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Remote Sens., Volume 16, Issue 7 (April-1 2024) – 197 articles

Cover Story (view full-size image): This study introduces PrISM, a method for estimating irrigation amounts using remote sensing soil moisture data. It adapts the Antecedent Precipitation Index model through data assimilation. Tested in Catalonia, Spain, for eight consecutive years and during a severe drought in 2023, PrISM accurately identified areas with water restrictions in 2023. It shows good performance, correlating well with in situ data (0.58 to 0.76) and exhibiting a cumulative weekly RMSE between 7 and 11 mm/week. PrISM's effectiveness varies with irrigation techniques, performing well with sprinkler and flood systems but facing challenges with drip irrigation. This paper fills a gap in remote sensing-based irrigation estimation, focusing on detecting significant reductions in water allocations during droughts. View this paper
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17 pages, 7005 KiB  
Article
Construction of Aerosol Model and Atmospheric Correction in the Coastal Area of Shandong Peninsula
by Kunyang Shan, Chaofei Ma, Jingning Lv, Dan Zhao and Qingjun Song
Remote Sens. 2024, 16(7), 1309; https://doi.org/10.3390/rs16071309 - 8 Apr 2024
Viewed by 623
Abstract
Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance (Rrs) products in the nearshore [...] Read more.
Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance (Rrs) products in the nearshore waters of the Shandong Peninsula, this study develops an aerosol model based on aerosol data collected from the Mu Ping site in the coastal area of the Shandong Peninsula, enabling tailored atmospheric correction for this specific region. Given the pronounced seasonal variations in aerosol optical properties, monthly aerosol models were developed. The monthly aerosol model is derived using the average values of aerosol microphysical properties. Compared to the standard aerosol model, this model is more effective in characterizing the absorption and scattering characteristics of aerosols in the study area. Corresponding lookup tables for the aerosol model were created and integrated into the NIR-SWIR atmospheric correction algorithm. According to the accuracy evaluation indexes of RMSD, MAE, and UPD, it can be found that the atmospheric correction results of the aerosol model established in this paper are better than those of the standard aerosol model, especially in the 547 nm band. It demonstrates that the new aerosol model outperforms the standard model in atmospheric correction performance. With the increasing availability of aerosol observational data, the aerosol model is expected to become more accurate and applicable to other satellite missions. Full article
(This article belongs to the Special Issue Aerosol and Atmospheric Correction)
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15 pages, 9406 KiB  
Article
How Land Use Transitions Contribute to the Soil Organic Carbon Accumulation from 1990 to 2020
by Zihui Zhang, Lang Xia, Zifei Zhao, Fen Zhao, Guanyu Hou, Shixin Wu, Xiao Sun, Shangrong Wu, Peng Yang and Yan Zha
Remote Sens. 2024, 16(7), 1308; https://doi.org/10.3390/rs16071308 - 8 Apr 2024
Viewed by 533
Abstract
Soil organic carbon stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal–spatial and vertical distribution characteristics of the SOCS in [...] Read more.
Soil organic carbon stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In this study, we investigated the temporal–spatial and vertical distribution characteristics of the SOCS in the Western Sichuan Plateau (WSP) using the sparrow search algorithm–random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal–spatial variations in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS in the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed a significant increase in the SOCS in the study area since 2010. There was a 27.88 Tg C increase in the SOCS in 2020 compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forests and grasslands are the main sources of SOCS the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total soil organic carbon (SOC) pool in 2020, respectively. The expansion of the wetlands, forest, and grassland areas could increase the SOCS in the study area. A timely and accurate understanding of the dynamics of SOC is crucial for developing effective land management strategies to enhance carbon sequestration and mitigate land degradation. Full article
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12 pages, 5145 KiB  
Technical Note
Instrumentation for Sub-Ampere Lightning Current Measurement on a Tall Meteorological Tower in Complex Electromagnetic Environment
by Shaoyang Wang, Yan Gao, Mingli Chen, Zongxu Qiu, Hongbo Zhuang and Runquan Huang
Remote Sens. 2024, 16(7), 1307; https://doi.org/10.3390/rs16071307 - 8 Apr 2024
Viewed by 506
Abstract
Measurement of lightning current plays a critical role in the field of atmospheric electricity. Traditionally, Rogowski coils or low-resistance shunts were employed for measuring lightning currents in the range from several amperes up to several hundreds of kilo-amperes, and high-value resistors were utilized [...] Read more.
