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Evaluating rainfall and graupel retrieval performance of the NASA TROPICS pathfinder through the NOAA MiRS system 通过NOAA MiRS系统评估NASA热带探路者的降雨和霰检索性能
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-12 DOI: 10.1016/j.rse.2024.114570
John Xun Yang , Yong-Keun Lee , Shuyan Liu , Christopher Grassotti , Quanhua Liu (Mark) , William Blackwell , Robert Vincent Leslie , Tom Greenwald , Ralf Bennartz , Scott Braun
The NASA TROPICS mission encompasses a constellation of CubeSats equipped with microwave radiometers, dedicated to investigating tropical meteorology and storm systems. In a departure from traditional microwave sounders, the TROPICS Microwave Sounder (TMS) employs new frequencies at F-band near 118 GHz and features an additional G-band channel at 205 GHz. We have expanded the capabilities of the Microwave Integrated Retrieval System (MiRS), a state-of-the-art one-dimensional variational (1DVAR) algorithm, for the retrieval of geophysical variables with the TROPICS Pathfinder early-phase data. Here we focus on assessing the retrieved precipitation in terms of rainfall and graupel. TROPICS captures well the spatial distribution and temporal evolution of Hurricane Ida and Super Typhoon Mindulle. TROPICS depicted the eyewall replacement cycle of Mindulle as it weakened and reintensified. The global precipitation distribution and dynamics are well represented by TROPICS. We compare TROPICS with other precipitation datasets, including Global Precipitation Mission (GPM) GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) products. For example, when compared with GMI, MiRS TROPICS instantaneous precipitation yields a correlation coefficient of 0.5 and an RMSE of 2.0 mm/h. For graupel, MiRS TROPICS retrievals show a correlation of 0.52 and an RMSE of 0.53 kg/m2. The retrieval performance is comparable to other sensors such as the Advanced Technology Microwave Sounder (ATMS), while the higher number of channels of ATMS, including its low-frequency channels serve to better constrain retrievals. TMS observes at higher spectral frequencies than the coincident ATMS channels, showing higher sensitivity to rainfall and graupel. The TMS high-frequency channels and lower orbit allow for greater resolution of precipitation features, while lower-frequency ATMS channels excel at resolving hurricane warm-core structures. The results underscore the value of the TROPICS mission for precipitation measurement and demonstrate the successful integration of TROPICS processing capability within the MiRS retrieval algorithm framework.
美国国家航空航天局的 TROPICS 任务包括一个配备微波辐射计的立方体卫星星座,专门用于调查热带气象学和风暴系统。与传统的微波探测仪不同,TROPICS微波探测仪(TMS)采用了F波段接近118千兆赫的新频率,并增加了一个205千兆赫的G波段通道。我们扩展了微波综合检索系统(MiRS)的功能,这是一种最先进的一维变分(1DVAR)算法,用于利用 TROPICS 探路者早期阶段数据检索地球物理变量。在此,我们重点从降雨和砂砾岩的角度对检索到的降水量进行评估。TROPICS 很好地捕捉了飓风 "艾达 "和超强台风 "明都尔 "的空间分布和时间演变。TROPICS 描述了 "明都 "在减弱和重新加强过程中的眼球替换周期。TROPICS 很好地表现了全球降水的分布和动态。我们将 TROPICS 与其他降水数据集,包括全球降水任务(GPM)微波成像仪(GMI)和双频降水雷达(DPR)产品进行了比较。例如,与 GMI 相比,MiRS TROPICS 瞬时降水量的相关系数为 0.5,均方根误差为 2.0 毫米/小时。对于谷雨,MiRS TROPICS 的检索结果显示相关系数为 0.52,有效误差为 0.53 kg/m2。检索性能与先进技术微波探测仪(ATMS)等其他传感器相当,而 ATMS 的信道数量较多,包括其低频信道,有助于更好地限制检索。TMS 观测的频谱频率高于 ATMS 的重合通道,对降雨和岩浆的灵敏度更高。TMS 的高频信道和较低的轨道使得降水特征的分辨率更高,而 ATMS 的低频信道则在解析飓风暖核结构方面表现出色。这些结果强调了 TROPICS 任务在降水测量方面的价值,并证明 TROPICS 处理能力与 MiRS 检索算法框架的成功整合。
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引用次数: 0
Retrieval of 1 km surface soil moisture from Sentinel-1 over bare soil and grassland on the Qinghai-Tibetan Plateau 青藏高原裸地和草地1公里表层土壤水分的Sentinel-1反演
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-12 DOI: 10.1016/j.rse.2024.114563
Zanpin Xing , Lin Zhao , Lei Fan , Gabrielle De Lannoy , Xiaojing Bai , Xiangzhuo Liu , Jian Peng , Frédéric Frappart , Kun Yang , Xin Li , Zhilan Zhou , Xiaojun Li , Jiangyuan Zeng , Defu Zou , Erji Du , Chong Wang , Lingxiao Wang , Zhibin Li , Jean-Pierre Wigneron
