Remote Sensing of Environment最新文献_第7页

Automated drone-borne GPR mapping of root-zone soil moisture for precision irrigation 用于精准灌溉的根区土壤湿度自动无人机探地雷达测绘

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-05 DOI: 10.1016/j.rse.2025.115110

Kaijun Wu , Jean Artois , Denis Tourneur , Merlin Mareschal , Maud Henrion , Sashini Pathirana , Lakshman Galagedara , Quentin Limbourg , Sébastien Lambot

High-resolution monitoring of root-zone soil moisture is essential for optimizing irrigation in precision agriculture. This study demonstrates the potential of drone-borne Ground-Penetrating Radar (GPR) to map spatial and temporal soil moisture dynamics across an agricultural field over an entire growing season. Using the innovative gprSense® system, which combines a frequency-domain radar with full-wave inversion, we achieved precise and automated data acquisition and processing. To our knowledge, this is the first study to implement time-lapse, root-zone soil moisture mapping using drone-borne GPR combined with real-time full-wave inversion. Time-lapse mapping was conducted in a spinach field, yielding eight high-resolution soil moisture maps that captured dynamic variations driven by precipitation and irrigation. Operating in the frequency range 110–120 MHz, the system measured soil moisture down to a depth of approximately 35–40 cm, with comparisons performed using Time Domain Reflectometry (TDR) sensors and mass balance analyses. Electrical Resistivity Tomography (ERT) provided complementary data into soil electrical conductivity patterns. Results revealed strong agreement between GPR-derived soil moisture estimates and conventional methods, with spatial patterns aligning closely with predictions from Boosted Regression Tree (BRT) models. These findings demonstrate the capacity of drone-borne GPR to deliver actionable, root-zone scale insights for real-time irrigation optimization and agricultural water management.

根区土壤水分的高分辨率监测对优化精准农业灌溉至关重要。这项研究展示了无人机载探地雷达（GPR）在整个生长季节绘制农田土壤水分时空动态地图的潜力。使用创新的gprSense®系统，该系统结合了频率域雷达和全波反演，我们实现了精确和自动化的数据采集和处理。据我们所知，这是第一次将无人机探地雷达与实时全波反演相结合，实现根区土壤湿度时移成像的研究。在菠菜田进行了延时绘图，生成了8张高分辨率的土壤湿度图，这些图捕捉了降水和灌溉驱动的动态变化。该系统在110-120 MHz的频率范围内工作，测量深度约为35-40 cm的土壤湿度，并使用时域反射（TDR）传感器和质量平衡分析进行比较。电阻率层析成像（ERT）为土壤电导率模式提供了补充数据。结果显示，gpr估算的土壤湿度与传统方法非常吻合，空间模式与增强回归树（boosting Regression Tree， BRT）模型的预测结果非常吻合。这些发现表明，无人机机载探地雷达能够为实时灌溉优化和农业用水管理提供可操作的根区规模的见解。

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引用次数: 0

Exploring global dark sky quality and potential dark sky park site selection: Integrating multi-source spatial data with random forest model 全球暗天质量与潜在暗天公园选址研究——基于随机森林模型的多源空间数据集成

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-05 DOI: 10.1016/j.rse.2025.115114

