Science of Remote Sensing最新文献_第2页

Adaptive Thresholding (AT) for snowmelt detection with Calibrated Enhanced-Resolution Brightness Temperatures (CETB): Timing and regional patterns for case study of Alaska 基于校准增强分辨率亮度温度（CETB）的自适应阈值（AT）融雪检测：阿拉斯加案例研究的时间和区域模式

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-02-07 DOI: 10.1016/j.srs.2026.100390

Mahboubeh Boueshagh , Joan M. Ramage , Mary J. Brodzik , Molly A. Hardman

Accurate detection of snowmelt timing is critical for understanding hydrologic processes in cold regions and monitoring environmental changes in heterogeneous snow-dominated regions. This study introduces a new data-driven framework, termed Adaptive Thresholding (AT), which optimizes enhanced-resolution passive microwave brightness temperature (

T_{b}

) thresholds from the 37 GHz vertically polarized SSM/I sensor (CETB dataset) and diurnal amplitude variation (DAV) thresholds for snowmelt detection. The approach integrates

T_{b}

histograms and DAV time series to identify melt transitions across diverse snow-covered environments. Across a regional domain surrounding Fairbanks, Alaska, our results show that the optimized

T_{b}

and DAV thresholds are generally consistent with commonly used legacy values, while the AT method yields improved performance across varying snow conditions. Using AT with these optimized thresholds, we evaluated melt onset date (MOD) estimates under seven threshold combination scenarios, ranging from fixed legacy values to fully adaptive configurations, across nine monitoring sites in interior Alaska from 2003 to 2007. Validation against reference data showed that AT achieved the highest accuracy, with mean absolute errors (MAE) as low as

\leq 1.0

day and the lowest standard deviations across sites, in contrast to legacy methods, which produced MAE values exceeding 6–8 days at several locations. Results also revealed that MOD estimates varied with snow class and terrain, representing the influence of local conditions on melt timing. Comparative MOD maps demonstrated that optimized thresholds captured spatial melt gradients more realistically than legacy methods. These results reveal the advantages of adaptive, physically interpretable thresholding for remote sensing of snowmelt in heterogeneous terrain and support its application to large-scale monitoring systems.

准确探测融雪时间对于了解寒区水文过程和监测非均质积雪地区的环境变化至关重要。本研究引入了一种新的数据驱动框架，称为自适应阈值（AT），该框架优化了37 GHz垂直极化SSM/I传感器（CETB数据集）的增强分辨率无源微波亮度温度（Tb）阈值和日振幅变化（DAV）阈值，用于融雪检测。该方法整合了结核直方图和DAV时间序列，以识别不同积雪环境中的融化转变。在阿拉斯加费尔班克斯周围的区域范围内，我们的研究结果表明，优化的Tb和DAV阈值与常用的遗留值基本一致，而AT方法在不同的雪况下产生了更好的性能。利用AT和这些优化阈值，我们评估了七个阈值组合情景下的融化开始日期（MOD）估计值，范围从固定的遗留值到完全自适应配置，涵盖阿拉斯加内陆9个监测点，从2003年到2007年。对照参考数据的验证表明，与传统方法相比，AT具有最高的准确性，平均绝对误差（MAE）低至≤1.0天，跨站点的标准偏差最低，而传统方法在某些位置产生的MAE值超过6-8天。结果还显示，MOD估计值随雪级和地形的变化而变化，这表明当地条件对融化时间的影响。对比MOD地图表明，优化后的阈值比传统方法更真实地捕获了空间融化梯度。这些结果揭示了自适应、物理可解释阈值法在非均质地形融雪遥感中的优势，支持了其在大尺度监测系统中的应用。

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引用次数: 0

Adaptive neighborhood aggregation algorithm for PolInSAR forest height estimation PolInSAR森林高度估计的自适应邻域聚合算法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-02-05 DOI: 10.1016/j.srs.2026.100385