Measurement of lightning current plays a critical role in the field of atmospheric electricity. Traditionally, Rogowski coils or low-resistance shunts were employed for measuring lightning currents in the range from several amperes up to several hundreds of kilo-amperes, and high-value resistors were utilized for measuring corona discharge currents at sub-ampere levels. However, these approaches were not suitable for continuously recording the vast range of lightning currents. For this sake, we have developed a lightning current measurement system equipped with a shock-tolerant low-noise amplifier module. With the system installed on a tall tower, sub-ampere level currents just before the lightning initiation were observed for the first time. To confirm the authenticity of the recorded currents, the background noise of the measurement system and surrounding environment were identified, and a digital multi-frequency notch filter was proposed for de-noising. Results show that the system can achieve a current identification level of 50 mA even in complex electromagnetic environments, while having a measurement capability of 220 kA. Full article
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23 pages, 33239 KiB  
Article
Lunar Surface Resource Exploration: Tracing Lithium, 7 Li and Black Ice Using Spectral Libraries and Apollo Mission Samples
by Susana del Carmen Fernández, Fernando Alberquilla, Julia María Fernández, Enrique Díez, Javier Rodríguez, Rubén Muñiz, Javier F. Calleja, Francisco Javier de Cos and Jesús Martínez-Frías
Remote Sens. 2024, 16(7), 1306; https://doi.org/10.3390/rs16071306 - 8 Apr 2024
Viewed by 669
Abstract
This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark [...] Read more.
This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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26 pages, 49819 KiB  
Article
Comparison of Cloud-Mask Algorithms and Machine-Learning Methods Using Sentinel-2 Imagery for Mapping Paddy Rice in Jianghan Plain
by Xinyi Gao, Hong Chi, Jinliang Huang, Yifei Han, Yifan Li and Feng Ling
Remote Sens. 2024, 16(7), 1305; https://doi.org/10.3390/rs16071305 - 8 Apr 2024
Viewed by 747
Abstract
Southern China, one of the traditional rice production bases, has experienced significant declines in the area of rice paddy since the beginning of this century. Monitoring the rice cropping area is becoming an urgent need for food security policy decisions. One of the [...] Read more.
Southern China, one of the traditional rice production bases, has experienced significant declines in the area of rice paddy since the beginning of this century. Monitoring the rice cropping area is becoming an urgent need for food security policy decisions. One of the main challenges for mapping rice in this area is the quantity of cloud-free observations that are vulnerable to frequent cloud cover. Another relevant issue that needs to be addressed is determining how to select the appropriate classifier for mapping paddy rice based on the cloud-masked observations. Therefore, this study was organized to quickly find a strategy for rice mapping by evaluating cloud-mask algorithms and machine-learning methods for Sentinel-2 imagery. Specifically, we compared four GEE-embedded cloud-mask algorithms (QA60, S2cloudless, CloudScore, and CDI (Cloud Displacement Index)) and analyzed the appropriateness of widely accepted machine-learning classifiers (random forest, support vector machine, classification and regression tree, gradient tree boost) for cloud-masked imagery. The S2cloudless algorithm had a clear edge over the other three algorithms based on its overall accuracy in evaluation and visual inspection. The findings showed that the algorithm with a combination of S2cloudless and random forest showed the best performance when comparing mapping results with field survey data, referenced rice maps, and statistical yearbooks. In general, the research highlighted the potential of using Sentinel-2 imagery to map paddy rice with multiple combinations of cloud-mask algorithms and machine-learning methods in a cloud-prone area, which has the potential to broaden our rice mapping strategies. Full article
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20 pages, 52640 KiB  
Article
Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model
by Guoqing Zhou, Haowen Li, Jing Huang, Ertao Gao, Tianyi Song, Xiaoting Han, Shuaiguang Zhu and Jun Liu
Remote Sens. 2024, 16(7), 1304; https://doi.org/10.3390/rs16071304 - 8 Apr 2024
Viewed by 624
Abstract
The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which [...] Read more.
The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. Full article
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22 pages, 5599 KiB  
Article
Landslide-Hazard-Avoiding Highway Alignment Selection in Mountainous Regions Based on SAR Images and High-Spatial-Resolution Precipitation Datasets: A Case Study in Southwestern China
by Zhiheng Wang, Yang Jia, Shengfu Li, Rui Zhang, Binzhi Xu and Xiaopeng Sun
Remote Sens. 2024, 16(7), 1303; https://doi.org/10.3390/rs16071303 - 8 Apr 2024
Viewed by 635
Abstract
Landslides recurrently cause severe damage and, in some cases, the full disruption of many highways in mountainous areas, which can last from a few days to even months. Thus, there is a high demand for monitoring tools and precipitation data to support highway [...] Read more.
