Pallavi Govekar, Christopher Griffin, Owen Embury, Jonathan Mittaz, Helen Mary Beggs, Christopher J. Merchant
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.
{"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":"44 1","pages":""},"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}
Wang Li, Fangsong Yang, Jiayi Yang, Renzhong Zhang, Juan Lin, Dongsheng Zhao, Craig M. Hancock
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.
{"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":"1 1","pages":""},"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}
Kunbo Liu, Shuai Liu, Kai Tan, Mingbo Yin, Pengjie Tao
Salt marshes provide diverse habitats for a wide range of creatures and play a key defensive and buffering role in resisting extreme marine hazards for coastal communities. Accurately obtaining the terrains of salt marshes is crucial for the comprehensive management and conservation of coastal resources and ecology. However, dense vegetation coverage, periodic tide inundation, and pervasive ditch distribution create challenges for measuring or estimating salt marsh terrains. These environmental factors make most existing techniques and methods ineffective in terms of data acquisition resolution, accuracy, and efficiency. Drone multi-line light detection and ranging (LiDAR) has offered a fire-new perspective in the 3D point cloud data acquisition and potentially exhibited great superiority in accurately deriving salt marsh terrains. The prerequisite for terrain characterization from drone multi-line LiDAR data is point cloud filtering, which means that ground points must be discriminated from the non-ground points. Existing filtering methods typically rely on either LiDAR geometric or intensity features. These methods may not perform well in salt marshes with dense, diverse, and complex vegetation. This study proposes a new filtering method for drone multi-line LiDAR point clouds in salt marshes based on the artificial neural network (ANN) machine learning model. First, a series of spatial–spectral features at the individual (e.g., elevation, distance, and intensity) and neighborhood (e.g., eigenvalues, linearity, and sphericity) scales are derived from the original data. Then, the derived spatial–spectral features are selected to remove the related and redundant ones for optimizing the performance of the ANN model. Finally, the reserved features are integrated as input variables in the ANN model to characterize their nonlinear relationships with the point categories (ground or non-ground) at different perspectives. A case study of two typical salt marshes at the mouth of the Yangtze River, using a drone 6-line LiDAR, demonstrates the effectiveness and generalization of the proposed filtering method. The average G-mean and AUC achieved were 0.9441 and 0.9450, respectively, outperforming traditional geometric information-based methods and other advanced machine learning methods, as well as the deep learning model (RandLA-Net). Additionally, the integration of spatial–spectral features at individual–neighborhood scales results in better filtering outcomes than using either single-type or single-scale features. The proposed method offers an innovative strategy for drone LiDAR point cloud filtering and salt marsh terrain derivation under the novel solution of deeply integrating geometric and radiometric data.
盐沼为多种生物提供了多样化的栖息地，在抵御极端海洋灾害方面为沿海社区发挥着重要的防御和缓冲作用。准确获取盐沼地形对于沿海资源和生态的综合管理和保护至关重要。然而，茂密的植被覆盖、周期性的潮水淹没和无处不在的沟渠分布，给盐沼地形的测量或估算带来了挑战。这些环境因素使得大多数现有技术和方法在数据采集分辨率、准确性和效率方面效果不佳。无人机多线光探测与测距（LiDAR）为三维点云数据采集提供了一个全新的视角，在准确推导盐沼地形方面可能表现出巨大的优势。利用无人机多线激光雷达数据进行地形特征描述的前提是点云过滤，这意味着必须将地面点与非地面点区分开来。现有的过滤方法通常依赖于激光雷达的几何特征或强度特征。这些方法在植被茂密、多样且复杂的盐沼中可能效果不佳。本研究基于人工神经网络（ANN）机器学习模型，提出了一种新的盐沼无人机多线激光雷达点云过滤方法。首先，从原始数据中导出一系列单个（如高程、距离和强度）和邻域（如特征值、线性度和球度）尺度的空间光谱特征。然后，对得出的空间光谱特征进行筛选，去除相关和冗余特征，以优化 ANN 模型的性能。最后，将保留的特征作为输入变量整合到 ANN 模型中，以描述它们与不同视角下的点类别（地面或非地面）之间的非线性关系。利用无人机 6 线激光雷达对长江口两片典型盐碱地进行了案例研究，证明了所提滤波方法的有效性和普适性。所获得的平均 G 均值和 AUC 分别为 0.9441 和 0.9450，优于传统的基于几何信息的方法和其他先进的机器学习方法，以及深度学习模型（RandLA-Net）。此外，与使用单一类型或单一尺度的特征相比，整合单个邻域尺度的空间光谱特征能带来更好的过滤效果。所提出的方法为无人机激光雷达点云滤波和盐沼地形推导提供了一种创新策略，是几何数据和辐射数据深度整合的新颖解决方案。
