Pub Date : 2024-11-20DOI: 10.1016/j.rse.2024.114517
Xi Zhao, Jiaxing Gong, Meng Qu, Lijuan Song, Xiao Cheng
Passive microwave remote sensing provides unique pan-Arctic light- and cloud-independent daily coverage of lead fraction (LF) for Arctic winter and spring. In this study, we conducted a quantitative assessment of various sea ice concentration (SIC) data products and LF retrieval algorithms to evaluate their accuracy in deriving lead fractions at both overall and pixel-wise levels. Our results indicate that SIC data products are not sensitive to refrozen leads in winter but tend to display clear lead structures in spring. However, the absolute SIC values differ significantly from LF and cannot be directly used as a proxy. As for the LF retrieval algorithms, we proved that the overall accuracy can be largely improved by adjusting upper tie-points. To further minimize errors, we developed an Artificial Neural Network model that outperformed conventional approaches at the pixel-wise level, offering a more reliable estimation method for absolute fraction values.
{"title":"Assessing lead fraction derived from passive microwave images and improving estimates at pixel-wise level","authors":"Xi Zhao, Jiaxing Gong, Meng Qu, Lijuan Song, Xiao Cheng","doi":"10.1016/j.rse.2024.114517","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114517","url":null,"abstract":"Passive microwave remote sensing provides unique pan-Arctic light- and cloud-independent daily coverage of lead fraction (LF) for Arctic winter and spring. In this study, we conducted a quantitative assessment of various sea ice concentration (SIC) data products and LF retrieval algorithms to evaluate their accuracy in deriving lead fractions at both overall and pixel-wise levels. Our results indicate that SIC data products are not sensitive to refrozen leads in winter but tend to display clear lead structures in spring. However, the absolute SIC values differ significantly from LF and cannot be directly used as a proxy. As for the LF retrieval algorithms, we proved that the overall accuracy can be largely improved by adjusting upper tie-points. To further minimize errors, we developed an Artificial Neural Network model that outperformed conventional approaches at the pixel-wise level, offering a more reliable estimation method for absolute fraction values.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"19 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satellite-based measurements have emerged as an effective method for the top-down estimates of anthropogenic CO2 emissions. Changes in the column-averaged dry-air mole fractions of CO2 (XCO2) in the atmosphere reflect contributions from both human activities and natural processes, posing challenges in accurately extracting anthropogenic XCO2 signals and quantifying urban CO2 emissions. Here, we introduce a novel method based on spatial autocorrelation to directly identify anthropogenic XCO2 signals from satellite measurements of Orbiting Carbon Observatory-2 (OCO-2). These signals serve as constraints for atmospheric transport model simulations, enabling the verification of emission inventory over urban areas. Utilizing 35 OCO-2 overpasses over the Yangtze River Delta urban agglomeration, we demonstrate the effectiveness of local Moran's I statistics in detecting localized anthropogenic XCO2 enhancements. The results show an average XCO2 increase of 1.36–4.41 ppm in proximity to major cities and areas with intensive industrial activity. A case study near Nanjing, based on eight overpasses, reveals XCO2 enhancements with peaks ranging from 2.26 to 4.72 ppm. To establish the relationship between these XCO2 enhancements and CO2 emissions, we conducted WRF-Chem simulations driven by emissions from the Emissions Database for Global Atmospheric Research (EDGAR). Discrepancies between observed and simulated XCO2 enhancements were primarily attributed to uncertainties in the prior emissions, the calculation of urban XCO2 enhancements from OCO-2 data, and complex atmospheric transport dynamics. From our estimates, the daily CO2 emissions in Nanjing is 0.65 ± 0.15 MtCO2/day, which is different from the EDGAR inventory by −10.5 % to 77.3 % (i.e., 0.17 ± 0.14 MtCO2 /day). Error analysis suggests an uncertainty in CO2 emission estimates associated with XCO2 enhancement and wind speed ranging from 16 % to 32 % (i.e., 0.08–0.15 MtCO2/day). This study proposes an objective approach to assess urban CO2 emissions, leveraging satellite XCO2 observations to improve accuracy and reliability in emission inventories.