Zhaobo Li, Ruihan Xing, Qiyao Ling, Ziran Wei, Zuo Wang, Qing Ji, Kaifang Shi

With the continuous advancement of global urbanization and industrialization, the pollution issue (e.g., light pollution) has become increasingly severe, posing a serious threat to the global dark sky quality. Evaluating the global dark sky quality and conducting site selection for potential dark sky parks not only contribute to the protection of natural ecosystems and the safeguarding of ecological security, but also provide scientific reference for sustainable ecotourism. Thus, our study integrated remote sensing nighttime light, cloud cover, and other data to develop a global dark sky quality identification based on the random forest model, effectively achieving interannual and seasonal dynamic identification of global dark sky quality. Then, the study defined global construction potential constraints and conducted site selection research for potential conventional dark sky parks and potential seasonal dark sky parks. The results show that in 2023, areas with high dark sky quality accounted for only 20.6 % globally, mainly distributed in high-altitude or arid regions worldwide, including the Qinghai-Tibet Plateau, inland Australia, and other areas. From 2012 to 2023, global dark sky quality exhibited an interannual fluctuation pattern of “decline–rise–decline” and further showed significant seasonal differences. In addition, the identified potential conventional dark sky parks are mainly distributed in southern Africa, the southwestern United States, and other regions, while potential seasonal dark sky parks are mainly located in the Korean Peninsula, southern Qinghai-Tibet Plateau, and other areas. Our study can provide a scientific reference for global light pollution control, ecotourism development, and policymaking on nighttime environmental protection, supporting strategic progress assessment of the United Nations Sustainable Development Goals (e.g., SDG 11: Sustainable Cities and SDG 15: Life on Land).

随着全球城市化、工业化进程的不断推进，光污染等污染问题日益严重，对全球暗天质量构成严重威胁。评价全球暗空质量，开展暗空公园选址，不仅有利于自然生态系统的保护和生态安全的保障，而且为可持续生态旅游提供科学参考。因此，本研究综合遥感夜间光照、云量等数据，建立了基于随机森林模型的全球暗天空质量识别方法，有效地实现了全球暗天空质量的年际和季节动态识别。然后，定义了全球潜在的建设约束条件，并对潜在的常规暗天公园和潜在的季节性暗天公园进行了选址研究。结果表明，2023年，暗天空质量高的区域仅占20个。2012 - 2023年，全球暗天质量呈现“下降-上升-下降”的年际波动格局，并进一步呈现显著的季节差异。该研究可为全球光污染控制、生态旅游开发和夜间环境保护政策制定提供科学参考，支持联合国可持续发展目标（如可持续发展目标11：可持续城市和可持续发展目标15：陆地生命）的战略进展评估。

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引用次数: 0

A comprehensive study of Surface Water and Ocean Topography (SWOT) Pixel Cloud data for flood extent extraction 地表水和海洋地形（SWOT）像元云数据在洪水范围提取中的综合研究

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-05 DOI: 10.1016/j.rse.2025.115101

Quentin Bonassies , Christophe Fatras , Santiago Peña-Luque , Pierre Dubois , Andrea Piacentini , Ludovic Cassan , Sophie Ricci , Thanh Huy Nguyen

Current disaster and emergency management services produce flood maps within hours using satellite data. To handle large-scale events efficiently, a reliable automated method is needed to generate an initial flood extent map, which can then be refined manually.

Launched in December 2022, the Surface Water and Ocean Topography (SWOT) satellite, equipped with the Ka-band Radar Interferometer (KaRIn), provides high-resolution radar observations used here for flood detection. While not initially designed for detailed flood mapping in vegetated or urban regions, the performance of SWOT’s Pixel Cloud products was assessed during four major flood events in Greece, France, Brazil, and the USA. Each event is paired with Sentinel-1 or Sentinel-2 imagery within a 3-hour time frame, providing a valuable opportunity to compare and evaluate SWOT’s flood detection capabilities.

Three radar variables of the Pixel Cloud products are studied for extracting flood extents:

σ_{0}

, coherent power, and interferometric coherence — which is computed from the two complex interferograms. They are compared to the built-in classification and flood masks computed from Sentinel-1/2. The study demonstrates the capabilities of the SWOT satellite in detecting flooded vegetation, flooded urban areas, and even regions with high snow cover. However, limitations are observed: (1) when high soil moisture is observed, causing signal saturation, (2) SWOT can be sensitive to the incidence angle, both of which lead to less reliable flood extent estimation. These findings highlight the potential of SWOT satellite for improving global flood mapping, as well as the need for further exploration to address current limitations and enhance flood monitoring capabilities in the near future.