Most existing soil moisture (SM) products from earth observations and land surface models over the Qinghai-Tibetan Plateau (QTP) have coarse resolutions or are mostly generated with high spatial resolutions based on downscaling methods. The former could hinder the applications in hydrological and ecological analyses at the regional scale and the performance of the latter could be limited by the intricate relationship between SM and downscaling factors in regions with complex topography. To address this issue, this paper aims to retrieve a 1 km SM product from 2017 to 2021 using Sentinel-1 Synthetic Aperture Radar (SAR) observations based on a semi-empirical method specific to the QTP region (SMS-1) as different from the previous downscaled SM products. The main interest in our retrievals is that the semi-empirical modeling approach allows exploring the relationships between microwave backscatters and the soil and vegetation parameters spatially based on well-defined mathematics. The SMS-1 retrievals were evaluated against the observations from five in-situ networks over the QTP and against six other existing downscaled 1 km SM products. The temporal evaluation against in-situ measurements showed that SMS-1 retrievals performed better than most 1 km SM products obtained from Machine Learning methods (median R = 0.57, ubRMSD = 0.064 m3/m3, RMSD = −0.107 m3/m3 and bias = −0.042 m3/m3) except for SMSg. Furthermore, the SMS-1 retrievals presented reasonable spatial patterns that are consistent with the spatial distribution of the grassland-type map. Our Sentinel-1 SAR-based method can therefore potentially serve as a foundation for the advance of active microwave remote sensing SM algorithm to retrieve spatially high-resolution SM.
现有的青藏高原地面观测和地表模式的土壤湿度产品大多具有粗分辨率或基于降尺度方法生成的高空间分辨率。前者会阻碍区域尺度水文和生态分析的应用,而后者则会受到地形复杂地区SM与降尺度因子之间复杂关系的限制。为了解决这一问题,本文旨在利用Sentinel-1合成孔径雷达(SAR)观测数据检索2017 - 2021年的1 km SM产品,该产品基于针对QTP地区的半经验方法(SMS-1),与之前缩小的SM产品不同。我们检索的主要兴趣在于,半经验建模方法允许探索微波后向散射与土壤和植被参数之间的空间关系,基于定义良好的数学。将SMS-1反演结果与QTP上5个原位网络的观测结果和6个其他缩小的1 km SM产品进行对比。对原位测量的时间评价表明,除SMSg外,SMS-1检索结果优于机器学习方法获得的大多数1公里SM产品(中位数R = 0.57, ubRMSD = 0.064 m3/m3, RMSD = - 0.107 m3/m3,偏差= - 0.042 m3/m3)。此外,SMS-1检索结果呈现出合理的空间格局,与草地类型地图的空间分布相一致。因此,基于Sentinel-1 sar的方法有可能为主动微波遥感SM算法的发展奠定基础,从而检索空间高分辨率SM。
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引用次数: 0
Entity-based image analysis: A new strategy to map rural settlements from Landsat images 基于实体的图像分析:利用陆地卫星图像绘制农村居民点的新策略
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-12 DOI: 10.1016/j.rse.2024.114549
Yan Wang , Xiaolin Zhu , Tao Wei , Fei Xu , Trecia Kay-Ann Williams , Helin Zhang
Accurate and timely mapping of rural settlements using medium-resolution satellite imagery, such as Landsat data, is crucial for evaluating rural infrastructure, estimating ecological service values, assessing the quality of life for rural populations, and promoting sustainable rural development. Current mapping techniques, including pixel-based and object-based classifications, primarily focus on identifying artificial surfaces, often failing to capture the complete spatial footprint of rural settlements. These settlements consist of diverse land cover elements, such as houses, roads, agricultural buildings, ponds, parks, and woodlands, which together form entities with distinct local characteristics. To address this limitation, we introduce a novel classification strategy: Entity-Based Image Analysis (EBIA). Inspired by cognitive principles of human visual perception, EBIA groups related land cover elements and differentiates settlements from their background. The key innovation of EBIA lies in its ability to incorporate semantic features within rural settlements, transforming pixel-level land cover classification results (Phase 1) into entity-level settlement mapping results (Phase 2). Our results demonstrate that EBIA effectively maps the comprehensive footprint of rural settlement entities, achieving F1 scores ranging from 0.79 to 0.88 across five globally selected experimental areas. Furthermore, EBIA can be utilized to monitor changes in rural settlements using long-term Landsat imagery. As a new classification strategy, EBIA holds potential for mapping other geographic entities.