{"title":"Estimating anthropogenic CO2 emissions from China's Yangtze River Delta using OCO-2 observations and WRF-Chem simulations","authors":"Mengya Sheng, Yun Hou, Hao Song, Xinxin Ye, Liping Lei, Peifeng Ma, Zhao-Cheng Zeng","doi":"10.1016/j.rse.2024.114515","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114515","url":null,"abstract":"Satellite-based measurements have emerged as an effective method for the top-down estimates of anthropogenic CO<sub>2</sub> emissions. Changes in the column-averaged dry-air mole fractions of CO<sub>2</sub> (XCO<sub>2</sub>) in the atmosphere reflect contributions from both human activities and natural processes, posing challenges in accurately extracting anthropogenic XCO<sub>2</sub> signals and quantifying urban CO<sub>2</sub> emissions. Here, we introduce a novel method based on spatial autocorrelation to directly identify anthropogenic XCO<sub>2</sub> signals from satellite measurements of Orbiting Carbon Observatory-2 (OCO-2). These signals serve as constraints for atmospheric transport model simulations, enabling the verification of emission inventory over urban areas. Utilizing 35 OCO-2 overpasses over the Yangtze River Delta urban agglomeration, we demonstrate the effectiveness of local Moran's I statistics in detecting localized anthropogenic XCO<sub>2</sub> enhancements. The results show an average XCO<sub>2</sub> increase of 1.36–4.41 ppm in proximity to major cities and areas with intensive industrial activity. A case study near Nanjing, based on eight overpasses, reveals XCO<sub>2</sub> enhancements with peaks ranging from 2.26 to 4.72 ppm. To establish the relationship between these XCO<sub>2</sub> enhancements and CO<sub>2</sub> emissions, we conducted WRF-Chem simulations driven by emissions from the Emissions Database for Global Atmospheric Research (EDGAR). Discrepancies between observed and simulated XCO<sub>2</sub> enhancements were primarily attributed to uncertainties in the prior emissions, the calculation of urban XCO<sub>2</sub> enhancements from OCO-2 data, and complex atmospheric transport dynamics. From our estimates, the daily CO<sub>2</sub> emissions in Nanjing is 0.65 ± 0.15 MtCO<sub>2</sub>/day, which is different from the EDGAR inventory by −10.5 % to 77.3 % (i.e., 0.17 ± 0.14 MtCO<sub>2</sub> /day). Error analysis suggests an uncertainty in CO<sub>2</sub> emission estimates associated with XCO<sub>2</sub> enhancement and wind speed ranging from 16 % to 32 % (i.e., 0.08–0.15 MtCO<sub>2</sub>/day). This study proposes an objective approach to assess urban CO<sub>2</sub> emissions, leveraging satellite XCO<sub>2</sub> observations to improve accuracy and reliability in emission inventories.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"6 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.rse.2024.114509
Hoang Hai Nguyen , Hyunglok Kim , Wade Crow , Simon Yueh , Wolfgang Wagner , Fangni Lei , Jean-Pierre Wigneron , Andreas Colliander , Frédéric Frappart
Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation – Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood, while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods.
Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cutting-edge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect.
