Remote Sensing Applications-Society and Environment最新文献_第9页

Composite Wildfire Impact (CWI) rating: Integrating fire intensity and burn severity earth observations 综合野火影响（CWI）评级：综合火灾强度和烧伤严重程度的地球观测

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101776

Konstantinos Chatzopoulos-Vouzoglanis , Karin J. Reinke , Mariela Soto-Berelov , Simon D. Jones

Current wildfire impact assessments at the landscape scale often overlook the complexity of active fire behaviour, focusing only on pre- and post-fire spectral differencing, despite remotely sensed active fire data being readily available. This study integrates high temporal resolution active fire intensity measures from geostationary satellite sensors and high spatial resolution normalised spectral differencing index products from polar-orbiting satellite sensors to produce a new approach for describing wildfire impact. Himawari-8 BRIGHT/AHI Fire Radiative Power (FRP) estimates are combined with Normalised Burn Ratio (NBR) metrics from Sentinel-2, to derive wildfire impact categories over Australia for one year of data, using a clustering approach. The wildfire impact categories summarise fire hotspot commonalities based on their maximum and total FRP, duration, differenced NBR (dNBR), burned area patchiness, and pre-fire NBR, and reveal expected 2019–2020 Australian fire season patterns. Furthermore, land cover emerges as an important factor, with forests and woodlands reflecting higher impact fires compared to grasslands and shrublands. Our wildfire impact categories show a moderate agreement with burn severity assessments conducted by state governments, further stressing the need for more diverse information inclusion in such assessments. The proposed composite wildfire impact rating combines diverse remotely sensed wildfire behaviour information and can assist in a better understanding of wildfire effects on a continental scale. More research, leveraging longer temporal and spatial baselines and fire ecology expertise, is needed to refine the used nomenclature for the improvement of wildfire impact assessments.

目前在景观尺度上的野火影响评估往往忽视了活火行为的复杂性，只关注火灾前和火灾后的光谱差异，尽管遥感活火数据很容易获得。本研究将地球静止卫星传感器的高时间分辨率活动性火灾强度测量和极轨卫星传感器的高空间分辨率归一化光谱差指数产品整合在一起，形成了一种描述野火影响的新方法。Himawari-8 BRIGHT/AHI火灾辐射功率（FRP）估计值与Sentinel-2的归一化燃烧比（NBR）指标相结合，使用聚类方法得出澳大利亚一年的野火影响类别数据。野火影响类别根据其最大和总FRP、持续时间、不同NBR （dNBR）、烧伤面积斑块和火灾前NBR总结了火灾热点的共性，并揭示了2019-2020年澳大利亚火灾季节的预期模式。此外，土地覆盖成为一个重要因素，与草原和灌丛地相比，森林和林地反映出更高的影响火灾。我们的野火影响类别与州政府进行的烧伤严重程度评估显示出适度的一致性，进一步强调了在此类评估中包含更多样化信息的必要性。拟议的野火影响综合评级结合了多种遥感野火行为信息，可以帮助更好地了解大陆范围内的野火影响。需要更多的研究，利用更长的时空基线和火灾生态学专业知识，来完善使用的术语，以改进野火影响评估。

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引用次数: 0

Change detection in Sentinel-2 images using deep learning ensembles 基于深度学习集成的Sentinel-2图像变化检测

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101764

Ewa Kopec , Agata M. Wijata , Jakub Nalepa

The recent advancements in satellite imaging bring various possibilities in Earth observation in numerous domains, including the analysis of the evolution of urban areas, precision agriculture, environmental monitoring, event detection and tracking, and many more. Change detection plays a key role in a multitude of applications, as it allows for precisely monitoring the changes within an area of interest. In this article, we tackle this issue and introduce deep learning ensembles for change detection in Sentinel-2 times series of multispectral images—the proposed ensembles benefit from different deep learning model architectures. The experimental study performed over the widely-adopted benchmark datasets showed that the ensembles combine the strengths of the individual models, thus they reduce false positives and false negatives of base learners. The ensembles compensated the under-performing models, ultimately obtaining the change detection accuracy that exceeds 95% over the unseen test scenes.

