Remote Sensing of Environment最新文献

An integrating pre-temperature description method for generating all-weather land surface temperature via passive microwave and thermal infrared remote sensing

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-23 DOI: 10.1016/j.rse.2025.114767

Weizhen Ji , Yunhao Chen , Xiaohui Li , Kangning Li , Haiping Xia , Ji Zhou , Han Gao

Integrating passive microwave (PMW) and thermal infrared (TIR) remote sensing to generate all-weather land surface temperature (LST) is essential for effective land thermal monitoring. Previous studies have attempted to adapt TIR-interactive kernel-driven downscaling techniques into the PMW-TIR integration process. However, large-scale spans often introduce significant uncertainties in the generated LST, potentially leading to spatial streaks. To address these challenges, it is critical to introduce a reliable temperature representation at the target resolution to generate accurate all-weather LST. In this study, we propose an integrated pre-temperature description model (ITDM) comprising three modules. The first module is a machine learning-based bias correction-driven generation module (BCDM), which generates relatively precise LST, particularly during the daytime, though it may smooth some spatial textures in certain regions. The second module, a spatial detail-aware generation module (SDAM), utilizes an annual temperature cycle model-based LST as a temperature description, ensuring spatial consistency in the generated LST. The third module integrates the two previous modules, addressing their differences to optimize the final output. Validation results based on MODIS LST indicate that the proposed method achieves a daytime root mean squared error (RMSE) of 3.20 K and a standard deviation of bias (STD) of 3.08 K. For nighttime, the RMSE and STD are 2.24 K and 2.15 K, respectively. Additionally, ten in-situ measurements reveal an average RMSE of 3.90 K in the daytime and 3.34 K in the nighttime. Comparative results with two other advanced methods based on MODIS LST and in-situ LST show that the proposed approach reduces RMSE by 0.04–0.91 K and mitigates streaking phenomena more effectively. The study also discusses feature importance, module performance, and the extendibility of the method. The proposed model significantly contributes to the generation of high-quality all-weather LST.

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引用次数: 0

The relationship between the ratio of far-red to red leaf SIF and leaf chlorophyll content: Theoretical derivation and experimental validation 远红外线与红叶 SIF 之比与叶绿素含量之间的关系：理论推导与实验验证

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-22 DOI: 10.1016/j.rse.2025.114762

Runfei Zhang , Peiqi Yang , Shan Xu , Long Li , Tingrui Guo , Dalei Han , Jing Liu

<div><div>Leaf chlorophyll content (LCC) is an important indicator of photosynthetic capacity. Sun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC. The far-red to red ratio of chlorophyll fluorescence (<em>F</em><sub>ratio</sub>) has been used to empirically estimate LCC in some previous studies. While these studies support the use of the <em>F</em><sub>ratio</sub> for LCC estimation, its theoretical underpinning remains less well-defined and its effectiveness across a wider range of scenarios remains unclear. In this study, we established the relationship between the <em>F</em><sub>ratio</sub> and LCC using the light use efficiency (LUE)-based SIF model and spectral invariant radiative transfer theory. Firstly, the LUE-based SIF model demonstrates that the change in the leaf <em>F</em><sub>ratio</sub> is controlled by the ratio of the fluorescence escape fraction (i.e., <em>f</em><sub><em>esc</em></sub> from the photosystem to the leaf surface) at the corresponding bands. Secondly, a <em>f</em><sub><em>esc</em></sub> modeling approach is presented using the spectral invariant theory and thus the <em>f</em><sub><em>esc</em></sub> ratio is linked to LCC. Theoretical analysis shows that the <em>F</em><sub>ratio</sub> has a strong correlation with LCC, which explains over 90 % of the variation in <em>F</em><sub>ratio</sub>. Both experimental measurements and model simulations from a radiative transfer model Fluspect were used to validate the relationship between LCC and three <em>F</em><sub>ratio</sub> (i.e., <span><math><msubsup><mi>F</mi><mtext>ratio</mtext><mo>↑</mo></msubsup></math></span>, <span><math><msubsup><mi>F</mi><mtext>ratio</mtext><mo>↓</mo></msubsup></math></span> and <span><math><msubsup><mi>F</mi><mtext>ratio</mtext><mi>tot</mi></msubsup></math></span>), which were derived from the upward and downward SIF of leaves, as well as the total SIF observed from both sides. The Fluspect simulations were used to assess the sensitivity of the <em>F</em><sub>ratio</sub>-LCC relationship to the leaf structure. Two types of experimental measurements, including the field measurements of three crops and the laboratory measurements of 20 tundra plants, were employed to examine the species dependence of the <em>F</em><sub>ratio</sub>-LCC relationship. The performance of <em>F</em><sub>ratio</sub> for LCC estimation was evaluated and compared with spectral indices and the PROSPECT model using the experimental measurements and leave-one-out cross-validation (LOOCV) approach. Both the Fluspect simulations and the experimental measurements indicate that the <em>F</em><sub>ratio</sub> is strongly correlated with LCC for a wide range of leaf scenarios. The <em>F</em><sub>ratio</sub>-LCC relationship remains relatively stable across different leaf structures and plant species, since the relationship is almost consistent. The LOOCV of experimental measurem

