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A radiative transfer model for characterizing photometric and polarimetric properties of leaf reflection: Combination of PROSPECT and a polarized reflection function 表征叶片反射光度和偏振特性的辐射传输模型:PROSPECT和偏振反射函数的组合
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-14 DOI: 10.1016/j.rse.2024.114559
Xiao Li , Zhongqiu Sun , Shan Lu , Kenji Omasa
Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R2 = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R2 = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R2 = 0.89, RMSE = 12.83 μg/cm2), equivalent water thickness (R2 = 0.90, RMSE = 0.0032 g/cm2), and leaf mass per area (R2 = 0.38, RMSE = 0.0031 g/cm2), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R2 = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.
光的光度和偏振特性是描述叶片反射光学特性的关键,在研究植被与大气系统的生物化学和表面结构特征反演以及辐射平衡等方面发挥着重要作用。虽然有几种物理模型可用,但考虑到光度和极化特征并结合生化和表面结构特征的综合模型的研究仍然不足。在本研究中,我们引入了PROPOLAR叶片模型,该模型考虑了叶片反射的极化和非极化分量,并将叶片反射与叶片性状联系起来。PROPOLAR采用PROSPECT模拟与生化性状相关的非极化成分,采用线性系数、折射率因子和叶片表面粗糙度三参数函数模拟极化成分。该模型使用从13种植物的533个样品中收集的数据集(由光度和偏振测量组成)在不同的照明观测几何形状下进行验证。结果表明,PROPOLAR在模拟光强方面优于PROSPECT和PROSPECULAR(表征BRF的叶片模型)(R2 = 0.98),在较宽光谱范围(450 ~ 2300 nm)和物种范围内有效模拟双向偏振反射因子(BPRF)和线性偏振度(Dolp), R2分别为0.92和0.80。此外,由于在校正过程中加入了偏振反射和线性系数,PROPOLAR提高了PROSPECT的精度,在多角度偏振测量的生化性状反演中,包括叶绿素(R2 = 0.89, RMSE = 12.83 μg/cm2)、等效水厚度(R2 = 0.90, RMSE = 0.0032 g/cm2)和每面积叶质量(R2 = 0.38, RMSE = 0.0031 g/cm2),其精度与PROSPECULAR相当。值得注意的是,PROPOLAR可以反演粗糙度,并且与实测粗糙度具有合理的一致性(R2 = 0.61)。这些结果证明了PROPOLAR在模拟叶片反射的光度和偏振特性方面的有效性,以及它在生化和表面结构特征反演方面的潜力。PROPOLAR可以通过整合光度和偏振特性来推进遥感在植被管理中的应用。
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引用次数: 0
Predicting drought vulnerability with leaf reflectance spectra in Amazonian trees 利用亚马逊树木的叶片反射光谱预测干旱脆弱性
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-14 DOI: 10.1016/j.rse.2024.114562
Maquelle N. Garcia , Lucas B.S. Tameirão , Juliana Schietti , Izabela Aleixo , Tomas F. Domingues , K. Fred Huemmrich , Petya K.E. Campell , Loren P. Albert
Hydraulic traits mediate trade-offs between growth and mortality in plants yet characterizing these traits at the community level remains challenging, particularly in the Amazon, where they vary widely across species and environments. While previous studies have used reflectance-based estimates, hydraulic traits, which arise from wood and/or whole-plant anatomy and physiology, have not been comprehensively explored.
For the first time, we comprehensively investigated the use of leaf reflectance to predict hydraulic traits alongside leaf functional traits in tropical evergreen and deciduous trees. For 196 Amazonian trees, we measured water potential, leaf mass per area (LMA), leaf reflectance, hydraulic conductivity curves (e.g., P50), and wood density (WD). We examined the relationships between leaf reflectance and traits using partial least square regression (PLSR).
Our findings indicate that leaf reflectance accurately predicts variation in LMA (R2 = 0.8), and reasonably estimates xylem water potential (R2 = 0.51) and WD (R2 = 0.52). However, P50 predictions were much less reliable (R2 = 0.27), with water absorption bands greatly influencing the PLSR model. Leaf phenological strategy had little impact on the results.
These findings suggest that reflectance-based remote sensing could monitor water status and forest carbon dynamics through water potential and wood density, respectively. However, our case study applying the PLSR approach to hyperspectral canopy spectra to predict wood density revealed challenges to upscaling. Despite these limitations, remote sensing of forest hydraulic traits at scale could enhance our understanding of drought vulnerability and carbon dynamics in Amazonian forests, with significant implications for conservation.