Landslides recurrently cause severe damage and, in some cases, the full disruption of many highways in mountainous areas, which can last from a few days to even months. Thus, there is a high demand for monitoring tools and precipitation data to support highway alignment selections before construction. In this study, we proposed a new system highway alignment selection method based on coherent scatter InSAR (CSI) and ~1 km high-spatial-resolution precipitation (HSRP) analysis. Prior to the CSI, we calculated and analyzed the feasibility of Sentinel-1A ascending and descending data. To illustrate the performance of the CSI, CSI and SBAS–InSAR were both utilized to monitor 80 slow-moving landslides, which were identified by optical remote-sensing interpretation and field investigation, along the Barkam–Kangting Highway Corridor (BKHC) in southwestern China, relying on 56 Sentinel-1A descending images from September 2019 to September 2021. The results reveal that CSI has clearer deformation signals and more measurement points (MPs) than SBAS-InSAR. And the maximum cumulative displacements and rates of the landslides reach −75 mm and −64 mm/year within the monitoring period (CSI results), respectively. Furthermore, the rates of the landslides near the Jinchuan River are higher than those of the landslides far from the river. Subsequently, to optimize the highway alignment selection, we analyzed the spatiotemporal evolution characteristics of feature points on a typical landslide by combining the −1 km HSRP, which was calculated from the 30′ Climatic Research Unit (CRU) time-series datasets, with the climatology datasets of WorldClim using delta spatial downscaling. The analysis shows that the sliding rates of landslides augment from the back edge to the tongue because of fluvial erosion and that accelerated sliding is highly related to the intense precipitation between April and September each year (ASP). Consequently, three solution types were established in our method by setting thresholds for the deformation rates and ASPs of every landslide. Afterward, the risk-optimal alignment selection of the BKHC was finalized according to the solution types and consideration of the construction’s possible impacts. Ultimately, the major problems and challenges for our method were discussed, and conclusions were given. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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13 pages, 8931 KiB  
Article
Early Identification of River Blockage Disasters Caused by Debris Flows in the Bailong River Basin, China
by Jianjun Zeng, Yan Zhao, Jiaoyu Zheng, Yongjun Zhang, Pengqing Shi, Yajun Li, Guan Chen, Xingmin Meng and Dongxia Yue
Remote Sens. 2024, 16(7), 1302; https://doi.org/10.3390/rs16071302 - 7 Apr 2024
Viewed by 797
Abstract
The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of [...] Read more.
The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas. Full article
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15 pages, 3942 KiB  
Technical Note
GPU Acceleration for SAR Satellite Image Ortho-Rectification
by Lei Dong, Tingtao Zhang, Fangjian Liu, Rui Liu and Hongjian You
Remote Sens. 2024, 16(7), 1301; https://doi.org/10.3390/rs16071301 - 7 Apr 2024
Viewed by 646
Abstract
Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric [...] Read more.
Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified. Full article
(This article belongs to the Special Issue Calibration and Validation of SAR Data and Derived Products)
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11 pages, 3477 KiB  
Communication
Separation of Rapidly-Varying and Slowly-Varying Processes and Development of Diffraction Decomposition Order Method in Radiative Transfer
by Meng Zhang, Chenxu Gao, Bingqiang Sun and Yijun Zhang
Remote Sens. 2024, 16(7), 1300; https://doi.org/10.3390/rs16071300 - 7 Apr 2024
Viewed by 575
Abstract
Single scattering in radiative transfer is separated into rapidly-varying and slowly-varying processes, where the rapidly-varying process (RVP) is mainly contributed by the diffraction effect. Accordingly, the diffraction decomposition order (DDO) method is developed to solve the vector radiative transfer equation (VRTE). Instead of [...] Read more.
Single scattering in radiative transfer is separated into rapidly-varying and slowly-varying processes, where the rapidly-varying process (RVP) is mainly contributed by the diffraction effect. Accordingly, the diffraction decomposition order (DDO) method is developed to solve the vector radiative transfer equation (VRTE). Instead of directly solving the original VRTE, we decompose it into a series of order equations, where the zeroth-order equation replaces the RVP with a δ-function while the high-order equations are the same as the zeroth-order one, except that the high decomposition orders of the RVP are used as driven sources. In this study, the DDO method is numerically realized using the successive order of the scattering method. The DDO is computationally efficient and accurate. More importantly, all physical processes in the VRTE are fully decomposed due to the order decomposition of the RVP and can be straightforwardly discussed. Full article
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21 pages, 6228 KiB  
Article
An Improved SAR Ship Classification Method Using Text-to-Image Generation-Based Data Augmentation and Squeeze and Excitation
by Lu Wang, Yuhang Qi, P. Takis Mathiopoulos, Chunhui Zhao and Suleman Mazhar
Remote Sens. 2024, 16(7), 1299; https://doi.org/10.3390/rs16071299 - 7 Apr 2024
Viewed by 726
Abstract
Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence [...] Read more.
Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network’s focus on essential features, thereby improving the model’s generalization ability. To assess the model’s effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods. Full article
(This article belongs to the Special Issue Deep Learning Techniques Applied in Remote Sensing)
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0 pages, 18976 KiB  
Article
Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China
by Kun Qin, Zhanpeng Wang, Shaoqing Dai, Yuchen Li, Manyao Li, Chen Li, Ge Qiu, Yuanyuan Shi, Chun Yin, Shujuan Yang and Peng Jia
Remote Sens. 2024, 16(7), 1298; https://doi.org/10.3390/rs16071298 - 7 Apr 2024
Viewed by 914
Abstract
Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment [...] Read more.
Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution. Full article
(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)
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18 pages, 4762 KiB  
Technical Note
SSEBop Evapotranspiration Estimates Using Synthetically Derived Landsat Data from the Continuous Change Detection and Classification Algorithm
by Mikael P. Hiestand, Heather J. Tollerud, Chris Funk, Gabriel B. Senay, Kate C. Fickas and MacKenzie O. Friedrichs
Remote Sens. 2024, 16(7), 1297; https://doi.org/10.3390/rs16071297 - 6 Apr 2024
Viewed by 867
Abstract
The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by [...] Read more.
The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC. Full article
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19 pages, 9215 KiB  
Article
Changes in Vegetation NDVI and Its Response to Climate Change and Human Activities in the Ferghana Basin from 1982 to 2015
by Heli Zhang, Lu Li, Xiaoen Zhao, Feng Chen, Jiachang Wei, Zhimin Feng, Tiyuan Hou, Youping Chen, Weipeng Yue, Huaming Shang, Shijie Wang and Mao Hu
Remote Sens. 2024, 16(7), 1296; https://doi.org/10.3390/rs16071296 - 6 Apr 2024
Viewed by 604
Abstract
Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological [...] Read more.
Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. Full article
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21 pages, 2358 KiB  
Article
Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar
by Xiaolong Zhou, Tian Jin, Yongpeng Dai, Yongping Song and Kemeng Li
Remote Sens. 2024, 16(7), 1295; https://doi.org/10.3390/rs16071295 - 6 Apr 2024
Viewed by 701
Abstract
In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task [...] Read more.
In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields. Full article
(This article belongs to the Special Issue Advances in Remote Sensing, Radar Techniques, and Their Applications)
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24 pages, 3929 KiB  
Article
Sentinel-1-Based Soil Freeze–Thaw Detection in Agro-Forested Areas: A Case Study in Southern Québec, Canada
by Shahabeddin Taghipourjavi, Christophe Kinnard and Alexandre Roy
Remote Sens. 2024, 16(7), 1294; https://doi.org/10.3390/rs16071294 - 6 Apr 2024
Viewed by 861
Abstract
Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) [...] Read more.
Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) data time series at two agro-forested study sites, St-Marthe and St-Maurice, in southern Québec, Canada. In total, 18 plots were instrumented to monitor soil temperature and derive soil freezing probabilities at 2 and 10 cm depths during 2020–21 and 2021–22. Three change detection algorithms were tested: backscatter differences (∆σ) derived from thawed reference (Delta), the freeze–thaw index (FTI), and a newly developed exponential freeze–thaw algorithm (EFTA). Various probabilistic mixed models were compared to identify the model and predictor variables that best predicted soil freezing probability. VH polarization backscatter signals processed with the EFTA and used as predictors in a logistic model led to improved predictions of soil freezing probability at 2 cm (Pseudo-R2 = 0.54) compared to other approaches. The EFTA could effectively address the limitations of the Delta algorithm caused by backscatter fluctuations in the shoulder seasons, resulting in more precise estimates of FT events. Furthermore, the inclusion of crop types as plot-level effects within the probabilistic model also slightly improved the soil freezing probability prediction at each monitored plot, with marginal and conditional R2 values of 0.59 and 0.61, respectively. The model accurately classified observed binary ‘frozen’ or ‘thawed’ states with 85.2% accuracy. Strong cross-level interactions were also observed between crop types and the EFTA derived from VH backscatter, indicating that crop type modulated the backscatter response to soil freezing. This study represents the first application of the EFTA and a probabilistic approach to detect frozen soil conditions in agro-forested areas in southern Quebec, Canada. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Monitoring Agricultural Management)
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17 pages, 4146 KiB  
Article
CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation
by Ming Zhang, Xin Gu, Ji Qi, Zhenshi Zhang, Hemeng Yang, Jun Xu, Chengli Peng and Haifeng Li
Remote Sens. 2024, 16(7), 1293; https://doi.org/10.3390/rs16071293 - 6 Apr 2024
Viewed by 555
Abstract
The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset [...] Read more.
