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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]

Addressing the formidable challenges posed by multiple jammers jamming multiple radars, which arise from spatial discretization, many degrees of freedom, numerous model input parameters, and the complexity of constraints, along with a multi-peaked objective function, this paper proposes a cooperative jamming resource allocation method, based on evolutionary reinforcement learning, that uses joint multi-domain information. Firstly, an adversarial scenario model is established, characterizing the interaction between multiple jammers and radars based on a multi-beam jammer model and a radar detection model. Subsequently, considering real-world scenarios, this paper analyzes the constraints and objective function involved in cooperative jamming resource allocation by multiple jammers. Finally, accounting for the impact of spatial, frequency, and energy domain information on jamming resource allocation, matrices representing spatial condition constraints, jamming beam allocation, and jamming power allocation are formulated to characterize the cooperative jamming resource allocation problem. Based on this foundation, the joint allocation of the jamming beam and jamming power is optimized under the constraints of jamming resources. Through simulation experiments, it was determined that, compared to the dung beetle optimizer (DBO) algorithm and the particle swarm optimization (PSO) algorithm, the proposed evolutionary reinforcement learning algorithm based on DBO and Q-Learning (DBO-QL) offers 3.03% and 6.25% improvements in terms of jamming benefit and 26.33% and 50.26% improvements in terms of optimization success rate, respectively. In terms of algorithm response time, the proposed hybrid DBO-QL algorithm has a response time of 0.11 s, which is 97.35% and 96.57% lower than the response times of the DBO and PSO algorithms, respectively. The results show that the method proposed in this paper has good convergence, stability, and timeliness. [ABSTRACT FROM AUTHOR]

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R 2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R 2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper's methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits. [ABSTRACT FROM AUTHOR]

One of the ideas behind vegetation monitoring is the ability to identify different vegetation units, such as species, communities, habitats, or vegetation types. Remote sensing data allow for obtaining such information remotely, which is especially valuable in areas that are difficult to explore (such as mountains or wetlands). At the same time, such techniques allow for limiting field research, which is particularly important in this context. Remote sensing has been utilized for vegetation inventories for many decades, using airborne and spaceborne platforms. Developing newer tools, algorithms and sensors is conducive to more new applications in the vegetation identification field. The Special Issue "Remote Sensing Applications in Vegetation Classification" is an overview of the applications of remote sensing data with different resolutions for the identification of vegetation at different levels of detail. In 14 research papers, the most frequent different types of crops were analysed. In three cases, the authors recognised different types of grasslands, whereas trees were the object of the studies in two papers. The most commonly used sensors were Copernicus Sentinel-1 and Sentinel-2; however, to a lesser extent, MODIS, airborne hyperspectral and multispectral data, as well as LiDAR products, were also utilised. There were articles that tested and compared different combinations of datasets, different terms of data acquisition, or different classifiers in order to achieve the highest classification accuracy. These accuracies were assessed quite satisfactorily in each publication; the overall accuracy (OA) for the best result varied from 72% to 98%. In all of the research papers, at least one of the two commonly used machine learning algorithms, random forest (RF) and support vector machines (SVM), was applied. Additionally, one paper presented software ARTMO's machine-learning classification algorithms toolbox, which allows for the testing of 13 different classifiers. The studies published in this Special Issue can be used by the vegetation research teams and practitioners to conduct deeper analysis via the utilization of the proposed solutions. [ABSTRACT FROM AUTHOR]

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges. [ABSTRACT FROM AUTHOR]

The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series. [ABSTRACT FROM AUTHOR]

The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of structural information of seismic data and the inferior generalizability. We propose a flexible attention-CNN (FACNN) and realized the denoising work of seismic data. This paper's main work and advantages were concentrated on the following three aspects: (i) We propose attention gates (AGs), which progressively suppressed features in irrelevant background parts and improved the denoising performance. (ii) We added a noise level map M as an additional channel, making a single CNN model expected to inherit the flexibility of handling noise models with different parameters, even spatially variant noises. (iii) We propose a mixed loss function based on MS_SSIM to improve the performance of FACNN further. Adding the noise level map can improve the network's generalization ability, and adding the attention structure with the mixed loss function can better protect the structural information of the seismic data. The numerical tests showed that our method has better generalization and can better protect the details of seismic events. [ABSTRACT FROM AUTHOR]

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R 2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms. [ABSTRACT FROM AUTHOR]