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引用次数: 0

Machine learning-based generation of high-resolution 3D full-coverage aerosol distribution data over China using multisource data 利用多源数据，基于机器学习生成中国上空高分辨率三维全覆盖气溶胶分布数据

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-21 DOI: 10.1016/j.rse.2025.114772

Wenze Li , Wenchao Han , Jiachen Meng , Zipeng Dong , Jun Xu , Qimeng Wang , Lulu Yuan , Han Wang , Zhongzhi Zhang , Miaomiao Cheng

Aerosol pollution significantly influences the interaction between solar radiation and the earth's atmosphere and seriously threatens human health. Numerous studies have applied machine learning models such as Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) to estimate aerosol-related parameters, including aerosol optical depth and particulate matter concentrations (e.g., PM_2.5). However, current aerosol products primarily provide horizontal or spatially discontinuous vertical data, lacking comprehensive three-dimensional (3D) coverage. To address this gap, we developed the XGBoost-LightGBM-Wavelet (XLW) model, integrating XGBoost, LightGBM, and wavelet transforms to merge multisource data. This approach, for the first time, produced high-resolution, three-dimensional, full-coverage aerosol distribution data for China in 2015. The model outputs a dataset of aerosol spatial distribution with a horizontal resolution of 0.05°, and 167 layers within 10 km in the vertical direction. The XLW model demonstrates excellent predictive ability, effectively filling gaps in aerosol distribution. It enhances signal continuity and strengthens lower-layer signals, closely matching ground LiDAR observations and providing a more accurate representation compared to the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) data. The dataset accurately reveals the 3D distribution of aerosols, which is meaningful for a comprehensive study of aerosol distribution at different altitudes in various regions. At 300 m height above ground level, the most polluted regions are the North China Plain and the Yangtze River Delta region, with an average aerosol extinction coefficient (AEC) of 0.34 and 0.40 km⁻¹, respectively. As the height increases to 1 km, the average AEC notably decreases to 0.23 and 0.24 km⁻¹ in the North China Plain and the Yangtze River Delta. By 3 km, aerosol distribution becomes sparse over most regions of China. For the vertical variations of aerosol distributions in typical cities, in the North China Plain and Yangtze River Delta, aerosol concentrations consistently decrease from the near-surface to 4 km. However, in the Pearl River Delta, aerosol concentrations decrease consistently from 0 to 2 km, with relatively stable between 2 and 3 km. Above 4 km, aerosol concentrations are nearly negligible in all typical cities. The XLW model can accurately produce a high-resolution, 3D, full-coverage aerosol spatial distribution dataset, which is vital for conducting thorough studies on aerosol transport, aerosol radiative effects, and climate change.

