Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
High-Resolution Remote Sensing of the Gradient Richardson Number in a Megacity Boundary Layer
Remote Sens. 2024, 16(6), 1075; https://doi.org/10.3390/rs16061075 (registering DOI) - 19 Mar 2024
Abstract
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The Gradient Richardson Number (Ri) is an important parameter for appraising the stability and turbulence exchange at the atmospheric boundary layer (ABL). However, high-resolution measurements of Ri profiles are rarely reported, especially in megacities. In this study, a Doppler
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The Gradient Richardson Number (Ri) is an important parameter for appraising the stability and turbulence exchange at the atmospheric boundary layer (ABL). However, high-resolution measurements of Ri profiles are rarely reported, especially in megacities. In this study, a Doppler wind lidar and a microwave radiometer were simultaneously utilized to measure the 2 km Ri vertical profile in downtown Beijing. These measurements were verified to have high accuracy compared with observations from a 325 m meteorological tower, with root-mean-square errors (RMSEs) of less than 1.66 K, 7.9%, and 1.45 m/s for the temperature, relative humidity, and wind speed (WS) for all altitudes and corresponding Pearson correlation coefficients (R) of 0.97, 0.93, and 0.81. The inter-comparisons of different spatial (25 m, 50 m, 100 m) and temporal resolutions (1 min, 30 min, 1 h) form a 3 × 3 resolution matrix of Ri, in which the 1 h temporal resolution of Ri overestimates the intensity and active area of turbulence. The Ri value retrieved from the 100 m spatial resolution data overestimates these by half as it misidentifies the height of the stable area at the near surface. There are significant differences between the data with a 1 min temporal resolution and a 25 m spatial resolution (defined as the standard resolution of Ri), and the rest of the data in the resolution matrix (defined as data at other resolutions), with an RMSE > 1 and an R < 0.8. The difference between data at the standard resolution and data at other resolutions increases with elevations, which results from frequent weather processes or from water-vapor blocking at higher altitudes. The Ri profiles reveal that the atmospheric layer at altitudes from 100 m to 500 m in daytime is unstable, with Ri < 0, while it is neutral, with 0 < Ri < 0.25, at night-time from 200 m to 400 m. The atmosphere above the ABL in a megacity is rather stable, with Ri > 0.25, whereas below the ABL, it is neutral or unstable, which is due to drastic changes in the WS and temperature that are affected by the topography and surface friction.
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Open AccessArticle
Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China
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Xin Tian, Jiejie Li, Fanyi Zhang, Haibo Zhang and Mi Jiang
Remote Sens. 2024, 16(6), 1074; https://doi.org/10.3390/rs16061074 (registering DOI) - 19 Mar 2024
Abstract
The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different
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The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.
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(This article belongs to the Special Issue SAR in Big Data Era III)
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Open AccessCommunication
Evaluation of Leaf Chlorophyll Content from Acousto-Optic Hyperspectral Data: A Multi-Crop Study
by
Anastasia Zolotukhina, Alexander Machikhin, Anastasia Guryleva, Valeria Gresis, Anastasia Kharchenko, Karina Dekhkanova, Sofia Polyakova, Denis Fomin, Georgiy Nesterov and Vitold Pozhar
Remote Sens. 2024, 16(6), 1073; https://doi.org/10.3390/rs16061073 - 18 Mar 2024
Abstract
Chlorophyll plays a crucial role in the process of photosynthesis and helps to regulate plants’ growth and development. Timely and accurate evaluation of leaf chlorophyll content provides valuable information about the health and productivity of plants as well as the effectiveness of agricultural
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Chlorophyll plays a crucial role in the process of photosynthesis and helps to regulate plants’ growth and development. Timely and accurate evaluation of leaf chlorophyll content provides valuable information about the health and productivity of plants as well as the effectiveness of agricultural treatments. For non-contact and high-performance chlorophyll content mapping in plants, spectral imaging techniques are the most widely used. Due to agility and rapid random-spectral-access tuning, acousto-optical imagers seem to be very attractive for the detection of vegetation indices and chlorophyll content assessment. This laboratory study demonstrates the capabilities of an acousto-optic imager for evaluation of leaf chlorophyll content in six crops with different biophysical properties: Ribes rubrum, Betula populifolia, Hibiscus rosa-sinensis, Prunus padus, Hordeum vulgare and Triticum aestivum. The experimental protocol includes plant collecting, reference spectrophotometric measurements, hyperspectral imaging data acquisition, processing and analysis and building a multi-crop chlorophyll model. For 90 inspected samples of plant leaves, the optimal vegetation index and model were found. Obtained values of chlorophyll concentrations correlate well with reference values (determination coefficient of 0.89 and relative error of 15%). Applying a multi-crop model to each pixel, we calculated chlorophyll content maps across all plant samples. The results of this study demonstrate that acousto-optic imagery is very promising for fast chlorophyll content assessment and other laboratory spectral-index-based measurements.