土壤湿度（SM）是水文气象和气候系统中的一个关键变量。随着人们对捕捉精细尺度的土壤水分变化以进行有效的水文气候应用的兴趣日益浓厚，利用全球导航卫星系统反射测量（GNSS-R）技术的星载 L 波段双向雷达系统在满足对高时空分辨率土壤水分数据的需求方面具有巨大潜力。第一个全球导航卫星系统-反射雷达卫星星座--气旋全球导航卫星系统（CYGNSS）任务虽然主要是为监测热带气旋而设计的，但由于其次日间隔的高重访频率，在其最初阶段的七年数据记录中，已显示出可靠监测昼夜SM动态的好处。然而，人们对 CYGNSS 的 SM 检索知识，特别是与其独特特征有关的知识，仍然知之甚少，而现有的许多不确定性和未决问题可能会限制其在下一个运行阶段的有效 SM 检索和实际应用。与其他综述论文不同的是，这项工作旨在根据对 CYGNSS 实际数据的分析，突出其值得注意的设计特性，同时综合介绍在消除外部不确定性因素和改进 SM 反演方法方面的最新进展，从而弥补 CYGNSS SM 检索方面的知识差距。尽管 CYGNSS 潜力巨大，但它的 SM 检索面临着一般和特殊挑战，这些挑战源于传统 GNSS-R 卫星检索算法中的常见问题以及与其技术设计相关的独特数据限制。关于 CYGNSS 信号总量中相干和非相干成分的贡献以及准确划分这两部分的科学争论，以及纠正植被和表面粗糙度的衰减效应，被确定为需要解决的与算法有关的关键挑战。与数据有关的挑战涉及不同地表条件下 CYGNSS 的空间足迹、时间频率和信号穿透深度的变化，对 CYGNSS 入射角变化考虑不周，过度依赖参考 SM 数据集进行反演模型校准/训练或验证，以及处理快速多采样 CYGNSS 数据检索的计算需求。未来的研究途径重点是利用最先进的机器学习/深度学习算法来提高 CYGNSS SM 数据的数量和质量，并更好地解释其与其他水文气候变量之间的复杂相互作用。将 CYGNSS SM 数据流同化到物理模型中，以改进对相关变量和极端气候的预测，也是一种前景广阔的方法。
{"title":"From theory to hydrological practice: Leveraging CYGNSS data over seven years for advanced soil moisture monitoring","authors":"Hoang Hai Nguyen , Hyunglok Kim , Wade Crow , Simon Yueh , Wolfgang Wagner , Fangni Lei , Jean-Pierre Wigneron , Andreas Colliander , Frédéric Frappart","doi":"10.1016/j.rse.2024.114509","DOIUrl":"10.1016/j.rse.2024.114509","url":null,"abstract":"<div><div>Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation – Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood, while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods.</div><div>Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cutting-edge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114509"},"PeriodicalIF":11.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.rse.2024.114497
Yanjun Wu , Zhenyue Peng , Yimin Hu , Rujing Wang , Taosheng Xu
With the rapid advancement of remote sensing technology, the recognition of agricultural field parcels using time-series remote sensing images has become an increasingly emphasized task. In this paper, we focus on identifying crops within scattered, irregular, and poorly defined agricultural fields in many Asian regions. We select two representative locations with small and scattered parcels and construct two new time-series remote sensing datasets (JM dataset and CF dataset). We propose a novel deep learning model DBL, the Dual-Branch Model with Long Short-Term Memory (LSTM), which utilizes main branch and supplementary branch to accomplish accurate crop-type mapping. The main branch is designed for capturing global receptive field and the supplementary is designed for temporal and spatial feature refinement. The experiments are conducted to evaluate the performance of the DBL compared with the state-of-the-art (SOTA) models. The results indicate that the DBL model performs exceptionally well on both datasets. Especially on the CF dataset characterized by scattered and irregular plots, the DBL model achieves an overall accuracy (OA) of 97.70% and a mean intersection over union (mIoU) of 90.70%. It outperforms all the SOTA models and becomes the only model to exceed 90% mark on the mIoU score. We also demonstrate the stability and robustness of the DBL across different agricultural regions.
