Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0021.zh
Jiaxin Song, Li Zhou, Guangsheng Zhou, Yujie Yan, Sen Zhang
Eddy correlation technology is an important observation method for the precise and long-term continuous measurement of carbon-water flux in ecosystems. The observations can provide important data support for studying carbon and water cycle processes and mechanisms in terrestrial ecosystems and evaluating ecosystem carbon income and expenditure. Damao Desert Steppe Station (Damao Station) is one of the member stations of the Chinese FLUX Observation and Research Network, located in Baotou City, central Inner Mongolia Autonomous Region. The vegetation in the station area is a typical example of warm desert grassland in China, primarily composed of needlegrass communities. Based on the eddy covariance method, Damao station has been carrying out integrated observations of ecosystem carbon and water fluxes for a long term. This dataset compiles the observation data from Damao Station from 2015 to 2018 pursuant to the strict data processing system of ChinaFLUX. It is a standardized dataset of ecosystem CO2 flux, water and heat flux, and corresponding meteorological elements, including the data products at half-hourly, daily, monthly, and yearly scales.
{"title":"A dataset of carbon and water fluxes of the temperate desert steppe in Damao Banner, Inner Mongolia (2015–2018)","authors":"Jiaxin Song, Li Zhou, Guangsheng Zhou, Yujie Yan, Sen Zhang","doi":"10.11922/11-6035.csd.2023.0021.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0021.zh","url":null,"abstract":"Eddy correlation technology is an important observation method for the precise and long-term continuous measurement of carbon-water flux in ecosystems. The observations can provide important data support for studying carbon and water cycle processes and mechanisms in terrestrial ecosystems and evaluating ecosystem carbon income and expenditure. Damao Desert Steppe Station (Damao Station) is one of the member stations of the Chinese FLUX Observation and Research Network, located in Baotou City, central Inner Mongolia Autonomous Region. The vegetation in the station area is a typical example of warm desert grassland in China, primarily composed of needlegrass communities. Based on the eddy covariance method, Damao station has been carrying out integrated observations of ecosystem carbon and water fluxes for a long term. This dataset compiles the observation data from Damao Station from 2015 to 2018 pursuant to the strict data processing system of ChinaFLUX. It is a standardized dataset of ecosystem CO2 flux, water and heat flux, and corresponding meteorological elements, including the data products at half-hourly, daily, monthly, and yearly scales.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47850254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.noda.2022.0002.zh
Jiao Guo, Jingyuan Bai, Yongkai Ye, Chaoyue Han, Wei-Tao Zhang
Satellite remote sensing technology can obtain the distribution of ground objects on a large scale in a timely manner, and provide great data and technical support for the acquisition of information on the planting structure of cash crops. Taking Yangling Agricultural Demonstration Area as the research area, this dataset is composed of four parts: remote sensing data, ground truth data, Yangling boundary and classification results. The remote sensing data consist of satellite data, such as Sentinel-2, Gaofen-1 (including Gaofen-1C satellite), Gaofen-2, and Gaofen-6 from April to September in 2021 after radiation correction, atmospheric correction, and remote sensing image processing such as orthorectification, image fusion, and image registration. Through on-the-spot investigation, visual interpretation of Google Earth, and near-ground remote sensing of UAVs in small areas, we established the ground truth distribution verification area. In terms of quality control, the remote sensing data are characteristic of little overall cloud content, uniform color, and a spatial resolution of 2m; the ground truth map, authentic and reliable, is drawn through field surveys. The dataset has been verified by random forest algorithm, and the overall classification accuracy is 86.17%. It can provide training samples for the research and application of related algorithms in the acquisition of cash crop planting structure, and can also provide data support for land use classification and changes as well as crop growth monitoring in Yangling Demonstration Zone.