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(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation II)
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Open AccessTechnical Note
Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring
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Wujiao Dai, Xin Li, Wenkun Yu, Xuanyu Qu and Xiaoli Ding
Remote Sens. 2024, 16(6), 1072; https://doi.org/10.3390/rs16061072 - 18 Mar 2024
Abstract
Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper,
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Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper, we propose an SHM scheme where IMU and multi-antenna GNSS are tightly integrated. The phase centers of multiple GNSS antennas are transformed into the IMU center, which increases the observation redundancy and strengthens the positioning model. To evaluate the performance of tight integration of IMU and multiple GNSS antennas, high-rate vibrational signals are simulated using a shaking table, and the errors of horizontal displacement of different positioning schemes are analyzed using recordings of a high-precision ranging laser as the reference. The results demonstrate that applying triple-antenna GNSS/IMU integration for measuring the displacement can achieve an accuracy of 2.6 mm, which is about 33.0% and 30.3% superior than the accuracy achieved by the conventional single-antenna GNSS-only and GNSS/IMU solutions, respectively.
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(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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Open AccessArticle
DCEF2-YOLO: Aerial Detection YOLO with Deformable Convolution–Efficient Feature Fusion for Small Target Detection
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Yeonha Shin, Heesub Shin, Jaewoo Ok, Minyoung Back, Jaehyuk Youn and Sungho Kim
Remote Sens. 2024, 16(6), 1071; https://doi.org/10.3390/rs16061071 (registering DOI) - 18 Mar 2024
Abstract
Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance
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Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance improvement. To improve the performance of small object detection, we propose DCEF -YOLO. Our proposed method enables efficient real-time small object detection by using a deformable convolution (DFConv) module and an efficient feature fusion structure to maximize the use of the internal feature information of objects. DFConv preserves small object information by preventing the mixing of object information with the background. The optimized feature fusion structure produces high-quality feature maps for efficient real-time small object detection while maximizing the use of limited information. Additionally, modifying the input data processing stage and reducing the detection layer to suit small object detection also contributes to performance improvement. When compared to the performance of the latest YOLO-based models (such as DCN-YOLO and YOLOv7), DCEF -YOLO outperforms them, with a mAP of +6.1% on the DOTA-v1.0 test set, +0.3% on the NWPU VHR-10 test set, and +1.5% on the VEDAI512 test set. Furthermore, it has a fast processing speed of 120.48 FPS with an RTX3090 for 512 × 512 images, making it suitable for real-time small object detection tasks.