In shallow water, Sentinel-2 multispectral imagery has only four visible bands and limited quantization levels, which easily leads to the occurrence of the same spectral profile but different depth (SSPBDD) phenomenon, resulting in a one-to-many relationship between water depth and spectral profile. Investigating the impact of this relationship on water depth inversion models is the main objective of this paper. The Stumpf model and three machine learning models (Random Forest, Support Vector Machine, and Mixture Density Network) are employed, and the performance of these models is analysed based on the spatial distribution of the training dataset and the input information composition of these models. The results show that the root mean square errors (RMSEs) of the depth inversion of Random Forest and Support Vector Machine are significantly affected by the spatial distribution of the training dataset, while minimal effects are observed for the Stumpf model and the Mixture Density Network model. The SSPBDD phenomenon is widespread in Sentinel-2 images at all depths, particularly between 5 m and 15 m, with most of the depth maximum difference of the SSPBDD pixels ranging from 0 to 5 m. The SSPBDDs phenomenon can significantly reduce the inversion accuracy of any model. The number and the depth maximum difference of the SSPBDDs pixels are the main influencing factors. However, by increasing the visible spectral information and the spatial neighbourhood information in the input layer of machine learning models, the inversion accuracy and stability of the models can be improved to a certain extent. Among the models, the Mixture Density Network achieves the best inversion accuracy and stability. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance. [ABSTRACT FROM AUTHOR]

In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation. [ABSTRACT FROM AUTHOR]

The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands' health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification. [ABSTRACT FROM AUTHOR]

When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques. [ABSTRACT FROM AUTHOR]

The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan. [ABSTRACT FROM AUTHOR]

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods. [ABSTRACT FROM AUTHOR]

The height of the F2 peak electron density (hmF2) is an essential parameter in studying ionospheric electrodynamics and high-frequency wireless communication. Based on ionosphere ray propagation theory, the physical relationship between M3000F2 and hmF2 is derived and visualized. Furthermore, based on the above physical theory and the machine learning method, this paper proposes a new model for determining hmF2 using propagation factor at a distance of 3000 km from the ionospheric F2 layer, time, and season. This proposed model is easy to understand and has the characteristics of clear principles, simple structure, and easy application. Furthermore, we used six stations in east Asia to verify this model and compare it with the other three models of the International Reference Ionosphere (IRI) model. The results show that the proposed model (PRO) has minor error and higher accuracy. Specifically the RMSE of the BSE, AMTB, SHU, and the PRO models were 20.35 km, 31.51 km, 13.59 km, and 5.68 km, respectively, and the RRMSE of the BSE, AMTB, SHU, and PRO models were 8.17%, 11.88%, 4.96%, and 2.12%, respectively. In addition, the experimental results show that the PRO model can better predict the trend of the hmF2 inflection point. This method can be further extended to add data sources and provide new ideas for studying the hmF2 over global regions. [ABSTRACT FROM AUTHOR]

The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a 'sequence' modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management. [ABSTRACT FROM AUTHOR]

Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progress that has been made in the field, this paper constitutes an overview of studies utilizing RS methods in wetland monitoring. It investigates publications from 1990 up to the middle of 2022, providing a systematic survey on RS data type, machine learning (ML) tools, publication details (e.g., authors, affiliations, citations, and publications date), case studies, accuracy metrics, and other parameters of interest for RS-based wetland studies by covering 344 papers. The RS data and ML combination is deemed helpful for wetland monitoring and multi-proxy studies, and it may open up new perspectives for research studies. In a rapidly changing wetlands landscape, integrating multiple RS data types and ML algorithms is an opportunity to advance science support for management decisions. This paper provides insight into the selection of suitable ML and RS data types for the detailed monitoring of wetland-associated systems. The synthesized findings of this paper are essential to determining best practices for environmental management, restoration, and conservation of wetlands. This meta-analysis establishes avenues for future research and outlines a baseline framework to facilitate further scientific research using the latest state-of-art ML tools for processing RS data. Overall, the present work recommends that wetland sustainability requires a special land-use policy and relevant protocols, regulation, and/or legislation. [ABSTRACT FROM AUTHOR]

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications. [ABSTRACT FROM AUTHOR]

Aiming at the low observable target detection under sea clutter backgrounds, this paper emphasizes the exploration of distinguishable full-polarization features between target and sea clutter echoes. To overcome the shortcomings of the existing polarization feature-based methods, the full-polarization features of sea clutter are modeled and analyzed in detail by using Van Zyl polarization decomposition. Then, three polarimetric features (the relative surface scattering energy, the relative dihedral scattering energy and the relative diffuse scattering energy) are extracted from the fully polarimetric radar sea clutter echoes, which improve the feature differences between sea clutter and targets. And a tri-polarimetric feature detector with constant false alarm rate (CFAR) is constructed based on the fast convex hull learning algorithm. The experimental results on the real measured IPIX radar datasets prove that the proposed full-polarization feature detector obtains more competitive detection performance and lower computational complexity than the several existing feature-based detectors. [ABSTRACT FROM AUTHOR]

Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called "Ping-pong CBUF" and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing. [ABSTRACT FROM AUTHOR]

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed. [ABSTRACT FROM AUTHOR]

The radio frequency (RF) data generated from a single-chip millimeter-wave (mmWave) ground-based multi-input multi-output (GB-MIMO) radar can provide a highly robust, precise measurement for deformation in harsh environments, overcoming challenges such as different lighting and weather conditions. Monitoring deformation is significant for safety factors in different applications, such as detecting and monitoring the ground stability of underground mines. However, radar images can experience different types of clutter and artifacts besides the spreading effects caused by the side lobes, resulting in the foremost challenge of suppressing clutter and monitoring deformation.In the state of the art, the introduced frameworks usually include many filters proposed for different types of noise, with commercial systems typically using an amplitude threshold. This paper proposes a framework for monitoring the deformation, where the essential process is to apply a data-driven threshold to the amplitude heatmap, detect the deformation, and eliminate noise. The proposed threshold is an iterative approach based on radar imagery statistics, and it performs well for the collected dataset. The principal advantage of our proposed framework is simplicity, reducing the burden of using different filters. We can consider the dynamic threshold based on data statistics as a data-driven machine learning tool. The results show promising performance for our method in monitoring the deformation and removing clutter compared to the benchmark method. [ABSTRACT FROM AUTHOR]

This paper focuses on large-scale landslide susceptibility modelling in NW Croatia. The objective of this research was to provide new insight into stable and unstable area sampling strategies on a representative inventory of small and shallow landslides mainly occurring in soil and soft rock. Four strategies were tested for stable area sampling (random points, stable area polygon, stable polygon buffering and stable area centroid) in combination with four strategies for unstable area sampling (landslide polygon, smoothing digital terrain model derived landslide conditioning factors, polygon buffering and landslide centroid), resulting in eight sampling scenarios. Using Logistic Regression, Neural Network, Random Forest and Support Vector Machine algorithm, 32 models were derived and analysed. The main conclusions reveal that polygon sampling of unstable areas is an imperative in large-scale modelling, as well as that subjective and/or biased stable area sampling leads to misleading models. Moreover, Random Forest and Neural Network proved to be more favourable methods (0.804 and 0.805 AUC, respectively), but also showed extreme sensitivity to the tested sampling strategies. In the comprehensive comparison, the advantages and disadvantages of 32 derived models were analysed through quantitative and qualitative parameters to highlight their application to large-scale landslide zonation. The results yielded by this research are beneficial to the susceptibility modelling step in large-scale landslide susceptibility assessments as they enable the derivation of more reliable zonation maps applicable to spatial and urban planning systems. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed's growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features. [ABSTRACT FROM AUTHOR]

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model's performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~ 7.3 × 10 − 4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6 × 10 − 4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. [ABSTRACT FROM AUTHOR]

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a 'LiDAR AGB model', to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015–2018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R2 = 0.80; RMSE = 55.2 Mg/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology. [ABSTRACT FROM AUTHOR]

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories. [ABSTRACT FROM AUTHOR]

Remote sensing technology has been modernized by artificial intelligence, which has made it possible for deep learning algorithms to extract useful information from images. However, overfitting and lack of uncertainty quantification, high-resolution images, information loss in traditional feature extraction, and background information retrieval for detected objects limit the use of deep learning models in various remote sensing applications. This paper proposes a Bayes by backpropagation (BBB)-based system for scene-driven identification and information retrieval in order to overcome the above-mentioned problems. We present the Bayes R-CNN, a two-stage object detection technique to reduce overfitting while also quantifying uncertainty for each object recognized within a given image. To extract features more successfully, we replace the traditional feature extraction model with our novel Multi-Resolution Extraction Network (MRENet) model. We propose the multi-level feature fusion module (MLFFM) in the inner lateral connection and a Bayesian Distributed Lightweight Attention Module (BDLAM) to reduce information loss in the feature pyramid network (FPN). In addition, our system incorporates a Bayesian image super-resolution model which enhances the quality of the image to improve the prediction accuracy of the Bayes R-CNN. Notably, MRENet is used to classify the background of the detected objects to provide detailed interpretation of the object. Our proposed system is comprehensively trained and assessed utilizing the state-of-the-art DIOR and HRSC2016 datasets. The results demonstrate our system's ability to detect and retrieve information from remote sensing scene images. [ABSTRACT FROM AUTHOR]

