Remote Sensing最新文献

{"title":"Research on Multiscale Atmospheric Chaos Based on Infrared Remote-Sensing and Reanalysis Data","authors":"Zhong Wang, Shengli Sun, Wenjun Xu, Rui Chen, Yijun Ma, Gaorui Liu","doi":"10.3390/rs16183376","DOIUrl":"https://doi.org/10.3390/rs16183376","url":null,"abstract":"The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review","authors":"Angelly de Jesus Pugliese Viloria, Andrea Folini, Daniela Carrion, Maria Antonia Brovelli","doi":"10.3390/rs16183374","DOIUrl":"https://doi.org/10.3390/rs16183374","url":null,"abstract":"With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to model susceptibility of hazardous events. This study consists of a systematic review of the ML/DL techniques applied to model the susceptibility of air pollution, urban heat islands, floods, and landslides, with the aim of providing a comprehensive source of reference both for techniques and modelling approaches. A total of 1454 articles published between 2020 and 2023 were systematically selected from the Scopus and Web of Science search engines based on search queries and selection criteria. ML/DL techniques were extracted from the selected articles and categorised using ad hoc classification. Consequently, a general approach for modelling the susceptibility of hazardous events was consolidated, covering the data preprocessing, feature selection, modelling, model interpretation, and susceptibility map validation, along with examples of related global/continental data. The most frequently employed techniques across various hazards include random forest, artificial neural networks, and support vector machines. This review also provides, per hazard, the definition, data requirements, and insights into the ML/DL techniques used, including examples of both state-of-the-art and novel modelling approaches.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Building Heights at Large Scales Using Sentinel-1 Radar Imagery and Nighttime Light Data","authors":"Mohammad Kakooei, Yasser Baleghi","doi":"10.3390/rs16183371","DOIUrl":"https://doi.org/10.3390/rs16183371","url":null,"abstract":"Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1’s signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method","authors":"Zongqing Cao, Bing Liu, Jianchao Yang, Ke Tan, Zheng Dai, Xingyu Lu, Hong Gu","doi":"10.3390/rs16183380","DOIUrl":"https://doi.org/10.3390/rs16183380","url":null,"abstract":"Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Attention Progressive Network for Infrared and Visible Image Fusion","authors":"Shuying Li, Muyi Han, Yuemei Qin, Qiang Li","doi":"10.3390/rs16183370","DOIUrl":"https://doi.org/10.3390/rs16183370","url":null,"abstract":"Visible and infrared image fusion is a strategy that effectively extracts and fuses information from different sources. However, most existing methods largely neglect the issue of lighting imbalance, which makes the same fusion models inapplicable to different scenes. Several methods obtain low-level features from visible and infrared images at an early stage of input or shallow feature extraction. However, these methods do not explore how low-level features provide a foundation for recognizing and utilizing the complementarity and common information between the two types of images. As a result, the complementarity and common information between the images is not fully analyzed and discussed. To address these issues, we propose a Self-Attention Progressive Network for the fusion of infrared and visible images in this paper. Firstly, we construct a Lighting-Aware Sub-Network to analyze lighting distribution, and introduce intensity loss to measure the probability of scene illumination. This approach enhances the model’s adaptability to lighting conditions. Secondly, we introduce self-attention learning to design a multi-state joint feature extraction module (MSJFEM) that fully utilizes the contextual information among input keys. It guides the learning of a dynamic attention matrix to strengthen the capacity for visual representation. Finally, we design a Difference-Aware Propagation Module (DAPM) to extract and integrate edge details from the source images while supplementing differential information. The experiments across three benchmark datasets reveal that the proposed approach exhibits satisfactory performance compared to existing methods.