卫星成像的最新进展为地球观测在许多领域带来了各种可能性，包括分析城市地区的演变、精准农业、环境监测、事件检测和跟踪等等。变更检测在许多应用程序中起着关键作用，因为它允许精确监视感兴趣区域内的变更。在本文中，我们解决了这个问题，并引入了用于Sentinel-2多光谱图像时间序列变化检测的深度学习集成——所提出的集成受益于不同的深度学习模型架构。在广泛采用的基准数据集上进行的实验研究表明，集成结合了单个模型的优势，从而减少了基础学习器的假阳性和假阴性。集成补偿了表现不佳的模型，最终在未见过的测试场景中获得了超过95%的变化检测精度。

引用次数: 0

Hidden forest loss: challenges in detecting wind- and insect-driven forest disturbances with global forest change landsat-based products in mixed southern boreal forests 隐性森林损失：利用基于全球森林变化的陆地卫星产品在南方北方混混林中探测风和昆虫驱动的森林扰动的挑战

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101798

Kirill Korznikov , Dmitriy Kislov , Jiří Doležal , Jan Altman

Wind- and insect-induced forest disturbances are becoming increasingly frequent and severe due to climate change, resulting in significant forest loss worldwide. Accurate detection of these disturbances is essential for understanding carbon storage, forest dynamics, ecosystem resilience, and for developing effective climate adaptation strategies. The Landsat-based Global Forest Change (GFC) product and the related Global Forest Watch web service are widely used for large-scale forest monitoring. However, its capacity to detect disturbances caused by wind and insect outbreaks remains uncertain. In this study, we assessed the accuracy of GFC forest loss detection by comparing it with U-Net neural network forest loss masks derived from very high-resolution satellite imagery. We analyzed several study areas in natural mixed-species southern boreal forests affected by windthrows and bark beetle outbreaks, evaluating true positive (TP), false negative (FN), and false positive (FP) detection rates. Our results show that GFC substantially underestimates forest loss, with TP detection rates ranging from 1.56 % to 62.18 % and FN errors reaching 37.82 %–98.44 %. In some cases, overestimation occurred due to high FP rates up to 65.55 %. The FPs happen when a small patch of forest loss within a 30 × 30 m Landsat pixel triggers the entire pixel to be classified as forest loss, even though most of the pixel remains undisturbed. The limitation stems from the small-scale nature of windthrows and insect-induced diebacks, which cannot be reliably captured by Landsat's spatial resolution. Our findings suggest that integrating higher-resolution satellite data is crucial for accurate area estimation and improved assessments of forest loss in the face of climate-driven disturbances such as windthrows and diebacks in natural mixed-species forests. Although GFC can be unsuitable for precisely mapping forest losses, it remains a valuable, globally consistent early warning tool due to its annual updates and broad coverage. Practitioners should treat GFC detections as indicative and conduct rapid visual or automated checks with higher-resolution imagery when assessing windthrow or insect-driven mortality, especially when disturbance patches are less than 450 m².