The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset plays an ever-shrinking role due to RSIs’ diverse distribution shifts. Instead, SSL promotes a ‘global-to-local’ transfer paradigm, in which generalized models pre-trained on arbitrarily large unlabeled datasets are fine-tuned to the target dataset to overcome data distribution shifts. However, the SSL pre-trained models may contain both useful and useless features for the downstream semantic segmentation task, due to the gap between the SSL tasks and the downstream task. To adapt such pre-trained models to semantic segmentation tasks, traditional supervised fine-tuning methods that use only a small number of labeled samples may drop out useful features due to overfitting. The main reason behind this is that supervised fine-tuning aims to map a few training samples from the high-dimensional, sparse image space to the low-dimensional, compact semantic space defined by the downstream labels, resulting in a degradation of the distinguishability. To address the above issues, we propose a class distinguishability-enhanced self-training (CDEST) method to support global-to-local transfer. First, the self-training module in CDEST introduces a semi-supervised learning mechanism to fully utilize the large amount of unlabeled data in the downstream task to increase the size and diversity of the training data, thus alleviating the problem of biased overfitting of the model. Second, the supervised and semi-supervised contrastive learning modules of CDEST can explicitly enhance the class distinguishability of features, helping to preserve the useful features learned from pre-training while adapting to downstream tasks. We evaluate the proposed CDEST method on four RSI semantic segmentation datasets, and our method achieves optimal experimental results on all four datasets compared to supervised fine-tuning as well as three semi-supervised fine-tuning methods. Full article
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20 pages, 51888 KiB  
Article
Introducing the Azimuth Cutoff as an Independent Measure for Characterizing Sea-State Dynamics in SAR Altimetry
by Ourania Altiparmaki, Samira Amraoui, Marcel Kleinherenbrink, Thomas Moreau, Claire Maraldi, Pieter N. A. M. Visser and Marc Naeije
Remote Sens. 2024, 16(7), 1292; https://doi.org/10.3390/rs16071292 - 6 Apr 2024
Viewed by 634
Abstract
This study presents the first azimuth cutoff analysis in Synthetic Aperture Radar (SAR) altimetry, aiming to assess its applicability in characterizing sea-state dynamics. In SAR imaging, the azimuth cutoff serves as a proxy for the shortest waves, in terms of wavelength, that can [...] Read more.
This study presents the first azimuth cutoff analysis in Synthetic Aperture Radar (SAR) altimetry, aiming to assess its applicability in characterizing sea-state dynamics. In SAR imaging, the azimuth cutoff serves as a proxy for the shortest waves, in terms of wavelength, that can be detected by the satellite under certain wind and wave conditions. The magnitude of this parameter is closely related to the wave orbital velocity variance, a key parameter for characterizing wind-wave systems. We exploit wave modulations exhibited in the tail of fully-focused SAR waveforms and extract the azimuth cutoff from the radar signal through the analysis of its along-track autocorrelation function. We showcase the capability of Sentinel-6A in deriving these two parameters based on analyses in the spatial and wavenumber domains, accompanied by a discussion of the limitations. We use Level-1A high-resolution Sentinel-6A data from one repeat cycle (10 days) globally to verify our findings against wave modeled data. In the spatial domain analysis, the estimation of azimuth cutoff involves fitting a Gaussian function to the along-track autocorrelation function. Results reveal pronounced dependencies on wind speed and significant wave height, factors primarily determining the magnitude of the velocity variance. In extreme sea states, the parameters are underestimated by the altimeter, while in relatively calm sea states and in the presence of swells, a substantial overestimation trend is observed. We introduce an alternative approach to extract the azimuth cutoff by identifying the fall-off wavenumber in the wavenumber domain. Results indicate effective mitigation of swell-induced errors, with some additional sensitivity to extreme sea states compared to the spatial domain approach. Full article
(This article belongs to the Special Issue Advances in Satellite Altimetry II)
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19 pages, 14368 KiB  
Communication
Sub-Hourly Variations of Wind Shear in the Mesosphere-Lower Thermosphere as Observed by the China Meteor Radar Chain
by Chi Long, Tao Yu, Jian Zhang, Xiangxiang Yan, Na Yang, Jin Wang, Chunliang Xia, Yu Liang and Hailun Ye
Remote Sens. 2024, 16(7), 1291; https://doi.org/10.3390/rs16071291 - 6 Apr 2024
Viewed by 418
Abstract
Wind shear has important implications for Kelvin–Helmholtz instability (KHI) and gravity waves (GWs) in the mesosphere–lower thermosphere (MLT) region where its momentum transport process is dominated by short-period (<1 h) GWs. However, the sub-hourly variation in wind shear is still not well quantified. [...] Read more.