{"title":"A dual-branch network for crop-type mapping of scattered small agricultural fields in time series remote sensing images","authors":"Yanjun Wu , Zhenyue Peng , Yimin Hu , Rujing Wang , Taosheng Xu","doi":"10.1016/j.rse.2024.114497","DOIUrl":"10.1016/j.rse.2024.114497","url":null,"abstract":"<div><div>With the rapid advancement of remote sensing technology, the recognition of agricultural field parcels using time-series remote sensing images has become an increasingly emphasized task. In this paper, we focus on identifying crops within scattered, irregular, and poorly defined agricultural fields in many Asian regions. We select two representative locations with small and scattered parcels and construct two new time-series remote sensing datasets (JM dataset and CF dataset). We propose a novel deep learning model DBL, the Dual-Branch Model with Long Short-Term Memory (LSTM), which utilizes main branch and supplementary branch to accomplish accurate crop-type mapping. The main branch is designed for capturing global receptive field and the supplementary is designed for temporal and spatial feature refinement. The experiments are conducted to evaluate the performance of the DBL compared with the state-of-the-art (SOTA) models. The results indicate that the DBL model performs exceptionally well on both datasets. Especially on the CF dataset characterized by scattered and irregular plots, the DBL model achieves an overall accuracy (OA) of 97.70% and a mean intersection over union (mIoU) of 90.70%. It outperforms all the SOTA models and becomes the only model to exceed 90% mark on the mIoU score. We also demonstrate the stability and robustness of the DBL across different agricultural regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114497"},"PeriodicalIF":11.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.rse.2024.114498
Yichen Lei , Xiuyuan Zhang , Shuping Xiong , Ge Tan , Shihong Du
Community Open Spaces (COS) refer to the fine-grained and micro-open areas within communities that offer residents convenient opportunities for social interaction and health benefits. The mapping of COS using Very High Resolution (VHR) imagery can provide critical community-scale data for monitoring urban sustainable development goals (SDGs). However, the three-dimensional structure of COS often results in layered occlusion in two-dimensional satellite imagery, leading to the invisibility and fragmentation of ground COS features in VHR images. This study presents a novel Layered Occlusion Perception Model (LOPM) to address these challenges by accurately modeling and reconstructing the intricate layered structure of COS. Our approach involves the automatic generation of a comprehensive COS database and the joint training of a deep learning network to decompose occlusion relationships. The developed dual-layer map product, COS-1m, includes various elements and their coupled spaces, with a resolution of 1 m, covering 31 major cities in China. The results demonstrate that the proposed method achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across these cities. COS-1m reveals that, on average, 60.51 km2 of COS area per city is occluded, constituting 10.18% of the total COS area. This research advances the technology for layered monitoring of COS, fills a critical gap in community-scale SDG assessments by providing fine-grained COS data products, and offers valuable insights for urban planners and policymakers to promote more effective and sustainable urban development.
社区开放空间 (COS) 指的是社区内的细粒度和微型开放区域，它们为居民提供了方便的社交互动机会和健康益处。利用甚高分辨率（VHR）图像绘制社区开放空间图可以为监测城市可持续发展目标（SDGs）提供重要的社区尺度数据。然而，COS 的三维结构往往会导致二维卫星图像中的分层遮挡，从而导致 VHR 图像中地面 COS 特征的不可见性和支离破碎。本研究提出了一种新颖的分层遮挡感知模型（LOPM），通过对 COS 复杂的分层结构进行精确建模和重建来应对这些挑战。我们的方法包括自动生成一个全面的 COS 数据库，并联合训练一个深度学习网络来分解遮挡关系。所开发的双层地图产品 COS-1m 包括各种要素及其耦合空间，分辨率为 1 米，覆盖中国 31 个主要城市。结果表明，所提出的方法在这些城市的总体准确率达到了 86.39%，平均 F1 分数为 77.47%。COS-1m 显示，平均每个城市有 60.51 平方公里的 COS 区域被遮挡，占 COS 总面积的 10.18%。这项研究推动了 COS 分层监测技术的发展，通过提供精细的 COS 数据产品，填补了社区尺度可持续发展目标评估的关键空白，并为城市规划者和决策者提供了宝贵的见解，以促进更有效和可持续的城市发展。