{"title":"ANN-Based Filtering of Drone LiDAR in Coastal Salt Marshes Using Spatial–Spectral Features","authors":"Kunbo Liu, Shuai Liu, Kai Tan, Mingbo Yin, Pengjie Tao","doi":"10.3390/rs16183373","DOIUrl":"https://doi.org/10.3390/rs16183373","url":null,"abstract":"Salt marshes provide diverse habitats for a wide range of creatures and play a key defensive and buffering role in resisting extreme marine hazards for coastal communities. Accurately obtaining the terrains of salt marshes is crucial for the comprehensive management and conservation of coastal resources and ecology. However, dense vegetation coverage, periodic tide inundation, and pervasive ditch distribution create challenges for measuring or estimating salt marsh terrains. These environmental factors make most existing techniques and methods ineffective in terms of data acquisition resolution, accuracy, and efficiency. Drone multi-line light detection and ranging (LiDAR) has offered a fire-new perspective in the 3D point cloud data acquisition and potentially exhibited great superiority in accurately deriving salt marsh terrains. The prerequisite for terrain characterization from drone multi-line LiDAR data is point cloud filtering, which means that ground points must be discriminated from the non-ground points. Existing filtering methods typically rely on either LiDAR geometric or intensity features. These methods may not perform well in salt marshes with dense, diverse, and complex vegetation. This study proposes a new filtering method for drone multi-line LiDAR point clouds in salt marshes based on the artificial neural network (ANN) machine learning model. First, a series of spatial–spectral features at the individual (e.g., elevation, distance, and intensity) and neighborhood (e.g., eigenvalues, linearity, and sphericity) scales are derived from the original data. Then, the derived spatial–spectral features are selected to remove the related and redundant ones for optimizing the performance of the ANN model. Finally, the reserved features are integrated as input variables in the ANN model to characterize their nonlinear relationships with the point categories (ground or non-ground) at different perspectives. A case study of two typical salt marshes at the mouth of the Yangtze River, using a drone 6-line LiDAR, demonstrates the effectiveness and generalization of the proposed filtering method. The average G-mean and AUC achieved were 0.9441 and 0.9450, respectively, outperforming traditional geometric information-based methods and other advanced machine learning methods, as well as the deep learning model (RandLA-Net). Additionally, the integration of spatial–spectral features at individual–neighborhood scales results in better filtering outcomes than using either single-type or single-scale features. The proposed method offers an innovative strategy for drone LiDAR point cloud filtering and salt marsh terrain derivation under the novel solution of deeply integrating geometric and radiometric data.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"60 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200470","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}
Matias F. Lucero, Carlos M. Hernández, Ana J. P. Carcedo, Ariel Zajdband, Pierre C. Guillevic, Rasmus Houborg, Kevin Hamilton, Ignacio A. Ciampitti
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.
{"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":"163 1","pages":""},"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}
For missile-borne platforms, traditional SAR technology consistently encounters two significant shortcomings: geometric distortion of 2D images and the inability to achieve forward-looking imaging. To address these issues, this paper explores the feasibility of using a maneuvering trajectory to enable forward-looking and three-dimensional imaging by analyzing the maneuvering characteristics of an actual missile-borne platform. Additionally, it derives the corresponding resolution characterization model, which lays a theoretical foundation for future applications. Building on this, the paper proposes a three-dimensional super-resolution imaging algorithm that combines axis rotation with compressed sensing. The axis rotation not only realizes the dimensionality reduction of data, but also can expand the observation scenario in the cross-track dimension. The proposed algorithm first focuses on the track-vertical plane to extract 2D position parameters. Then, a compressed sensing-based process is applied to extract reflection coefficients and super-resolution cross-track position parameters, thereby achieving precise 3D imaging reconstruction. Finally, numerical simulation results confirm the effectiveness and accuracy of the proposed algorithm.