利用中分辨率卫星图像(如Landsat数据)准确、及时地绘制农村居民点地图,对于评估农村基础设施、估算生态服务价值、评估农村人口生活质量和促进农村可持续发展至关重要。目前的制图技术,包括基于像素和基于物体的分类,主要侧重于识别人工表面,往往无法捕捉农村住区的完整空间足迹。这些聚落由不同的土地覆盖元素组成,如房屋、道路、农业建筑、池塘、公园和林地,这些元素共同构成了具有鲜明地方特色的实体。为了解决这一限制,我们引入了一种新的分类策略:基于实体的图像分析(EBIA)。EBIA受人类视觉感知认知原理的启发,将相关的土地覆盖元素分组,并将聚落与其背景区分开来。EBIA的关键创新在于它能够将语义特征融入乡村聚落,将像素级土地覆盖分类结果(第一阶段)转化为实体级聚落制图结果(第二阶段)。研究结果表明,EBIA有效地绘制了乡村聚落实体的综合足迹,在全球选择的五个试验区中获得了0.79至0.88的F1分数。此外,EBIA可用于利用长期陆地卫星图像监测农村住区的变化。作为一种新的分类策略,EBIA具有映射其他地理实体的潜力。
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引用次数: 0
Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction 多模态遥感数据自适应融合优化子田作物产量预测
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-12 DOI: 10.1016/j.rse.2024.114547
Francisco Mena , Deepak Pathak , Hiba Najjar , Cristhian Sanchez , Patrick Helber , Benjamin Bischke , Peter Habelitz , Miro Miranda , Jayanth Siddamsetty , Marlon Nuske , Marcela Charfuelan , Diego Arenas , Michaela Vollmer , Andreas Dengel
Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, industry stakeholders, and policymakers in optimizing agricultural practices. However, this task is complex and depends on multiple factors, such as environmental conditions, soil properties, and management practices. Leveraging Remote Sensing (RS) technologies, multi-modal data from diverse global data sources can be collected to enhance predictive model accuracy. However, combining heterogeneous RS data poses a fusion challenge, like identifying the specific contribution of each modality in the predictive task. In this paper, we present a novel multi-modal learning approach to predict crop yield for different crops (soybean, wheat, rapeseed) and regions (Argentina, Uruguay, and Germany). Our multi-modal input data includes multi-spectral optical images from Sentinel-2 satellites and weather data as dynamic features during the crop growing season, complemented by static features like soil properties and topographic information. To effectively fuse the multi-modal data, we introduce a Multi-modal Gated Fusion (MMGF) model, comprising dedicated modality-encoders and a Gated Unit (GU) module. The modality-encoders handle the heterogeneity of data sources with varying temporal resolutions by learning a modality-specific representation. These representations are adaptively fused via a weighted sum. The fusion weights are computed for each sample by the GU using a concatenation of the multi-modal representations. The MMGF model is trained at sub-field level with 10 m resolution pixels. Our evaluations show that the MMGF outperforms conventional models on the same task, achieving the best results by incorporating all the data sources, unlike the usual fusion results in the literature. For Argentina, the MMGF model achieves an R2 value of 0.68 at sub-field yield prediction, while at the field level evaluation (comparing field averages), it reaches around 0.80 across different countries. The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task. This novel method has proven its effectiveness in enhancing the accuracy of the challenging sub-field crop yield prediction. Our investigation indicates that the gated fusion approach promises a significant advancement in the field of agriculture and precision farming.
准确的作物产量预测对农业决策至关重要,可帮助农民、行业利益相关者和政策制定者优化农业实践。然而,这项任务十分复杂,取决于环境条件、土壤特性和管理方法等多种因素。利用遥感(RS)技术,可以从不同的全球数据源收集多模式数据,从而提高预测模型的准确性。然而,结合异构 RS 数据会带来融合方面的挑战,如确定每种模式在预测任务中的具体贡献。在本文中,我们提出了一种新颖的多模态学习方法,用于预测不同作物(大豆、小麦、油菜籽)和地区(阿根廷、乌拉圭和德国)的作物产量。我们的多模态输入数据包括来自 Sentinel-2 卫星的多光谱光学图像和作物生长季节的气象数据作为动态特征,并辅以土壤特性和地形信息等静态特征。为有效融合多模态数据,我们引入了多模态门控融合(MMGF)模型,该模型由专用模态编码器和门控单元(GU)模块组成。模态编码器通过学习特定模态的表示来处理具有不同时间分辨率的数据源的异质性。这些表征通过加权和进行自适应融合。融合权重由 GU 使用多模态表征的连接为每个样本计算。MMGF 模型是在 10 米分辨率像素的子场级别进行训练的。我们的评估结果表明,在同一任务中,MMGF 的表现优于传统模型,它通过整合所有数据源取得了最佳结果,这与文献中通常的融合结果不同。在阿根廷,MMGF 模型在分田产量预测方面的 R2R2 值达到 0.68,而在田间水平评估(比较田间平均值)方面,不同国家的 R2R2 值达到 0.80 左右。基于国家和作物类型,GU 模块学习了不同的权重,与每个数据源对预测任务的变量重要性相一致。事实证明,这种新方法能有效提高具有挑战性的分田作物产量预测的准确性。我们的调查表明,门控融合方法有望在农业和精准农业领域取得重大进展。
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引用次数: 0
Canopy height estimation from PlanetScope time series with spatio-temporal deep learning 基于时空深度学习的PlanetScope时间序列冠层高度估计
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-12 DOI: 10.1016/j.rse.2024.114518
Dan J. Dixon, Yunzhe Zhu, Yufang Jin