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引用次数: 0

GDCM: Generalized data completion model for satellite observations

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-17 DOI: 10.1016/j.rse.2025.114760

Haoyu Wang , Yinfei Zhou , Xiaofeng Li

Ocean remote sensing data is crucial in understanding the global climate system. Due to satellite orbital coverage gaps and cloud cover, satellite ocean remote sensing products have significant data gaps. This paper introduces a Generalized Data Completion Model (GDCM) based on deep learning to reconstruct gap-free and cloud-free key oceanic variables such as sea surface temperature (SST), wind speed, water vapor, cloud liquid water, and precipitation rate derived from polar-orbiting satellite sensors including Advanced Microwave Scanning Radiometer 2 (AMSR2), the Special Sensor Microwave Imager (SSMI), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Utilizing Convolutional Neural Networks (CNNs) and attention mechanisms, the GDCM model effectively leverages spatio-temporal information within remote sensing data to fill in missing regions accurately. We use reanalysis data to simulate various data missing scenarios during model training for model development. We tested the model with the US East Coast region's global-coverage AMSR2/SSMI and local-coverage MODIS datasets. The experiments demonstrate that the GDCM model successfully and precisely completes the data for different satellites and types of missing data. To enable the model to capture enough data for the dynamical change patterns, we used seven consecutive days of observation data as inputs to improve the model's data-completion ability, significantly enhancing the handling of MODIS SST missing data due to cloud cover. When the input data's duration increased from one day to seven days, the model's R² value improved from 0.062 to 0.93, and the Root Mean Square Difference (RMSD) decreased from 6.58 to 0.92. Besides the model framework design, we implemented the incremental learning training strategy to enhance the model's data completion capability for different types of missing data, especially for SST data from AMSR2 satellites. The model's completed SST data R² value improved from 0.93 to 0.99, and the RMSD decreased from 2.64 °C to 0.50 °C. The Mean Absolute Difference (MAD) of water vapor data decreased from 0.88 kg/m² to 0.76 kg/m², and the RMSD decreased from 1.39 kg/m² to 1.27 kg/m². This study provides a generalized new solution to the problem of missing ocean data at different resolutions, contributing to a more comprehensive and supporting ocean science research and related applications.

海洋遥感数据对于了解全球气候系统至关重要。由于卫星轨道覆盖缺口和云层覆盖，卫星海洋遥感产品存在严重的数据缺口。本文介绍了一种基于深度学习的广义数据补全模型（GDCM），用于重建来自极轨卫星传感器（包括高级微波扫描辐射计 2（AMSR2）、特殊传感器微波成像仪（SSMI）和中分辨率成像分光仪（MODIS））的无间隙、无云的关键海洋变量，如海面温度（SST）、风速、水蒸气、云液水和降水率。利用卷积神经网络（CNN）和注意力机制，GDCM 模型能有效利用遥感数据中的时空信息，准确填补缺失区域。在模型开发的模型训练过程中，我们使用再分析数据模拟了各种数据缺失情况。我们使用美国东海岸地区的全球覆盖 AMSR2/SSMI 数据集和本地覆盖 MODIS 数据集对该模型进行了测试。实验证明，GDCM 模型能够成功、精确地补全不同卫星和不同类型的缺失数据。为了使模型能够捕捉到足够的动态变化模式数据，我们使用了连续七天的观测数据作为输入，以提高模型的数据补全能力，显著增强了对因云层覆盖而缺失的 MODIS SST 数据的处理能力。当输入数据的时间从一天增加到七天时，模型的 R2 值从 0.062 提高到 0.93，均方根差（RMSD）从 6.58 减小到 0.92。除了模型框架设计外，我们还实施了增量学习训练策略，以提高模型对不同类型缺失数据的数据补全能力，尤其是对 AMSR2 卫星的 SST 数据的补全能力。模型完成的 SST 数据 R2 值从 0.93 提高到 0.99，RMSD 从 2.64 ℃ 下降到 0.50 ℃。水汽数据的平均绝对差值（MAD）从 0.88 kg/m2 降至 0.76 kg/m2，RMSD 从 1.39 kg/m2 降至 1.27 kg/m2。这项研究为解决不同分辨率下海洋数据缺失问题提供了一种通用的新方案，有助于更全面地支持海洋科学研究和相关应用。