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(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing III)
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Open AccessArticle
ADF-Net: An Attention-Guided Dual-Branch Fusion Network for Building Change Detection near the Shanghai Metro Line Using Sequences of TerraSAR-X Images
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Peng Chen, Jinxin Lin, Qing Zhao, Lei Zhou, Tianliang Yang, Xinlei Huang and Jianzhong Wu
Remote Sens. 2024, 16(6), 1070; https://doi.org/10.3390/rs16061070 - 18 Mar 2024
Abstract
Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth
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Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth observation compared with optical remote sensors. Deep learning algorithms have extensively been applied for BCD to realize the automatic detection of building changes. However, existing deep learning-based BCD methods with SAR images suffer limited accuracy due to the speckle noise effect and insufficient feature extraction. In this paper, an attention-guided dual-branch fusion network (ADF-Net) is proposed for urban BCD to address this limitation. Specifically, high-resolution SAR images collected by TerraSAR-X have been utilized to detect building changes near metro line 8 in Shanghai with the ADF-Net model. In particular, a dual-branch structure is employed in ADF-Net to extract heterogeneous features from radiometrically calibrated TerraSAR-X images and log ratio images (i.e., difference images (DIs) in dB scale). In addition, the attention-guided cross-layer addition (ACLA) blocks are used to precisely locate the features of changed areas with the transformer-based attention mechanism, and the global attention mechanism with the residual unit (GAM-RU) blocks is introduced to enhance the representation learning capabilities and solve the problems of gradient fading. The effectiveness of ADF-Net is verified using evaluation metrics. The results demonstrate that ADF-Net generates better building change maps than other methods, including U-Net, FC-EF, SNUNet-CD, A2Net, DMINet, USFFCNet, EATDer, and DRPNet. As a result, some building area changes near metro line 8 in Shanghai have been accurately detected by ADF-Net. Furthermore, the prediction results are consistent with the changes derived from high-resolution optical remote sensing images.
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(This article belongs to the Special Issue New Approaches in High-Resolution SAR Imaging)
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Open AccessArticle
A Remote Sensing Approach to Estimating Cropland Sustainability in the Lateritic Red Soil Region of China
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Dingding Duan, Xiao Sun, Chenrui Wang, Yan Zha, Qiangyi Yu and Peng Yang
Remote Sens. 2024, 16(6), 1069; https://doi.org/10.3390/rs16061069 - 18 Mar 2024
Abstract
Spatiotemporal assessment and a comprehensive understanding of cropland sustainability are prerequisites for ensuring food security and promoting sustainable development. However, a remote sensing-based approach framework that is suitable for large-scale and high-precision assessment and can reflect the overall sustainability of cropland has not
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Spatiotemporal assessment and a comprehensive understanding of cropland sustainability are prerequisites for ensuring food security and promoting sustainable development. However, a remote sensing-based approach framework that is suitable for large-scale and high-precision assessment and can reflect the overall sustainability of cropland has not yet been developed. This study considered a typical lateritic red soil region of Guangdong Province, China, as an example. Cropland sustainability was examined from three aspects: natural capacity, management level, and food productivity. Ten typical indicators, including soil organic matter, pH, irrigation guarantee capability, multiple cropping index, and food productivity, among others, were constructed using remote sensing technology and selected to represent these three aspects. Based on the indicator system, we assessed the spatiotemporal patterns of cropland sustainability from 2010 to 2020. The results showed that the natural capacity, management level, and food productivity of cropland had improved over the 10 years. The cropland sustainability score increased from 67.95 to 69.08 over this period. The sustainability scores for 68.64% of cropland were increased and were largely distributed in the eastern and western region of the study area. The croplands with declining sustainability scores were mostly distributed in the central region. The prefecture-level regions differed in cropland sustainability, with Zhongshan, Zhuhai, and Qingyuan cities exhibiting the highest values, and Zhanjiang the lowest. Exploring the underlying mechanisms of cropland sustainability and proposing improvement measures can guide decision-making, cropland protection, and efficient utilization, especially in similar lateritic red soil regions of the world.