<div><div>Canopy height mapping is critical for assessing forest structure, forest resilience, carbon stocks, habitat, and biodiversity, all of which are threatened by changing climate and weather extremes. While current tools utilizing lidar (e.g., GEDI) and multispectral imagery (e.g., Landsat, Sentinel-2, airborne imagery) produce canopy height products, significant challenges remain, particularly in capturing fine-scale spatial details across large areas with high frequency. PlanetScope CubeSat imagery, with its 3 m spatial resolution and near-daily frequency, offers a unique opportunity to estimate woody plant structure by capturing fine-scale texture and temporal patterns that shift throughout the year. In this study, we adapted a 3D Spatio-Temporal Convolutional Neural Network (ST-CNN) to estimate canopy height at 3 m resolution, utilizing sequential PlanetScope time series over five months, summer Sentinel-1 radar imagery, and solar illumination layers as inputs. We generated a large and diverse reference database covering 2,296 sample scenes (each scene = 768 × 768 m, totaling <span><math><mo>∼</mo></math></span>135,000 ha) using a semi-automatic labeling process that leverages 23 aerial lidar surveys conducted in California between 2016 and 2021. Trained on a random selection of 2,046 scenes, the accuracy assessment on the remaining 250 scenes demonstrates strong performance across various ecoregions, capturing 80.8% of the observed variance in live canopy height with a mean absolute error (MAE) of 3.6 m and a bias of -0.53 m compared with aerial lidar. Analysis of all 681 GEDI footprints over the same testing scenes estimates the MAE of 6.5 m, bias of -1.82 m, and R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.58 for the GEDI L2A Vector Canopy Top Height RH98 product. The ST-CNN model accurately identifies heterogeneous canopy structures, and shows sensitivity to canopies reaching 50 to 60 m in height. We found a major contribution from the PlanetScope time series, compared to a single PlanetScope image, and marginal benefits of including Sentinel-1 and terrain-based solar irradiance layers to improve performance on dense canopies or diverse topography. Example applications demonstrate the ability to generalize to different years, maintaining consistent predictions between years and capturing changes in canopy height over a seven year period (2017–2023) within 400 plots representing regrowth, minimal change, selective logging, and clear cut areas. We also demonstrate improved canopy height estimation compared to existing products from Landsat (MAE = 8.41 m) and Sentinel-2 (MAE = 7.19 m). A visualization tool displays our data alongside existing products for the Sierra Nevada in 2022. The Planet ST-CNN model, using a 15-day PlanetScope satellite time series, offers a scalable approach for annual canopy height estimation in California, achieving a high level of detail, often down to the resolution of in
冠层高度测绘对于评估森林结构、森林恢复力、碳储量、栖息地和生物多样性至关重要,所有这些都受到气候变化和极端天气的威胁。虽然目前利用激光雷达(例如GEDI)和多光谱图像(例如Landsat、Sentinel-2、航空图像)的工具可以产生冠层高度产品,但仍然存在重大挑战,特别是在以高频率捕获大面积的精细尺度空间细节方面。PlanetScope CubeSat图像具有3米的空间分辨率和接近每日的频率,通过捕捉精细尺度的纹理和全年变化的时间模式,提供了一个独特的机会来估计木本植物的结构。在这项研究中,我们采用了3D时空卷积神经网络(ST-CNN)来估计3米分辨率的冠层高度,利用连续PlanetScope时间序列超过5个月,夏季Sentinel-1雷达图像和太阳照射层作为输入。我们利用2016年至2021年间在加州进行的23次空中激光雷达调查,使用半自动标记过程生成了一个涵盖2296个样本场景(每个场景= 768 × 768 m,总计约135,000公顷)的大型多样化参考数据库。在随机选择的2046个场景上进行训练,剩余的250个场景的准确性评估在不同的生态区域表现出色,与空中激光雷达相比,捕获了80.8%的观测到的活冠层高度方差,平均绝对误差(MAE)为3.6 m,偏差为-0.53 m。对相同测试场景下所有681个GEDI足迹的分析估计,GEDI L2A矢量冠层顶部高度RH98产品的MAE为6.5 m,偏差为-1.82 m, R22为0.58。ST-CNN模型能准确识别非均匀冠层结构,并对50 ~ 60 m高度的冠层表现出敏感性。与单一的PlanetScope图像相比,我们发现PlanetScope时间序列的主要贡献,以及包括Sentinel-1和基于地形的太阳辐照层的边际效益,以提高在密集的冠层或不同的地形上的性能。示例应用展示了将其推广到不同年份的能力,在不同年份之间保持一致的预测,并在代表再生、最小变化、选择性采伐和采伐地区的400个样地中捕获7年(2017-2023年)冠层高度的变化。与Landsat (MAE = 8.41 m)和Sentinel-2 (MAE = 7.19 m)的现有产品相比,我们还展示了改进的冠层高度估算。一个可视化工具将我们的数据与2022年内华达山脉的现有产品一起显示。Planet ST-CNN模型使用了15天的PlanetScope卫星时间序列,为加利福尼亚的年冠层高度估计提供了一种可扩展的方法,实现了高水平的细节,通常可以降低到单个树木的分辨率。森林结构监测能力的提高有望为评估和跟踪森林碳、生物多样性和脆弱性提供关键、全面的数据,最终促进数据驱动的战略,以提高森林的复原力。
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引用次数: 0
Seafloor motion from offshore man-made structures using satellite radar images – A case study in the Adriatic Sea 利用卫星雷达图像分析近海人造结构的海底运动——以亚得里亚海为例
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-12 DOI: 10.1016/j.rse.2024.114543
Fanghui Deng , Mark Zumberge