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引用次数: 0

High-resolution anthropogenic emission inventories with deep learning in northern South America

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-17 DOI: 10.1016/j.rse.2025.114761

Franz Pablo Antezana Lopez , Alejandro Casallas , Guanhua Zhou , Kai Zhang , Guifei Jing , Aamir Ali , Ellie Lopez-Barrera , Luis Carlos Belalcazar , Nestor Rojas , Hongzhi Jiang

Air quality in northern South America faces significant challenges due to insufficient high-resolution emission inventories and sparse atmospheric studies. This study addresses these gaps by developing a novel framework that integrates high-resolution nighttime light data from SDGSAT-1 and multisource remote sensing datasets with deep learning techniques to downscale emission inventories. The refined inventories are coupled with meteorological inputs into the Weather Research and Forecasting (WRF-Chem) model, enabling precise simulation of pollutant dynamics. Validated against ground measurements from Colombia's SISAIRE monitoring network, demonstrates significant improvements in spatiotemporal accuracy, particularly for particulate matter (PM) and nitrogen dioxide (NO₂) with error reductions of 22–30 % and correlation coefficients increasing from 0.68 to 0.85. These findings underscore the critical role of satellite-enhanced inventories in resolving localized emission patterns and seasonal variability, such as dry-season PM₁₀ spikes (150 % increase from wildfires). The framework provides policymakers with actionable insights to prioritize mitigation in rapidly urbanizing regions and manage transboundary pollution. By bridging data scarcity gaps, this replicable methodology offers transformative potential for global air quality management and public health protection, advocating for expanded ground monitoring networks and real-time satellite data integration in future applications.

由于高分辨率排放清单不足和大气研究稀少，南美洲北部的空气质量面临重大挑战。本研究通过开发一个新颖的框架，将 SDGSAT-1 和多源遥感数据集提供的高分辨率夜间光照数据与深度学习技术相结合，以缩小排放清单的规模，从而弥补这些不足。改进后的排放清单与气象研究和预测（WRF-Chem）模型中的气象输入相结合，实现了对污染物动态的精确模拟。根据哥伦比亚 SISAIRE 监测网络的地面测量结果进行验证，结果表明时空精确度显著提高，尤其是颗粒物（PM）和二氧化氮（NO₂），误差减少了 22-30%，相关系数从 0.68 提高到 0.85。这些发现强调了卫星增强清单在解决局部排放模式和季节变异性（如旱季 PM₁₀峰值（野火导致增加 150%））方面的关键作用。该框架为政策制定者提供了可操作的见解，以便在快速城市化的地区优先考虑缓解措施，并管理跨境污染。通过弥合数据稀缺的差距，这种可复制的方法为全球空气质量管理和公共健康保护提供了变革潜力，倡导在未来的应用中扩大地面监测网络和实时卫星数据集成。

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引用次数: 0

Estimating NOx emissions of individual ships from TROPOMI NO2 plumes

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-17 DOI: 10.1016/j.rse.2025.114734

T. Christoph V.W. Riess , K. Folkert Boersma , Aude Prummel , Bart J.H. van Stratum , Jos de Laat , Jasper van Vliet

Maritime transportation is a substantial contributor to anthropogenic NO

_{x}

emissions and coastal air pollution. Recognizing this, the International Maritime Organization (IMO) has steadily implemented stepwise stricter emission standards for ships in recent years. However, monitoring emissions from sea-bound vessels poses inherent challenges, prompting the exploration of satellite observations as a promising solution. Here we use TROPOMI measurements of NO