目前的灾害和应急管理服务利用卫星数据在数小时内绘制洪水地图。为了有效地处理大规模事件，需要一种可靠的自动化方法来生成初始洪水范围图，然后人工对其进行细化。地表水和海洋地形（SWOT）卫星于2022年12月发射，配备ka波段雷达干涉仪（KaRIn），提供用于洪水探测的高分辨率雷达观测。虽然最初并不是为植被或城市地区的详细洪水测绘而设计的，但SWOT的像素云产品在希腊、法国、巴西和美国的四次主要洪水事件中进行了评估。每个事件在3小时内与Sentinel-1或Sentinel-2图像配对，为比较和评估SWOT的洪水检测能力提供了宝贵的机会。研究了像素云产品的三个雷达变量：σ0σ0、相干功率和干涉相干度——从两个复杂干涉图中计算。将它们与内置分类和从Sentinel-1/2计算的洪水掩码进行比较。该研究证明了SWOT卫星在探测淹没植被、淹没城区甚至高积雪地区方面的能力。但也存在局限性：(1)当观测到土壤湿度较高时，会导致信号饱和；(2)SWOT对入射角敏感，导致洪水范围估计的可靠性较低。这些发现突出了SWOT卫星在改善全球洪水制图方面的潜力，以及在不久的将来进一步探索解决当前限制和增强洪水监测能力的必要性。

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引用次数: 0

The fully-automatic Sentinel-1 Global Flood Monitoring service: Scientific challenges and future directions 全自动Sentinel-1全球洪水监测服务：科学挑战和未来方向

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115108

Wolfgang Wagner , Bernhard Bauer-Marschallinger , Florian Roth , Tobias Raiger-Stachl , Christoph Reimer , Niall McCormick , Patrick Matgen , Marco Chini , Yu Li , Sandro Martinis , Marc Wieland , Franziska Kraft , Davide Festa , Muhammed Hassaan , Mark Edwin Tupas , Jie Zhao , Michaela Seewald , Michael Riffler , Luca Molini , Richard Kidd , Peter Salamon

Sentinel-1 is a unique resource for global flood monitoring, providing systematic, weather-independent Synthetic Aperture Radar (SAR) imagery with unprecedented coverage. To overcome limitations of on-demand flood mapping services that depend on human operators to collect and interpret satellite images, a fundamentally new approach was adopted by the Global Flood Monitoring (GFM) service. This service, which was launched in 2021 as part of the Copernicus Emergency Management Service (CEMS), processes all Sentinel-1 land images acquired in VV polarisation fully automatically in near-real time. This article presents the first comprehensive analysis of GFM’s scientific achievements and challenges during its initial years of operation. To map floods reliably under diverse environmental conditions, GFM combines three complementary flood-mapping algorithms with reference water datasets to differentiate flooded areas from permanent and seasonal water bodies. The service also offers a novel flood-likelihood layer and contextual information to highlight areas where flood mapping is unreliable or not feasible. These data layers were derived from a global 20 m backscatter datacube containing approximately 379 billion land surface pixels. This datacube also made it possible to generate the first global Sentinel-1 flood archive (2015 to present). Our performance analysis shows that GFM typically delivers flood maps within five hours of image acquisition. However, a significant percentage of floods may go undetected due to coverage gaps. Initial evaluation results show that good accuracies are achieved for larger-scale floods and regions in the temperate and tropical zones, while accuracies are lower for smaller-scale floods and arid environments. The GFM service will continue to improve service quality by enhancing flood detection capabilities using improved algorithms and additional data, such as the VH channel from Sentinel-1 or L-band data from the upcoming ROSE-L mission.