Space geodetic techniques have achieved centimeter to even millimeter precision in measuring earth surface deformation. However, a large data gap remains in the offshore area. Offshore man-made structures (e.g., oil/gas platforms) anchored to the ocean bottom provide an opportunity to study seafloor motion in some areas. Although satellite InSAR (Interferometric Synthetic Aperture Radar) has been widely used to study earth surface deformation, its application to offshore regions is extremely limited. Continuous GNSS (Global Navigation Satellite System) observations at several tens of offshore platforms in the Adriatic Sea have recently been released. Measuring the same platforms with InSAR provides a great opportunity to assess the feasibility of applying this technique to study seafloor motion on a regional scale using offshore structures. We processed a six-year-long time series of SAR images from the Sentinel-1A satellite using the Permanent Scatterer InSAR (PS-InSAR) method. We assessed the feasibility of phase unwrapping using synthetic data with different velocity fields and noise levels. Correct phase unwrapping could be achieved in the Adriatic Sea and two other large offshore oil/gas fields: the Gulf of Mexico and the North Sea. Different calibration strategies were applied, and we suggest that the InSAR results could be calibrated with limited and even no GNSS stations. Our InSAR results show good agreement with the GNSS measurements and the InSAR observations from the European Ground Motion Service. In addition, our InSAR results provide deformation measurements at about twenty offshore structures where GNSS stations are not present. Most of the offshore structures have a subsidence rate of no more than 5 mm/year, while a few of them reach about 10 mm/year. Our work demonstrates that it is feasible to apply the InSAR technique to measure displacement of discrete offshore man-made structures (fixed to the ocean bottom) on a regional scale but still on a case-by-case basis. Pre-acquired information including geological settings, existing geodetic observations, and human activity records (e.g., hydrocarbon production) are useful information to assess the feasibility and to validate the InSAR results.
空间大地测量技术在测量地表变形方面已经达到了厘米到毫米的精度。然而,在近海地区仍然存在很大的数据缺口。锚定在海底的海上人造结构(如石油/天然气平台)为研究某些地区的海底运动提供了机会。卫星干涉合成孔径雷达(InSAR, Interferometric Synthetic Aperture Radar)已广泛应用于地表形变研究,但其在近海地区的应用极为有限。最近发布了亚得里亚海数十个海上平台的连续GNSS(全球导航卫星系统)观测数据。利用InSAR测量相同平台提供了一个很好的机会来评估应用该技术在区域范围内利用海上结构研究海底运动的可行性。我们使用永久散射体InSAR (PS-InSAR)方法处理了Sentinel-1A卫星6年时间序列的SAR图像。我们使用不同速度场和噪声水平的合成数据来评估相位展开的可行性。在亚得里亚海和另外两个大型海上油气田:墨西哥湾和北海,可以实现正确的阶段展开。采用了不同的校准策略,我们建议在有限的甚至没有GNSS站的情况下对InSAR结果进行校准。我们的InSAR结果与GNSS测量结果和欧洲地面运动服务处的InSAR观测结果吻合良好。此外,我们的InSAR结果提供了大约20个没有GNSS站的海上结构的变形测量。大部分海上构造沉降速率不超过5 mm/年,少数达到10 mm/年左右。我们的工作表明,在区域尺度上应用InSAR技术来测量离散的近海人造结构(固定在海底)的位移是可行的,但仍然是个案的基础上。预先获取的信息,包括地质环境、现有大地测量观测和人类活动记录(如油气生产),是评估可行性和验证InSAR结果的有用信息。
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引用次数: 0
A geostatistical approach to enhancing national forest biomass assessments with Earth Observation to aid climate policy needs 利用地球观测加强国家森林生物量评估的地质统计学方法,以满足气候政策的需要
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-11 DOI: 10.1016/j.rse.2024.114557
Neha Hunka , Paul May , Chad Babcock , José Armando Alanís de la Rosa , Maria de los Ángeles Soriano-Luna , Rafael Mayorga Saucedo , John Armston , Maurizio Santoro , Daniela Requena Suarez , Martin Herold , Natalia Málaga , Sean P. Healey , Robert E. Kennedy , Andrew T. Hudak , Laura Duncanson
Earth Observation (EO) data can provide added value to nations’ assessments of vegetation aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open access to global-scale EO datasets of vegetation heights or biomass, nor the availability of computational power, has proven sufficient for their wide uptake in climate policy-related assessments. Using Mexico as an example, one of the primary obstacles to enhancing their National Forest Inventory (NFI) with such global EO datasets is the lack of statistically defensible methodologies that do so, while addressing the nation’s existing reporting needs and gaps. In collaboration with the Comisión Nacional Forestal (CONAFOR), this study develops a geostatistical model that integrates vegetation height and AGBD estimates from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and ESA’s Climate Change Initiative (CCI) with Mexico’s NFI to attain sub-national and geographically-explicit biomass predictions. The posited model includes spatially varying parameters, allowing flexibility to capture non-stationary