Wei Zhao , Bing Zhang , Zhongchao Hu , Jichao Zhang , Weidong Song

Forest height is a core parameter for characterizing vertical forest structure, estimating biomass, and studying global carbon cycles. Polarized interferometric synthetic aperture radar (PolInSAR) technology possesses unique detection capabilities for vegetation vertical structure and has become an important technical means for large-scale forest height estimation. However, under single-baseline PolInSAR configurations, forest height estimation based on the random volume over ground (RVoG) model suffers from parameter solution rank deficiency. Therefore, in this paper, we propose a novel forest height estimation method based on an adaptive neighborhood aggregation algorithm (ANAA). This method overcomes the limitations of traditional fixed windows by using scattering mechanism similarity as the core metric. It quantifies differences in polarization coherence matrices using Wishart distance to dynamically construct adaptive windows, ensuring that pixels within each window satisfy the RVoG model assumptions at the physical level. Furthermore, it integrates multi-pixel observations within each window into a unified estimation framework, simultaneously solving for shared forest heights. This approach fully exploits the potential of joint observation information, fundamentally improving the rank deficiency issue. The scattering-characteristic-driven window size adaptation strategy in this article eliminates reliance on empirical window size settings. Experimental conducted in the Mabounie tropical rainforest region demonstrate that the ANAA significantly outperforms both fixed-jumping window and sliding window approaches in the key metrics of root mean square error (RMSE) and coefficient of determination (R²). RMSE is reduced by 28.8% and 20.6%, respectively, effectively enhancing estimation accuracy and robustness in complex heterogeneous forest areas. This provides an efficient and feasible solution for precise tropical rainforest forest height estimation.

森林高度是表征森林垂直结构、估算森林生物量和研究全球碳循环的核心参数。偏振干涉合成孔径雷达（PolInSAR）技术对植被垂直结构具有独特的探测能力，已成为大规模森林高度估算的重要技术手段。然而，在单基线PolInSAR配置下，基于RVoG模型的森林高度估计存在参数解秩不足的问题。为此，本文提出了一种基于自适应邻域聚合算法（ANAA）的森林高度估计方法。该方法以散射机制相似度为核心度量，克服了传统固定窗方法的局限性。利用Wishart距离量化极化相干矩阵的差异，动态构建自适应窗口，确保每个窗口内的像素在物理层满足RVoG模型的假设。此外，它将每个窗口内的多像素观测数据整合到统一的估计框架中，同时解决共享森林高度的问题。这种方法充分挖掘了联合观测信息的潜力，从根本上改善了秩不足问题。本文中的散射特征驱动的窗口大小适应策略消除了对经验窗口大小设置的依赖。在Mabounie热带雨林地区进行的实验表明，ANAA方法在均方根误差（RMSE）和决定系数（R2）等关键指标上明显优于固定跳窗和滑动窗方法。RMSE分别降低28.8%和20.6%，有效提高了复杂异质林区的估计精度和鲁棒性。这为热带雨林森林高度的精确估算提供了一种高效可行的解决方案。

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引用次数: 0

Mapping hidden heritage: Self-supervised pre-training on high-resolution LiDAR DEM derivatives for archaeological stone wall detection 绘制隐藏遗产：用于考古石墙探测的高分辨率LiDAR DEM衍生品的自监督预训练

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-14 DOI: 10.1016/j.srs.2026.100372

Zexian Huang , Mashnoon Islam , Brian Armstrong , Billy Bell , Kourosh Khoshelham , Martin Tomko

Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management during dry seasons in Australia. However, many of these stone structures in remote or vegetated landscapes remain undocumented due to limited accessibility and the high cost of manual mapping. Deep learning–based segmentation offers a scalable approach for automated mapping of such features, but challenges remain: 1. the visual occlusion of low-lying dry-stone walls by dense vegetation and 2. the scarcity of labeled training data. This study presents DINO-CV, a self-supervised cross-view pre-training framework based on knowledge distillation, designed for accurate and data-efficient mapping of dry-stone walls using Digital Elevation Models (DEMs) derived from high-resolution airborne LiDAR. By learning invariant geometric and geomorphic features across DEM-derived views, (i.e., Multi-directional Hillshade and Visualization for Archaeological Topography), DINO-CV addresses the occlusion by vegetation and data scarcity challenges. Applied to the Budj Bim Cultural Landscape at Victoria, Australia, a UNESCO World Heritage site, the approach achieves a mean Intersection over Union (mIoU) of 68.6% on test areas and maintains 63.8% mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for large-scale, automated mapping of cultural heritage features in complex and vegetated environments. Beyond archaeology, this approach offers a scalable solution for environmental monitoring and heritage preservation across inaccessible or environmentally sensitive regions.