Sentinel-1是全球洪水监测的独特资源，提供系统的、与天气无关的合成孔径雷达（SAR）图像，覆盖范围前所未有。全球洪水监测（GFM）服务采用了一种全新的方法，以克服依赖人工操作员收集和解释卫星图像的按需洪水测绘服务的局限性。该服务于2021年作为哥白尼应急管理服务（CEMS）的一部分启动，以近乎实时的方式自动处理所有在VV偏振下获得的Sentinel-1陆地图像。本文首次全面分析了GFM在最初几年的科学成就和挑战。为了在不同环境条件下可靠地绘制洪水地图，GFM将三种互补的洪水制图算法与参考水数据集相结合，以区分洪水区域与永久性和季节性水体。该服务还提供了一个新颖的洪水可能性层和上下文信息，以突出显示洪水地图不可靠或不可行的地区。这些数据层来自一个包含大约3790亿个地表像元的全球20米背向散射数据集。该数据库还可以生成第一个全球Sentinel-1洪水档案（2015年至今）。我们的性能分析表明，GFM通常在图像采集后5小时内提供洪水地图。然而，由于覆盖范围的差距，很大比例的洪水可能未被发现。初步评价结果表明，在温带和热带地区的大尺度洪涝环境和区域具有较好的精度，而在小尺度洪涝环境和干旱环境中精度较低。GFM服务将继续提高服务质量，通过使用改进的算法和额外的数据来增强洪水探测能力，例如来自Sentinel-1的VH信道或来自即将到来的ROSE-L任务的l波段数据。

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引用次数: 0

Boundary conditions for studying branch-scale tree growth strategies using tree quantitative structure model time series 树形定量结构模型时间序列研究树枝尺度树木生长策略的边界条件

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115105

Hanna Elisabet Sorokina , Mariana Campos , Pasi Raumonen , Anna Shcherbacheva , Rami Echriti , Juha Hyyppä , Eetu Puttonen , Yunsheng Wang

Advances in Light Detection and Ranging (LiDAR) technology, along with point cloud modeling techniques like Quantitative Structure Models (QSM), have improved the accuracy of non-destructive above-ground tree biomass estimation. However, branch-level tree growth analysis using QSM time series remains underexplored due to challenges in data quality and methodology. This study investigates the boundary conditions in terms of data and species to facilitate robust QSM generation, which could enable branch-level growth analysis using multi-temporal LiDAR data and QSM. A multi-scan terrestrial laser scanning dataset and a dataset from a tower-based system were used to assess the impact of data acquisition setup and data quality on QSM reconstruction for birch (Betula pendula), pines (Pinus sylvestris), and spruces (Picea abies). The results show that reliable QSMs for detecting branch-level growth require a minimum spatial resolution of approximately 500 pts./m³ with a uniform point density, a maximum uniform 3D point distance of 2 cm, and gaps smaller than around 20 cm. Although smaller spherical noise clusters can be removed using denoising techniques, larger and denser noise clusters (e.g., > 9500 points within 1 m radius) are more likely to be misidentified as additional branches. Foliage removal methods risk modeling accuracy by inadvertently removing woody points. Regarding species, birches were more accurately modeled than pines and spruces. While QSMs are reproducible for single time points, comparing branches over time is challenging due to inconsistencies in modeled branching order and scanner positioning. Nonetheless, tree-level QSM metrics remain statistically consistent, revealing diverse growth strategies within and across species.