由于气候变化，风和昆虫引起的森林干扰日益频繁和严重，导致世界范围内的重大森林损失。准确探测这些干扰对于理解碳储量、森林动态、生态系统恢复力以及制定有效的气候适应战略至关重要。基于陆地卫星的全球森林变化（GFC）产品和相关的全球森林观察网络服务被广泛用于大规模森林监测。然而，它探测由风和昆虫爆发引起的干扰的能力仍然不确定。在这项研究中，我们通过比较来自高分辨率卫星图像的U-Net神经网络森林损失掩模，评估了GFC森林损失检测的准确性。我们分析了受大风和树皮甲虫暴发影响的自然混合树种南方北方森林的几个研究区域，评估了真阳性（TP）、假阴性（FN）和假阳性（FP）的检出率。结果表明，GFC严重低估了森林损失，TP检出率为1.56% ~ 62.18%，FN误差为37.82% ~ 98.44%。在某些情况下，由于FP率高达65.55%，出现了高估。当一个30 × 30 m的Landsat像元内的一小块森林损失触发整个像元被分类为森林损失时，FPs就发生了，即使大部分像元没有受到干扰。这种限制源于风力和昆虫引起的枯枝枯落的小规模性质，陆地卫星的空间分辨率无法可靠地捕捉到这些特征。我们的研究结果表明，整合更高分辨率的卫星数据对于面对气候驱动的干扰（如自然混合物种森林中的风和枯死）进行准确的面积估算和改进森林损失评估至关重要。虽然GFC可能不适合精确绘制森林损失地图，但由于其年度更新和广泛的覆盖范围，它仍然是一个有价值的、全球一致的早期预警工具。从业人员应将GFC检测视为指示性的，在评估风投或昆虫导致的死亡率时，特别是当干扰斑块小于450平方米时，应使用更高分辨率的图像进行快速视觉或自动检查。

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引用次数: 0

A lightweight georeferencing workflow for dynamic UAV footage using feature-matching and minimal drone metadata 使用特征匹配和最小无人机元数据的动态无人机镜头的轻量级地理参考工作流

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101801

Muhammad Waqas Ahmed , Muhammad Adnan , Muhammad Ahmed , Davy Janssens , Geert Wets , Afzal Ahmed , Wim Ectors

The emergence of unmanned aerial vehicles (UAVs), commonly known as drones, has transformed aerial imaging and photogrammetry, offering a cost-effective and flexible alternative to traditional methods. While commercially available drones are useful and affordable, the metadata provided in flight logs often falls short for robust photogrammetric applications. To address this limitation, we propose a novel method for the automated georeferencing of UAV footage that combines a feature-matching algorithm, Scale Invariant Feature Transform (SIFT), with telemetry data. Our system begins by initializing the homography by matching an input frame with a reference orthomosaic with a known spatial projection. Subsequently, the homography of the following frames is adjusted using the translation component estimated from the drone's telemetry. For the drone's rotation, the Oriented FAST and Rotated BRIEF (ORB) algorithm was utilized to detect changes between consecutive frames, allowing for reinitialization of the homography when needed. To quantify uncertainty and assess temporal dependence in frame-wise accuracy, a moving-block bootstrap (MBB) approach was employed for estimating confidence intervals. The proposed workflow is designed to be modular, meaning that the algorithms can be swapped out based on the data and conditions. Experimental results indicate that the method achieves sub-meter accuracy, with mean RMSE ranging from 54.9 to 95.9 cm.

无人驾驶飞行器（uav）的出现，通常被称为无人机，已经改变了航空成像和摄影测量，为传统方法提供了一种经济高效且灵活的替代方案。虽然商用无人机有用且价格合理，但飞行日志中提供的元数据往往无法满足强大的摄影测量应用。为了解决这一限制，我们提出了一种将特征匹配算法尺度不变特征变换（SIFT）与遥测数据相结合的无人机镜头自动地理参考的新方法。我们的系统首先通过将输入帧与已知空间投影的参考正交匹配来初始化单应性。随后，使用从无人机遥测估计的平移分量调整以下帧的单应性。对于无人机的旋转，定向FAST和旋转BRIEF （ORB）算法被用来检测连续帧之间的变化，允许在需要时重新初始化单应性。为了量化不确定性和评估逐帧精度的时间依赖性，采用了一种移动块自举（MBB）方法来估计置信区间。所提出的工作流被设计为模块化，这意味着算法可以根据数据和条件进行交换。实验结果表明，该方法可达到亚米级精度，平均RMSE范围为54.9 ~ 95.9 cm。