水力性状调节植物生长和死亡之间的权衡,但在群落水平上表征这些性状仍然具有挑战性,特别是在亚马逊地区,它们在不同物种和环境中差异很大。虽然以前的研究使用了基于反射率的估计,但由于木材和/或整株植物的解剖和生理,水力特性尚未得到全面探索。本文首次全面研究了利用叶片反射率预测热带常绿和落叶乔木水力性状和叶片功能性状的方法。对于196棵亚马逊树,我们测量了水势、每面积叶质量(LMA)、叶片反射率、水力导率曲线(如P50)和木材密度(WD)。利用偏最小二乘法(PLSR)分析了叶片反射率与性状之间的关系。结果表明,叶片反射率能准确预测叶片LMA的变化(R2 = 0.8),能合理预测木质部水势(R2 = 0.51)和WD (R2 = 0.52)。然而,P50预测的可靠性要低得多(R2 = 0.27),吸水带对PLSR模型的影响很大。叶片物候策略对结果影响不大。这些结果表明,基于反射率的遥感可以分别通过水势和木材密度监测水分状况和森林碳动态。然而,我们的案例研究将PLSR方法应用于高光谱冠层光谱来预测木材密度,揭示了升级的挑战。尽管存在这些限制,大规模的森林水力特征遥感可以增强我们对亚马逊森林干旱脆弱性和碳动态的理解,对保护具有重要意义。
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引用次数: 0
SIFFI: Bayesian solar-induced fluorescence retrieval algorithm for remote sensing of vegetation SIFFI:植被遥感贝叶斯太阳诱导荧光检索算法
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-13 DOI: 10.1016/j.rse.2024.114558
Antti Kukkurainen , Antti Lipponen , Ville Kolehmainen , Antti Arola , Sergio Cogliati , Neus Sabater
Remote sensing of solar-induced vegetation chlorophyll fluorescence (SIF) has a rich history of more than 50 years of research covering active and passive techniques from leaf, canopy, and satellite scale. Current satellite-derived SIF products primarily focus on the far-red spectral range, with variations in techniques dependent on sensor capabilities. However, these retrieval methods often rely on parametric spectral models and are constrained to narrow absorption regions. In this paper, we introduce a novel Bayesian retrieval technique, referred to as SIFFI (Siffi Is For Fluorescence Inference), designed for the flexible and robust estimation of spectrally resolved fluorescence spectra. SIFFI utilizes spectral representations for both fluorescence and surface reflectance, enabling its application to distinct spectral ranges, e.g., red, far-red, and full spectral range. Also, its applicability extends to top-of-canopy (TOC) and top-of-atmosphere (TOA) measurements, with the latter being possible when auxiliary information about the atmospheric state is available. For the assessment of SIFFI, we performed an extensive proof-of-concept simulation exercise involving diverse scenarios that integrated measured leaf-level fluorescence and reflectance signals, propagated them to the TOC and TOA levels, and perturbed the resultant signal with instrument Gaussian noise to simulate realistic conditions. Additionally, we extend our assessment exercise to TOC measurements acquired by a fluorescence box (FloX) instrument during two diurnal cycles on sunlit and cloudy conditions. In all the TOC cases, simulations- and measured-based scenarios, we compared our SIF estimates with the results from two well-established methods: the improved Fraunhofer line discrimination method (iFLD) and the Spectral Fitting (SpecFit) method covering the full fluorescence spectra. Notably, our results highlight the versatility and accuracy of SIFFI in estimating spectrally resolved fluorescence, achieving Mean Absolute Error (MAE) values of 0.07 (0.09) [mW/(m2srnm)] in the TOC (TOA) simulation scenarios, improving the SpecFit method estimates, and being aligned with the iFLD method results at the oxygen bands. SIFFI represents a significant advancement in SIF retrieval, providing a robust approach that exploits the full spectral information from the red to the near-infrared regions.