光探测和测距（LiDAR）技术的进步，以及定量结构模型（QSM）等点云建模技术，提高了非破坏性地上树木生物量估算的准确性。然而，由于数据质量和方法方面的挑战，使用QSM时间序列的分支级树生长分析仍未得到充分探索。本研究探讨了数据和物种的边界条件，以促进稳健的QSM生成，从而可以使用多时相激光雷达数据和QSM进行枝级生长分析。利用多扫描地面激光扫描数据集和基于塔式系统的数据集，评估了数据采集设置和数据质量对桦木（Betula pendula）、松树（Pinus sylvestris）和云杉（Picea abies） QSM重建的影响。结果表明，用于检测分支级生长的可靠QSMs需要大约500 pts的最小空间分辨率。/m3，点密度均匀，最大均匀三维点距为2cm，间隙小于20cm左右。虽然使用去噪技术可以去除较小的球形噪声簇，但较大和较密集的噪声簇（例如，1米半径内的>； 9500个点）更有可能被误认为是额外的分支。树叶移除方法会因无意中移除木质点而影响建模的准确性。在物种方面，桦树比松树和云杉更准确地被建模。虽然qsm在单个时间点上是可重复的，但由于建模的分支顺序和扫描仪位置不一致，比较分支随着时间的变化是具有挑战性的。尽管如此，树级QSM指标在统计上保持一致，揭示了物种内部和物种间不同的生长策略。

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引用次数: 0

Modeling, prediction, and retrieval of surface soil moisture from InSAR closure phase InSAR封闭期地表土壤水分的建模、预测和反演

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115104

Yujie Zheng , Heresh Fattahi

We present a discretized, multi-layer soil moisture model that links soil moisture variability to single-look SAR measurements. Our model reveals distinct closure phase signatures arising from variation of soil moisture, radar frequencies, and soil textures. Specifically, our model predicts that positive asymmetric soil moisture anomalies produce positive closure phase step-changes, and negative asymmetric anomalies yield negative closure phase step-changes, consistent with observed data. Additionally, our analysis reveals that low-frequency radar (e.g., L-band) exhibits heightened sensitivity to the vertical distribution of soil moisture. We identify an approximate transfer function between soil moisture anomalies and closure phase responses and introduce a scalable algorithm for retrieving the InSAR Soil Moisture Index, a relative soil moisture product. We demonstrate the retrieval algorithm in two diverse environments: the Mojave Desert and the Central Valley in California. Good agreements between the derived InSAR Soil Moisture Index, in situ soil moisture measurements, and SMAP/Sentinel-1 soil moisture measurements highlight the potential for large-scale soil moisture monitoring using InSAR closure phase.

我们提出了一个离散的多层土壤湿度模型，将土壤湿度变化与单一SAR测量联系起来。我们的模型揭示了由土壤湿度、雷达频率和土壤质地变化引起的明显的闭合相位特征。具体而言，我们的模型预测，正非对称土壤湿度异常产生正闭合相位阶跃变化，而负非对称异常产生负闭合相位阶跃变化，与观测数据一致。此外，我们的分析表明，低频雷达（如l波段）对土壤水分的垂直分布表现出更高的敏感性。我们确定了土壤湿度异常和闭合阶段响应之间的近似传递函数，并引入了一种可扩展的算法来检索InSAR土壤湿度指数（一个相对土壤湿度产品）。我们在两个不同的环境中演示了检索算法：莫哈韦沙漠和加利福尼亚州的中央山谷。衍生的InSAR土壤湿度指数、原位土壤湿度测量和SMAP/Sentinel-1土壤湿度测量之间的良好一致性突出了利用InSAR封闭阶段进行大规模土壤湿度监测的潜力。

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引用次数: 0

Towards an easy-to-use algorithm to estimate longwave cloud radiative forcing: algorithm development and preliminary evaluation 迈向一种易于使用的长波云辐射强迫估算算法：算法发展和初步评估