历史上的干石墙具有重要的文化和环境意义，作为历史标志，有助于澳大利亚干旱季节的生态系统保护和野火管理。然而，由于有限的可达性和手工测绘的高成本，许多这些偏远或植被景观中的石头结构仍然没有记录。基于深度学习的分割为这些特征的自动映射提供了一种可扩展的方法，但挑战仍然存在：1。低矮的干石墙被茂密的植被和2。标记训练数据的稀缺性。本研究提出了DINO-CV，这是一种基于知识蒸馏的自监督交叉视图预训练框架，旨在使用高分辨率机载激光雷达衍生的数字高程模型（dem）对干石墙进行准确和高效的数据映射。通过学习dem衍生视图中不变的几何和地貌特征（即考古地形的多向遮阳和可视化），DINO-CV解决了植被遮挡和数据稀缺性的挑战。应用于澳大利亚维多利亚州的Budj Bim文化景观（联合国教科文组织世界遗产），该方法在测试区域实现了68.6%的平均交叉点（mIoU），并且在仅使用10%标记数据进行微调时保持了63.8%的mIoU。这些结果表明，在复杂的植被环境中，自监督学习在高分辨率DEM衍生工具上的潜力，可以用于大规模、自动绘制文化遗产特征。除了考古学，这种方法还为难以进入或环境敏感地区的环境监测和遗产保护提供了可扩展的解决方案。

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引用次数: 0

Harmonized Tasseled Cap Transformation coefficients for Landsat 8 and 9 OLI sensors using surface reflectance from near-coincident underfly observations Landsat 8和9 OLI传感器的协调流苏帽转换系数，利用接近一致的下飞观测的表面反射率

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-14 DOI: 10.1016/j.srs.2025.100353

Shuai Wang , Lijuan Yang , Tingting Shi , Jin Chen

Tasseled Cap Transformation (TCT) is a widely applied remote sensing technique for dimensionality reduction and physical feature enhancement, valued for its interpretability and efficiency. While TCT coefficients have been developed for numerous sensors, no dedicated coefficient set has been proposed to date for Landsat 9 Operational Land Imager-2 (OLI-2) sensor. This study addresses this gap by deriving the first TCT coefficients for Landsat 9 OLI-2 based on surface reflectance data. To ensure cross-sensor consistency, we simultaneously recalculated Landsat 8 Operational Land Imager (OLI) coefficients using strictly matched underfly image pairs and identical sample selection protocols. This harmonized derivation strategy minimizes methodological and sampling-induced discrepancies, enhancing compatibility between the two Landsat sensors. More importantly, the proposed coefficients offer improved performance in capturing wetness-related information across diverse ecological settings. This makes them especially suitable for applications involving soil moisture monitoring, vegetation stress detection, and hydrological modeling in spatially and temporally heterogeneous landscapes. The validation results demonstrate that the newly proposed coefficients effectively enhance spectral differences among surface features with varying brightness, greenness, and moisture content. Moreover, the TCT components from Landsat 8 OLI and Landsat 9 OLI-2 exhibit strong agreement across all three components (R² > 0.96, approaching 1), underscoring the high consistency of the derived coefficients. Sensitivity analyses further reveal that the wetness and greenness components remain highly stable under varying sample selection conditions, while the brightness component, though slightly more sensitive, still maintains angular differences within 5°. The results highlight the improved physical consistency and cross-sensor compatibility of the proposed coefficients, facilitating more robust long-term environmental monitoring and multi-source data integration in Earth observation studies.

流苏帽变换（TCT）是一种应用广泛的遥感降维和物理特征增强技术，以其可解释性和高效性而受到重视。虽然已经为许多传感器开发了TCT系数，但迄今为止还没有为Landsat 9操作陆地成像仪-2 （OLI-2）传感器提出专用系数集。本研究通过基于地表反射率数据推导Landsat 9 OLI-2的第一个TCT系数来解决这一问题。为了确保跨传感器的一致性，我们使用严格匹配的下飞图像对和相同的样本选择协议，同时重新计算了Landsat 8操作陆地成像仪（OLI）系数。这种协调的推导策略最大限度地减少了方法和采样引起的差异，增强了两个陆地卫星传感器之间的兼容性。更重要的是，所提出的系数在捕获不同生态环境中与湿度相关的信息方面提供了更好的性能。这使得它们特别适用于涉及土壤湿度监测、植被应力检测和空间和时间异质性景观的水文建模的应用。验证结果表明，新提出的系数有效地增强了不同亮度、绿色和含水量地物之间的光谱差异。此外，Landsat 8 OLI和Landsat 9 OLI-2的TCT分量在所有三个分量之间表现出很强的一致性（R2 > 0.96，接近1），强调了推导系数的高度一致性。灵敏度分析进一步表明，湿度和绿色成分在不同的样品选择条件下保持高度稳定，而亮度成分虽然稍微敏感，但仍然保持在5°内的角差。结果表明，所提出的系数提高了物理一致性和跨传感器兼容性，有助于在地球观测研究中进行更稳健的长期环境监测和多源数据集成。

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引用次数: 0

Development of a 10 m daily seamless surface reflectance data cube based on Sentinel-2 constellation for generating the reference true-value products at Wanglang mountain area, China 基于Sentinel-2星座的10 m日无缝地表反射率数据立方生成参考真值产品