Wind shear has important implications for Kelvin–Helmholtz instability (KHI) and gravity waves (GWs) in the mesosphere–lower thermosphere (MLT) region where its momentum transport process is dominated by short-period (<1 h) GWs. However, the sub-hourly variation in wind shear is still not well quantified. This study aims to improve current understanding of vertical wind shear by analyzing multi-year meteor radar measurements at the Mohe (MH, 53.5°N, 122.3°E), Beijing (BJ, 40.3°N, 116.2°E), Wuhan (WH, 30.5°N, 114.6°E), and Fuke (FK, 19.5°N, 109.1°E) stations in China. The wind field is estimated by a new algorithm, e.g., the damped least squares fitting. Taking the wind shear estimated by normal products as a criterion, the shear produced by the new algorithm has more statistical convergence as compared to the traditional algorithm, e.g., the least squares fitting. Therefore, we argue that the 10 min DLSA wind probably produces a more reasonable vertical shear. Both intensive wind shears and GW kinetic energy can be simultaneously captured during the 0600–1600 UTs of May at MH and during the 1300–2400 UTs of March at FK, possibly implying that the up-propagation of GWs could contribute to the production of large wind shears. The sub-hourly variation in wind shears is potentially valuable for understanding the interrelationship between shear (or KHI) and GWs. Full article
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34 pages, 6717 KiB  
Article
Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning
by Heather D. Couture, Madison Alvara, Jeremy Freeman, Aaron Davitt, Hannes Koenig, Ali Rouzbeh Kargar, Joseph O’Connor, Isabella Söldner-Rembold, André Ferreira, Jeyavinoth Jeyaratnam, Jordan Lewis, Colin McCormick, Tiffany Nakano, Charmaine Dalisay, Christy Lewis, Gabriela Volpato, Matthew Gray and Gavin McCormick
Remote Sens. 2024, 16(7), 1290; https://doi.org/10.3390/rs16071290 - 6 Apr 2024
Viewed by 1281
Abstract
Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants [...] Read more.
Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant water vapor plumes as proxy signals to estimate electric power generation and CO2 emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, then an aggregation model predicted plant utilization over a 30-day period. Lastly, emission factors specific to region, fuel, and plant technology were used to convert the estimated electricity generation into CO2 emissions. Models were trained with reported hourly electricity generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed the baseline approaches. In validating our model results against available generation and emissions reported data, we calculated the root mean square error as 1.75 TWh (236 plants across 17 countries over 4 years) and 2.18 Mt CO2 (207 plants across 17 countries over 4 years), respectively. Ultimately, we applied our ML method to plants that constitute 32% of global power plant CO2 emissions, as estimated by Climate TRACE, averaged over the period 2015–2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is made freely available to the public to support global emissions reduction. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change II)
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22 pages, 7541 KiB  
Article
Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network
by Yao Zhao, Chengwen Ou, He Tian, Bingo Wing-Kuen Ling, Ye Tian and Zhe Zhang
Remote Sens. 2024, 16(7), 1289; https://doi.org/10.3390/rs16071289 - 5 Apr 2024
Viewed by 702
Abstract
Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse [...] Read more.
Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes. Full article
(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)
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24 pages, 39812 KiB  
Article
Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective
by Bohan Zeng, Shan Gao, Yuelei Xu, Zhaoxiang Zhang, Fan Li and Chenghang Wang
Remote Sens. 2024, 16(7), 1288; https://doi.org/10.3390/rs16071288 - 5 Apr 2024
Viewed by 686
Abstract
Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and [...] Read more.
Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors. Full article
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22 pages, 15570 KiB  
Article
Time-Series Cross-Radiometric Calibration and Validation of GF-6/WFV Using Multi-Site
by Yingxian Wang, Yaokai Liu, Weiwei Zhao, Jian Zeng, Huixian Wang, Renfei Wang, Zhaopeng Xu and Qijin Han
Remote Sens. 2024, 16(7), 1287; https://doi.org/10.3390/rs16071287 - 5 Apr 2024
Viewed by 575
Abstract
The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these [...] Read more.