Full article
(This article belongs to the Special Issue Land Use Monitoring Based on Remote Sensing and Artificial Intelligence)
Open AccessArticle
Wind Profile Retrieval Based on LSTM Algorithm and Mobile Observation of Brightness Temperature over the Tibetan Plateau
by
Bing Chen, Xinghong Cheng, Debin Su, Xiangde Xu, Siying Ma and Zhiqun Hu
Remote Sens. 2024, 16(6), 1068; https://doi.org/10.3390/rs16061068 - 18 Mar 2024
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Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR
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Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR over the Three-River-Source Region of the Tibetan Plateau from 18 to 30 July 2021, we develop a direct retrieval method for the wind profile (WP) based on the Long Short-Term Memory (LSTM) network technique, and obtain the reliable dynamic variation characteristics of the WP in the region. Furthermore, the ground-based radiative transfer model for TOVS (RTTOV-gb) was employed to validate the reliability of the TB observation, and we analyzed the impact of weather conditions, altitude, observational mode, and TB diurnal variation on the accuracy of the TB measurement and the retrieval of the WP. Results show that the TB from the mobile observation (MOTB) on clear and cloudy days are close to those of the simulated TB with the RTTOV-gb model, while TB measurements on rainy days are far larger than the modeled TBs. When compared with radiosonde observations, the WPs retrieved with the LSTM algorithm are better than the ERA5 reanalysis data, especially below 350 hPa, where the root mean square errors for both wind speed and wind direction are smaller than those of ERA5. The major factors influencing WP retrieval include the weather conditions, altitude, observational mode, and TB diurnal variation. Under clear-sky and cloudy conditions, the LSTM retrieval method can reproduce the spatio-temporal evolution of wind field and vertical wind shear characteristics. The findings of this study help to improve our understanding of meso-scale atmospheric dynamic structures, characteristics of vertical wind shear, atmospheric boundary layer turbulence, and enhance the assessment and forecasting accuracy of wind energy resources.
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Open AccessArticle
A Unitary Transformation Extension of PolSAR Four-Component Target Decomposition
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Tingting Wang, Zhiyong Suo, Jingjing Ti, Boya Yan, Hongli Xiang and Jiabao Xi
Remote Sens. 2024, 16(6), 1067; https://doi.org/10.3390/rs16061067 - 18 Mar 2024
Abstract
As an improvement on the traditional model-based Yamaguchi four-component decomposition method, in recent years, to fully utilize the polarization information in the coherency matrix, four-component target decomposition methods Y4R and S4R have been proposed, which are based on the rotation of the coherency
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As an improvement on the traditional model-based Yamaguchi four-component decomposition method, in recent years, to fully utilize the polarization information in the coherency matrix, four-component target decomposition methods Y4R and S4R have been proposed, which are based on the rotation of the coherency matrix and the expansion of the volume model, respectively. At the same time, there is also an improved G4U method proposed based on Y4R and S4R. Although these methods have achieved certain decomposition results, there are still problems with overestimation of volume scattering and insufficient utilization of polarization information. In this paper, a unitary transformation extension to the four-component target decomposition method of PolSAR based on the properties of the Jacobi method is proposed. By analyzing the terms in the basic scattering models, such as volume scattering, in the existing four-component decomposition methods, it is clear that the reason for the existence of the residual matrix in the existing decomposition methods is that the off-diagonal term and the real part of of the coherency matrix do not participate in the four-component decomposition. On this basis, a matrix transformation method is proposed to decouple terms and , and the residual matrix decomposed based on this method is derived. The performance of the proposed method was validated and evaluated using two datasets. The experimental results indicate that, compared with model-based methods such as Y4R, S4R and G4U, the proposed method can enhance the contribution of double-bounce scattering and odd-bounce scattering power in urban areas in both sets of data. The computational time of the proposed method is equivalent to Y4R, S4R, etc.
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(This article belongs to the Section Engineering Remote Sensing)
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Open AccessArticle
Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data
by
Elizabeth Baby George, Cécile Gomez and Nagesh D. Kumar
Remote Sens. 2024, 16(6), 1066; https://doi.org/10.3390/rs16061066 - 18 Mar 2024
Abstract
The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels.
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The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction ( ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions.