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-09 DOI: 10.1016/j.srs.2025.100350

Jinhu Bian , Siyuan Li , Zhengjian Zhang , Yi Deng , Guangbin Lei , Xi Nan , Amin Naboureh , Ainong Li

Mountain ecosystems, characterized by high heterogeneity and rapid dynamics, require high spatiotemporal resolution remote sensing products for accurate monitoring. However, rugged terrain induces significant radiometric distortion in satellite imagery, and persistent cloud cover leads to data gaps, severely limiting the application of optical satellites in these regions. This study developed an integrated method to generate a daily, seamless, 10-m resolution, Sentinel-2-like surface reflectance data cube for the topographically complex Wanglang mountain area in China. Our method synergistically combined a physically-based topographic correction model that incorporates atmospheric and bidirectional reflectance distribution function (BRDF) effects, a harmonic model for temporal reconstruction and gap-filling, and a validation and calibration framework using a tower-based multi-angle in-situ surface reflectance observation. The results demonstrate that the physically-based topographic correction model outperformed the operational L2A product of Sentinel-2, reducing the RMSE in the near-infrared (NIR) band from 0.025–0.035 to 0.0106–0.0206 and effectively eliminating the dependence on solar incidence angle (reducing R² to as low as 0.007 during peak season). The harmonic model accurately reconstructed seamless daily data, achieving well prediction accuracy (R² of 0.83–0.87 for NIR band and 0.81–0.89 for blue band) across different phenological stages. Following linear calibration, the final data cube achieved exceptional radiometric agreement with independent in-situ measurements, with R² > 0.60 for all visible and NIR bands and RMSE as low as 0.0081–0.0171. This study provides not only a high-fidelity and seamless surface reflectance product in Wanglang complex terrains for the research community but also a replicable framework for generating reference true-value products in the challenging mountain area. The resulting surface reflectance data cube serves as a critical foundation for biophysical parameters estimation, thereby enhancing our ability to validate and develop mountain remote sensing algorithms and products, and further understanding vulnerable mountain ecosystems as well.

山地生态系统具有高度异质性和快速动态的特点，需要高时空分辨率的遥感产品进行精确监测。然而，崎岖的地形导致卫星图像出现明显的辐射失真，持续的云层覆盖导致数据空白，严重限制了光学卫星在这些地区的应用。本研究开发了一种集成方法，为中国地形复杂的王朗山区生成每日、无缝、10米分辨率、类似sentinel -2的地表反射率数据立方体。该方法将包含大气和双向反射率分布函数（BRDF）效应的基于物理的地形校正模型、用于时间重建和间隙填充的调和模型以及基于塔的多角度原位地表反射率观测的验证和校准框架协同结合起来。结果表明，基于物理的地形校正模型优于Sentinel-2的实际L2A产品，将近红外（NIR）波段的RMSE从0.025-0.035降低到0.0106-0.0206，有效消除了对太阳入射角的依赖（旺季时R2低至0.007）。调和模型准确地重建了无缝的日数据，在不同物候阶段取得了较好的预测精度（近红外波段R2为0.83-0.87，蓝波段R2为0.81-0.89）。在线性校准之后，最终的数据立方体与独立的原位测量结果达到了非常好的辐射一致性，所有可见光和近红外波段的R2 >； 0.60， RMSE低至0.0081-0.0171。本研究不仅为研究界提供了王朗复杂地形下的高保真无缝地表反射率产品，也为在具有挑战性的山区生成参考真值产品提供了可复制的框架。由此产生的地表反射率数据立方体可以作为生物物理参数估计的重要基础，从而提高我们验证和开发山地遥感算法和产品的能力，并进一步了解脆弱的山地生态系统。

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引用次数: 0

The rayleigh effects on the atmospheric correction of the ultraviolet imager on HY-1C and HY-1D satellites HY-1C和HY-1D卫星紫外成像仪大气校正的瑞利效应

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-08 DOI: 10.1016/j.srs.2025.100349

Zhihua Mao , Xianliang Zhang , Dayi Yin , Longwei Zhang , Yiwei Zhang , Qifei Wang , Bangyi Tao , Jianyu Chen , Zengzhou Hao , Qiankun Zhu , Haiqing Huang