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引用次数: 0

Morphology-based correction improves plant height estimation from drone RGB SfM photogrammetry in dense salt marsh canopy 基于形态的校正改进了无人机RGB SfM摄影测量在密集盐沼冠层中的植物高度估计

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101796

Ernie I.H. Lee, Saurabh Amin, Heidi Nepf

The height of salt marsh vegetation is a key biophysical trait used to assess ecological health, monitor marsh restoration, and evaluate coastal protection from wave attenuation. This study defined a morphology-based correction that improved the accuracy of digital surface model (DSM)-derived canopy height for Spartina alterniflora. Using imagery collected at six drone altitudes (3 m to 120 m), corresponding to ground sampling distances (GSDs) of 1 mm/pixel to 34 mm/pixel, mean canopy height was estimated within 0.5 m by 0.5 m quadrats and validated against in situ RTK-GNSS Rover measurements. DSMs from drone altitudes of 60 m or lower (GSD ≤ 17 mm/pixel) yielded consistent elevation estimates with mean error < 5 cm. Increasing point cloud densification by changing image scale and point density settings did not improve DSM accuracy. Canopy height derived from DSMs was, on average, 60% of the true canopy height measured by Rover. A novel, quantitative assessment of canopy structure showed that the mean vertical position of projected horizontal canopy area was also 60% of the canopy height, suggesting that SfM reconstruction was capturing this position in the canopy, rather than the uppermost plant tips. The canopy structure methodology could be used for other species to estimate the underestimation of canopy height derived from SfM. Overall, this study provides a framework for selecting drone flight settings, processing parameters, and predicting canopy-height correction factors to improve DSM-based plant height measurements in dense canopies and heterogeneous vegetation surfaces.

盐沼植被高度是评价盐沼生态健康状况、监测盐沼恢复状况和评价海岸防波衰减的重要生物物理特征。本研究定义了一种基于形态学的校正方法，提高了数字表面模型（DSM）衍生的互花米草冠层高度的精度。利用在6个无人机高度（3米至120米）收集的图像，对应于1毫米/像素至34毫米/像素的地面采样距离（gsd），在0.5米× 0.5米的样方内估计平均冠层高度，并根据RTK-GNSS Rover的原位测量结果进行验证。无人机高度为60米或更低（GSD≤17毫米/像素）的DSMs得出的高度估计值一致，平均误差为5厘米。通过改变图像尺度和点密度设置来增加点云密度并不能提高DSM的精度。从DSMs得到的冠层高度平均为Rover测量的真实冠层高度的60%。一项新的冠层结构定量评估表明，投影水平冠层面积的平均垂直位置也是冠层高度的60%，这表明SfM重建捕获的是冠层中的这一位置，而不是最上层的植物尖端。冠层结构方法可用于其他物种的冠层高度估算。总的来说，本研究为选择无人机飞行设置、处理参数和预测冠层高度校正因子提供了一个框架，以改进基于dsm的密集冠层和非均匀植被表面的植物高度测量。

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引用次数: 0

Continuity of Sargassum biomass estimation in the East China Sea between GOCI and GOCI-II GOCI与GOCI- ii之间东海马尾藻生物量估算的连续性

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101782

Jisun Shin , Seung-Hwan Min , Young-Heon Jo

Sargassum honeri in the East China Sea (ECS) has flowed toward Jeju Island and the Korean Peninsula annually since 2015, causing considerable damage to aqua-farming sites and the coastal environments. Effective mitigation of these transboundary events requires not only distribution mapping but also high-frequency (<1 day) monitoring of Sargassum biomass. In this study, we identified the continuity of Sargassum biomass estimation in the ECS across the Geostationary Ocean Color Imager (GOCI) and its successor, GOCI-II, which provide high temporal resolution ocean color observations. Hourly overlapping observations from GOCI and GOCI-II Rayleigh-corrected reflectance (