{"title":"Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities","authors":"Yichen Lei , Xiuyuan Zhang , Shuping Xiong , Ge Tan , Shihong Du","doi":"10.1016/j.rse.2024.114498","DOIUrl":"10.1016/j.rse.2024.114498","url":null,"abstract":"<div><div>Community Open Spaces (COS) refer to the fine-grained and micro-open areas within communities that offer residents convenient opportunities for social interaction and health benefits. The mapping of COS using Very High Resolution (VHR) imagery can provide critical community-scale data for monitoring urban sustainable development goals (SDGs). However, the three-dimensional structure of COS often results in layered occlusion in two-dimensional satellite imagery, leading to the invisibility and fragmentation of ground COS features in VHR images. This study presents a novel Layered Occlusion Perception Model (LOPM) to address these challenges by accurately modeling and reconstructing the intricate layered structure of COS. Our approach involves the automatic generation of a comprehensive COS database and the joint training of a deep learning network to decompose occlusion relationships. The developed dual-layer map product, COS-1m, includes various elements and their coupled spaces, with a resolution of 1 m, covering 31 major cities in China. The results demonstrate that the proposed method achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across these cities. COS-1m reveals that, on average, 60.51 km<sup>2</sup> of COS area per city is occluded, constituting 10.18% of the total COS area. This research advances the technology for layered monitoring of COS, fills a critical gap in community-scale SDG assessments by providing fine-grained COS data products, and offers valuable insights for urban planners and policymakers to promote more effective and sustainable urban development.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114498"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.rse.2024.114516
Lun Gao , Kaiyu Guan , Chongya Jiang , Xiaoman Lu , Sheng Wang , Elizabeth A. Ainsworth , Xiaocui Wu , Min Chen
Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.
{"title":"Incorporating environmental stress improves estimation of photosynthesis from NIRvP in US Great Plains pasturelands and Midwest croplands","authors":"Lun Gao , Kaiyu Guan , Chongya Jiang , Xiaoman Lu , Sheng Wang , Elizabeth A. Ainsworth , Xiaocui Wu , Min Chen","doi":"10.1016/j.rse.2024.114516","DOIUrl":"10.1016/j.rse.2024.114516","url":null,"abstract":"<div><div>Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114516"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.rse.2024.114511
Mengyang Cai , Yao Zhang , Xiaobin Guan , Jinghao Qiu
Satellite-derived normalized difference vegetation index (NDVI) is inevitably contaminated by clouds and aerosols, causing large uncertainties in depicting the seasonal and interannual variations of terrestrial ecosystems, and potentially misrepresents their responses to climate change and climate extremes. Although various methods have been developed to reconstruct NDVI time series using the similarity in time, space or their combination, they typically require known and accurate data quality information. It is still challenging to effectively reconstruct high-quality NDVI from Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3g+), which is one of the longest observation records but lacks reliable data quality information. This study introduces an adaptive spatiotemporal tensor reconstruction algorithm that leverages the spatial and temporal patterns of vegetation to produce high-quality long-term NDVI datasets without the need of data quality information. The reconstruction process employs two different tensor completion models to satisfy the low-rank constraints. These two models can effectively remove the high-frequency noises originating from atmospheric contamination, while preserving the abrupt or low-frequency changes attributable to disturbances such as drought, even in the absence of data quality information. The resultant NDVI shows good consistency with observations from geostationary satellites. Regions that show a strong correlation (r > 0.7) with geostationary satellite NDVI increased from 46.7 % (original GIMMS-3g+) to 62.2 % and 62.3 % (two reconstructions results) for East Asia, and from 41.4 % to 58.0 % and 59.0 % for Amazon. Our method also demonstrates superior performance to traditional methods such as Whittaker, HANTS, SG-filter, and comparable performance with the state-of-the-art ST-Tensor method when the fraction of contaminated observations is low. The proposed method can also be applied to other datasets such as EVI, LAI, etc., without additional data quality inputs. The resultant vegetation index dataset has the potential to improve plant phenology retrievals and evaluation of ecosystem responses to extremes.