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(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)
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Open AccessArticle
Landsat 9 Thermal Infrared Sensor-2 (TIRS-2) Pre- and Post-Launch Spatial Response Performance
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Rehman Eon, Brian N. Wenny, Ethan Poole, Sarah Eftekharzadeh Kay, Matthew Montanaro, Aaron Gerace and Kurtis J. Thome
Remote Sens. 2024, 16(6), 1065; https://doi.org/10.3390/rs16061065 - 18 Mar 2024
Abstract
The launch of Landsat 9 (L9) on 27 September 2021 marks the ongoing commitment of the Landsat mission to delivering users with calibrated Earth observations for fifty years. The two imaging sensors on L9 are the Thermal Infrared Sensor-2 (TIRS-2) and the Operational
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The launch of Landsat 9 (L9) on 27 September 2021 marks the ongoing commitment of the Landsat mission to delivering users with calibrated Earth observations for fifty years. The two imaging sensors on L9 are the Thermal Infrared Sensor-2 (TIRS-2) and the Operational Land Imager-2 (OLI-2). Shortly after launch, the image data from OLI-2 and TIRS-2 were evaluated for both radiometric and geometric quality. This paper provides a synopsis of the evaluation of the spatial response of the TIRS-2 instrument. The assessment focuses on determining the instrument’s ability to detect a perfect knife edge. The spatial response was evaluated both pre- and post-launch. Pre-launch testing was performed at NASA Goddard Space Flight Center (GSFC) under flight-like thermal vacuum (TVAC) conditions. On orbit, coastline targets were identified to evaluate the spatial response and compared against Landsat 8 (L8). The pre-launch results indicate that the spatial response of the TIRS-2 sensor is consistent with its predecessor on board L8, with no noticeable decline in image quality to compromise any TIRS science objectives. Similarly, the post-launch analysis shows no apparent degradation of the TIRS-2 focus during the launch and the initial operational timeframe.
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(This article belongs to the Special Issue Landsat 9 Pre-launch, Commissioning, and Early On-Orbit Imaging Performance)
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Open AccessEditorial
Editorial of Special Issue “Remote Sensing Observations to Improve Knowledge of Lithosphere–Atmosphere–Ionosphere Coupling during the Preparatory Phase of Earthquakes”
by
Dedalo Marchetti, Yunbin Yuan and Kaiguang Zhu
Remote Sens. 2024, 16(6), 1064; https://doi.org/10.3390/rs16061064 - 18 Mar 2024
Abstract
We launched this Special Issue with the aim of collecting papers that use satellite data and new methodologies to understand the preparatory phase of medium–large earthquakes in the world [...]
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(This article belongs to the Special Issue Remote Sensing Observations to Improve Knowledge of Lithosphere–Atmosphere–Ionosphere Coupling during the Preparatory Phase of Earthquakes)
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Open AccessArticle
METEO-DLNet: Quantitative Precipitation Nowcasting Net Based on Meteorological Features and Deep Learning
by
Jianping Hu, Bo Yin and Chaoqun Guo
Remote Sens. 2024, 16(6), 1063; https://doi.org/10.3390/rs16061063 - 17 Mar 2024
Abstract
Precipitation prediction plays a crucial role in people’s daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms
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Precipitation prediction plays a crucial role in people’s daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms based on radar echo extrapolation for short-term precipitation forecasting. However, there are inadequately addressed issues with radar echo extrapolation methods based on deep learning, particularly when considering the inherent meteorological characteristics of precipitation on spatial and temporal scales. Additionally, traditional forecasting methods face challenges in handling local images that deviate from the overall trend. To address these problems, we propose the METEO-DLNet short-term precipitation prediction network based on meteorological features and deep learning. Experimental results demonstrate that the Meteo-LSTM of METEO-DLNet, utilizing spatial attention and differential attention, adequately learns the influence of meteorological features on spatial and temporal scales. The fusion mechanism, combining self-attention and gating mechanisms, resolves the divergence between local images and the overall trend. Quantitative and qualitative experiments show that METEO-DLNet outperforms current mainstream deep learning precipitation prediction models in natural spatiotemporal sequence problems.