ρ_{c}

) products revealed sensor degradation of GOCI's blue bands (412 and 443 nm). Using in situ biomass density measurements, we established second-order polynomial relationships between the alternative floating algae index (AFAI) and Sargassum biomass density (SBD) for both sensors and generated corresponding SBD ground-truth maps. Machine learning models, including Fine trees, boosted trees, and bagged trees, were trained using eight GOCI and twelve GOCI-II spectral bands as inputs and the ground-truth SBD as the output. Among these, the bagged trees model achieved the best performance (GOCI: R² = 0.70; RMSE = 0.022 kg m⁻²; GOCI-II: R² = 0.79; RMSE = 0.016 kg m⁻²) and produced the most realistic spatial patterns. After model prediction, Sargassum areal overage and total biomass were derived by summing all pixels with SBD ≥0.1 kg m⁻². Biomass distributions from both sensors exhibited good cross-sensor consistency in areal coverage (R² = 0.77; RMSE = 16.95 km²) and high consistency in total biomass (R² = 0.88; RMSE = 0.052 million tonne), despite inherent differences in sensor characteristics and acquisition conditions. Overall, this semi-automated framework ensures seamless continuity between GOCI and GOCI-II, enabling near-real-time estimation of Sargassum biomass and early detection of bloom events under varying cloud conditions. Our approach provides a transferable and operational tool for coastal monitoring, disaster preparedness, and policy-oriented marine management in East Asia and other Sargassum-affected regions.

从2015年开始，东中国海（ECS）的马尾藻（Sargassum honeri）每年都流向济州岛和韩半岛，对水产养殖场和沿海环境造成了相当大的破坏。有效缓解这些跨界事件不仅需要绘制分布图，还需要对马尾藻生物量进行高频（1天）监测。在这项研究中，我们确定了通过地球同步海洋颜色成像仪（GOCI）及其后续产品GOCI- ii，在ECS中马尾藻生物量估算的连续性，这些仪器提供了高时间分辨率的海洋颜色观测。GOCI和GOCI- ii瑞利校正反射率（ρc）产品每小时的重叠观测显示GOCI的蓝带（412和443 nm）的传感器退化。利用原位生物量密度测量，我们建立了两个传感器的备选浮藻指数（AFAI）和马尾藻生物量密度（SBD）之间的二阶多项式关系，并生成了相应的SBD地面真值图。使用8个GOCI和12个GOCI- ii光谱带作为输入，ground-truth SBD作为输出，对Fine tree、boosting tree和bagged trees等机器学习模型进行了训练。其中，套袋树模型表现最佳（GOCI: R2 = 0.70, RMSE = 0.022 kg m−2;GOCI- ii: R2 = 0.79, RMSE = 0.016 kg m−2），产生的空间格局最真实。模型预测后，将SBD≥0.1 kg m−2的所有像元相加，得到马尾藻面积覆盖和总生物量。尽管传感器特性和采集条件存在固有差异，但两种传感器的生物量分布在面积覆盖上具有良好的跨传感器一致性（R2 = 0.77, RMSE = 16.95 km2），在总生物量上具有较高的一致性（R2 = 0.88, RMSE = 0.052万吨）。总的来说，这种半自动化的框架确保了GOCI和GOCI- ii之间的无缝连续性，能够近实时地估计马尾藻生物量，并在不同的云条件下早期发现水华事件。我们的方法为东亚和其他受沙藻影响地区的沿海监测、备灾和以政策为导向的海洋管理提供了一种可转移的可操作工具。

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引用次数: 0

Pixel-based satellite mapping for coral island seabed classification: Application to the Maupiti island, French Polynesia 基于像素的珊瑚岛海底分类卫星制图：在法属波利尼西亚Maupiti岛的应用

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101762

Teo Nguyen , Damien Sous , Benoit Liquet , Samuel Meulé , Kerrie Mengersen , Frédéric Bouchette

This study introduces a novel pixel-based satellite mapping approach for classifying coral island seabed. The model combines a pixel-based approach and a segmentation technique, to smooth the predictions into coherent objects. The model is applied to Maupiti Island (French Polynesia) and compared with an expert-based mapping mostly based on the Reef Cover classification. Results demonstrate high accuracy, ranging between 87% and 90% for various spatial resolutions. The developed tool is open-source and flexible, allowing users to retrain it for different classification schemes and environments. The study highlights the potential of automated satellite mapping for monitoring coral reef ecosystems and supporting conservation efforts.