_{2}

plumes together with information on ship position and identity, and atmospheric models to quantify the NO

_{x}

emissions of 130 plumes from individual ships in the eastern Mediterranean Sea in 2019. Because most of the emitted NO

_{x}

is in the form of NO, which is not immediately converted into detectable NO

_{2}

, plumes show their NO

_{2}

maximum some 15-30 km downwind of the ship’s stack. Further downwind NO

_{2}

decreases because of plume dispersion and photochemical oxidation. Background ozone and wind speed play a significant role both in detectability of the NO

_{2}

plume and the relationship between NO

_{x}

emissions and observed NO

_{2}

, explaining the good detection conditions in the eastern Mediterranean summertime, where ozone levels are high. Taking such effects of emissions, dispersion, entrainment, and in-plume chemistry in full account, we find emission strengths of 10-317 g (NO

_{2}

) s⁻¹. We then calculate emission factors of the detected ship plumes using AIS and ship specific data and find that newer Tier II ships have higher emission factors compared to older Tier I ships. This is especially the case when running at lower engine loads, which is the most frequently observed mode of operation in our ensemble. Additionally, at the time of detection around half of the emission factors detected for Tier II ships lie above the IMO weighted average limits. The presented method sets the stage for automated ship emission monitoring at sea, contributing to better air quality management.

{"title":"Estimating NOx emissions of individual ships from TROPOMI NO2 plumes","authors":"T. Christoph V.W. Riess , K. Folkert Boersma , Aude Prummel , Bart J.H. van Stratum , Jos de Laat , Jasper van Vliet","doi":"10.1016/j.rse.2025.114734","DOIUrl":"10.1016/j.rse.2025.114734","url":null,"abstract":"<div><div>Maritime transportation is a substantial contributor to anthropogenic NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span> emissions and coastal air pollution. Recognizing this, the International Maritime Organization (IMO) has steadily implemented stepwise stricter emission standards for ships in recent years. However, monitoring emissions from sea-bound vessels poses inherent challenges, prompting the exploration of satellite observations as a promising solution. Here we use TROPOMI measurements of NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> plumes together with information on ship position and identity, and atmospheric models to quantify the NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span> emissions of 130 plumes from individual ships in the eastern Mediterranean Sea in 2019. Because most of the emitted NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span> is in the form of NO, which is not immediately converted into detectable NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, plumes show their NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> maximum some 15-30 km downwind of the ship’s stack. Further downwind NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> decreases because of plume dispersion and photochemical oxidation. Background ozone and wind speed play a significant role both in detectability of the NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> plume and the relationship between NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span> emissions and observed NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, explaining the good detection conditions in the eastern Mediterranean summertime, where ozone levels are high. Taking such effects of emissions, dispersion, entrainment, and in-plume chemistry in full account, we find emission strengths of 10-317 g (NO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) s<sup>−1</sup>. We then calculate emission factors of the detected ship plumes using AIS and ship specific data and find that newer Tier II ships have higher emission factors compared to older Tier I ships. This is especially the case when running at lower engine loads, which is the most frequently observed mode of operation in our ensemble. Additionally, at the time of detection around half of the emission factors detected for Tier II ships lie above the IMO weighted average limits. The presented method sets the stage for automated ship emission monitoring at sea, contributing to better air quality management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114734"},"PeriodicalIF":11.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A gradient-based nonlinear multi-pixel physical method for simultaneously separating component temperature and emissivity from nonisothermal mixed pixels with DART

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-17 DOI: 10.1016/j.rse.2025.114738

Zhijun Zhen , Shengbo Chen , Nicolas Lauret , Abdelaziz Kallel , Tiangang Yin , Jonathan León-Tavares , Biao Cao , Jean-Philippe Gastellu-Etchegorry

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m²·sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUST-DART is distributed together with DART (https://dart.omp.eu).