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115107

Chuanye Shi , Tianxing Wang , Zheng Li , Dahui Li , Husi Letu

Estimation of surface longwave cloud radiative forcing (LWCRF) is crucial for understanding cloud-climate interactions but remains challenging due to the complexity of radiative transfer simulations and the difficulty in accuracy evaluation. Using only five readily available parameters: digital elevation model, column water vapor, cloud top temperature, cloud optical thickness, and cloud fraction ratio, this study develops a lightweight algorithm to estimate LWCRF, defined as the contribution of clouds to surface downward longwave radiation. The algorithm achieves a theoretical RMSE (root-mean-square error) of 5.47 W/m² under rigorous radiative transfer-based testing. When applied to the CERES CRS (Clouds and the Earth's Radiant Energy System, Clouds and Radiative Swath) data which is based on pixel-level Langley Fu-Liou radiative transfer simulations, it exhibits an RMSE of 12.03 W/m² across 812 million global samples, demonstrating strong consistency in spatial patterns. This work provides a practical and efficient alternative when numerous inputs required by radiative transfer model are unavailable. This advancement allows for easy assessment of the impact of clouds on the global radiation balance, thereby advancing the understanding of how much clouds warm or cool the Earth.

地表长波云辐射强迫（LWCRF）的估算对于理解云-气候相互作用至关重要，但由于辐射传输模拟的复杂性和准确性评估的困难，仍然具有挑战性。本研究仅利用数字高程模型、柱状水汽、云顶温度、云光学厚度和云分数比五个可用参数，开发了一种轻量级的LWCRF估算算法，LWCRF定义为云对地表向下长波辐射的贡献。在基于辐射转移的严格测试下，该算法的理论均方根误差（RMSE）为5.47 W/m2。当应用基于像素级Langley Fu-Liou辐射传输模拟的CERES CRS（云与地球辐射能系统，云与辐射带）数据时，它在8.12亿个全球样本中显示出12.03 W/m2的RMSE，显示出很强的空间格局一致性。当辐射传输模型所需的大量输入无法获得时，本工作提供了一种实用而有效的替代方法。这一进展使评估云对全球辐射平衡的影响变得容易，从而提高了对云对地球增温或降温程度的理解。

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引用次数: 0

Accuracy of SeaHawk-HawkEye Ocean color CubeSat remote sensing reflectance products in globally distributed aquatic sites 海鹰-鹰眼海洋彩色立方体卫星遥感反射率产品在全球分布的水生地点的精度

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115111

Srinivas Kolluru , Sara Rivero-Calle , Philip J. Bresnahan , Susanne Kratzer , Timothy S. Moore , Thomas Schroeder , Sean Bailey , Alan Holmes , John M. Morrison , Gene F. Feldman , Liang Hong , Hiroto Higa , Kohei Arai

HawkEye was an ocean color instrument onboard SeaHawk, the first dedicated Ocean Color CubeSat, launched in 2018 to provide targeted high spatial resolution (∼130 m) images of open ocean, coastal, inland, and estuarine waters with eight spectral bands in the visible NIR region. This study presents the first comprehensive assessment of HawkEye standard Level-2 remote sensing reflectance (R_rs) products. A total of 51 cloud-free HawkEye scenes over 15 globally distributed inland and coastal water sites with contrasting optical conditions were found to be suitable for match-up analysis, spanning two years. Compared to R_rs from ocean color component of the Aerosol Robotic Network (AERONET- OC) and in situ hyperspectral measurements from coastal Georgia waters (USA), HawkEye R_rs accuracy varied between 31 and 61 % for Median Symmetric Accuracy (MdSA) depending on the wavelength. The R_rs at 510 nm was the most accurate (31 % MdSA), and the accuracy decreased towards the blue bands, with 412 nm being the least accurate (61.7 % MdSA). HawkEye R_rs is more accurate in low Chlorophyll-a waters compared to high Chlorophyll-a waters. HawkEye products accuracy improved with a site-specific atmospheric correction (e.g. the Management Unit of the North Sea Mathematical Models (MUMM) correction improved results in two turbid coastal waters tested). HawkEye R_rs product accuracy was comparable with ocean color sensors, OLCI, AQUA and VIIRS. These results thus indicate that HawkEye imagery is suitable for aquatic remote sensing applications. Further, the results serve as a reference and offer potential areas of improvement for future ocean color CubeSat missions.