本研究提出了一种新的基于像素的珊瑚岛海底分类卫星制图方法。该模型结合了基于像素的方法和分割技术，将预测平滑成连贯的对象。该模型应用于Maupiti岛（法属波利尼西亚），并与主要基于珊瑚礁覆盖分类的专家制图进行了比较。结果表明，在不同空间分辨率下，精度在87% ~ 90%之间。开发的工具是开源和灵活的，允许用户重新训练它以适应不同的分类方案和环境。这项研究强调了自动卫星测绘在监测珊瑚礁生态系统和支持保护工作方面的潜力。

引用次数: 0

Recovering the disappearing Yamnaya kurgan landscape of northeastern Bulgaria by multi-method remote sensing 多方法遥感恢复保加利亚东北部消失的扬纳亚库尔干景观

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101789

Nikolai Paukkonen , Henry Skorna , Feiko Wilkes , Stelian Dimitrov , Stefan Alexandrov , Vladimir Slavchev , Volker Heyd

Kurgans — earthen burial mounds, mostly erected by Yamnaya people c. 3100-2500 BCE — constitute a vital element of the cultural heritage and prehistoric landscape of Eastern Europe. While the largest and most prominent mounds have been extensively documented and archaeologically excavated, but often also looted beforehand, the detection and study of smaller, eroded, or partially destroyed mounds remains a significant challenge. These less-visible features are often overlooked, yet they are crucial for reconstructing past settlement patterns and funerary landscapes. Moreover, ongoing agricultural activity poses a constant threat to their preservation.

This study presents the results of a non-invasive, multi-method archaeological survey conducted near the village of Vetrino in northeastern Bulgaria. The field campaign integrated drone-mounted LiDAR, magnetometry, multispectral imaging, and high-resolution photogrammetry. By applying advanced visualization techniques and cross-referencing datasets within a GIS environment, we identified several previously undocumented kurgan-like features. Our results not only demonstrate the efficacy of combining remote sensing and geophysical methods in kurgan detection but also emphasize the urgency of documenting these vulnerable monuments before they are entirely lost. Simultaneously, these methods offer a possibility to study features outside the kurgans themselves, such as ring ditches around them.

库尔干是一种土冢，主要由扬纳亚人在公元前3100-2500年建造，是东欧文化遗产和史前景观的重要组成部分。虽然最大和最突出的土丘已经被广泛地记录和考古发掘，但往往事先也被洗劫一空，但对较小的、被侵蚀的或部分被破坏的土丘的探测和研究仍然是一个重大挑战。这些不太明显的特征往往被忽视，但它们对于重建过去的定居模式和丧葬景观至关重要。此外，正在进行的农业活动不断威胁着它们的保存。这项研究介绍了在保加利亚东北部Vetrino村附近进行的一项非侵入性、多方法考古调查的结果。战场战役集成了无人机安装的激光雷达、磁强计、多光谱成像和高分辨率摄影测量。通过在GIS环境中应用先进的可视化技术和交叉引用数据集，我们确定了几个以前未记录的类似库尔干的特征。我们的研究结果不仅证明了遥感和地球物理方法相结合在库尔干探测中的有效性，而且强调了在这些脆弱的古迹完全消失之前记录它们的紧迫性。同时，这些方法提供了一种研究库尔干本身以外特征的可能性，比如它们周围的环形沟渠。

