Pub Date : 2024-06-21DOI: 10.5194/essd-16-2917-2024
Weixin Zhu, Siqi Liu, Shiming Xu, Lu Zhou
Abstract. The wave-affected marginal ice zone (MIZ) is an essential part of the sea ice cover and crucial to the atmosphere–ice–ocean interaction in the polar region. While we primarily rely on in situ campaigns for studying MIZs, significant challenges exist for the remote sensing of MIZs by satellites. This study develops a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter on board CryoSat-2 (CS2). CS2 waveform power and waveform stack statistics are used to determine the part of the sea ice cover affected by waves. Based on the CS2 data since 2010, we generate a climate record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022 (https://doi.org/10.5281/zenodo.8176585, Zhu et al., 2023). The MIZ record indicates no significant change in the mean MIZ width or the extreme width, although large temporal and spatial variability is present. In particular, extremely wide MIZ events (over 300 km) are observed in the Barents Sea, whereas in other parts of the Atlantic Arctic, MIZ events are typically narrower. We also compare the CS2-based retrieval with the retrievals based on the laser altimeter of ICESat2 and the synthetic aperture radar images from Sentinel-1. Under spatial and temporal collocation, we attain good agreement among the MIZ retrievals based on the three different types of satellite payloads. Moreover, the traditional sea-ice-concentration-based definition of MIZ yields systematically narrower MIZs than CS2, and no statistically significant correlation exists between the two. Beyond its application to CS2, the proposed retrieval algorithm can be adapted to historical and future radar altimetry campaigns. The synergy of multiple satellites can improve the spatial and temporal representation of the altimeters' observation of the MIZs.
摘要。受波浪影响的边缘冰区（MIZ）是海冰覆盖的重要组成部分，对极地地区大气-冰-海洋的相互作用至关重要。虽然我们主要依靠现场活动来研究边缘冰区，但卫星遥感边缘冰区也面临巨大挑战。本研究基于 CryoSat-2 （CS2）上的延迟多普勒雷达测高仪，开发了一种新型的受波浪影响的 MIZ 检索算法。CS2 波形功率和波形堆统计用于确定受海浪影响的海冰覆盖部分。根据自 2010 年以来的 CS2 数据，我们生成了大西洋北极地区受波浪影响的 MIZ 气候记录，时间跨度为 2010 年至 2022 年的 12 个冬季（https://doi.org/10.5281/zenodo.8176585，Zhu 等人，2023 年）。该记录表明，尽管存在较大的时空变异性，但平均 MIZ 宽度或极端宽度没有明显变化。特别是在巴伦支海观测到了极宽的 MIZ 事件（超过 300 公里），而在北极大西洋的其他地区，MIZ 事件通常较窄。我们还将基于 CS2 的检索与基于 ICESat2 激光测高仪和哨兵-1 合成孔径雷达图像的检索进行了比较。在空间和时间搭配的情况下，基于三种不同类型卫星有效载荷的 MIZ 检索结果具有良好的一致性。此外，传统的基于海冰浓度的 MIZ 定义得到的 MIZ 系统性地比 CS2 更窄，两者之间不存在统计意义上的显著相关性。除了应用于 CS2 外，所提出的检索算法还可适用于过去和未来的雷达测高活动。多颗卫星的协同作用可以改进测高仪对 MIZ 观测的空间和时间表示。
{"title":"A 12-year climate record of wintertime wave-affected marginal ice zones in the Atlantic Arctic based on CryoSat-2","authors":"Weixin Zhu, Siqi Liu, Shiming Xu, Lu Zhou","doi":"10.5194/essd-16-2917-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2917-2024","url":null,"abstract":"Abstract. The wave-affected marginal ice zone (MIZ) is an essential part of the sea ice cover and crucial to the atmosphere–ice–ocean interaction in the polar region. While we primarily rely on in situ campaigns for studying MIZs, significant challenges exist for the remote sensing of MIZs by satellites. This study develops a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter on board CryoSat-2 (CS2). CS2 waveform power and waveform stack statistics are used to determine the part of the sea ice cover affected by waves. Based on the CS2 data since 2010, we generate a climate record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022 (https://doi.org/10.5281/zenodo.8176585, Zhu et al., 2023). The MIZ record indicates no significant change in the mean MIZ width or the extreme width, although large temporal and spatial variability is present. In particular, extremely wide MIZ events (over 300 km) are observed in the Barents Sea, whereas in other parts of the Atlantic Arctic, MIZ events are typically narrower. We also compare the CS2-based retrieval with the retrievals based on the laser altimeter of ICESat2 and the synthetic aperture radar images from Sentinel-1. Under spatial and temporal collocation, we attain good agreement among the MIZ retrievals based on the three different types of satellite payloads. Moreover, the traditional sea-ice-concentration-based definition of MIZ yields systematically narrower MIZs than CS2, and no statistically significant correlation exists between the two. Beyond its application to CS2, the proposed retrieval algorithm can be adapted to historical and future radar altimetry campaigns. The synergy of multiple satellites can improve the spatial and temporal representation of the altimeters' observation of the MIZs.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435781","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 : 2024-06-20DOI: 10.5194/essd-16-2877-2024