{"title":"A gradient-based nonlinear multi-pixel physical method for simultaneously separating component temperature and emissivity from nonisothermal mixed pixels with DART","authors":"Zhijun Zhen , Shengbo Chen , Nicolas Lauret , Abdelaziz Kallel , Tiangang Yin , Jonathan León-Tavares , Biao Cao , Jean-Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.114738","DOIUrl":"10.1016/j.rse.2025.114738","url":null,"abstract":"<div><div>Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (<em>e.g.</em>, spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m<sup>2</sup>·sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUST-DART is distributed together with DART (<span><span>https://dart.omp.eu</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114738"},"PeriodicalIF":11.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Slip surface, volume and evolution of active landslide groups in Gongjue County, eastern Tibetan Plateau from 15-year InSAR observations

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-16 DOI: 10.1016/j.rse.2025.114763

Bo Chen , Zhenhong Li , Chuang Song , Chen Yu , Roberto Tomás , Jiantao Du , Xinlong Li , Adrien Mugabushaka , Wu Zhu , Jianbing Peng

Landslides stand as a prevalent geological risk in mountainous areas, presenting substantial danger to human habitation. The slip surface (SSF), volume, type and evolution of landslides constitute crucial information from which to understand landslide mechanisms and assess landslide risk. However, current methods for obtaining this information, relying primarily on field surveys, are usually time-consuming, labor-intensive and costly, and are more applicable to individual landslides than large-scale landslide groups. To tackle these challenges, we present a novel method utilizing multi-orbit Synthetic Aperture Radar (SAR) data to deduce the SSF, volume and type of active landslides. In this method, the SSF of landslides over a wide area is determined from three-dimensional deformation fields by assuming that the most authentic direction of the landslide movement aligns parallel to the SSF, on the basis of which the volume and type of active landslides can also be inferred. This approach was utilized with landslide groups in Gongjue County (LGGC), situated in the eastern Tibetan Plateau, which pose grave peril to community members and critical construction along the upstream/downstream of the Jinsha River. Firstly, SAR images were gathered and interferometrically processed from four separate platforms, spanning the period from July 2007 to August 2022. Then, three-dimensional displacement time series were inverted based on Interferometric Synthetic Aperture Radar (InSAR) observations and a topography-constrained model, from which the SSF, volume and type were determined using our proposed method. Finally, the Tikhonov regularization method was applied to reconstruct 15-year displacement time series along the sliding surface, and potential driving factors of landslide motion were identified. Results indicate that 53 landslides were detected in the LGGC region, of which ∼70 % were active and complex landslides with maximum cumulative displacement along the sliding surface reaching 1.5 m over the past ∼15 years. In addition, the deepest SSF of these landslides was found to reach 114 m, with volumes ranging from 1.66 × 10⁵ m³ to 1.72 × 10⁸ m³. Independent in-situ measurements validate the reliability of the SSF obtained in this study. More particularly, we found that the 2018 failure of the Baige landslide (approximately 50 km from LGCC) had caused persistent acceleration to those wading landslides, highlighting the prolonged impact of external factors on landslide evolution. These insights provide a deeper understanding of landslide dynamics and mechanisms, which is crucial when implementing early warning systems and forecasting future failure events.

