resolution imaging of geostationary satellites conducted by ground-based observatories.

Satellite based oil spill emergency response and recovery support

Oil spills at sea pose a complex and challenging problem. Detecting, reporting, and potentially acting on the spill are difficult tasks, especially if the information is only obtained from the perspective of a vessel. Near-real time and relevant information is crucial for effective management of an oil spill event. This paper demonstrates how satellite-based images, combined with methods for thickness extraction and oil drift trajectory, can provide end-users with actionable and timely information for emergency response and recovery support. The results show the high value of quick delivery and the importance of using multiple sensors and data sources during an emergency, which enhance situational awareness and decision making. We highlight the value of satellite imagery for oil detection and show how it can be used to document the polluter, identify relative oil thickness, model oil drift, and compare different sensors.

Environmental Inequality in Peri-Urban Areas: A Case Study of Huangpu District, Guangzhou City

This research investigates environmental inequalities within Guangzhou’s Huangpu District against the backdrop of rapid urbanisation and industrial expansion in Asia. This study identifies environmental hotspots, particularly in socially vulnerable areas characterised by high industrial density, using a vulnerability framework and analysing census and pollution data. Utilising satellite imagery, urban planning documents, and field research, we delve into the internal environmental conflicts arising from industrial land use. Our findings reveal how diverse stakeholders, guided by their rationales and interests, collectively contribute to spatial inequalities within a market-driven context. Importantly, we emphasise that environmental inequality transcends mere conflicts of interest among stakeholders and is fundamentally shaped by the prevailing market-oriented spatial development model in peri-urban areas. This model results in urban segmentation, socio-economic stratification, and an uneven distribution of environmental risks and resources. Our study advocates for a paradigmatic shift in China’s peri-urban spatial development and the integration of environmental protection and social equity alongside economic growth. We recommend moving away from short-term speculative practices and promoting long-term, community-engaged urban renewal strategies that harmonise economic progress with improved living standards and environmental sustainability.

The Egyptian pyramid chain was built along the now abandoned Ahramat Nile Branch

The largest pyramid field in Egypt is clustered along a narrow desert strip, yet no convincing explanation as to why these pyramids are concentrated in this specific locality has been given so far. Here we use radar satellite imagery, in conjunction with geophysical data and deep soil coring, to investigate the subsurface structure and sedimentology in the Nile Valley next to these pyramids. We identify segments of a major extinct Nile branch, which we name The Ahramat Branch, running at the foothills of the Western Desert Plateau, where the majority of the pyramids lie. Many of the pyramids, dating to the Old and Middle Kingdoms, have causeways that lead to the branch and terminate with Valley Temples which may have acted as river harbors along it in the past. We suggest that The Ahramat Branch played a role in the monuments’ construction and that it was simultaneously active and used as a transportation waterway for workmen and building materials to the pyramids’ sites.

A large-scale high-resolution numerical model for sea-ice fragmentation dynamics

Abstract. Forecasts of sea-ice motion and fragmentation are of vital importance for all human interactions with sea ice, ranging from those involving indigenous hunters to shipping in polar regions. Sea-ice models are also important for simulating long-term changes in a warming climate. Here, we apply the Helsinki Discrete Element Model (HiDEM), originally developed for glacier calving, to sea-ice breakup and dynamics. The code is highly optimized to utilize high-end supercomputers to achieve an extreme time and space resolution. Simulated fracture patterns and ice motion are compared with satellite images of the Kvarken region of the Baltic Sea from March 2018. A second application of HiDEM involves ice ridge formation in the Gulf of Riga. With a few tens of graphics processing units (GPUs), the code is capable of reproducing observed ice patterns that in nature may take a few days to form; this is done over an area of ∼100km×100km, with an 8 m resolution, in computations lasting ∼10 h. The simulations largely reproduce observed fracture patterns, ice motion, fast-ice regions, floe size distributions, and ridge patterns. The similarities and differences between observed and computed ice dynamics and their relation to initial conditions, boundary conditions, and applied driving forces are discussed in detail. The results reported here indicate that the HiDEM has the potential to be developed into a detailed high-resolution model for sea-ice dynamics at short timescales, which, when combined with large-scale and long-term continuum models, may form an efficient framework for forecasts of sea-ice dynamics.

Coastal Sediment Grain Size Estimates on Gravel Beaches Using Satellite Synthetic Aperture Radar (SAR)

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2≥0.75) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs.