Earth System Science Data最新文献

{"title":"HUST-Grace2024: a new GRACE-only gravity field time series based on more than 20 years of satellite geodesy data and a hybrid processing chain","authors":"Hao Zhou, Lijun Zheng, Yaozong Li, Xiang Guo, Zebing Zhou, Zhicai Luo","doi":"10.5194/essd-16-3261-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3261-2024","url":null,"abstract":"Abstract. To improve the accuracy of monthly temporal gravity field models for the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On (GRACE-FO) missions, a new series named HUST-Grace2024 is determined based on the updated L1B datasets (GRACE L1B RL03 and GRACE-FO L1B RL04) and the newest atmosphere and ocean de-aliasing product (AOD1B RL07). Compared to the previous HUST temporal gravity field model releases, we have made the following improvements related to updating the background models and the processing chain: (1) during the satellite onboard events, the inter-satellite pointing angles are calculated to pinpoint outliers in the K-band ranging (KBR) range-rate and accelerometer observations. To exclude outliers, the advisable threshold is 50 mrad for KBR range rates and 20 mrad for accelerations. (2) To relieve the impacts of KBR range-rate noise at different frequencies, a hybrid data-weighting method is proposed. Kinematic empirical parameters are used to reduce the low-frequency noise, while a stochastic model is designed to relieve the impacts of random noise above 10 mHz. (3) A fully populated scale factor matrix is used to improve the quality of accelerometer calibration. Analyses in the spectral and spatial domains are then implemented, which demonstrate that HUST-Grace2024 yields a noticeable reduction of 10 % to 30 % in noise level and retains consistent amplitudes of signal content over 48 river basins compared with the official GRACE and GRACE-FO solutions. These evaluations confirm that our aforementioned efforts lead to a better temporal gravity field series. This data set is identified with the following DOI: https://doi.org/10.5880/ICGEM.2024.001 (Zhou et al., 2024).\u0000","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654207","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}

{"title":"Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea","authors":"Jisun Shin, Dae-Won Kim, So-Hyun Kim, Gi Seop Lee, B. Khim, Young-Heon Jo","doi":"10.5194/essd-16-3193-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3193-2024","url":null,"abstract":"Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).\u0000","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662657","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}

{"title":"ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021","authors":"Qing Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, Fulu Tao","doi":"10.5194/essd-16-3213-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3213-2024","url":null,"abstract":"Abstract. Soybean, an essential food crop, has witnessed a steady rise in demand in recent years. There is a lack of high-resolution annual maps depicting soybean-planting areas in China, despite China being the world's largest consumer and fourth-largest producer of soybean. To address this gap, we developed the novel Regional Adaptation Spectra-Phenology Integration method (RASP) based on Sentinel-2 remote sensing images from the Google Earth Engine (GEE) platform. We utilized various auxiliary data (e.g., cropland layer, detailed phenology observations) to select the specific spectra and indices that differentiate soybeans most effectively from other crops across various regions. These features were then input for an unsupervised classifier (K-means), and the most likely type was determined by a cluster assignment method based on dynamic time warping (DTW). For the first time, we generated a dataset of soybean-planting areas across China, with a high spatial resolution of 10 m, spanning from 2017 to 2021 (ChinaSoyArea10m). The R2 values between the mapping results and the census data at both the county and prefecture levels were consistently around 0.85 in 2017–2020. Moreover, the overall accuracy of the mapping results at the field level in 2017, 2018, and 2019 was 77.08 %, 85.16 %, and 86.77 %, respectively. Consistency with census data was improved at the county level (R2 increased from 0.53 to 0.84) compared to the existing 10 m crop-type maps in Northeast China (Crop Data Layer, CDL) based on field samples and supervised classification methods. ChinaSoyArea10m is very spatially consistent with the two existing datasets (CDL and GLAD (Global Land Analysis and Discovery) maize–soybean map). ChinaSoyArea10m provides important information for sustainable soybean production and management as well as agricultural system modeling and optimization. ChinaSoyArea10m can be downloaded from an open-data repository (DOI: https://doi.org/10.5281/zenodo.10071427, Mei et al., 2023).\u0000","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662812","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}

{"title":"CCD-Rice: A long-term paddy rice distribution dataset in China at 30 m resolution","authors":"Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, Wenping Yuan","doi":"10.5194/essd-2024-147","DOIUrl":"https://doi.org/10.5194/essd-2024-147","url":null,"abstract":"<strong>Abstract.</strong> As one of the most widely cultivated grain crops, paddy rice is a vital staple food in China and plays a crucial role in ensuring food security. Over the past decades, the planting area of paddy rice in China has shown substantial variability. Yet, there are no long-term high-resolution rice distribution maps in China, which hinders our ability to estimate greenhouse gas fluxes and crop production. This study developed a new optical satellite-based rice mapping method using a machine learning model and appropriate data preprocessing strategies to address the challenges of cloud contamination and missing data in optical remote sensing observations. This study produced CCD-Rice (China Crop Dataset-Rice), the first high-resolution rice distribution dataset in China from 1990 to 2016. Based on 391,659 validation samples, the overall accuracy of the distribution maps in each provincial administrative region averaged 90.26 %. Compared with 20,759 county-level statistical data, the coefficients of determination (<em>R</em>²) of single- and double-season rice in each year averaged 0.84 and 0.80, respectively. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.15865 (Shen et al., 2024a).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561462","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}

{"title":"A new repository of electrical resistivity tomography and ground-penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard","authors":"Francesca Pace, Andrea Vergnano, Alberto Godio, Gerardo Romano, Luigi Capozzoli, Ilaria Baneschi, Marco Doveri, Alessandro Santilano","doi":"10.5194/essd-16-3171-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3171-2024","url":null,"abstract":"Abstract. We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (western Svalbard, Brøggerhalvøya Peninsula, Norway) as part of the project ICEtoFLUX. The aim of the investigation is to characterize the role of groundwater flow through the active layer as well as through and/or below the permafrost. The data set is composed of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. Overall, 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). The resistivity models revealed deep resistive structures, probably related to the heterogeneous permafrost, which are often interrupted by electrically conductive regions that may relate to aquifers and/or faults. The interpretation of these data can support the identification of the active layer, the occurrence of spatial variation in soil conditions at depth, and the presence of groundwater flow through the permafrost. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate change studies of the Ny-Ålesund area. The data set is of major relevance because there are few geophysical data published about the Ny-Ålesund area. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, and geomorphology, etc. The geophysical data are provided in a free repository and can be accessed at https://doi.org/10.5281/zenodo.10260056 (Pace et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561186","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}