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, Jacek Kozak
Abstract. Accurate information on forest tree species composition is vital for various scientific applications, as well as for forest inventory and management purposes. Country-wide, detailed species maps are a valuable resource for environmental management, conservation, research, and planning. Here, we performed the classification of 16 dominant tree species and genera in Poland using time series of Sentinel-2 imagery. To generate comprehensive spectral–temporal information, we created Sentinel-2 seasonal aggregations known as spectral–temporal metrics (STMs) within the Google Earth Engine (GEE). STMs were computed for short periods of 15–30 d during spring, summer, and autumn, covering multi-annual observations from 2018 to 2021. The Polish Forest Data Bank served as reference data, and, to obtain robust samples with pure stands only, the data were validated through automated and visual inspection based on very-high-resolution orthoimagery, resulting in 4500 polygons serving as training and test data. The forest mask was derived from available land cover datasets in GEE, namely the ESA WorldCover and Dynamic World dataset. Additionally, we incorporated various topographic and climatic variables from GEE to enhance classification accuracy. The random forest algorithm was employed for the classification process, and an area-adjusted accuracy assessment was conducted through cross-validation and test datasets. The results demonstrate that the country-wide forest stand species mapping achieved an accuracy exceeding 80 %; however, this varies greatly depending on species, region, and observation frequency. We provide freely accessible resources, including the forest tree species map and training and test data: https://doi.org/10.5281/zenodo.10180469 (Grabska-Szwagrzyk, 2023a).
{"title":"Map of forest tree species for Poland based on Sentinel-2 data","authors":"Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, Jacek Kozak","doi":"10.5194/essd-16-2877-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2877-2024","url":null,"abstract":"Abstract. Accurate information on forest tree species composition is vital for various scientific applications, as well as for forest inventory and management purposes. Country-wide, detailed species maps are a valuable resource for environmental management, conservation, research, and planning. Here, we performed the classification of 16 dominant tree species and genera in Poland using time series of Sentinel-2 imagery. To generate comprehensive spectral–temporal information, we created Sentinel-2 seasonal aggregations known as spectral–temporal metrics (STMs) within the Google Earth Engine (GEE). STMs were computed for short periods of 15–30 d during spring, summer, and autumn, covering multi-annual observations from 2018 to 2021. The Polish Forest Data Bank served as reference data, and, to obtain robust samples with pure stands only, the data were validated through automated and visual inspection based on very-high-resolution orthoimagery, resulting in 4500 polygons serving as training and test data. The forest mask was derived from available land cover datasets in GEE, namely the ESA WorldCover and Dynamic World dataset. Additionally, we incorporated various topographic and climatic variables from GEE to enhance classification accuracy. The random forest algorithm was employed for the classification process, and an area-adjusted accuracy assessment was conducted through cross-validation and test datasets. The results demonstrate that the country-wide forest stand species mapping achieved an accuracy exceeding 80 %; however, this varies greatly depending on species, region, and observation frequency. We provide freely accessible resources, including the forest tree species map and training and test data: https://doi.org/10.5281/zenodo.10180469 (Grabska-Szwagrzyk, 2023a).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430373","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}
Abstract. The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 available stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha−1·h−1·yr−1, with areas exceeding 4000 MJ·mm·ha−1·h−1·yr−1 mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China’s mean annual rainfall erosivity by 31 %~65 %, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206; Chen, 2024).