太阳诱导的植被叶绿素荧光遥感(SIF)研究已有 50 多年的丰富历史,涵盖了从叶片、冠层到卫星尺度的主动和被动技术。目前的卫星 SIF 产品主要集中在远红外光谱范围,根据传感器能力的不同,技术也有所不同。然而,这些检索方法通常依赖于参数光谱模型,并受限于狭窄的吸收区域。在本文中,我们介绍了一种新颖的贝叶斯检索技术,即 SIFFI(Siffi Is For Fluorescence Inference),它专为灵活、稳健地估计光谱分辨荧光光谱而设计。SIFFI 利用荧光和表面反射率的光谱表示法,可应用于不同的光谱范围,如红光、远红光和全光谱范围。此外,它的适用范围还扩展到树冠顶(TOC)和大气层顶(TOA)测量,后者可在有大气状态辅助信息的情况下进行。为了对 SIFFI 进行评估,我们进行了广泛的概念验证模拟演练,其中涉及各种不同的情况,将测量到的叶片级荧光和反射信号整合在一起,传播到 TOC 和 TOA 级,并用仪器高斯噪声对由此产生的信号进行扰动,以模拟现实条件。此外,我们还将评估工作扩展到荧光盒(FloX)仪器在日照和多云条件下的两个昼夜周期中获取的 TOC 测量值。在所有的 TOC 案例、模拟和测量场景中,我们将 SIF 估计值与两种成熟方法的结果进行了比较:改进的弗劳恩霍夫线判别法(iFLD)和覆盖整个荧光光谱的光谱拟合法(SpecFit)。值得注意的是,我们的结果凸显了 SIFFI 在估算光谱分辨荧光方面的多功能性和准确性,在 TOC (TOA) 模拟场景中,其平均绝对误差 (MAE) 值为 0.07 (0.09) [mW/(m2srnm)] [mW/(m2srnm)],改善了 SpecFit 方法的估算结果,并且在氧波段与 iFLD 方法的结果一致。SIFFI 在 SIF 检索方面取得了重大进展,提供了一种利用从红外到近红外区域全部光谱信息的稳健方法。
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引用次数: 0
Retrieval of global surface soil and vegetation temperatures based on multisource data fusion 基于多源数据融合的全球地表土壤和植被温度检索
IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2024-12-13 DOI: 10.1016/j.rse.2024.114564
Xiangyang Liu , Zhao-Liang Li , Si-Bo Duan , Pei Leng , Menglin Si
Soil and vegetation temperatures are crucial for various fields, including ecology, agriculture, and climate change. However, there remains a lack of entirely observation-based global datasets for these two component temperatures. To fill this gap, this study developed a multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT). This novel method not only utilizes temporal and spatial information from MODIS data by adopting a temperature cycle model to capture temporal variation and using adjacent pixels to consider spatial differences and increase the number of equations solved, but also leverages ERA5-Land data to reduce unknown parameters, effectively compensating for the limitations of satellite observations. Its performances were comprehensively evaluated with simulated data, high-resolution satellite products, and in situ measurements, demonstrating competitive accuracy with root mean square errors below 2 K and Biases of under 1 K in most cases. Compared to previous retrieval method that relies solely on satellite-based temporal and spatial information, FuSVeT present enhanced accuracy, more complete spatial coverage, and improved computational efficiency, making it more applicable for global soil and vegetation temperature mapping. Using this method, we generated global 0.05° monthly mean soil and vegetation temperatures for January and July 2020. These data can capture more pronounced temperature heterogeneities within biomes than existing soil temperature products, indicating its superiority for global analyses. Importantly, FuSVeT can also be applied to satellite observations with higher spatiotemporal resolution, holding significant potential for providing accurate, long-term, global maps of surface soil and vegetation temperatures.
土壤和植被温度对包括生态、农业和气候变化在内的各个领域都至关重要。然而,对于这两个分量的温度,仍然缺乏完全基于观测的全球数据集。为了填补这一空白,本研究开发了一种基于多源数据融合的全球地表土壤和植被温度检索方法(FuSVeT)。该方法不仅利用MODIS数据的时空信息,采用温度循环模型捕捉时间变化,利用相邻像元考虑空间差异,增加方程求解数量,而且利用ERA5-Land数据减少未知参数,有效补偿了卫星观测的局限性。通过模拟数据、高分辨率卫星产品和现场测量对其性能进行了全面评估,在大多数情况下,其均方根误差低于2 K,偏差低于1 K,具有竞争力的精度。与以往单纯依赖卫星时空信息的检索方法相比,FuSVeT精度提高,空间覆盖更全面,计算效率提高,更适用于全球土壤和植被温度制图。利用该方法,我们生成了2020年1月和7月全球0.05°的月平均土壤和植被温度。这些数据可以捕获比现有土壤温度产品更明显的生物群系温度异质性,表明其在全球分析中的优越性。重要的是,FuSVeT还可以应用于具有更高时空分辨率的卫星观测,具有提供准确、长期、全球地表土壤和植被温度地图的巨大潜力。
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引用次数: 0
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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Remote Sensing of Environment
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