Image semantic segmentation using deep learning algorithms plays a vital role in identifying different rock-forming minerals. In this paper, we employ the U-net model for its architecture that guarantees precise localization and efficient data utilization. We implement this deep learning model across two distinct datasets: (1) the first dataset from the ALEX Streckeisen website, and (2) the second dataset from the Gabal Nikeiba area, South Eastern Desert of Egypt. Our model exhibits excellent performance in both datasets, with an average accuracy of precision at 0.89 and 0.83, recall at 0.80 and 0.78, and F1 score at 0.82 and 0.79, respectively, helping in identifying and detecting rock-forming minerals in thin-section images. The model's most exceptional performance is clearly in eleven different basement rock-forming minerals with precision up to 0.89, recall at 0.80, and F1 score at 0.82 on average. This study is significant as it represents the key to identifying and detecting minerals in the thin sections of rock samples in Egypt and the Arabian–Nubian Shield as a whole. By significantly reducing analysis time and improving accuracy compared to manual methods, it revolutionizes geological research and resource exploration in the region. [ABSTRACT FROM AUTHOR]

Combining remote sensing images with deep learning algorithms plays an important role in wide applications. However, it is difficult to have large-scale labeled datasets for remote sensing images because of acquisition conditions and costs. How to use the limited acquisition budget to obtaina better remote sensing image dataset is a problem worth studying. In response to this problem, this paper proposes a remote sensing image quality evaluation method based on node entropy, which can be combined with active learning to provide low-cost guidance for remote sensing image collection and labeling. The method includes a node selection module and a remote sensing image quality evaluation module. The function of the node selection module is to select representative images, and the remote sensing image quality evaluation module evaluates the remote sensing image information quality by calculating the node entropy of the images. The image at the decision boundary of the existing images has a higher information quality. To validate the method proposed in this paper, experiments are performed on two public datasets. The experimental results confirm the superiority of this method compared with other methods. [ABSTRACT FROM AUTHOR]

GNSS is still not well applied in indoor environments. This is an important challenge for seamless positioning and navigation. Using other sensors to replace and connect is the mainstream practice at present. No matter what technology is used, the problem of real-time optimal station selection is faced in complex indoor environments. In this paper, we first verified the impact of random interference from walkers on positioning signals in an indoor environment. Based on this phenomenon, we proposed a novel real-time dynamic Beacons selection method (RD) in the field of indoor positioning. First, we introduced a machine learning algorithm for real-time anomaly detection of received signals from different Beacons. Then the Beacon selection is completed based on the real-time anomaly detection results and RSSI. In an indoor scene, we verified the positioning accuracy of three other methods when selecting various numbers of Beacons. Then we used the best selection strategies to compare with the RD method. Experiments showed that the RD method can use the least Beacons to obtain higher accuracy and stable positioning results. This paper provides a new idea for real-time optimal selection of signal sources in a complex indoor environment. [ABSTRACT FROM AUTHOR]

As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research. [ABSTRACT FROM AUTHOR]

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing's subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing's Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [ABSTRACT FROM AUTHOR]

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study's findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers' ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study's approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions. [ABSTRACT FROM AUTHOR]

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration. [ABSTRACT FROM AUTHOR]

Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology. [ABSTRACT FROM AUTHOR]

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named "INSTANCE," comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs). [ABSTRACT FROM AUTHOR]