{"title":"Rainfall erosivity mapping in mainland China using 1-minute precipitation data from densely distributed weather stations","authors":"Yueli Chen, Yun Xie, Xingwu Duan, Minghu Ding","doi":"10.5194/essd-2024-195","DOIUrl":"https://doi.org/10.5194/essd-2024-195","url":null,"abstract":"<strong>Abstract.</strong> The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 available stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha<sup>−1</sup>·h<sup>−1</sup>·yr<sup>−1</sup>, with areas exceeding 4000 MJ·mm·ha<sup>−1</sup>·h<sup>−1</sup>·yr<sup>−1</sup> mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China’s mean annual rainfall erosivity by 31 %~65 %, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206; Chen, 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430346","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 : 2024-06-19DOI: 10.5194/essd-16-2831-2024
David Winker, Xia Cai, Mark Vaughan, Anne Garnier, Brian Magill, Melody Avery, Brian Getzewich
Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).
{"title":"A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements","authors":"David Winker, Xia Cai, Mark Vaughan, Anne Garnier, Brian Magill, Melody Avery, Brian Getzewich","doi":"10.5194/essd-16-2831-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2831-2024","url":null,"abstract":"Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00\u0000(Winker et al., 2018).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425405","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 : 2024-06-19DOI: 10.5194/essd-16-2857-2024
Zhe Jin, Xiangjun Tian, Yilong Wang, Hongqin Zhang, Min Zhao, Tao Wang, Jinzhi Ding, Shilong Piao
Abstract. Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations in carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 and −2.32 ± 0.18 Pg C yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 Pg C yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 Pg C yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with Total Carbon Column Observing Network (TCCON) retrievals as well as Observation Package (ObsPack) surface flask observations and aircraft observations. Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error (RMSE) in posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at https://doi.org/10.5281/zenodo.8368846 (Jin et al., 2023a).
{"title":"A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system","authors":"Zhe Jin, Xiangjun Tian, Yilong Wang, Hongqin Zhang, Min Zhao, Tao Wang, Jinzhi Ding, Shilong Piao","doi":"10.5194/essd-16-2857-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2857-2024","url":null,"abstract":"Abstract. Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations in carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 and −2.32 ± 0.18 Pg C yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 Pg C yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 Pg C yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with Total Carbon Column Observing Network (TCCON) retrievals as well as Observation Package (ObsPack) surface flask observations and aircraft observations. Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error (RMSE) in posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at https://doi.org/10.5281/zenodo.8368846 (Jin et al., 2023a).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425521","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}
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, Lucie Homolová
Abstract. Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), forest floor (based on in situ measurements) and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at: Hovi et al. 2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9 (for laboratory and field data) and Hovi et al. 2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f (for airborne data).
{"title":"A spectral-structural characterization of European temperate, hemiboreal and boreal forests","authors":"Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, Lucie Homolová","doi":"10.5194/essd-2024-154","DOIUrl":"https://doi.org/10.5194/essd-2024-154","url":null,"abstract":"<strong>Abstract.</strong> Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), forest floor (based on in situ measurements) and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at: Hovi et al. 2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9 (for laboratory and field data) and Hovi et al. 2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f (for airborne data).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334324","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}
Murray S. A. Thompson, Izaskun Preciado, Federico Maioli, Valerio Bartolino, Andrea Belgrano, Michele Casini, Pierre Cresson, Elena Eriksen, Gema Hernandez-Milian, Ingibjörg G. Jónsdóttir, Stefan Neuenfeldt, John F. Pinnegar, Stefán Ragnarsson, Sabine Schueckel, Ulrike Schueckel, Brian E. Smith, María Á. Torres, Thomas J. Webb, Christopher P. Lynam
Abstract. International efforts to assess the status of marine ecosystems have been hampered by insufficient observations of food web interactions across many species, their various life stages, and geographic ranges. Hence, we collated data from multiple databases of fish stomach contents from samples taken across the North Atlantic and Arctic Oceans containing 944,129 stomach samples from larvae to adults, with 14,196 unique interactions between 227 predator species and 2158 prey taxa. We use these data to develop a data-driven, reproducible approach to classifying broad functional feeding guilds and then apply these to fish survey data from the Northeast Atlantic shelf seas to reveal spatial and temporal changes in ecosystem structure and functioning. In doing so, we construct predator-prey body size scaling models to predict the biomass of prey functional groups, e.g., zooplankton, benthos, and fish, for different predator species. These predictions provide empirical estimates of species- and size-specific feeding traits of fish, such as predator-prey mass ratios, individual prey mass, and the biomass contribution of different prey to predator diets. The functional groupings and feeding traits provided here help to further resolve our understanding of interactions within marine food webs and support the use of trait-based indicators in biodiversity assessments. The data used and predictions generated in this study are published on the Cefas Data Hub at: https://doi.org/10.14466/CefasDataHub.149 (Thompson et al., 2024).