Ultraviolet (UV) light is a key component of solar radiation, significantly impacting marine ecosystems. It is necessary to measure the ultraviolet light on the ocean which is now available from the Ultraviolet Imager (UVI) on the two ocean color satellites (HY-1C and HY-1D). The atmospheric correction (AC) procedure of the UVI data is based on the Layer Removal Scheme for Atmospheric Correction (LRSAC). Evaluation with Marine Optical Buoy in-situ data shows water-leaving reflectance (Rrs) accuracy with mean relative error (MRE) of 0.84 % (high sensitivity) and 0.13 % (low sensitivity) at Band 1 of HY-1C, and 6.79 % (high sensitivity) and −5.55 % (low sensitivity) at Band 2 of HY-1C, respectively. The mean absolute error (MAE) values are 0.0023 sr⁻¹ (high sensitivity) and 0.0034 sr⁻¹ (low sensitivity) at Band 1 of HY-1C, and 0.0021 sr⁻¹ (high sensitivity) and −0.0028 sr⁻¹ (low sensitivity) at Band 2 of HY-1C, respectively. The MRE values are 13.96 % (high sensitivity) and 20.27 % (low sensitivity) at Band 1 of HY-1D, and 8.09 % (high sensitivity) and 6.99 % (low sensitivity) at Band 2 of HY-1D, respectively. The MAE values are 0.0021 sr⁻¹ (high sensitivity) and 0.0022 sr⁻¹ (low sensitivity) at Band 1 of HY-1D, and 0.0023 sr⁻¹ (high sensitivity) and 0.0022 sr⁻¹ (low sensitivity) at Band 2 of HY-1D, respectively. The global daily and the 8-day composite images of the UVI demonstrate the spatial patterns of Rrs in the ultraviolet region, similar to the Rrs products of the Chinese Ocean Color and Temperature Scanner at blue bands. The accuracy of the Rayleigh can affect the performance of the AC mainly due to the largest part in the satellite-received radiance of the UVI. The selection of different volume scattering phases can cause about 1 % of MRE in the generation of the lookup table of Rayleigh varying with the solar and viewing angles. The ozone concentrations, sea surface winds, and atmospheric pressure of the global daily climatology have been generated and used to estimate the Rayleigh scattering at the ultraviolet bands. The ozone concentrations can cause about −0.6 % of MRE with about −0.8 % for winds and −0.4 % for pressure on the global Rayleigh distributions. The products are now available on the website for the oceanography study.

紫外线是太阳辐射的重要组成部分，对海洋生态系统有重要影响。有必要测量海洋上的紫外线，目前可以从两颗海洋彩色卫星（HY-1C和HY-1D）上的紫外线成像仪（UVI）获得。UVI数据的大气校正（AC）过程基于大气校正的分层去除方案（LRSAC）。利用海洋光学浮标原位数据进行评价，在HY-1C波段1的平均相对误差（MRE）分别为0.84%（高灵敏度）和0.13%（低灵敏度），在HY-1C波段2的平均相对误差分别为6.79%（高灵敏度）和- 5.55%（低灵敏度）。在HY-1C波段1的平均绝对误差（MAE）分别为0.0023 sr−1（高灵敏度）和0.0034 sr−1（低灵敏度），在HY-1C波段2的平均绝对误差（MAE）分别为0.0021 sr−1（高灵敏度）和- 0.0028 sr−1（低灵敏度）。在HY-1D波段1的MRE值分别为13.96%（高灵敏度）和20.27%（低灵敏度），在HY-1D波段2的MRE值分别为8.09%（高灵敏度）和6.99%（低灵敏度）。在HY-1D波段1的MAE值分别为0.0021 sr−1（高灵敏度）和0.0022 sr−1（低灵敏度），在HY-1D波段2的MAE值分别为0.0023 sr−1（高灵敏度）和0.0022 sr−1（低灵敏度）。全球日和8天的UVI合成图像显示了紫外线区域Rrs的空间格局，与中国海洋颜色和温度扫描仪在蓝色波段的Rrs产品相似。瑞利雷达的精度会影响交流雷达的性能，主要是因为卫星接收到的紫外线辐射占最大的比例。在瑞利查找表的生成过程中，不同体积散射相位的选择会导致约1%的MRE随太阳和视角的变化而变化。生成了全球日气候学的臭氧浓度、海面风和大气压力，并用于估算紫外线波段的瑞利散射。在全球瑞利分布上，臭氧浓度可引起约- 0.6%的MRE，约- 0.8%的风和- 0.4%的压力。这些产品现在可以在海洋学研究网站上找到。

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引用次数: 0

High-resolution winter cover crop mapping with PlanetScope imagery: Comparative analysis of Random Forest, Convolutional Neural Network, and unsupervised classification PlanetScope图像的高分辨率冬季覆盖作物制图：随机森林、卷积神经网络和无监督分类的比较分析

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-12 DOI: 10.1016/j.srs.2025.100351