{"title":"An adaptive spatiotemporal tensor reconstruction method for GIMMS-3g+ NDVI","authors":"Mengyang Cai , Yao Zhang , Xiaobin Guan , Jinghao Qiu","doi":"10.1016/j.rse.2024.114511","DOIUrl":"10.1016/j.rse.2024.114511","url":null,"abstract":"<div><div>Satellite-derived normalized difference vegetation index (NDVI) is inevitably contaminated by clouds and aerosols, causing large uncertainties in depicting the seasonal and interannual variations of terrestrial ecosystems, and potentially misrepresents their responses to climate change and climate extremes. Although various methods have been developed to reconstruct NDVI time series using the similarity in time, space or their combination, they typically require known and accurate data quality information. It is still challenging to effectively reconstruct high-quality NDVI from Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3g+), which is one of the longest observation records but lacks reliable data quality information. This study introduces an adaptive spatiotemporal tensor reconstruction algorithm that leverages the spatial and temporal patterns of vegetation to produce high-quality long-term NDVI datasets without the need of data quality information. The reconstruction process employs two different tensor completion models to satisfy the low-rank constraints. These two models can effectively remove the high-frequency noises originating from atmospheric contamination, while preserving the abrupt or low-frequency changes attributable to disturbances such as drought, even in the absence of data quality information. The resultant NDVI shows good consistency with observations from geostationary satellites. Regions that show a strong correlation (<em>r</em> > 0.7) with geostationary satellite NDVI increased from 46.7 % (original GIMMS-3g+) to 62.2 % and 62.3 % (two reconstructions results) for East Asia, and from 41.4 % to 58.0 % and 59.0 % for Amazon. Our method also demonstrates superior performance to traditional methods such as Whittaker, HANTS, SG-filter, and comparable performance with the state-of-the-art ST-Tensor method when the fraction of contaminated observations is low. The proposed method can also be applied to other datasets such as EVI, LAI, etc., without additional data quality inputs. The resultant vegetation index dataset has the potential to improve plant phenology retrievals and evaluation of ecosystem responses to extremes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114511"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.rse.2024.114487
Xuerong Sun , Robert J.W. Brewin , Shubha Sathyendranath , Giorgio Dall’Olmo , David Antoine , Ray Barlow , Astrid Bracher , Malika Kheireddine , Mengyu Li , Dionysios E. Raitsos , Fang Shen , Gavin H. Tilstone , Vincenzo Vellucci
In the first part of this paper series (Sun et al., 2023), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.
{"title":"Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling","authors":"Xuerong Sun , Robert J.W. Brewin , Shubha Sathyendranath , Giorgio Dall’Olmo , David Antoine , Ray Barlow , Astrid Bracher , Malika Kheireddine , Mengyu Li , Dionysios E. Raitsos , Fang Shen , Gavin H. Tilstone , Vincenzo Vellucci","doi":"10.1016/j.rse.2024.114487","DOIUrl":"10.1016/j.rse.2024.114487","url":null,"abstract":"<div><div>In the first part of this paper series (<span><span>Sun et al., 2023</span></span>), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114487"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.rse.2024.114466
Wade T. Crow , Andrew F. Feldman
Successful surface soil moisture (SM) retrieval from space has been enabled by microwave satellite measurements of Earth's upwelling brightness temperature (TB). Nevertheless, correction for the impact of vegetation on TB emission remains a challenge for SM retrieval algorithms. Such correction is often performed in a simplified manner. For example, the Single Channel Algorithm (SCA) uses ancillary climatological normalized vegetation difference index values as a proxy for vegetation optical depth (τ) - resulting in SM retrievals that do not account for interannual τ variability. Official NASA Soil Moisture Active/Passive (SMAP) mission SM products are all based, to varying degrees, on the SCA. Here, we utilize an instrumental variable analysis and alternative SMAP SM retrievals derived from the Multi-Temporal Dual Channel Algorithm (MTDCA) – that better account for time variations in τ – as a benchmark for examining SMAP Level 3 SM retrievals for the presence of signal crosstalk associated with the neglect of interannual τ variability. Results suggest that failing to account for such variability introduces a spurious vegetation-based signal into monthly climatological SMAP SM anomalies. The SMAP Dual Channel Algorithm (DCA), which serves as the current SMAP baseline algorithm, reduces - but does not eliminate – this crosstalk. Results therefore suggest the need for caution when applying SMAP SM retrievals to science applications aimed at understanding SM coupling with the terrestrial biosphere.