{"title":"Fish functional groups of the North Atlantic and Arctic Oceans","authors":"Murray S. A. Thompson, Izaskun Preciado, Federico Maioli, Valerio Bartolino, Andrea Belgrano, Michele Casini, Pierre Cresson, Elena Eriksen, Gema Hernandez-Milian, Ingibjörg G. Jónsdóttir, Stefan Neuenfeldt, John F. Pinnegar, Stefán Ragnarsson, Sabine Schueckel, Ulrike Schueckel, Brian E. Smith, María Á. Torres, Thomas J. Webb, Christopher P. Lynam","doi":"10.5194/essd-2024-102","DOIUrl":"https://doi.org/10.5194/essd-2024-102","url":null,"abstract":"<strong>Abstract.</strong> International efforts to assess the status of marine ecosystems have been hampered by insufficient observations of food web interactions across many species, their various life stages, and geographic ranges. Hence, we collated data from multiple databases of fish stomach contents from samples taken across the North Atlantic and Arctic Oceans containing 944,129 stomach samples from larvae to adults, with 14,196 unique interactions between 227 predator species and 2158 prey taxa. We use these data to develop a data-driven, reproducible approach to classifying broad functional feeding guilds and then apply these to fish survey data from the Northeast Atlantic shelf seas to reveal spatial and temporal changes in ecosystem structure and functioning. In doing so, we construct predator-prey body size scaling models to predict the biomass of prey functional groups, e.g., zooplankton, benthos, and fish, for different predator species. These predictions provide empirical estimates of species- and size-specific feeding traits of fish, such as predator-prey mass ratios, individual prey mass, and the biomass contribution of different prey to predator diets. The functional groupings and feeding traits provided here help to further resolve our understanding of interactions within marine food webs and support the use of trait-based indicators in biodiversity assessments. The data used and predictions generated in this study are published on the Cefas Data Hub at: https://doi.org/10.14466/CefasDataHub.149 (Thompson et al., 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141333692","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}
Abstract. The Landsat series constitutes an unparalleled repository of multi-decadal Earth observations, serving as a cornerstone in global environmental monitoring. However, the inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses significant challenges to land monitoring over large geographical extents. In this study, we developed a full-chain processing framework for the multi-sensor data fusion of Landsat-5, 7, 8, 9 and MODIS Terra surface reflectance products. Based on this framework, a global, 30-m resolution, and daily Seamless Data Cube (SDC) of land surface reflectance was generated, spanning from 2000 to 2022. A thorough evaluation of the SDC was undertaken using a leave-one-out approach and a cross-comparison with NASA’s Harmonized Landsat and Sentinel-2 (HLS) products. The leave-one-out validation at 425 global test sites assessed the agreement between the SDC with actual Landsat surface reflectance values (not used as input), revealing an overall Mean Absolute Error (MAE) of 0.014 (the valid range of surface reflectance values is 0–1). The cross-comparison with the HLS products at 22 Military Grid Reference System (MGRS) tiles revealed an overall Mean Absolute Deviation (MAD) of 0.017 with L30 (Landsat-8-based 30-m HLS product) and a MAD of 0.021 with S30 (Sentinel-2-based 30-m HLS product). Moreover, experimental results underscore the advantages of employing the SDC for global land cover classification, achieving a sizable improvement in overall accuracy (2.4 %~11.3 %) over that obtained using Landsat composite and interpolated datasets. A web-based interface has been developed for researchers to freely access the SDC dataset, which is available at https://doi.org/10.12436/SDC30.26.20240506 (Chen et al., 2024).