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(This article belongs to the Special Issue Recent Advances in Precipitation Radar)
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Open AccessArticle
Effects of Geomorphic Spatial Differentiation on Vegetation Distribution Based on Remote Sensing and Geomorphic Regionalization
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Hua Xu, Weiming Cheng, Baixue Wang, Keyu Song, Yichi Zhang, Ruibo Wang and Anming Bao
Remote Sens. 2024, 16(6), 1062; https://doi.org/10.3390/rs16061062 - 17 Mar 2024
Abstract
As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is
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As the core area of human activities and economic development in the Xinjiang Autonomous Region, the hilly oasis zone of Xinjiang directly affects the regional sustainable development and stability of the ecosystem. Understanding the effects of different geomorphic types on vegetation distribution is crucial for maintaining vegetation growth and development, especially the improvement in the terrestrial ecological environment in arid areas under the background of climate change. However, there are few studies on the effect of spatial differences in detailed geomorphic types on vegetation distribution patterns. Therefore, this paper divides the Xinjiang hilly oasis zone into six geomorphologic level zones and innovatively investigates the influence of detailed geomorphologic types on the spatial distribution of vegetation and vegetation cover. Further, the area proportion of detailed landform types corresponding to different vegetation coverage in each geomorphic area was quantitatively calculated. Finally, the Geodetector method was used to detect the drivers of interactions between vegetation and the environment. The findings are shown as follows: (1) In the same climate zone, the spatial differentiation of landforms has a great influence on the vegetation distribution, manifesting as the significantly different vegetation distribution in different landform types. Grassland is the main vegetation type in the erosion and denudation of Nakayama; cultivated vegetation and meadows have a larger coverage in the alluvial flood plain and alluvial plain; and the distribution of vegetation in the Tianshan economic zone is characterized by obvious vertical zoning with the geomorphology. (2) The landform type and morphological types are the strongest driving factors for vegetation coverage with q values of 0.433 and 0.295, respectively, which effectually fill the gap caused by only using two terrain indicators, slope and elevation, to study the relationship between landforms and vegetation. (3) In addition, the improved nonlinear interaction resulting from the double factor of landform type and slope is 0.486, which has a stronger control on vegetation coverage than the single factor of landform type. These findings are conducive to enhancing the supply services of vegetation to the ecosystem in arid areas as well as providing important scientific guidance for the construction of ecological civilization and sustainable development in Xinjiang.
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(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle
CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field
by
Qiang Wu, Liang Huang, Bo-Hui Tang, Jiapei Cheng, Meiqi Wang and Zixuan Zhang
Remote Sens. 2024, 16(6), 1061; https://doi.org/10.3390/rs16061061 - 16 Mar 2024
Abstract
Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the
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Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection.
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(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing)
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Open AccessArticle
Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains
by
Yuanqi Li, Ronghai Hu, Yuzhen Xing, Zhe Pang, Zhi Chen and Haishan Niu
Remote Sens. 2024, 16(6), 1060; https://doi.org/10.3390/rs16061060 - 16 Mar 2024
Abstract
Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the
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Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.