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引用次数: 0

The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-16 DOI: 10.1016/j.rse.2025.114723

Junchang Ju , Qiang Zhou , Brian Freitag , David P. Roy , Hankui K. Zhang , Madhu Sridhar , John Mandel , Saeed Arab , Gail Schmidt , Christopher J. Crawford , Ferran Gascon , Peter A. Strobl , Jeffrey G. Masek , Christopher S.R. Neigh

<div><div>Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 ref

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引用次数: 0

Soil and vegetation cover estimation for global imaging spectroscopy using spectral mixture analysis

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-04-14 DOI: 10.1016/j.rse.2025.114746

Francisco Ochoa , Philip G. Brodrick , Gregory S. Okin , Eyal Ben-Dor , Thoralf Meyer , David R. Thompson , Robert O. Green

The Earth surface Mineral dust source InvesTigation (EMIT) is a visible-to-shortwave infrared imaging spectrometer currently aboard the International Space Station. Derivations of fractional cover from spectral unmixing algorithms have provided insights into various ecosystem functions. In the case of EMIT, they will be used by multiple global Earth systems models to constrain the sign of dust-related radiative forcing. This study aims to evaluate the efficacy of different approaches for estimating fractional cover and quantifying the corresponding uncertainty, and serves as a model to encapsulate the true error budget for EMIT. We simulated surface reflectance from a spectral library compiled from various drylands to generate millions of candidate spectra made up of different random fractions of nonphotosynthetic vegetation (NPV), green vegetation (GV), and soil. Simulated spectra were used as-is but we also tested the impact of atmospheric conditions/surface reflectance retrieval by using them to calculate top-of-atmosphere radiance then using the current EMIT surface reflectance retrieval algorithm to estimate apparent surface reflectance. We tested approaches to unmixing these simulated spectra using multiple strategies for dealing with spectrum brightness, within-class spectral variability, and library selection. We also incorporated a Monte Carlo approach to stabilize fractional cover retrievals and quantify uncertainty. The best spectral unmixing approaches produced mean absolute error < 0.10 for NPV and soil and < 0.06 for GV with uncertainties

\leq \pm

0.02 for all classes. We named this innovative approach EndMember Combination Monte Carlo, E(MC)², unmixing and found that our fractional cover retrievals are insensitive to atmospheric residuals in the surface reflectance data.

{"title":"Soil and vegetation cover estimation for global imaging spectroscopy using spectral mixture analysis","authors":"Francisco Ochoa , Philip G. Brodrick , Gregory S. Okin , Eyal Ben-Dor , Thoralf Meyer , David R. Thompson , Robert O. Green","doi":"10.1016/j.rse.2025.114746","DOIUrl":"10.1016/j.rse.2025.114746","url":null,"abstract":"<div><div>The Earth surface Mineral dust source InvesTigation (EMIT) is a visible-to-shortwave infrared imaging spectrometer currently aboard the International Space Station. Derivations of fractional cover from spectral unmixing algorithms have provided insights into various ecosystem functions. In the case of EMIT, they will be used by multiple global Earth systems models to constrain the sign of dust-related radiative forcing. This study aims to evaluate the efficacy of different approaches for estimating fractional cover and quantifying the corresponding uncertainty, and serves as a model to encapsulate the true error budget for EMIT. We simulated surface reflectance from a spectral library compiled from various drylands to generate millions of candidate spectra made up of different random fractions of nonphotosynthetic vegetation (NPV), green vegetation (GV), and soil. Simulated spectra were used as-is but we also tested the impact of atmospheric conditions/surface reflectance retrieval by using them to calculate top-of-atmosphere radiance then using the current EMIT surface reflectance retrieval algorithm to estimate apparent surface reflectance. We tested approaches to unmixing these simulated spectra using multiple strategies for dealing with spectrum brightness, within-class spectral variability, and library selection. We also incorporated a Monte Carlo approach to stabilize fractional cover retrievals and quantify uncertainty. The best spectral unmixing approaches produced mean absolute error < 0.10 for NPV and soil and < 0.06 for GV with uncertainties <span><math><mo>≤</mo><mo>±</mo></math></span> 0.02 for all classes. We named this innovative approach EndMember Combination Monte Carlo, E(MC)<sup>2</sup>, unmixing and found that our fractional cover retrievals are insensitive to atmospheric residuals in the surface reflectance data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"324 ","pages":"Article 114746"},"PeriodicalIF":11.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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