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引用次数: 0

Contributions of meteorological and vegetation factors to surface and root zone soil moisture variability across India's Agro-ecological regions 气象和植被因子对印度农业生态区表层和根区土壤水分变异的贡献

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101803

Adigarla Yeswanth Naidu , Sarmistha Singh , K. Sreelash , Hari Shanker Srivastava , Bhaskar Ramchandra Nikam

Understanding soil moisture variability plays a crucial role in comprehending land-atmosphere interactions influenced by vegetation and meteorological factors. Previous studies related to soil moisture variability at the field scale often focused on smaller spatial scales and, using the modeled or observed data with uncertainties related to topography and soil characteristics. Although the relative effects of multiple climatic factors are highly valuable for agriculture drought prediction, a complete understanding of these effects remains unclear. This study used a dominance analysis approach to investigate the influence of metrological forcings and vegetation on NASA's SMAP surface soil moisture (SSM) and root zone soil moisture (RZSM) across different agro-ecological regions of India. Precipitation and evapotranspiration (ET) identified as the most influential factors for SSM and RZSM variability, respectively, for most of the study area. However, these contributions vary across regions, with precipitation (∼0.5) dominates SSM in arid and semi-arid regions, while ET governs both SSM and RZSM in sub-humid and humid zones, and VPD (∼0.4) significantly influences coastal areas. Seasonally, monsoon precipitation drives SSM in semi-arid regions, however, ET dominating RZSM across most seasons, except pre-monsoon where vapor pressure deficit (VPD) prevails. Furthermore, a causal discovery approach using PCMCI analysis shows bidirectional SSM-ET relationships in arid regions, lagged precipitation effects of 2–3 weeks on RZSM in humid zones, and strong VPD driven drying in coastal areas. Together, they highlight the temporal decoupling between surface and root zone soil moisture and their distinct responses to climatic drivers. Overall, this improved understanding of SSM–RZSM dynamics and land–atmosphere interactions can enhance the prediction of agricultural droughts and flash floods across diverse agro-ecological regions of India.

了解土壤水分变率对理解受植被和气象因子影响的陆-气相互作用具有重要意义。以往有关野外尺度土壤水分变异的研究往往集中在较小的空间尺度上，并且使用与地形和土壤特征有关的不确定性的模拟或观测数据。尽管多种气候因子的相对影响对农业干旱预测非常有价值，但对这些影响的全面理解仍不清楚。本研究采用优势度分析方法，研究了气象强迫和植被对印度不同农业生态区NASA SMAP地表土壤湿度（SSM）和根区土壤湿度（RZSM）的影响。在大部分研究区，降水和蒸散发分别是影响SSM和RZSM变率的主要因子。然而，这些贡献在不同地区有所不同，在干旱和半干旱地区，降水（~ 0.5）主导SSM，而在半湿润和湿润地区，ET主导SSM和RZSM， VPD（~ 0.4）显著影响沿海地区。季节上，季风降水驱动半干旱区SSM，但在大部分季节，除了季风前水汽压差（VPD）盛行外，ET主导RZSM。此外，利用PCMCI分析的因果发现方法表明，干旱区SSM-ET存在双向关系，湿润地区2 ~ 3周降水对RZSM的滞后效应，沿海地区强VPD驱动的干燥。总之，他们强调了地表和根区土壤水分之间的时间解耦及其对气候驱动因素的独特响应。总体而言，这种对SSM-RZSM动力学和陆地-大气相互作用的改进理解可以增强对印度不同农业生态区域农业干旱和山洪的预测。

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引用次数: 0

Retrieving water surface elevation over arctic thermokarst lakes using ICESat-2 and Sentinel-3 alti metry data 利用ICESat-2和Sentinel-3高度计数据反演北极热岩溶湖水面高程

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2025-11-01 DOI: 10.1016/j.rsase.2025.101751