鹰眼（HawkEye）是海鹰卫星（SeaHawk）上的一种海洋色彩仪器，海鹰卫星是第一颗专用的海洋色彩立方体卫星，于2018年发射，用于在可见光近红外区域提供有针对性的高空间分辨率（~ 130米）图像，包括公海、沿海、内陆和河口水域的8个光谱波段。本研究首次对鹰眼标准2级遥感反射率（Rrs）产品进行了综合评估。在全球分布的15个内陆和沿海水域地点的51个无云鹰眼场景中，对比了光学条件，发现适合进行匹配分析，历时两年。与气溶胶机器人网络（AERONET- OC）海洋颜色组件的Rrs和美国乔治亚沿海水域的原位高光谱测量相比，HawkEye Rrs的中位数对称精度（MdSA）的准确度在31%到61%之间，具体取决于波长。510 nm处的Rrs最准确（31% MdSA），准确度向蓝色波段下降，412 nm处最不准确（61.7% MdSA）。与叶绿素a含量高的水体相比，在叶绿素a含量低的水体中，鹰眼Rrs更为准确。HawkEye产品的精度随着特定地点的大气校正而提高（例如北海数学模型管理单元（MUMM）校正改善了两个浑浊沿海水域测试的结果）。HawkEye Rrs产品精度与海洋颜色传感器、OLCI、AQUA和VIIRS相当。这些结果表明，鹰眼图像适用于水生遥感应用。此外，结果可作为参考，并为未来的海洋彩色立方体卫星任务提供潜在的改进领域。

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引用次数: 0

Retrieving fine-scale leaf and soil spectral properties from canopy reflectance with differentiable 3D radiative transfer 基于可微三维辐射传输的冠层反射率反演精细尺度叶片和土壤光谱特性

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115115

Jianbo Qi , Jifan Wei , Mengqi Xia , Donghui Xie

Accurate estimation of leaf spectral properties or biochemical parameters from canopy-level reflectance has long posed a significant challenge in quantitative remote sensing due to the inherent variability and complexity of canopy structures. This study presents 3D-Diff, an innovative differentiable three-dimensional (3D) radiative transfer approach for retrieving leaf and soil spectral properties using remote sensing imagery with known 3D canopy structures. Implemented within the LESS (LargE-Scale remote sensing data and image Simulation) model, 3D-Diff employs computer graphics-derived differentiable modeling to compute reflectance derivatives relative to scene parameters during forward simulation. A gradient-based optimization algorithm then minimizes discrepancies between simulated and observed images to estimate scene parameters. Validations using both virtual experiments and real measurements demonstrated robust performance across varying parameter quantities. Notably, the results show that the reflectance/transmittance of all scene parameters were accurately estimated with maximum RMSE of 0.015 for virtual experiments, including non-directly-observed elements, and maximum RMSE of 0.096 for real measurements. Spatial resolution significantly affected accuracy, with relative RMSE values of 0.07 % (0.05 m) and 1.4 % (2.5 m) for a dense virtual forest scene. Although computationally slower than analytic models, this work establishes differentiable radiative transfer as a promising framework for fine-scale vegetation mapping, enabling simultaneous multi-parameter retrieval while maintaining physical interpretability.

由于冠层结构固有的变异性和复杂性，从冠层反射率中准确估计叶片的光谱特性或生化参数一直是定量遥感的一个重大挑战。本研究提出了3D- diff，一种创新的可微三维（3D）辐射传输方法，用于利用已知三维冠层结构的遥感图像检索叶片和土壤的光谱特性。3D-Diff在LESS（大规模遥感数据和图像仿真）模型中实现，使用计算机图形导出的可微建模来计算正演仿真期间相对于场景参数的反射率导数。然后，基于梯度的优化算法最小化模拟图像和观测图像之间的差异，以估计场景参数。使用虚拟实验和实际测量的验证证明了不同参数量的稳健性能。结果表明，包括非直接观测元素在内的虚拟实验中，所有场景参数的反射率/透射率均能准确估计，RMSE最大值为0.015，真实测量的RMSE最大值为0.096。空间分辨率显著影响精度，对于茂密的虚拟森林场景，相对RMSE值分别为0.07% （0.05 m）和1.4% （2.5 m）。虽然计算速度比解析模型慢，但这项工作建立了可微辐射传输作为精细尺度植被制图的一个有前途的框架，在保持物理可解释性的同时实现多参数同步检索。