Kanru Chen

Effective monitoring of agricultural conservation practices is essential for evaluating environmental outcomes and guiding land management strategies. This study assessed three satellite-based classification methods to estimate winter cover crop adoption across Benton County, Indiana, from 2021 to 2023, and compared their results with traditional field-based transect surveys. Using 3-m PlanetScope imagery and a consistent preprocessing and validation pipeline, we implemented (1) unsupervised Iso-cluster classification with five vegetation indices, (2) supervised Random Forest (RF) models, and (3) deep learning-based Convolutional Neural Networks (CNNs), tailored separately for December and April imagery. Supervised methods outperformed the unsupervised approach. RF models achieved F1 scores of 0.98 (December) and 0.96 (April), while CNNs reached 0.97 and 0.92, respectively. Unsupervised classification yielded lower accuracy (F1 ≤ 0.77), particularly under heterogeneous spring conditions. While transect surveys reported 24–128 % higher cover crop acreage than satellite-based estimates, the spatial and temporal patterns captured by both methods were similar, highlighting trends such as higher adoption after corn than soybean and substantial seasonal variation. Multi-year analysis revealed that less than 1 % of fields maintained continuous cover cropping across three consecutive winters, indicating predominantly intermittent adoption. These findings underscore the value of satellite imagery for full-coverage, repeatable assessments of conservation practice adoption. Scalable, remotely sensed classification enables timely evaluation of program effectiveness and supports adaptive land management to improve soil health and water quality at county scales.

有效监测农业保护措施对于评估环境结果和指导土地管理战略至关重要。本研究评估了三种基于卫星的分类方法，以估计2021年至2023年印第安纳州本顿县冬季覆盖作物的采用情况，并将其结果与传统的基于实地的样带调查进行了比较。利用3米PlanetScope图像和一致的预处理和验证管道，我们实现了(1)基于5个植被指数的无监督等聚类分类，(2)监督随机森林（RF）模型，(3)基于深度学习的卷积神经网络（cnn），分别针对12月和4月的图像进行了定制。有监督方法优于无监督方法。RF模型的F1得分分别为0.98（12月）和0.96（4月），cnn分别为0.97和0.92。非监督分类的准确率较低（F1≤0.77），特别是在异质弹簧条件下。虽然样带调查报告的覆盖作物面积比基于卫星的估计高24 - 128%，但两种方法捕获的时空模式相似，突出了玉米种植后的采用率高于大豆，以及明显的季节变化等趋势。多年分析显示，只有不到1%的农田在连续三个冬季保持覆盖种植，这表明主要是间歇性采用。这些发现强调了卫星图像对保护实践采用的全覆盖、可重复评估的价值。可扩展的遥感分类能够及时评估项目有效性，并支持适应性土地管理，以改善县尺度上的土壤健康和水质。

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引用次数: 0

Rule-based training sample generation using Sentinel-2 GCVI time series for winter wheat mapping 基于规则的Sentinel-2 GCVI时间序列冬小麦制图训练样本生成

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-06 DOI: 10.1016/j.srs.2026.100364

Fangjie Li , Inbal Becker-Reshef , Josef Wagner , Françoise Nerry

Timely and accurate winter wheat mapping is essential for agricultural monitoring and food security. However, efficiently acquiring high-quality training data for supervised classification remains a challenge. In this study, we developed a rule-based method to automatically generate training samples using Sentinel-2 green chlorophyll vegetation index (GCVI) time series. Then, the key phenological periods were identified through feature importance analysis, and spectral features from these periods were used with a Random Forest (RF) classifier to produce 10 m resolution winter wheat distribution maps for Hengshui, Kaifeng, and Xiangyang in 2022 and 2023. To evaluate temporal transferability, the automatically generated training samples from 2022 to 2023 were transferred to subsequent years, enabling winter wheat mapping for 2023 and 2024 based on cross-year training data. Accuracy assessments showed that the proposed method achieved high performance, with average overall accuracy (OA) of 96.04 ± 1.97 % and 94.81 ± 2.14 % in 2022 and 2023, respectively, and average F1 scores of 91.21 % and 90.83 %. The winter wheat maps generated using transferred samples also demonstrated good temporal transferability, and maintained high accuracy, with average OA of 94.06 ± 2.19 % in 2023 and 94.58 ± 2.01 % in 2024. Area estimates from stratified random sampling showed that winter wheat planting areas in Hengshui, Kaifeng, and Xiangyang were 290.8 ± 16.82, 199.16 ± 10.65, and 318.05 ± 44.16 thousand hectares (kha) in 2022, increasing to 379.34 ± 19.75, 209.27 ± 12.68, and 342.02 ± 42.81 kha in 2023, respectively. Compared with existing winter wheat products, the map generated in this study achieved higher classification accuracy and finer spatial detail. Overall, this study provides a practical and effective approach for automatic training sample generation in winter wheat mapping, and offers valuable guidance for large-scale, long-term agricultural monitoring.