relations between the EO-based covariates and NFI-estimated AGBD. Inference is conducted with Bayesian methods, allowing the computation of summary statistics, such as the standard deviations for single-location and area-wide predictions of AGBD. This enables the transparent disclosure and traceability of sources of uncertainty throughout the prediction approach. Results indicate strong model performance; the EO-based covariates explain 79% of the variance in NFI-estimated AGBD in a randomly withheld sample of 10% of observations and a heuristic root mean squared error (RMSE) of 21.55 Mg/ha. Approximately 96% of the observations falling within the 95% credible intervals of our predictions, with some systematic under-prediction observed at AGBD ranges of >100 Mg/ha. To ease the operational uptake of the model for policy purposes, source code based in the ‘R’ language with the optional use of urban and (non)forest masks for AGBD predictions is released. It includes demonstrations for predicting AGBD in Mexico’s Natural Protected Areas, terrestrial ecological strata, and community forest management or payment for environmental services projects, which are commonly used delineations in its climate policy reports. For other nations considering the presented approach for policy purposes, the study discusses challenges concerning the use of EO-based covariates and the limitations of the model. It concludes with a broader call toward ensuring consistency in EO data streams, and prioritizing the co-development of EO-NFI integration approaches with nations in the future, thereby directly addressing their long-term climate policy needs.
地球观测(EO)数据可以以最小的额外成本为各国对植被地上生物量密度(AGBD)的评估提供附加价值。然而,无论是开放获取植被高度或生物量的全球尺度EO数据集,还是计算能力的可用性,都不足以在与气候政策相关的评估中广泛采用它们。以墨西哥为例,利用此类全球生态数据集加强国家森林清查(NFI)的主要障碍之一是,在解决该国现有报告需求和差距的同时,缺乏统计上站得住脚的方法。与Comisión国家森林(CONAFOR)合作,本研究开发了一个地质统计模型,该模型整合了美国宇航局全球生态系统动力学调查(GEDI)和欧航局气候变化倡议(CCI)与墨西哥NFI的植被高度和AGBD估计,以获得次国家和地理上明确的生物量预测。假设的模型包括空间变化的参数,允许灵活地捕获基于eo的协变量和nfi估计的AGBD之间的非平稳关系。使用贝叶斯方法进行推断,可以计算汇总统计量,例如单位置和全区域AGBD预测的标准差。这使得整个预测方法的不确定性来源的透明披露和可追溯性成为可能。结果表明,模型性能较好;在随机保留的10%的观察样本中,基于生态系统的协变量解释了nfi估计的AGBD方差的79%,启发式均方根误差(RMSE)为21.55 Mg/ha。大约96%的观测值落在我们预测的95%可信区间内,在100毫克/公顷的AGBD范围内观察到一些系统的预测不足。为了便于政策目的对模型的操作吸收,发布了基于R语言的源代码,可选择使用城市和(非)森林遮罩进行AGBD预测。它包括预测墨西哥自然保护区、陆地生态层、社区森林管理或环境服务项目的AGBD的示范,这些都是墨西哥气候政策报告中常用的描述。对于考虑为政策目的提出的方法的其他国家,该研究讨论了有关使用基于eo的协变量的挑战和模型的局限性。最后,报告更广泛地呼吁确保观测数据流的一致性,并优先考虑未来与各国共同开发观测-国家金融机构一体化方法,从而直接满足其长期气候政策需求。
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引用次数: 0
Long-term prediction of Arctic sea ice concentrations using deep learning: Effects of surface temperature, radiation, and wind conditions 利用深度学习对北极海冰浓度的长期预测:地表温度、辐射和风况的影响
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-11 DOI: 10.1016/j.rse.2024.114568
Young Jun Kim , Hyun-cheol Kim , Daehyeon Han , Julienne Stroeve , Jungho Im
Over the last five decades, Arctic sea ice has been shrinking in area and thickness. As a result, increased marine traffic has created a need for improved sea ice forecasting on seasonal to annual time-scales. In this study, we introduce a novel UNET-based deep learning model to forecast sea ice concentration up to 12 months. Based on yearly hindcast validation, the UNET 3-, 6-, 9-, and 12-month predictions provided more accurate and stable predictions than did the four baseline models: the Copernicus Climate Change Service (C3S), the damped anomaly persistence (DP) forecast, and two deep learning approach, the Convolutional Neural Network (CNN) models and Convolutional Long Short-Term Memory (ConvLSTM). During years with large departures from the long-term trend, the proposed UNET model exhibited promising SIC prediction results with root-mean-square errors (RMSEs), which were reduced from 17.35 to 7.07 % compared to the four baseline models. Our findings also confirmed the relative importance of each predictor variable (temperature, incoming solar radiation, wind speed and direction) in long-term prediction. Past SIC conditions, together with surface temperature emerged as the most important factors for SIC prediction, especially in the marginal ice zone. Incoming solar radiation and wind speed and direction showed greater sensitivity in predicting SICs in areas with thin ice. This model offers the potential to shape Arctic development and management plans and strategies, ensuring extended forecasting periods and enhanced prediction accuracy.