Plain language summary
Satellite observations of natural microwave emission from Earth's land surface can be converted into estimates of both surface soil moisture and vegetation water content. Such estimates have a variety of applications. However, the separation of the vegetation signal from the soil moisture signal is challenging and often performed using only approximate methods. This paper uses a novel approach to evaluate how accurately state-of-the-art soil moisture retrieval algorithms perform such partitioning. Results suggests that spurious vegetation signals remain in existing soil moisture products - with some approaches removing it more than others. Such “crosstalk” between soil- and vegetation-based signals limits the value of existing satellite soil moisture products for agricultural and ecohydrological applications and motivates the development of improved retrieval algorithms.
通过微波卫星对地球上涌亮度温度（TB）的测量，可以成功地从空间进行地表土壤湿度（SM）检索。然而，修正植被对 TB 辐射的影响仍然是 SM 检索算法面临的一项挑战。这种校正通常以简化的方式进行。例如，单通道算法（SCA）使用辅助气候学归一化植被差异指数值作为植被光学深度（τ）的替代值--导致土壤水分检索不考虑年际τ变化。美国国家航空航天局（NASA）土壤水分主动/被动（SMAP）任务的官方土壤水分产品都在不同程度上基于SCA。在这里，我们利用工具变量分析和从多时空双通道算法（MTDCA）中得出的替代 SMAP SM 检索结果（更好地考虑了 τ 的时间变化）作为基准，检查 SMAP 第 3 级 SM 检索结果是否存在与忽略年际 τ 变化相关的信号串扰。结果表明，如果不考虑这种变率，就会在 SMAP SM 月度气候异常中引入虚假的植被信号。作为当前 SMAP 基线算法的 SMAP 双通道算法（DCA）可以减少--但不能消除--这种串扰。因此，结果表明，在将 SMAP SM 检索应用于旨在了解 SM 与陆地生物圈耦合的科学应用时需要谨慎。这些估算值有多种用途。然而，将植被信号从土壤水分信号中分离出来是一项挑战，通常只能使用近似方法。本文采用一种新方法来评估最先进的土壤水分检索算法如何准确地进行这种分离。结果表明，在现有的土壤水分产品中仍然存在虚假的植被信号--有些方法比其他方法更能去除这些信号。土壤和植被信号之间的这种 "串扰 "限制了现有卫星土壤水分产品在农业和生态水文应用方面的价值，并促使人们开发改进的检索算法。
{"title":"Vegetation signal crosstalk present in official SMAP surface soil moisture retrievals","authors":"Wade T. Crow , Andrew F. Feldman","doi":"10.1016/j.rse.2024.114466","DOIUrl":"10.1016/j.rse.2024.114466","url":null,"abstract":"<div><div>Successful surface soil moisture (SM) retrieval from space has been enabled by microwave satellite measurements of Earth's upwelling brightness temperature (<em>T</em><sub>B</sub>). Nevertheless, correction for the impact of vegetation on <em>T</em><sub>B</sub> emission remains a challenge for SM retrieval algorithms. Such correction is often performed in a simplified manner. For example, the Single Channel Algorithm (SCA) uses ancillary climatological normalized vegetation difference index values as a proxy for vegetation optical depth (<em>τ</em>) - resulting in SM retrievals that do not account for interannual <em>τ</em> variability. Official NASA Soil Moisture Active/Passive (SMAP) mission SM products are all based, to varying degrees, on the SCA. Here, we utilize an instrumental variable analysis and alternative SMAP SM retrievals derived from the Multi-Temporal Dual Channel Algorithm (MTDCA) – that better account for time variations in <em>τ</em> – as a benchmark for examining SMAP Level 3 SM retrievals for the presence of signal crosstalk associated with the neglect of interannual <em>τ</em> variability. Results suggest that failing to account for such variability introduces a spurious vegetation-based signal into monthly climatological SMAP SM anomalies. The SMAP Dual Channel Algorithm (DCA), which serves as the current SMAP baseline algorithm, reduces - but does not eliminate – this crosstalk. Results therefore suggest the need for caution when applying SMAP SM retrievals to science applications aimed at understanding SM coupling with the terrestrial biosphere.</div></div><div><h3>Plain language summary</h3><div>Satellite observations of natural microwave emission from Earth's land surface can be converted into estimates of both surface soil moisture and vegetation water content. Such estimates have a variety of applications. However, the separation of the vegetation signal from the soil moisture signal is challenging and often performed using only approximate methods. This paper uses a novel approach to evaluate how accurately state-of-the-art soil moisture retrieval algorithms perform such partitioning. Results suggests that spurious vegetation signals remain in existing soil moisture products - with some approaches removing it more than others. Such “crosstalk” between soil- and vegetation-based signals limits the value of existing satellite soil moisture products for agricultural and ecohydrological applications and motivates the development of improved retrieval algorithms.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114466"},"PeriodicalIF":11.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.rse.2024.114507