及时、准确的冬小麦制图对农业监测和粮食安全至关重要。然而，如何有效地获取高质量的监督分类训练数据仍然是一个挑战。在这项研究中，我们开发了一种基于规则的方法，以哨兵-2绿色叶绿素植被指数（GCVI）时间序列自动生成训练样本。然后，通过特征重要性分析确定关键物候期，利用这些物候期的光谱特征与随机森林（Random Forest， RF）分类器构建2022年和2023年衡水、开封和襄阳地区10 m分辨率冬小麦分布图。为了评估时间可转移性，将自动生成的2022 - 2023年训练样本转移到后续年份，实现了基于跨年训练数据的2023年和2024年冬小麦制图。准确率评估表明，该方法取得了良好的性能，2022年和2023年的平均总体准确率（OA）分别为96.04±1.97%和94.81±2.14%，平均F1分数为91.21%和90.83%。利用转移样本生成的冬小麦图谱具有良好的时间可转移性，保持了较高的精度，2023年和2024年的平均OA分别为94.06±2.19%和94.58±2.01%。分层随机抽样估算结果显示，2022年衡水、开封和襄阳冬小麦种植面积分别为290.8±16.82、199.16±10.65和318.05±44.16千公顷，2023年分别增加到379.34±19.75、209.27±12.68和342.02±42.81千公顷。与现有冬小麦产品相比，本研究生成的地图具有更高的分类精度和更精细的空间细节。综上所述，本研究为冬小麦制图中训练样本的自动生成提供了实用有效的方法，对大规模、长期的农业监测具有重要的指导意义。

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引用次数: 0

Combining single-date mobile and multitemporal airborne laser scanning for retrospective estimation of individual tree growth over a 10-year period in boreal forests 结合单日期移动和多时相机载激光扫描对北方森林单株树木生长10年的回顾性估计

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-02 DOI: 10.1016/j.srs.2025.100345

Daniella Tavi , Jesse Muhojoki , Valtteri Soininen , Eric Hyyppä , Teemu Hakala , Ville Luoma , Antero Kukko , Xiaowei Yu , Mikko Vastaranta , Juha Hyyppä

Accurate estimation of individual tree growth is essential for forest inventories and carbon stock assessments, yet traditional manual methods remain labor-intensive and poorly scalable. Laser scanning offers promising alternatives, but slow tree growth rates, sensor limitations, and limited temporal availability of accurate stem-level data challenge growth estimation. This study presents a novel framework combining a single-date mobile laser scanning (MLS) dataset from 2024 with airborne laser scanning (ALS) datasets from 2014 and 2023 to estimate 10-year growth (2014–2024) in diameter at breast height (DBH) and stem volume at the individual tree level. MLS was used for detecting trees and modeling their stem curves, enabling DBH estimation in 2024. These stem curves, alongside ALS-derived heights, were used for volume estimation in 2024. A linear scaling approach, based on ALS-derived height growth factors, was used to model past DBH and stem curves to obtain 2014 attributes, eliminating the need for historical MLS data. Across eight boreal forest plots, growth and one-time attribute estimation accuracy were evaluated against manual DBH measurements and ALS-based reference heights, with analyses across forest complexities, tree species, and size classes. Volume change estimation achieved

R^{2}

values of 0.6–0.8 compared to 0.3–0.4 for DBH change estimation. Root mean square errors (RMSEs) were 0.9–1.7 cm (30%–64%) for DBH change and 0.04–0.10 m³ (25%–65%) for volume change. Growth estimation was most accurate for pines, medium-sized trees (DBH 20–35 cm), and in sparse stands. Although accuracy varied by environment, the proposed method offers a scalable approach for retrospective growth estimation, with potential to enhance the efficiency and cost-effectiveness of forest monitoring.