在过去的50年里,北极海冰的面积和厚度一直在缩小。因此,由于海上交通的增加,需要改进季节性到年度时间尺度的海冰预报。在这项研究中,我们引入了一种新的基于unet的深度学习模型来预测长达12个月的海冰浓度。基于年度后预测验证,UNET 3、6、9和12个月的预测比四种基线模型提供了更准确和稳定的预测:哥白尼气候变化服务(C3S)、衰减异常持久性(DP)预测和两种深度学习方法,卷积神经网络(CNN)模型和卷积长短期记忆(ConvLSTM)。在与长期趋势偏离较大的年份,所提出的UNET模型显示出令人满意的SIC预测结果,其均方根误差(rmse)从17.35%降至7.07%。我们的研究结果也证实了每个预测变量(温度、入射太阳辐射、风速和风向)在长期预测中的相对重要性。过去的碳化硅条件和地表温度是碳化硅预测的最重要因素,特别是在边缘冰带。在薄冰地区,入射太阳辐射、风速和风向对预测铯的灵敏度更高。该模型提供了形成北极开发和管理计划和战略的潜力,确保延长预测周期并提高预测准确性。
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引用次数: 0
Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data 基于GEDI激光雷达和TanDEM-X InSAR数据的大尺度泛热带森林冠层高度制图
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-09 DOI: 10.1016/j.rse.2024.114534
Wenlu Qi , John Armston , Changhyun Choi , Atticus Stovall , Svetlana Saarela , Matteo Pardini , Lola Fatoyinbo , Konstantinos Papathanassiou , Adrian Pascual , Ralph Dubayah
NASA's Global Ecosystem Dynamic Investigation (GEDI) mission provides billions of lidar samples of canopy structure over the Earth's temperate and pantropical forests. Using the GEDI sample data alone, gridded height and biomass products have been created at a spatial resolution of 1 km or coarser. However, this resolution may be too coarse for some applications. In this study, we present a new method of mapping high spatial resolution forest height across large areas using fusion of data acquired by GEDI and TanDEM-X (TDX) Interferometric Synthetic Aperture Radar (InSAR). Our method utilizes GEDI waveforms to provide vertical profiles of scatterers needed to invert a physically-based InSAR model to solve for canopy height. We then use 2-year GEDI canopy height and adaptive wavenumber (kZ)-based calibration models to reduce errors in the inverted canopy height caused by the limited penetration capability of the X-band signal in dense tropical forests and the impact of terrain. We apply this novel method over large areas including Gabon, Mexico, French Guiana and most of the Amazon basin, and generate continuous forest height products at 25 m and 100 m. After validating against airborne lidar data, we find that our canopy height products have a bias of 0.31 m and 0.46 m, and a root mean square error (RMSE) of 8.48 m (30.02 %) and 6.91 m (24.08 %) at 25 m and 100 m respectively, for all sites combined. Compared to existing data products that integrate GEDI with passive optical data using machine learning approaches, our method reduces bias, has a lower RMSE, and does not saturate for tall canopy heights up to 56 m. A key feature of this study is that our canopy height product is complemented with an uncertainty of prediction map which provides information on the predictor's uncertainty around the actual value —an advancement over the standard error maps used in earlier studies, which provide uncertainty around the expectation of the predicted value. This integration approach enables the first-ever accurate and high-resolution mapping of forest canopy heights at unprecedented large areas from GEDI and TDX InSAR data fusion, serving as an essential foundation for pantropical aboveground biomass mapping.