{"title":"Global 30-m seamless data cube (2000–2022) of land surface reflectance generated from Landsat-5,7,8,9 and MODIS Terra constellations","authors":"Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, Peng Gong","doi":"10.5194/essd-2024-178","DOIUrl":"https://doi.org/10.5194/essd-2024-178","url":null,"abstract":"<strong>Abstract.</strong> The Landsat series constitutes an unparalleled repository of multi-decadal Earth observations, serving as a cornerstone in global environmental monitoring. However, the inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses significant challenges to land monitoring over large geographical extents. In this study, we developed a full-chain processing framework for the multi-sensor data fusion of Landsat-5, 7, 8, 9 and MODIS Terra surface reflectance products. Based on this framework, a global, 30-m resolution, and daily Seamless Data Cube (SDC) of land surface reflectance was generated, spanning from 2000 to 2022. A thorough evaluation of the SDC was undertaken using a leave-one-out approach and a cross-comparison with NASA’s Harmonized Landsat and Sentinel-2 (HLS) products. The leave-one-out validation at 425 global test sites assessed the agreement between the SDC with actual Landsat surface reflectance values (not used as input), revealing an overall Mean Absolute Error (MAE) of 0.014 (the valid range of surface reflectance values is 0–1). The cross-comparison with the HLS products at 22 Military Grid Reference System (MGRS) tiles revealed an overall Mean Absolute Deviation (MAD) of 0.017 with L30 (Landsat-8-based 30-m HLS product) and a MAD of 0.021 with S30 (Sentinel-2-based 30-m HLS product). Moreover, experimental results underscore the advantages of employing the SDC for global land cover classification, achieving a sizable improvement in overall accuracy (2.4 %~11.3 %) over that obtained using Landsat composite and interpolated datasets. A web-based interface has been developed for researchers to freely access the SDC dataset, which is available at https://doi.org/10.12436/SDC30.26.20240506 (Chen et al., 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141333690","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 : 2024-06-14DOI: 10.5194/essd-16-2773-2024
Robert W. Schlegel, Rakesh Kumar Singh, Bernard Gentili, Simon Bélanger, Laura Castro de la Guardia, Dorte Krause-Jensen, Cale A. Miller, Mikael Sejr, Jean-Pierre Gattuso
Abstract. Most inhabitants of the Arctic live near the coastline, which includes fjord systems where socio-ecological coupling with coastal communities is dominant. It is therefore critically important that the key aspects of Arctic fjords be measured as well as possible. Much work has been done to monitor temperature and salinity, but in-depth knowledge of the light environment throughout Arctic fjords is lacking. This is particularly problematic knowing the importance of light for benthic ecosystem engineers such as macroalgae, which also play a major role in ecosystem function. Here we document the creation and implementation of a high-resolution (∼50–150 m) gridded dataset for surface photosynthetically available radiation (PAR), diffuse attenuation of PAR through the water column (KPAR), and PAR available at the seafloor (bottom PAR) for seven Arctic fjords distributed throughout Svalbard, Greenland, and Norway during the period 2003–2022. In addition to KPAR and bottom PAR being available at a monthly resolution over this time period, all variables are available as a global average, annual averages, and monthly climatologies, with standard deviations provided for the latter two. Throughout most Arctic fjords, the interannual variability of monthly bottom PAR is too large to determine any long-term trends. However, in some fjords, bottom PAR increases in spring and autumn and decreases in summer. While a full investigation into these causes is beyond the scope of the description of the dataset presented here, it is hypothesized that this shift is due to a decrease in seasonal ice cover (i.e. enhanced surface PAR) in the shoulder seasons and an increase in coastal runoff (i.e. increased turbidity and decreased surface PAR) in summer. A demonstration of the usability of the dataset is given by showing how it can be combined with known PAR requirements of macroalgae to track the change in the potential distribution area for macroalgal habitats within fjords with time. The datasets are available on PANGAEA at https://doi.org/10.1594/PANGAEA.962895 (Gentili et al., 2023a) and https://doi.org/10.1594/PANGAEA.965460 (Gentili et al., 2024). A toolbox for downloading and working with this dataset is available in the form of the FjordLight R package, which is available via CRAN (Gentili et al., 2023b, https://doi.org/10.5281/zenodo.10259129) or may be installed via GitHub: https://face-it-project.github.io/FjordLight (last access: 29 April 2024).