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FD-Net: A Single-Stage Fire Detection Framework for Remote Sensing in Complex Environments","authors":"Jianye Yuan, Haofei Wang, Minghao Li, Xiaohan Wang, Weiwei Song, Song Li, Wei Gong","doi":"10.3390/rs16183382","DOIUrl":"https://doi.org/10.3390/rs16183382","url":null,"abstract":"Fire detection is crucial due to the exorbitant annual toll on both human lives and the economy resulting from fire-related incidents. To enhance forest fire detection in complex environments, we propose a new algorithm called FD-Net for various environments. Firstly, to improve detection performance, we introduce a Fire Attention (FA) mechanism that utilizes the position information from feature maps. Secondly, to prevent geometric distortion during image cropping, we propose a Three-Scale Pooling (TSP) module. Lastly, we fine-tune the YOLOv5 network and incorporate a new Fire Fusion (FF) module to enhance the network’s precision in identifying fire targets. Through qualitative and quantitative comparisons, we found that FD-Net outperforms current state-of-the-art algorithms in performance on both fire and fire-and-smoke datasets. This further demonstrates FD-Net’s effectiveness for application in fire detection.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring","authors":"Jing Ning, Yunjun Yao, Joshua B. Fisher, Yufu Li, Xiaotong Zhang, Bo Jiang, Jia Xu, Ruiyang Yu, Lu Liu, Xueyi Zhang, Zijing Xie, Jiahui Fan, Luna Zhang","doi":"10.3390/rs16183372","DOIUrl":"https://doi.org/10.3390/rs16183372","url":null,"abstract":"As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m3/m3) and the highest coefficient of determination (R2, above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology","authors":"Pallavi Govekar, Christopher Griffin, Owen Embury, Jonathan Mittaz, Helen Mary Beggs, Christopher J. Merchant","doi":"10.3390/rs16183381","DOIUrl":"https://doi.org/10.3390/rs16183381","url":null,"abstract":"As a contribution to the Integrated Marine Observing System (IMOS), the Bureau of Meteorology introduces new reprocessed Himawari-8 satellite-derived Sea Surface Temperature (SST) products. The Radiative Transfer Model and a Bayesian cloud clearing method is used to retrieve SSTs every 10 min from the geostationary satellite Himawari-8. An empirical Sensor Specific Error Statistics (SSES) model, introduced herein, is applied to calculate bias and standard deviation for the retrieved SSTs. The SST retrieval and compositing method, along with validation results, are discussed. The monthly statistics for comparisons of Himawari-8 Level 2 Product (L2P) skin SST against in situ SST quality monitoring (iQuam) in situ SST datasets, adjusted for thermal stratification, showed a mean bias of −0.2/−0.1 K and a standard deviation of 0.4–0.7 K for daytime/night-time after bias correction, where satellite zenith angles were less than 60° and the quality level was greater than 2. For ease of use, these native resolution SST data have been composited using a method introduced herein that retains retrieved measurements, to hourly, 4-hourly and daily SST products, and projected onto the rectangular IMOS 0.02 degree grid. On average, 4-hourly products cover ≈10% more of the IMOS domain, while one-night composites cover ≈25% more of the IMOS domain than a typical 1 h composite. All available Himawari-8 data have been reprocessed for the September 2015–December 2022 period. The 10 min temporal resolution of the newly developed Himawari-8 SST data enables a daily composite with enhanced spatial coverage, effectively filling in SST gaps caused by transient clouds occlusion. Anticipated benefits of the new Himawari-8 products include enhanced data quality for applications like IMOS OceanCurrent and investigations into marine thermal stress, marine heatwaves, and ocean upwelling in near-coastal regions.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023","authors":"Wang Li, Fangsong Yang, Jiayi Yang, Renzhong Zhang, Juan Lin, Dongsheng Zhao, Craig M. Hancock","doi":"10.3390/rs16183375","DOIUrl":"https://doi.org/10.3390/rs16183375","url":null,"abstract":"The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Alfalfa Biomass Prediction: An Innovative Framework Using Remote Sensing Data","authors":"Matias F. Lucero, Carlos M. Hernández, Ana J. P. Carcedo, Ariel Zajdband, Pierre C. Guillevic, Rasmus Houborg, Kevin Hamilton, Ignacio A. Ciampitti","doi":"10.3390/rs16183379","DOIUrl":"https://doi.org/10.3390/rs16183379","url":null,"abstract":"Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet’s PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model’s. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r2: 0.61; RMSE: 0.29 kg.m−2). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers’ management decisions.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}