NASA的全球生态系统动态调查(GEDI)任务提供了数十亿份地球温带和泛热带森林树冠结构的激光雷达样本。仅使用GEDI样本数据,网格化的高度和生物量产品已在1公里或更大的空间分辨率下创建。然而,这个分辨率对于某些应用程序来说可能太粗糙了。在这项研究中,我们提出了一种利用GEDI和TanDEM-X (TDX)干涉合成孔径雷达(InSAR)获取的数据融合来绘制大面积高空间分辨率森林高度的新方法。我们的方法利用GEDI波形来提供散射体的垂直剖面,这些散射体需要用于反演基于物理的InSAR模型来求解冠层高度。然后,利用2年GEDI冠层高度和基于自适应波数(kZ)的校准模型,降低由于x波段信号在茂密的热带森林中穿透能力有限和地形影响而导致的反演冠层高度误差。我们将这种新方法应用于包括加蓬、墨西哥、法属圭亚那和亚马逊盆地大部分地区在内的大片地区,并在25米和100米处生成连续的森林高度产品。在对机载激光雷达数据进行验证后,我们发现我们的冠层高度产品偏差为0.31 m和0.46 m,均方根误差(RMSE)分别为8.48 m(30.2%)和6.91 m(24.08%),在所有站点组合的25 m和100 m。与使用机器学习方法将GEDI与被动光学数据集成在一起的现有数据产品相比,我们的方法减少了偏差,具有更低的RMSE,并且在高达56 m的高树冠高度下不会饱和。本研究的一个关键特征是,我们的冠层高度产品与预测的不确定性图相辅相成,该图提供了预测者对实际值的不确定性的信息,这是比早期研究中使用的标准误差图的进步,后者提供了对预测值期望的不确定性。这种整合方法使GEDI和TDX InSAR数据融合能够在前所未有的大范围内首次精确和高分辨率地绘制森林冠层高度图,为泛热带地上生物量制图奠定了重要基础。
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引用次数: 0
A temporal attention-based multi-scale generative adversarial network to fill gaps in time series of MODIS data for land surface phenology extraction 基于时间关注的多尺度生成对抗网络填补MODIS数据时间序列空白,用于地表物候提取
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-09 DOI: 10.1016/j.rse.2024.114546
Yidan Wang , Wei Wu , Zhicheng Zhang , Ziming Li , Fan Zhang , Qinchuan Xin
High-quality and continuous satellite data are essential for land surface studies such as monitoring of land surface phenology, but factors such as cloud contamination and sensor malfunction degrade the quality of remote sensing images and limit their utilization. Filling gaps and recovering missing information in time series of remote sensing images are vital for a wide range of downstream applications, such as land surface phenology extraction. Most existing gap-filling and cloud removal methods focus on individual or multi-temporal image reconstruction, but struggle with continuous and overlapping missing areas in time series data. In this study, we propose a Temporal Attention-Based Multi-Scale Generative Adversarial Network (TAMGAN) to reconstruct time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data. TAMGAN leverages a Generative Adversarial Network (GAN) with a 3-dimensional Convolution Neural Networks (3DCNN) in its generator to reconstruct the missing areas in the annual time series of remote sensing images simultaneously. The temporal attention blocks are designed to capture the changing trends of surface reflectance over time. And multi-scale feature extraction and progressive concatenation are introduced to enhance spectral consistency and provide detailed texture information. Experiments are carried out on MOD09A1 products to evaluate the performance of the proposed network. The results show that TAMGAN outperformed the comparison methods across various evaluation metrics, particularly in handling large and continuous missing areas in the time series. Furthermore, we showcase an example of downstream application by extracting phenological information from the gap-filled products. By effectively filling gaps and removing clouds, our method offers spatial-temporal continuous MODIS surface reflectance data, benefiting downstream applications such as phenology extraction and highlighting the potential of artificial intelligence technique in remote sense data processing.
高质量和连续的卫星数据对于监测地表物候等陆地表面研究至关重要,但云污染和传感器故障等因素降低了遥感图像的质量并限制了它们的利用。在时间序列遥感图像中填补空白和恢复缺失信息对于陆地表面物候特征提取等下游广泛应用至关重要。现有的空白填充和去云方法大多侧重于单个或多时间图像的重建,但难以处理时间序列数据中连续和重叠的缺失区域。在这项研究中,我们提出了一种基于时间注意力的多尺度生成对抗网络(TAMGAN)来重建中分辨率成像光谱仪(MODIS)数据的时间序列。TAMGAN利用生成器中的生成对抗网络(GAN)和三维卷积神经网络(3DCNN)同时重建遥感图像年度时间序列中的缺失区域。时间注意块的设计是为了捕捉表面反射率随时间的变化趋势。引入多尺度特征提取和逐级拼接,增强了光谱一致性,提供了详细的纹理信息。在MOD09A1产品上进行了实验,以评估所提出网络的性能。结果表明,TAMGAN在各种评估指标上都优于比较方法,特别是在处理时间序列中大而连续的缺失区域方面。此外,我们还展示了一个下游应用的例子,即从空白填充的产品中提取物候信息。通过有效地填补空白和去除云层,我们的方法提供了时空连续的MODIS地表反射率数据,有利于物候提取等下游应用,并突出了人工智能技术在遥感数据处理中的潜力。
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Remote Sensing of Environment
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