摘要北极地区的大多数居民都生活在海岸线附近，其中包括峡湾系统，在峡湾系统中，与沿海社区的社会生态耦合是主要的。因此，尽可能好地测量北极峡湾的关键方面至关重要。在监测温度和盐度方面已经做了大量工作，但对整个北极峡湾的光环境还缺乏深入了解。鉴于光对大型藻类等底栖生物生态系统工程师的重要性，这一点尤其成问题，而大型藻类在生态系统功能中也发挥着重要作用。在此，我们记录了 2003-2022 年期间为分布在斯瓦尔巴群岛、格陵兰岛和挪威的七个北极峡湾创建和实施高分辨率（50-150 米）网格数据集的情况，包括地表光合可利用辐射（PAR）、PAR 在水体中的漫射衰减（KPAR）和海底可利用 PAR（底层 PAR）。在这一时期，除了以月为分辨率提供 KPAR 和海底 PAR 外，还提供所有变量的全球平均值、年平均值和月气候值，并提供后两者的标准偏差。在大多数北极峡湾，月底层 PAR 的年际变化太大，无法确定任何长期趋势。不过，在一些峡湾，底部 PAR 在春季和秋季增加，而在夏季减少。虽然对这些原因的全面调查超出了本文数据集描述的范围，但可以推测，这种变 化是由于肩季季节性冰盖减少（即表面 PAR 增加）和夏季沿岸径流增加（即浊度增加和表面 PAR 减少）造成的。通过展示如何将该数据集与已知大型藻类对 PAR 的要求结合起来，跟踪峡湾内大型藻类栖息地潜在分布区随时间的变 化，证明了该数据集的可用性。数据集可在 PANGAEA 网站 https://doi.org/10.1594/PANGAEA.962895（Gentili 等，2023a）和 https://doi.org/10.1594/PANGAEA.965460（Gentili 等，2024）上查阅。FjordLight R 软件包是下载和使用该数据集的工具箱，可通过 CRAN 获取（Gentili 等，2023b, https://doi.org/10.5281/zenodo.10259129），也可通过 GitHub 安装：https://face-it-project.github.io/FjordLight（最后访问日期：2024 年 4 月 29 日）。
{"title":"Underwater light environment in Arctic fjords","authors":"Robert W. Schlegel, Rakesh Kumar Singh, Bernard Gentili, Simon Bélanger, Laura Castro de la Guardia, Dorte Krause-Jensen, Cale A. Miller, Mikael Sejr, Jean-Pierre Gattuso","doi":"10.5194/essd-16-2773-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2773-2024","url":null,"abstract":"Abstract. Most inhabitants of the Arctic live near the coastline, which includes fjord systems where socio-ecological coupling with coastal communities is dominant. It is therefore critically important that the key aspects of Arctic fjords be measured as well as possible. Much work has been done to monitor temperature and salinity, but in-depth knowledge of the light environment throughout Arctic fjords is lacking. This is particularly problematic knowing the importance of light for benthic ecosystem engineers such as macroalgae, which also play a major role in ecosystem function. Here we document the creation and implementation of a high-resolution (∼50–150 m) gridded dataset for surface photosynthetically available radiation (PAR), diffuse attenuation of PAR through the water column (KPAR), and PAR available at the seafloor (bottom PAR) for seven Arctic fjords distributed throughout Svalbard, Greenland, and Norway during the period 2003–2022. In addition to KPAR and bottom PAR being available at a monthly resolution over this time period, all variables are available as a global average, annual averages, and monthly climatologies, with standard deviations provided for the latter two. Throughout most Arctic fjords, the interannual variability of monthly bottom PAR is too large to determine any long-term trends. However, in some fjords, bottom PAR increases in spring and autumn and decreases in summer. While a full investigation into these causes is beyond the scope of the description of the dataset presented here, it is hypothesized that this shift is due to a decrease in seasonal ice cover (i.e. enhanced surface PAR) in the shoulder seasons and an increase in coastal runoff (i.e. increased turbidity and decreased surface PAR) in summer. A demonstration of the usability of the dataset is given by showing how it can be combined with known PAR requirements of macroalgae to track the change in the potential distribution area for macroalgal habitats within fjords with time. The datasets are available on PANGAEA at https://doi.org/10.1594/PANGAEA.962895 (Gentili et al., 2023a) and https://doi.org/10.1594/PANGAEA.965460 (Gentili et al., 2024). A toolbox for downloading and working with this dataset is available in the form of the FjordLight R package, which is available via CRAN (Gentili et al., 2023b, https://doi.org/10.5281/zenodo.10259129) or may be installed via GitHub: https://face-it-project.github.io/FjordLight (last access: 29 April 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141320025","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 : 2024-06-14DOI: 10.5194/essd-16-2789-2024
Chu Zou, Shanshan Du, Xinjie Liu, Liangyun Liu