The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these quantitative applications. Currently, the absolute radiometric calibration of GF-6/WFV relies primarily on vicarious calibration conducted by the China Center for Resources Satellite Data and Application (CRESDA). However, annual vicarious calibration may not adequately capture the radiometric performance of GF-6/WFV due to performance degradation. Therefore, increasing the frequency of on-orbit radiometric calibration throughout the lifetime of GF-6/WFV is essential. This study proposes a method for conducting long-term cross-radiometric calibrations of GF-6/WFV by taking the multispectral imager (MSI) onboard the Sentinel-2 satellite as a reliable reference sensor and the sites from RadCalNet as reference ground targets. Firstly, we conducted 62 on-orbit cross-radiometric calibrations of GF-6/WFV since its launch by tracking with the Sentinel-2/MSI sensor after correcting the discrepancy spectrum and solar zenith angle. Then, validation of cross-radiometric calibration results against RadCalNet products indicated an average absolute relative error between 3.55% and 4.64%. Cross-validation with additional reference sensors, including Landsat-8/OLI and MODIS, confirmed the reliability of calibration, demonstrating relative differences from GF-6/WFV of less than 5%. Furthermore, the overall uncertainty of the cross-radiometric calibration was estimated to be from 4.08% to 4.89%. Finally, trend analysis of the time-series radiometric performance was also conducted and revealed an annual degradation rate ranging from 0.57% to 2.31%. This degradation affects surface reflectance retrieval, introducing a bias of approximately 0.0073 to 0.0084. Our findings highlight the operational effectiveness of the proposed method in achieving long-time-series on-orbit radiometric calibration and degradation monitoring of GF-6/WFV. The study also demonstrates that the radiometric performance of GF-6/WFV is relatively stable and suitable for further quantitative applications, especially for long-term monitoring applications. Full article
(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation)
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22 pages, 15350 KiB  
Article
Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach
by Gulam Mohiuddin and Jan-Peter Mund
Remote Sens. 2024, 16(7), 1286; https://doi.org/10.3390/rs16071286 - 5 Apr 2024
Viewed by 1023
Abstract
Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering [...] Read more.
Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering the urban heat island (UHI). This study aims to elucidate the spatiotemporal variations and alterations of LST in urban areas compared to LULC changes. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development. Using Landsat images from 2000 to 2021, the analysis employed an exploratory time-series analysis of LST. The study revealed a noticeable variability in LST (20 to 69 °C), which was predominantly influenced by seasonal variability and LULC changes. The study also provided insights into how LST varies within different LULC at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes. This study accounts for changing land surfaces’ complex physical energy interaction over time. The methodology offers a replicable model for other similarly structured, rapidly urbanised regions utilising novel semi-automatic processing of LST from Landsat images, potentially inspiring future research in various urban planning and monitoring contexts. Full article
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25 pages, 7926 KiB  
Article
Generative Adversarial Network and Mutual-Point Learning Algorithm for Few-Shot Open-Set Classification of Hyperspectral Images
by Tuo Xu, Ying Wang, Jie Li and Yuefan Du
Remote Sens. 2024, 16(7), 1285; https://doi.org/10.3390/rs16071285 - 5 Apr 2024
Viewed by 551
Abstract
Existing approaches addressing the few-shot open-set recognition (FSOSR) challenge in hyperspectral images (HSIs) often encounter limitations stemming from sparse labels, restricted category numbers, and low openness. These limitations compromise stability and adaptability. In response, an open-set HSI classification algorithm based on data wandering [...] Read more.
Existing approaches addressing the few-shot open-set recognition (FSOSR) challenge in hyperspectral images (HSIs) often encounter limitations stemming from sparse labels, restricted category numbers, and low openness. These limitations compromise stability and adaptability. In response, an open-set HSI classification algorithm based on data wandering (DW) is introduced in this research. Firstly, a K-class classifier suitable for a closed set is trained, and its internal encoder is leveraged to extract features and estimate the distribution of known categories. Subsequently, the classifier is fine-tuned based on feature distribution. To address the scarcity of samples, a sample density constraint based on the generative adversarial network (GAN) is employed to generate synthetic samples near the decision boundary. Simultaneously, a mutual-point learning method is incorporated to widen the class distance between known and unknown categories. In addition, a dynamic threshold method based on DW is devised to enhance the open-set performance. By categorizing drifting synthetic samples into known and unknown classes and retraining them together with the known samples, the closed-set classifier is optimized, and a (K + 1)-class open-set classifier is trained. The experimental results in this research demonstrate the superior FSOSR performance of the proposed method across three benchmark HSI datasets. Full article
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17 pages, 6317 KiB  
Article
Spectral Reconstruction from Thermal Infrared Multispectral Image Using Convolutional Neural Network and Transformer Joint Network
by Enyu Zhao, Nianxin Qu, Yulei Wang and Caixia Gao
Remote Sens. 2024, 16(7), 1284; https://doi.org/10.3390/rs16071284 - 5 Apr 2024
Viewed by 545
Abstract
Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth’s surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely [...] Read more.