{"title":"A dataset of multi-source and multi-temporal remote sensing data of cash crop planting structure in Yangling Agricultural Demonstration Zone","authors":"Jiao Guo, Jingyuan Bai, Yongkai Ye, Chaoyue Han, Wei-Tao Zhang","doi":"10.11922/11-6035.noda.2022.0002.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0002.zh","url":null,"abstract":"Satellite remote sensing technology can obtain the distribution of ground objects on a large scale in a timely manner, and provide great data and technical support for the acquisition of information on the planting structure of cash crops. Taking Yangling Agricultural Demonstration Area as the research area, this dataset is composed of four parts: remote sensing data, ground truth data, Yangling boundary and classification results. The remote sensing data consist of satellite data, such as Sentinel-2, Gaofen-1 (including Gaofen-1C satellite), Gaofen-2, and Gaofen-6 from April to September in 2021 after radiation correction, atmospheric correction, and remote sensing image processing such as orthorectification, image fusion, and image registration. Through on-the-spot investigation, visual interpretation of Google Earth, and near-ground remote sensing of UAVs in small areas, we established the ground truth distribution verification area. In terms of quality control, the remote sensing data are characteristic of little overall cloud content, uniform color, and a spatial resolution of 2m; the ground truth map, authentic and reliable, is drawn through field surveys. The dataset has been verified by random forest algorithm, and the overall classification accuracy is 86.17%. It can provide training samples for the research and application of related algorithms in the acquisition of cash crop planting structure, and can also provide data support for land use classification and changes as well as crop growth monitoring in Yangling Demonstration Zone.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43534504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.ncdc.2022.0007.zh
L. Ji, Hairong Tang, Kai Yu, Qinyu Zhao, Yuqi Bai, Jiancheng Shi
Under the background of global warming, the Tibet Plateau shows a trend of warming and wetting. Remote sensing data can be used to effectively monitor the spatial and temporal changes of the surface water on the Tibet Plateau. Due to the dynamic characteristics of the water, and in order to study the long-term impact of climate and other factors on the water of the Qinghai-Tibet Plateau, we used the AVHRR daily reflectance time series from 1982 to 2020 to produce the 39-year daily water body mapping product (including water freezing information) on the Qinghai-Tibet Plateau. The overall accuracy of our product is up to 88.18% based on validation samples selected from 30m Landsat images. This product can provide fundamental data support for long-term water monitoring on the Qinghai Tibet Plateau.
{"title":"A dataset of daily surface water mapping products with a resolution of 0.05° on the Qinghai–Tibet Plateau during","authors":"L. Ji, Hairong Tang, Kai Yu, Qinyu Zhao, Yuqi Bai, Jiancheng Shi","doi":"10.11922/11-6035.ncdc.2022.0007.zh","DOIUrl":"https://doi.org/10.11922/11-6035.ncdc.2022.0007.zh","url":null,"abstract":"Under the background of global warming, the Tibet Plateau shows a trend of warming and wetting. Remote sensing data can be used to effectively monitor the spatial and temporal changes of the surface water on the Tibet Plateau. Due to the dynamic characteristics of the water, and in order to study the long-term impact of climate and other factors on the water of the Qinghai-Tibet Plateau, we used the AVHRR daily reflectance time series from 1982 to 2020 to produce the 39-year daily water body mapping product (including water freezing information) on the Qinghai-Tibet Plateau. The overall accuracy of our product is up to 88.18% based on validation samples selected from 30m Landsat images. This product can provide fundamental data support for long-term water monitoring on the Qinghai Tibet Plateau.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41831942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Yellow River Basin is located in the arid, semi-arid climate zone and semi-humid climate zones. According to the statistics of the Second National Comprehensive Water Resources Planning, its annual average water resources total 71.94 billion m3, accounting for 2.5% of the country's total water resources. However, it is the most severely affected by drought among the major river basins in China, and ecological protection faces severe challenges. With the global climate change, the drought problem in the Yellow River Basin has attracted more and more attention. Based on MODIS vegetation index products (MYD13A2, V6) and surface temperature radiation products (MYD11A2, V6). The dataset is obtained by approaches including projection conversion, cloud removal, mosaic clipping, time series interpolation and SG filter smoothing and other preprocessing. Using the calculation method proposed by Kogan in 1995, the VCI and TCI indices were calculated according to the corresponding MODIS products, respectively, and the monthly 1km resolution VCI and TCI drought index datasets in the Yellow River Basin from 2003 to 2021 were generated. Through data sharing, it is expected to provide important data support for drought monitoring and research in the Yellow River Basin under the background of global change.