{"title":"SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea","authors":"Lars Kaleschke, Xiangshan Tian-Kunze, Stefan Hendricks, Robert Ricker","doi":"10.5194/essd-16-3149-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3149-2024","url":null,"abstract":"Abstract. Accurate satellite measurements of the thickness of Antarctic sea ice are urgently needed but pose a particular challenge. The Antarctic data presented here were produced using a method to derive the sea ice thickness from 1.4 GHz brightness temperatures previously developed for the Arctic, with only modified auxiliary data. The ability to observe the thickness of thin sea ice using this method is limited to cold conditions, meaning it is only reasonable during the freezing period, typically March to October. The Soil Moisture and Ocean Salinity (SMOS) level-3 sea ice thickness product contains estimates of the sea ice thickness and its uncertainty up to a thickness of about 1 m. The sea ice thickness is provided as a daily average on a polar stereographic projection grid with a sample resolution of 12.5 km, while the SMOS brightness temperature data used have a footprint size of about 35–40 km in diameter. Data from SMOS have been available since 2010, and the mission's operation has been extended to continue until at least the end of 2025. Here we compare two versions of the SMOS Antarctic sea ice thickness product which are based on different level-1 input data (v3.2 based on SMOS L1C v620 and v3.3 based on SMOS L1C 724). A validation is performed to generate a first baseline reference for future improvements of the retrieval algorithm and synergies with other sensors. Sea ice thickness measurements to validate the SMOS product are particularly rare in Antarctica, especially during the winter season and for the valid range of thicknesses. From the available validation measurements, we selected datasets from the Weddell Sea that have varying degrees of representativeness: Helicopter-based EM Bird (HEM), Surface and Under-Ice Trawl (SUIT), and stationary Upward-Looking Sonars (ULS). While the helicopter can measure hundreds of kilometres, SUIT's use is limited to distances of a few kilometres and thus only captures a small fraction of an SMOS footprint. Compared to SMOS, the ULS are point measurements and multi-year time series are necessary to enable a statistically representative comparison. Only four of the ULS moorings have a temporal overlap with SMOS in the year 2010. Based on selected averaged HEM flights and monthly ULS climatologies, we find a small mean difference (bias) of less than 10 cm and a root mean square deviation of about 20 cm with a correlation coefficient R > 0.9 for the valid sea ice thickness range between 0 and about 1 m. The SMOS sea ice thickness showed an underestimate of about 40 cm with respect to the less representative SUIT validation data in the marginal ice zone. Compared with sea ice thickness outside the valid range, we find that SMOS strongly underestimates the real values, which underlines the need for combination with other sensors such as altimeters. In summary, the overall validity of the SMOS sea ice thickness for thin sea ice up to a thickness of about 1 m has been demonstrated through validat","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141557201","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}

{"title":"Global Greenhouse Gas Reconciliation 2022","authors":"Zhu Deng, Philippe Ciais, Liting Hu, Adrien Martinez, Marielle Saunois, Rona L. Thompson, Kushal Tibrewal, Wouter Peters, Brendan Byrne, Giacomo Grassi, Paul I. Palmer, Ingrid T. Luijkx, Zhu Liu, Junjie Liu, Xuekun Fang, Tengjiao Wang, Hanqin Tian, Katsumasa Tanaka, Ana Bastos, Stephen Sitch, Benjamin Poulter, Clément Albergel, Aki Tsuruta, Shamil Maksyutov, Rajesh Janardanan, Yosuke Niwa, Bo Zheng, Joël Thanwerdas, Dmitry Belikov, Arjo Segers, Frédéric Chevallier","doi":"10.5194/essd-2024-103","DOIUrl":"https://doi.org/10.5194/essd-2024-103","url":null,"abstract":"<strong>Abstract.</strong> In this study, we provide an update of the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO₂) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH₄), and nitrous oxide (N₂O), we separate anthropogenic emissions from natural sources based directly on the inversion results, to make them compatible with NGHGIs. Our global harmonized NGHGIs database was updated with inventory data until February 2023 by compiling data from periodical UNFCCC inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by National Communications and Biennial Update Reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO₂, CH₄ and N₂O coordinated by the Global Carbon Project with ancillary data. The CO₂ inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study, and an improved managed land mask. As a result, although significant differences exist between the CO₂ inversion estimates, both satellite and in-situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH₄ and N₂O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slow declining or stable trend in emissions. Much denser sampling and higher atmospheric CO₂ and CH₄ concentrations by different satellites, are expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.10841716 (Deng et al., 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553433","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}