Accurate estimation of surface evapotranspiration (ET) in the Heihe River Basin using remote sensing data is crucial for understanding water dynamics in arid regions. In this paper, by coupling physical constraints and machine learning for hybrid modeling, we develop a hybrid model based on surface conductance optimization. A hybrid modeling algorithm, two physical process-based ET algorithms (Penman–Monteith-based and Priestley–Taylor-based ET algorithms), and three pure machine learning algorithms (Random Forest, Extreme Gradient Boosting, and K Nearest Neighbors) are comparatively analyzed for estimating the ET. The results showed that, in general, the machine learning model optimized by parameters was able to better predict the surface conductance of the hybrid model. Driver analyses showed that radiation, normalized difference vegetation index (NDVI), and air temperature had high correlations with ET. The hybrid model had a better prediction performance for ET than the other five models, and it improved the R2 of the two physical process-based algorithms to 0.9, reduced the root mean square error (RMSE) to 0.5 mm/day, reduced the BIAS to 0.2 mm/day, and improved the Kling–Gupta efficiency (KGE) to 0.9. The hybrid model outperformed the others across different time scales, displaying lower BIAS, RMSE, and higher KGE. Spatially, its ET patterns aligned with regional vegetation changes, with superior accuracy in annual ET estimation compared to the other models. Comparison with other ET products shows that the estimation results based on the hybrid model have better performance. This approach not only improves the accuracy of ET estimation but also improves the understanding of the physical mechanism of ET estimation by pure machine learning models. This study can provide important support for understanding ET and hydrological processes under different climatic and biotic vegetation in other arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a vital prerequisite in decision-making for maintaining sustainable agriculture. However, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved. Based on soil samples, synthetic image of bare soil and geographical data, this paper predicted SOC density of croplands using Random Forest model in the Black Soil Region of Jilin Province, China in 2005 and 2020, and analyzed its influencing factors. Results showed that random forest model that integrates bare soil composite images improve the accuracy and robustness of SOC density prediction. From 2005 to 2020, the total SOC storage in croplands decreased from 89.96 to 82.79 Tg C with an annual decrease of 0.48 Tg C yr−1. The mean value of SOC density of croplands decreased from 3.42 to 3.32 kg/m2, and high values are distributed in middle parts. Changes of SOC represented significant heterogeneity spatially. 62.14% of croplands with SOC density greater than 4.0 kg/m2 decreased significantly, and 38.60% of croplands with SOC density between 2.5 and 3.0 kg/m2 significantly increased. Climatic factors made great contributions to SOC density, however, their relative importance (RI) to SOC density decreased from 44.65% to 37.26% during the study period. Synthetic images of bare soil constituted 23.54% and 26.29% of RI in the SOC density prediction, respectively, and the contribution of each band was quite different. The RIs of topographic and vegetation factors were low but increased significantly from 2005 to 2020. This study can aid local land managers and governmental agencies in assessing carbon sequestration potential and carbon credits, thus contributing to the protection and sustainable use of black soils. [ABSTRACT FROM AUTHOR]

Indoor localization has long been a challenging task due to the complexity and dynamism of indoor environments. This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. Firstly, a dynamic feature filtering based on unsupervised learning and moving consistency checks is developed to eliminate the features of dynamic objects. Secondly, an improved line feature extraction algorithm based on LSD is proposed to optimize the effect of geometric feature extraction. Thirdly, geometric features are used to optimize localizability estimation, and an adaptive weight model and attention mechanism are built using the method of region delimitation and region growth. Finally, to verify the effectiveness and robustness of localizability estimation, multiple indoor experiments using the EuRoC dataset and TUM RGB-D dataset are conducted. Compared with ORBSLAM2, the experimental results demonstrate that absolute trajectory accuracy can be improved by 95% for equivalent processing speed in walking sequences. In fr3/walking_xyz and fr3/walking_half, ULG-SLAM tracks more trajectories than DS-SLAM, and the ATE RMSE is improved by 36% and 6%, respectively. Furthermore, the improvement in robot localizability over DynaSLAM is noteworthy, coming in at about 11% and 3%, respectively. [ABSTRACT FROM AUTHOR]

Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. It proposes a new testing methodology that systematically analyzes the quality of the spatial transfer of trained models. In this study, the classification results of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and a Majority Voting of all models and their spatial transferability are assessed. The proposed testing methodology comprises 18 test scenarios to investigate phenological, temporal, spatial, and quantitative (quantitative regarding available training data) influences. Results show that the model accuracies tend to decrease with increasing time due to the differences in phenological phases in different regions, with a combined F1-score of 82% (XGBoost) when trained on a single day, 72% (XGBoost) when trained on the half-season, and 61% when trained over the entire growing season (Majority Voting). [ABSTRACT FROM AUTHOR]

Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identification accuracy, poor timeliness, and limitation to main lobe reconnaissance, an efficient identification method based on sidelobe reconnaissance and machine learning is proposed in this paper. It can identify four classical SAR operating modes, including stripmap, scan, spotlight and ground moving target indication (GMTI). Firstly, the signal models of different operating modes are presented from the perspective of sidelobe reconnaissance. By setting the parameters differently, such as the SAR trajectory height, antenna length, transmit/receive gain and loss, signal–noise ratio, and so on, the feature samples based on multiple parameters can be obtained, respectively. Then, based on the generated database of feature samples, the initialized neural network can be pre-trained. As a result, in practice, with the intercepted sidelobe signal and the pre-trained network, we can precisely infer the SAR operating mode before the arrival of the main lobe beam footprint. Finally, the effect of SNR and the jammer's position on the identification accuracy is experimentally detailed in the simulation. The simulation results show that the identification accuracy can reach above 91%. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research. [ABSTRACT FROM AUTHOR]