Joseph J. Everest , Elisa Van Cleemput , Alison L. Beamish , Marko J. Spasojevic , Hope C. Humphries , Sarah C. Elmendorf
Plant functional traits are key drivers of ecosystem processes. However, plot-based monitoring of functional composition across both large spatial and temporal extents is a time-consuming and expensive undertaking. Airborne and satellite remote sensing platforms collect data across large spatial expanses, often repeatedly over time, raising the tantalising prospect of detection of biodiversity change over space and time through remotely sensed methods. Here, we test the degree to which in situ measurements of taxonomic and functional β-diversity, defined as pairwise dissimilarity either between sites, or between years within individual sites, is detectable in airborne hyperspectral imagery across both space and time in an alpine vascular plant community in the Front Range, Colorado, USA. Functional and taxonomic dissimilarity were significantly related to spectral dissimilarity across space, but lacked robust relationships with spectral dissimilarity over time. Biomass showed stronger relationships with spectral dissimilarity than either taxonomic or functional dissimilarity over space, but exhibited no significant associations with spectral dissimilarity over time. Comparative analyses using NDVI revealed that NDVI alone explains much of the variation explained by the full-range spectra. Our results support the use of hyperspectral data to detect fine-scale changes in vascular plant β-diversity over space, but suggest that methodological limitations still preclude the use of this technology for long-term monitoring and change detection.
{"title":"Evaluating the utility of hyperspectral data to monitor local-scale β-diversity across space and time","authors":"Joseph J. Everest , Elisa Van Cleemput , Alison L. Beamish , Marko J. Spasojevic , Hope C. Humphries , Sarah C. Elmendorf","doi":"10.1016/j.rse.2024.114507","DOIUrl":"10.1016/j.rse.2024.114507","url":null,"abstract":"<div><div>Plant functional traits are key drivers of ecosystem processes. However, plot-based monitoring of functional composition across both large spatial and temporal extents is a time-consuming and expensive undertaking. Airborne and satellite remote sensing platforms collect data across large spatial expanses, often repeatedly over time, raising the tantalising prospect of detection of biodiversity change over space and time through remotely sensed methods. Here, we test the degree to which in situ measurements of taxonomic and functional β-diversity, defined as pairwise dissimilarity either between sites, or between years within individual sites, is detectable in airborne hyperspectral imagery across both space and time in an alpine vascular plant community in the Front Range, Colorado, USA. Functional and taxonomic dissimilarity were significantly related to spectral dissimilarity across space, but lacked robust relationships with spectral dissimilarity over time. Biomass showed stronger relationships with spectral dissimilarity than either taxonomic or functional dissimilarity over space, but exhibited no significant associations with spectral dissimilarity over time. Comparative analyses using NDVI revealed that NDVI alone explains much of the variation explained by the full-range spectra. Our results support the use of hyperspectral data to detect fine-scale changes in vascular plant β-diversity over space, but suggest that methodological limitations still preclude the use of this technology for long-term monitoring and change detection.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114507"},"PeriodicalIF":11.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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