{"title":"Thorough Understanding and 3D Super-Resolution Imaging for Forward-Looking Missile-Borne SAR via a Maneuvering Trajectory","authors":"Tong Gu, Yifan Guo, Chen Zhao, Jian Zhang, Tao Zhang, Guisheng Liao","doi":"10.3390/rs16183378","DOIUrl":"https://doi.org/10.3390/rs16183378","url":null,"abstract":"For missile-borne platforms, traditional SAR technology consistently encounters two significant shortcomings: geometric distortion of 2D images and the inability to achieve forward-looking imaging. To address these issues, this paper explores the feasibility of using a maneuvering trajectory to enable forward-looking and three-dimensional imaging by analyzing the maneuvering characteristics of an actual missile-borne platform. Additionally, it derives the corresponding resolution characterization model, which lays a theoretical foundation for future applications. Building on this, the paper proposes a three-dimensional super-resolution imaging algorithm that combines axis rotation with compressed sensing. The axis rotation not only realizes the dimensionality reduction of data, but also can expand the observation scenario in the cross-track dimension. The proposed algorithm first focuses on the track-vertical plane to extract 2D position parameters. Then, a compressed sensing-based process is applied to extract reflection coefficients and super-resolution cross-track position parameters, thereby achieving precise 3D imaging reconstruction. Finally, numerical simulation results confirm the effectiveness and accuracy of the proposed algorithm.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"4 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200477","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}
Juan José Martín-Sotoca, Ernesto Sanz, Antonio Saa-Requejo, Rubén Moratiel, Andrés F. Almeida-Ñauñay, Ana M. Tarquis
The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being ZVCI and ZWCI, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the ZWCI had a strong probability of informing in advance, where the negative ZVCI will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of ZWCI to aid the prediction of ZVCI. During these months, using ZWCI as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón.
{"title":"Relationship between Vegetation and Soil Moisture Anomalies Based on Remote Sensing Data: A Semiarid Rangeland Case","authors":"Juan José Martín-Sotoca, Ernesto Sanz, Antonio Saa-Requejo, Rubén Moratiel, Andrés F. Almeida-Ñauñay, Ana M. Tarquis","doi":"10.3390/rs16183369","DOIUrl":"https://doi.org/10.3390/rs16183369","url":null,"abstract":"The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being ZVCI and ZWCI, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the ZWCI had a strong probability of informing in advance, where the negative ZVCI will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of ZWCI to aid the prediction of ZVCI. During these months, using ZWCI as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"17 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200468","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}
To address the high cost associated with acquiring hyperspectral data, spectral reconstruction (SR) has emerged as a prominent research area. However, contemporary SR techniques are more focused on image processing tasks in computer vision than on practical applications. Furthermore, the prevalent approach of employing single-dimensional features to guide reconstruction, aimed at reducing computational overhead, invariably compromises reconstruction accuracy, particularly in complex environments with intricate ground features and severe spectral mixing. Effectively utilizing both local and global information in spatial and spectral dimensions for spectral reconstruction remains a significant challenge. To tackle these challenges, this study proposes an integrated network of 3D CNN and U-shaped Transformer for heterogeneous spectral reconstruction, ICTH, which comprises a shallow feature extraction module (CSSM) and a deep feature extraction module (TDEM), implementing a coarse-to-fine spectral reconstruction scheme. To minimize information loss, we designed a novel spatial–spectral attention module (S2AM) as the foundation for constructing a U-transformer, enhancing the capture of long-range information across all dimensions. On three hyperspectral datasets, ICTH has exhibited remarkable strengths across quantitative, qualitative, and single-band detail assessments, while also revealing significant potential for subsequent applications, such as generalizability and vegetation index calculations) in two real-world datasets.