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引用次数: 0

Remote sensing applications for monitoring restoration outcomes in boreal forestry-drained peatlands - Reviewed applications and future potential 监测北方森林排水泥炭地恢复结果的遥感应用-综述应用和未来潜力

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-04 DOI: 10.1016/j.rse.2025.115093

Lauri Ikkala , Ismail , Franziska Wolff , Hannu Marttila , Anna-Kaisa Ronkanen , Pavel Alekseychik , Parvez Rana , Marko Kohv , Teemu Tahvanainen , Anne Tolvanen , Ali Torabi Haghighi , Timo Kumpula , Christopher Osborne , Jari Ilmonen , Tuomas Haapalehto , Bjørn Kløve , Aleksi Räsänen

Peatland restoration can halt biodiversity loss and organic soil degradation and mitigate climate change. Monitoring of restoration impacts requires novel approaches that can be scaled to large site networks. On a smaller scale, the restoration practitioners would likewise benefit from spatially and temporally comprehensive and objective monitoring data. This narrative review compiles potential remote sensing methods for practical monitoring of the ecohydrological processes in restoration of forestry-drained boreal peatlands to support and complement conventional ground monitoring approaches that are restricted by spatial coverage. Remote sensing provides tools for tracking the changes in soil surface moisture, water flow routes, vegetation cover and structure, topography, peat depth and greenhouse gas emissions. We emphasize that the suitable indicators of restoration success, platforms and sensors should be tailored to specific restoration cases with their own initial site conditions and restoration targets. The choice of spatial and temporal resolutions of data is defined by the scale and change rate of the restoration indicators. Data acquisition campaigns and selection of existing datasets require meticulous planning due to seasonal changes in hydrology and vegetation. We also compiled practical experiences on selecting remote sensing tools and ensuring satisfactory data quality to facilitate the implementation of remote-sensing-based monitoring. Finally, we provide recommendations on how the rapid development of remote sensing technology, encompassing new uncrewed applications and novel sensors on conventional platforms, can offer a range of monitoring tools to cater to the growing needs for spatially comprehensive data amounts assessing peatland restoration success in boreal conditions.

泥炭地恢复可以阻止生物多样性丧失和有机土壤退化，减缓气候变化。监测修复影响需要新的方法，可以扩展到大型站点网络。在较小的尺度上，恢复从业者同样会受益于空间和时间上全面和客观的监测数据。本述评汇编了在森林排水的北方泥炭地恢复过程中对生态水文过程进行实际监测的潜在遥感方法，以支持和补充受空间覆盖范围限制的传统地面监测方法。遥感为跟踪土壤表面湿度、水流路线、植被覆盖和结构、地形、泥炭深度和温室气体排放的变化提供了工具。我们强调，应根据具体的恢复案例，根据各自的初始场地条件和恢复目标，量身定制适合的恢复成功指标、平台和传感器。数据时空分辨率的选择由恢复指标的尺度和变化率决定。由于水文和植被的季节性变化，数据采集活动和现有数据集的选择需要细致的规划。我们还在选择遥感工具和确保令人满意的数据质量方面汇编了实践经验，以促进遥感监测的实施。最后，我们就快速发展的遥感技术（包括新的无人驾驶应用和传统平台上的新型传感器）如何提供一系列监测工具提供建议，以满足对评估北方条件下泥炭地恢复成功的空间综合数据量日益增长的需求。

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引用次数: 0