Thermal infrared remotely sensed data, by capturing the thermal radiation characteristics emitted by the Earth’s surface, plays a pivotal role in various domains, such as environmental monitoring, resource exploration, agricultural assessment, and disaster early warning. However, the acquisition of thermal infrared hyperspectral remotely sensed imagery necessitates more complex and higher-precision sensors, which in turn leads to higher research and operational costs. In this study, a novel Convolutional Neural Network (CNN)–Transformer combined block, termed CTBNet, is proposed to address the challenge of thermal infrared multispectral image spectral reconstruction. Specifically, the CTBNet comprises blocks that integrate CNN and Transformer technologies (CTB). Within these CTBs, an improved self-attention mechanism is introduced, which not only considers features across spatial and spectral dimensions concurrently, but also explicitly extracts incremental features from each channel. Compared to other algorithms, the proposed method more closely aligns with the true spectral curves in the reconstruction of hyperspectral images across the spectral dimension. Through a series of experiments, this approach has been proven to ensure robustness and generalizability, outperforming some state-of-the-art algorithms across various metrics. Full article
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23 pages, 7359 KiB  
Article
Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years
by Jie Liu, Baofu Li and Mengqiu Ma
Remote Sens. 2024, 16(7), 1283; https://doi.org/10.3390/rs16071283 - 5 Apr 2024
Viewed by 525
Abstract
In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics [...] Read more.
In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics and causes of typical extreme precipitation events. The results indicated the following: (1) The total number and duration of precipitation events show a nonsignificant upward trend, while the average and extreme rainfall intensities show a nonsignificant downward trend. (2) Extreme precipitation events are primarily concentrated in Qingdao, Jinan, Heze, and Binzhou, with fewer events occurring in central Shandong Province. (3) Extreme precipitation events are classified into four types (namely, patterns I, II, III, and IV). Pattern I exhibits two rain peaks, with the primary rain peak occurring after the secondary rain peak. Similarly, pattern II also displays two rain peaks, with equivalent rainfall amounts for both peaks. In contrast, pattern III has multiple, evenly distributed rain peaks. Finally, pattern IV shows a rain peak during the first half of the precipitation event. Pattern I has the highest occurrence probability (46%), while pattern IV has the lowest (7%). (4) The spatial distributions of the different rain patterns are similar, with most being found in the eastern coastal and western regions. (5) Extreme precipitation events result from interactions between large-scale circulation configurations and mesoscale convective systems. The strong blocking situation and significant circulation transport at middle and low latitudes in East Asia, along with strong convergent uplift, abnormally high specific humidity, and high-water-vapor convergence centers, play crucial roles in supporting large-scale circulation systems and triggering mesoscale convective systems. Full article
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19 pages, 10533 KiB  
Article
Interannual Variations in the Summer Coastal Upwelling in the Northeastern South China Sea
by Wuyang Chen, Yifeng Tong, Wei Li, Yang Ding, Junmin Li, Wenhua Wang and Ping Shi
Remote Sens. 2024, 16(7), 1282; https://doi.org/10.3390/rs16071282 - 5 Apr 2024
Viewed by 468
Abstract
This study scrutinizes interannual (2003–2023) variations in coastal upwelling along the Guangdong Province during summers (June–August) in the northeastern South China Sea (NESCS) by comprehensively applying the moderate-resolution imaging spectroradiometer (MODIS) remote sensing sea surface temperature (SST) and chlorophyll concentration (CHL) data and [...] Read more.
This study scrutinizes interannual (2003–2023) variations in coastal upwelling along the Guangdong Province during summers (June–August) in the northeastern South China Sea (NESCS) by comprehensively applying the moderate-resolution imaging spectroradiometer (MODIS) remote sensing sea surface temperature (SST) and chlorophyll concentration (CHL) data and the model reanalysis product. The results show that SST and upwelling intensity in the sea area have significant (p < 0.05) rising trends in the last 21 years. The CHL shows an upward but insignificant trend, which is affected simultaneously by the rise in SST and the enhancement of upwelling. Further analysis reveals that the interannual variations in upwelling are robustly related to the wind fields’ variations in the coastal region. A clockwise/counter-clockwise anomaly in the wind field centered on the NESCS facilitates alongshore/onshore winds near the Guangdong coast, which can strengthen/weaken coastal upwelling. Based on the correlation between wind field variations and large-scale climate factors, long-term variations in the upwelling intensity can be primarily predicted by the Oceanic Niño Index. Full article
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22 pages, 7233 KiB  
Article
High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data
by Cesar Alvites, Hannah O’Sullivan, Saverio Francini, Marco Marchetti, Giovanni Santopuoli, Gherardo Chirici, Bruno Lasserre, Michela Marignani and Erika Bazzato
Remote Sens. 2024, 16(7), 1281; https://doi.org/10.3390/rs16071281 - 5 Apr 2024
Viewed by 1291
Abstract
Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has [...] Read more.
Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). Full article
(This article belongs to the Special Issue Vegetation Structure Monitoring with Multi-Source Remote Sensing Data)
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20 pages, 15304 KiB  
Article
Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data
by Haiying Yu, Qianhua Yang, Shouzheng Jiang, Bao Zhan and Cun Zhan
Remote Sens. 2024, 16(7), 1280; https://doi.org/10.3390/rs16071280 - 5 Apr 2024
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Abstract
Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate [...] Read more.
Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)
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