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(This article belongs to the Topic Remote Sensing and Visualization Methods: Monitoring, Modeling, Simulations and Interaction of Forest Resource)
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Open AccessArticle
Investigating a Possible Correlation between NOAA-Satellite-Detected Electron Precipitations and South Pacific Tectonic Events
by
Cristiano Fidani, Serena D’Arcangelo, Angelo De Santis, Loredana Perrone and Maurizio Soldani
Remote Sens. 2024, 16(6), 1059; https://doi.org/10.3390/rs16061059 - 16 Mar 2024
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On 4 March 2021, a devastating M8.1 earthquake struck the Kermadec Islands of New Zealand. Given the tremendous energy released during the event, we sought to investigate the event’s potential impact on the ionosphere and the inner Van Allen Belt using data from
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On 4 March 2021, a devastating M8.1 earthquake struck the Kermadec Islands of New Zealand. Given the tremendous energy released during the event, we sought to investigate the event’s potential impact on the ionosphere and the inner Van Allen Belt using data from the high-energy electron detectors on board the NOAA-18 satellite. The survey was also extended to the strongest shallow M6.5+ earthquakes occurring between 150° and 190° in longitude, and between −5° and −35° in latitude over the previous ten years. In nearly all cases, evident electron fluxes entering the loss cone were observed. To explore the possibility of a connection between ionospheric signals and tectonic events in this intensely active region, we analyzed electron losses from the inner Van Allen Belt, taking into account latitude, longitude, day/night times, and proximity to the South Atlantic Anomaly. Compared to previous studies, here only the most significant loss phenomena persistent in the ionosphere were considered. Particular interest was reserved for the intense electron loss events that had a duration spanning from a few to several minutes and occurred several hours before and after strong seismic events. Thereafter, time series of electron counting rates and strong Southern Pacific earthquakes were transformed into binary series, and the series multiplication was investigated. The results suggest four peaks of association, including a first couple between electron perturbations detected for ascending semi-orbits and seismic events and a second one between electron perturbations detected in the southern ionosphere and seismic events. They both anticipated the occurrence of earthquakes, occurring around 4 h before them. Other couples were observed between electron perturbations detected for descending semi-orbits and seismic events and between electron perturbations detected in the northern ionosphere and seismic events. They both occurred around 3 h after the occurrence of earthquakes. The case of perturbations anticipating seismic events has the intriguing properties of sustaining the hypothesis that a physical interaction occurred around 6 h before seismic events as in the West Pacific case. A physical model of electrons detected far several thousands of km from the earthquake epicenters was also presented. However, a simulation of random seismic events suggested that the null hypothesis cannot be fully rejected for these associations, prompting many more analyses and case studies.
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Open AccessArticle
Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet
by
Xiaomao Chen, Shanshan Zhang, Xiaofeng Qin and Jinfeng Lin
Remote Sens. 2024, 16(6), 1058; https://doi.org/10.3390/rs16061058 - 16 Mar 2024
Abstract
Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address
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Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano.
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(This article belongs to the Special Issue SAR in Big Data Era III)
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Finding Navigable Paths through Tidal Flats with Synthetic Aperture Radar
by
Ruaridh A. Clark, Ciara N. McGrath, Astrid A. Werkmeister, Christopher J. Lowe, Gwilym Gibbons and Malcolm Macdonald
Remote Sens. 2024, 16(6), 1057; https://doi.org/10.3390/rs16061057 - 16 Mar 2024
Abstract
Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through
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Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path.
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(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)
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The Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction
by
Fabien H. Wagner, Samuel Favrichon, Ricardo Dalagnol, Mayumi C. M. Hirye, Adugna Mullissa and Sassan Saatchi
Remote Sens. 2024, 16(6), 1056; https://doi.org/10.3390/rs16061056 - 16 Mar 2024
Abstract
The Amazon, the world’s largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water
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The Amazon, the world’s largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The accuracy of the water surface model was high, with an F1-score of 0.93. A 12-day mosaic time series of the water surface was generated from the Sentinel-1 prediction. The water surface mask demonstrated relatively consistent agreement with the global surface water (GSW) product from the Joint Research Centre (F1-score: 0.708) and with the Brazilian MapBiomas Water initiative (F1-score: 0.686). The main errors of the map were omission errors in flooded woodland, in flooded shrub, and because of clouds. Rio Negro water surfaces reached their lowest level around the 25th of November 2023 and were reduced to 68.1% (9559.9 km2) of the maximum water surfaces observed in the period 2022–2023 (14,036.3 km2). Synthetic aperture radar (SAR) data, in conjunction with deep learning techniques, can significantly improve near-real-time mapping of water surfaces in tropical regions.
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(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)
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