Abstract. Satellite-based solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for monitoring the photosynthesis of vegetation globally. The Global Ozone Monitoring Experiment-2A (GOME-2A) SIF product has gained widespread popularity, particularly due to its extensive global coverage since 2007. However, serious temporal degradation of the GOME-2A instrument is a problem, and there is currently a lack of time-consistent GOME-2A SIF products that meet the needs of temporal trend analysis. In this paper, the GOME-2A instrument's temporal degradation was first calibrated using a pseudo-invariant method, which revealed 16.21 % degradation of the GOME-2A radiance at the near-infrared (NIR) band from 2007 to 2021. Based on the calibration results, the temporal degradation of the GOME-2A radiance spectra was successfully corrected by using a fitted quadratic polynomial function whose determination coefficient (R2) was 0.851. Next, a data-driven algorithm was applied for SIF retrieval at the 735–758 nm window. Also, a photosynthetically active radiation (PAR)-based upscaling model was employed to upscale the instantaneous clear-sky observations to monthly average values to compensate for the changes in cloud conditions and atmospheric scattering. Accordingly, a global temporally consistent GOME-2A SIF dataset (TCSIF) for 2007 to 2021 with the correction of temporal degradation was successfully generated, and the spatiotemporal pattern of global SIF was then investigated. Corresponding trend maps of the global temporally consistent GOME-2A SIF showed that 62.91 % of vegetated regions underwent an increase in SIF, and the global annual averaged SIF exhibited a trend of increasing by 0.70 % yr−1 during the 2007–2021 period. The TCSIF dataset is available at https://doi.org/10.5281/zenodo.8242928 (Zou et al., 2023).
{"title":"TCSIF: a temporally consistent global Global Ozone Monitoring Experiment-2A (GOME-2A) solar-induced chlorophyll fluorescence dataset with the correction of sensor degradation","authors":"Chu Zou, Shanshan Du, Xinjie Liu, Liangyun Liu","doi":"10.5194/essd-16-2789-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2789-2024","url":null,"abstract":"Abstract. Satellite-based solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for monitoring the photosynthesis of vegetation globally. The Global Ozone Monitoring Experiment-2A (GOME-2A) SIF product has gained widespread popularity, particularly due to its extensive global coverage since 2007. However, serious temporal degradation of the GOME-2A instrument is a problem, and there is currently a lack of time-consistent GOME-2A SIF products that meet the needs of temporal trend analysis. In this paper, the GOME-2A instrument's temporal degradation was first calibrated using a pseudo-invariant method, which revealed 16.21 % degradation of the GOME-2A radiance at the near-infrared (NIR) band from 2007 to 2021. Based on the calibration results, the temporal degradation of the GOME-2A radiance spectra was successfully corrected by using a fitted quadratic polynomial function whose determination coefficient (R2) was 0.851. Next, a data-driven algorithm was applied for SIF retrieval at the 735–758 nm window. Also, a photosynthetically active radiation (PAR)-based upscaling model was employed to upscale the instantaneous clear-sky observations to monthly average values to compensate for the changes in cloud conditions and atmospheric scattering. Accordingly, a global temporally consistent GOME-2A SIF dataset (TCSIF) for 2007 to 2021 with the correction of temporal degradation was successfully generated, and the spatiotemporal pattern of global SIF was then investigated. Corresponding trend maps of the global temporally consistent GOME-2A SIF showed that 62.91 % of vegetated regions underwent an increase in SIF, and the global annual averaged SIF exhibited a trend of increasing by 0.70 % yr−1 during the 2007–2021 period. The TCSIF dataset is available at https://doi.org/10.5281/zenodo.8242928 (Zou et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319959","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}
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