{"title":"A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet","authors":"Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, Chuqun Chen","doi":"10.5194/essd-16-3125-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3125-2024","url":null,"abstract":"Abstract. Satellite remote sensing of sea surface chlorophyll products sometimes yields a significant amount of sporadic missing data due to various variables, such as weather conditions and operational failures of satellite sensors. The limited nature of satellite observation data impedes the utilization of satellite data in the domain of marine research. Hence, it is highly important to investigate techniques for reconstructing satellite remote sensing data to obtain spatially and temporally uninterrupted and comprehensive data within the desired area. This approach will expand the potential applications of remote sensing data and enhance the efficiency of data usage. To address this series of problems, based on the demand for research on the ecological effects of multiscale dynamic processes in the South China Sea, this paper combines the advantages of the optimal interpolation (OI) method and SwinUnet and successfully develops a deep-learning model based on the expected variance in data anomalies, called OI-SwinUnet. The OI-SwinUnet method was used to reconstruct the MODIS chlorophyll-a concentration products of the South China Sea from 2013 to 2017. When comparing the performances of the data-interpolating empirical orthogonal function (DINEOF), OI, and Unet approaches, it is evident that the OI-SwinUnet algorithm outperforms the other algorithms in terms of reconstruction. We conduct a reconstruction experiment using different artificial missing patterns to assess the resilience of OI-SwinUnet. Ultimately, the reconstructed dataset was utilized to examine the seasonal variations and geographical distribution of chlorophyll-a concentrations in various regions of the South China Sea. Additionally, the impact of the plume front on the dispersion of phytoplankton in upwelling areas was assessed. The potential use of reconstructed products to investigate the process by which individual mesoscale eddies affect sea surface chlorophyll is also examined. The reconstructed daily chlorophyll-a dataset is freely accessible at https://doi.org/10.5281/zenodo.10478524 (Ye et al., 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546083","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}

{"title":"A Sentinel-2 Machine Learning Dataset for Tree Species Classification in Germany","authors":"Maximilian Freudenberg, Sebastian Schnell, Paul Magdon","doi":"10.5194/essd-2024-206","DOIUrl":"https://doi.org/10.5194/essd-2024-206","url":null,"abstract":"<strong>Abstract.</strong> We present a machine learning dataset for tree species classification in Sentinel-2 satellite image time series of bottom of atmosphere reflectance. The dataset is based on the German national forest inventory of 2012, as well as analysis ready satellite imagery computed using the FORCE processing pipeline. From the national forest inventory data, we extracted the tree positions, filtered 387 775 trees in the upper canopy layer and automatically extracted the corresponding bottom of atmosphere reflectance time series from Sentinel-2 L2A images. These time series are labeled with the corresponding tree species, which allows pixel-wise classification tasks. Furthermore, we provide auxiliary information such as the approximate tree position, the year of possible disturbance events or the diameter at breast height. Temporally, the dataset spans the years from July 2015 to end of October 2022 with ca. 75.3 million data points for trees of 51 species and species groups, as well as 13.8 million observations for non-tree background. Spatially, it covers entire Germany. The dataset is available under following DOI (Freudenberg et al., 2024): https://doi.org/10.3220/DATA20240402122351-0","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546084","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}

{"title":"Annual vegetation maps in Qinghai-Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery","authors":"Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, Mengzi Zhou","doi":"10.5194/essd-2024-193","DOIUrl":"https://doi.org/10.5194/essd-2024-193","url":null,"abstract":"<strong>Abstract.</strong> The Qinghai Tibet Plateau (QTP), known as the \"Third Pole\" of the Earth\" and the \"Water Tower of Asia,\" plays a crucial role in global climate regulation, biodiversity conservation, and regional socio-economic development. Continuous annual vegetation types and their geographical distribution data are essential for studying the response and adaptation of vegetation to climate change. However, there is very limited data on vegetation types and their geographical distributions on the QTP due to harsh natural environment. Currently, land cover/surface vegetation (LCSV) data are typically obtained using independent classification methods for each period's product, based on remote sensing information. These approaches do not consider the time continuity of vegetation to presence, and leads to a gradual increase in the number of misclassified pixels and the uncertainty of their locations, consequently decreasing the interpretability of the long-time series remote sensing products. To address this issue, this study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the QTP from 2000 to 2022 at a 500 m spatial resolution through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping from 2000 to 2022 reached 80.9 % based on 733 samples from literature, with the reference annual 2020 reaching an accuracy of 86.5 % and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-term continuous annual vegetation.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546101","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}