为了解决与获取高光谱数据相关的高成本问题，光谱重建（SR）已成为一个突出的研究领域。然而，当代的光谱重建技术更侧重于计算机视觉中的图像处理任务，而非实际应用。此外，为了减少计算开销，普遍采用单维度特征来指导重建，这无形中降低了重建精度，尤其是在地面特征错综复杂、光谱混合严重的复杂环境中。有效利用空间和光谱维度的局部和全局信息进行光谱重建仍然是一项重大挑战。为了应对这些挑战，本研究提出了一种用于异构光谱重建的三维 CNN 和 U 型变换器集成网络 ICTH，它由浅层特征提取模块（CSSM）和深层特征提取模块（TDEM）组成，实现了从粗到细的光谱重建方案。为了最大限度地减少信息损失，我们设计了一个新颖的空间-光谱关注模块（S2AM），作为构建 U 型变换器的基础，增强了对所有维度长距离信息的捕捉。在三个高光谱数据集上，ICTH 在定量、定性和单波段细节评估方面都表现出了显著的优势，同时在两个真实世界数据集上也显示出了后续应用的巨大潜力，如通用性和植被指数计算。
{"title":"ICTH: Local-to-Global Spectral Reconstruction Network for Heterosource Hyperspectral Images","authors":"Haozhe Zhou, Zhanhao Liu, Zhenpu Huang, Xuguang Wang, Wen Su, Yanchao Zhang","doi":"10.3390/rs16183377","DOIUrl":"https://doi.org/10.3390/rs16183377","url":null,"abstract":"To address the high cost associated with acquiring hyperspectral data, spectral reconstruction (SR) has emerged as a prominent research area. However, contemporary SR techniques are more focused on image processing tasks in computer vision than on practical applications. Furthermore, the prevalent approach of employing single-dimensional features to guide reconstruction, aimed at reducing computational overhead, invariably compromises reconstruction accuracy, particularly in complex environments with intricate ground features and severe spectral mixing. Effectively utilizing both local and global information in spatial and spectral dimensions for spectral reconstruction remains a significant challenge. To tackle these challenges, this study proposes an integrated network of 3D CNN and U-shaped Transformer for heterogeneous spectral reconstruction, ICTH, which comprises a shallow feature extraction module (CSSM) and a deep feature extraction module (TDEM), implementing a coarse-to-fine spectral reconstruction scheme. To minimize information loss, we designed a novel spatial–spectral attention module (S2AM) as the foundation for constructing a U-transformer, enhancing the capture of long-range information across all dimensions. On three hyperspectral datasets, ICTH has exhibited remarkable strengths across quantitative, qualitative, and single-band detail assessments, while also revealing significant potential for subsequent applications, such as generalizability and vegetation index calculations) in two real-world datasets.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"7 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200501","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}
Yongcheng Li, Huaqiang Xu, Jiawei Xu, Hao Chen, Qiying An, Kangming Hou, Jingjing Wang
Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.
{"title":"Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA","authors":"Yongcheng Li, Huaqiang Xu, Jiawei Xu, Hao Chen, Qiying An, Kangming Hou, Jingjing Wang","doi":"10.3390/rs16173342","DOIUrl":"https://doi.org/10.3390/rs16173342","url":null,"abstract":"Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"90 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200502","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}
Shiyao Liu, Baorong Yan, Wei Guo, Yu Hua, Shougang Zhang, Jun Lu, Lu Xu, Dong Yang
Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.
{"title":"Research on ELoran Demodulation Algorithm Based on Multiclass Support Vector Machine","authors":"Shiyao Liu, Baorong Yan, Wei Guo, Yu Hua, Shougang Zhang, Jun Lu, Lu Xu, Dong Yang","doi":"10.3390/rs16173349","DOIUrl":"https://doi.org/10.3390/rs16173349","url":null,"abstract":"Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"28 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200506","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}
The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R2 ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately.
{"title":"Quantifying Qiyi Glacier Surface Dirtiness Using UAV and Sentinel-2 Imagery","authors":"Jiangtao Chen, Ninglian Wang, Yuwei Wu, Anan Chen, Chenlie Shi, Mingjie Zhao, Longjiang Xie","doi":"10.3390/rs16173351","DOIUrl":"https://doi.org/10.3390/rs16173351","url":null,"abstract":"The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R2 ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"14 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200362","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}
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