准确估计单株树木的生长对森林资源清查和碳储量评估至关重要，但传统的人工方法仍然是劳动密集型的，而且难以推广。激光扫描提供了很有希望的替代方法，但树木生长速度慢，传感器的局限性，以及准确的茎级数据的有限时间可用性，给生长估计带来了挑战。本研究提出了一个新的框架，将2024年的单日期移动激光扫描（MLS）数据集与2014年和2023年的机载激光扫描（ALS）数据集相结合，以估计10年（2014 - 2024年）树木胸高直径（DBH）和单树水平茎体积的增长。MLS用于树木检测和树干曲线建模，实现了2024年的胸径估计。这些茎杆曲线与als导出的高度一起用于2024年的体积估计。基于als衍生的高度生长因子，采用线性缩放方法对过去的胸径和茎干曲线进行建模，以获得2014年的属性，从而消除了对历史MLS数据的需求。在8个北方针叶林样地，利用人工胸径测量和基于als的参考高度对生长和一次性属性估计的准确性进行了评估，并对森林复杂性、树种和大小类别进行了分析。体积变化估计的R2值为0.6-0.8，而胸径变化估计的R2值为0.3-0.4。胸径变化的均方根误差（rmse）为0.9 ~ 1.7 cm(30% ~ 64%)，体积变化的均方根误差为0.04 ~ 0.10 m3（25% ~ 65%）。对松木、中等乔木（胸径20 ~ 35 cm）和稀疏林分的生长估算最准确。虽然准确性因环境而异，但提议的方法提供了一种可扩展的回顾性生长估计方法，有可能提高森林监测的效率和成本效益。

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引用次数: 0

Observing irrigation using SWOT SAR Ka-band data from daily calibration and validation acquisitions 使用SWOT SAR ka波段数据观察灌溉情况，这些数据来自日常校准和验证获取

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-21 DOI: 10.1016/j.srs.2026.100378

Henri Bazzi , Nicolas Baghdadi , Cecile Cazals , Sami Najem , Damien Desroches , Frédéric Frappart , Mehrez Zribi , François Charron

While primarily designed for ocean and inland water monitoring through Interferometric SAR (InSAR) technology, the Surface Water and Ocean Topography (SWOT) Ka-band SAR sensor also presents a novel potential for agricultural applications. This study explores the sensitivity of SWOT's Ka-band backscatter to soil moisture variations, focusing on detecting irrigation events using daily observations collected during the calibration/validation (Cal/Val) phase. Daily backscatter variations from the SWOT Level 1B High-Rate Single-look Complex product were examined over an experimental irrigated grassland site, in response to irrigation events and rainfall. The analysis included first evaluating the stability of SWOT Ka-band backscatter signal, the temporal responses to both irrigation and rainfall, and the influence of vegetation density on Ka-band SAR signal penetration. Main findings showed that the Ka-band SAR data was sensitive to soil moisture variation due to irrigation, inducing an increased backscattering by an average of 4.3 dB on the same day of irrigation. For some cases of flooded vegetation persisting after irrigation, specular reflection and/or double-bounce scattering mechanisms were observed, causing an extreme increase in the Ka-band backscattering. Following complete infiltration, irrigation events induced an average increase of about 2 dB one day after irrigation which dropped back to previous levels two days later due to natural soil drying. Despite the Ka-band's short wavelength, typically limiting canopy penetration, SWOT's near-vertical incidence angle appears to enhance its ability to penetrate dense vegetation cover reaching the soil surface and detecting soil moisture dynamics. These findings open new perspectives for leveraging the daily CAL/VAL SWOT acquisitions to map irrigated areas and support agricultural water management.

虽然主要设计用于通过干涉SAR （InSAR）技术监测海洋和内陆水域，但地表水和海洋地形（SWOT） ka波段SAR传感器也具有农业应用的新潜力。本研究探讨了SWOT的ka波段反向散射对土壤湿度变化的敏感性，重点是利用在校准/验证（Cal/Val）阶段收集的日常观测数据来检测灌溉事件。在一个试验灌溉草地上，研究了SWOT 1B级高速率单视复杂产品的日反向散射变化，以响应灌溉事件和降雨。分析首先评估了SWOT ka波段后向散射信号的稳定性、灌溉和降雨对SWOT ka波段后向散射信号的时间响应以及植被密度对ka波段SAR信号穿透的影响。结果表明：ka波段SAR数据对灌水引起的土壤水分变化较为敏感，灌水当天的后向散射平均增加4.3 dB；在一些淹水植被在灌溉后持续存在的情况下，观察到镜面反射和/或双反弹散射机制，导致ka波段后向散射急剧增加。在完全入渗后，灌溉事件在灌溉后1天平均增加约2 dB， 2天后由于土壤自然干燥而回落到之前的水平。尽管ka波段波长较短，通常会限制冠层穿透，但SWOT的近垂直入射角似乎增强了其穿透茂密植被覆盖到达土壤表面并探测土壤水分动态的能力。这些发现为利用每日CAL/VAL SWOT数据绘制灌溉区地图和支持农业用水管理开辟了新的视角。

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引用次数: 0