Shamsudeen Yekeen , Ben DeVries , Aaron Berg , Philip Marsh

Information on lake Water Surface Elevation (WSE) in the Arctic permafrost region is essential for understanding the impacts of climate warming on water storage and hydrological processes. The location of these lakes, in remote areas with limited access to in-situ monitoring capacity, remains a significant constraint in retrieving WSE. To overcome these issues, we leveraged the high spatial along-track resolution of ICESat-2, the higher temporal resolution of Sentinel-3, and the monitoring accuracy of installed gauges for retrieving WSE time series for several Arctic lakes. We derived in-situ WSE benchmark from 20 out of the 36 HOBOware gauges installed on a series of Arctic lakes over the non-frozen period spanning mid-June to the end of September 2022. The ICESat-2 ATL13 product (2018–2024) and Sentinel-3 SRAL data sets (2016–2024) were acquired over the study area. A multiple linear regression model (MLRM) was used to estimate bias between the different data sources. The comparison between in-situ measured WSE and whole-lake ICESat-2 WSE estimates revealed a root-mean-square-difference (RMSD) of 0.36 m, although with positive bias from ICESat-2. Using our in-situ data and MLRM, we calibrated the ICESat-2 product for this Arctic Lake region that produced an RMSD of 0.03 m. With the goal of combining the two WSE products from the ICESat-2 and the Sentinel-3 satellites to attain a WSE time series we compared the satellite products. The RMSD observed between ICESat-2 and the Sentinel-3 was 1.7 m for this region. To improve the time series WSE, we calibrated the instruments to each other, resulting in an RMSD of 0.17 m between the two satellites' WSE. After producing a combined WSE from the calibrated ICESat-2 and Sentinel-3, an accuracy assessment with the DAHITI database WSE revealed that our product had an RMSD of 0.03 m. This study demonstrates the combination of these sources of data towards WSE monitoring in Arctic lakes experiencing permafrost-related changes. It is also expected to serve as a baseline inventory for the recently launched SWOT altimetry satellites in producing long-temporal monitoring for the sizes of lakes in Arctic permafrost regions.

北极多年冻土层湖泊水面海拔（WSE）信息对于理解气候变暖对水储存和水文过程的影响至关重要。这些湖泊位于偏远地区，现场监测能力有限，这仍然是检索WSE的一个重大制约因素。为了克服这些问题，我们利用了ICESat-2的高空间分辨率，Sentinel-3的高时间分辨率，以及安装的测量仪的监测精度来检索几个北极湖泊的WSE时间序列。在2022年6月中旬至9月底的非冻结期，我们从安装在一系列北极湖泊上的36个HOBOware仪表中的20个获得了原位WSE基准。在研究区域获取了ICESat-2 ATL13产品（2018-2024）和Sentinel-3 SRAL数据集（2016-2024）。使用多元线性回归模型（MLRM）来估计不同数据源之间的偏差。原位测量的WSE与全湖ICESat-2 WSE估计值的比较显示，均方根差（RMSD）为0.36 m，尽管ICESat-2有正偏差。利用我们的原位数据和MLRM，我们校准了北极湖地区的ICESat-2产品，RMSD为0.03 m。为了结合来自ICESat-2和Sentinel-3卫星的两个WSE产品来获得WSE时间序列，我们比较了卫星产品。在该区域，ICESat-2和Sentinel-3观测到的RMSD为1.7 m。为了提高时间序列的WSE，我们对两颗卫星的WSE进行了相互校准，得到了0.17 m的RMSD。在使用校准后的ICESat-2和Sentinel-3生成综合WSE后，使用DAHITI数据库WSE进行精度评估，结果显示我们的产品的RMSD为0.03 m。这项研究展示了这些数据来源对北极湖泊的WSE监测的组合，这些湖泊正在经历与永久冻土相关的变化。预计它还将作为最近发射的SWOT测高卫星的基线清单，用于对北极永久冻土区湖泊的大小进行长期监测。

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