{"title":"A dataset of monthly VCI and TCI drought indices at a resolution of 1km in the Yellow River Basin (2003–2021)","authors":"Longxin Qiao, Zelin Zheng, Xiaoyan Ma, Xingwang Zhang, Xutong Ru, Jie Peng, Xiaoyang Zhao, Haoming Xia","doi":"10.11922/11-6035.ncdc.2022.0004.zh","DOIUrl":"https://doi.org/10.11922/11-6035.ncdc.2022.0004.zh","url":null,"abstract":"The Yellow River Basin is located in the arid, semi-arid climate zone and semi-humid climate zones. According to the statistics of the Second National Comprehensive Water Resources Planning, its annual average water resources total 71.94 billion m3, accounting for 2.5% of the country's total water resources. However, it is the most severely affected by drought among the major river basins in China, and ecological protection faces severe challenges. With the global climate change, the drought problem in the Yellow River Basin has attracted more and more attention. Based on MODIS vegetation index products (MYD13A2, V6) and surface temperature radiation products (MYD11A2, V6). The dataset is obtained by approaches including projection conversion, cloud removal, mosaic clipping, time series interpolation and SG filter smoothing and other preprocessing. Using the calculation method proposed by Kogan in 1995, the VCI and TCI indices were calculated according to the corresponding MODIS products, respectively, and the monthly 1km resolution VCI and TCI drought index datasets in the Yellow River Basin from 2003 to 2021 were generated. Through data sharing, it is expected to provide important data support for drought monitoring and research in the Yellow River Basin under the background of global change.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45183774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0024.zh
Xing-chang Wang, Keming Hu, Fan Liu, Yuan-zhi Zhu, Q. Zhang, Chuankuan Wang
Forest ecosystem dominates the terrestrial ecosystem carbon (C) cycle, thus the accurate estimation of C flux in the forest ecosystem is essential to understanding the impact of global change on global C cycle. Based on the micrometeorology theory, the eddy covariance technique is one of the standard methods for C flux monitoring in terrestrial ecosystems, which has been widely used in the long-term monitoring of C flux in forests, grasslands, croplands and other ecosystems. Heilongjiang Maoershan Forest Ecosystem National Observation and Research Station has a continental monsoon climate, dominated by natural secondary forests (temperate deciduous broad-leaved forestd) which are typical in the montane forests of Northeast China. In this dataset, we compiled the measured C flux data and routine meteorological data of a deciduous broad-leaved forest at Maoershan Station from 2016 to 2018, including gross primary productivity, ecosystem respiration, net ecosystem exchange, incoming solar radiation, incoming photosynthetically active radiation, air temperature, soil temperature, soil moisture and precipitation. The dataset is divided into four time scales: half-hourly, daily, monthly and yearly. The establishment and sharing of this dataset will provide necessary, accurate and reliable data to support the evaluation of the role of natural secondary forests in the regional C cycle and the optimization of C cycle models.
{"title":"A dataset of carbon fluxes of the deciduous broad-leaved forest at Maoershan Station from 2016 to 2018","authors":"Xing-chang Wang, Keming Hu, Fan Liu, Yuan-zhi Zhu, Q. Zhang, Chuankuan Wang","doi":"10.11922/11-6035.csd.2023.0024.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0024.zh","url":null,"abstract":"Forest ecosystem dominates the terrestrial ecosystem carbon (C) cycle, thus the accurate estimation of C flux in the forest ecosystem is essential to understanding the impact of global change on global C cycle. Based on the micrometeorology theory, the eddy covariance technique is one of the standard methods for C flux monitoring in terrestrial ecosystems, which has been widely used in the long-term monitoring of C flux in forests, grasslands, croplands and other ecosystems. Heilongjiang Maoershan Forest Ecosystem National Observation and Research Station has a continental monsoon climate, dominated by natural secondary forests (temperate deciduous broad-leaved forestd) which are typical in the montane forests of Northeast China. In this dataset, we compiled the measured C flux data and routine meteorological data of a deciduous broad-leaved forest at Maoershan Station from 2016 to 2018, including gross primary productivity, ecosystem respiration, net ecosystem exchange, incoming solar radiation, incoming photosynthetically active radiation, air temperature, soil temperature, soil moisture and precipitation. The dataset is divided into four time scales: half-hourly, daily, monthly and yearly. The establishment and sharing of this dataset will provide necessary, accurate and reliable data to support the evaluation of the role of natural secondary forests in the regional C cycle and the optimization of C cycle models.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42554807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a key area of China’s animal husbandry development, dairy farming plays a pivotal role in promoting agricultural structural adjustment and driving social and economic development. The occurrence of dairy cow disease not only brings great economic losses to animal husbandry, but also seriously threatens food safety and human health. The task of disease prevention and control is important and arduous. The construction of dairy cow disease knowledge graph is the basis of disease prevention and control, which is of great significance to the development of animal husbandry. In the dataset of dairy cow diseases in China for knowledge graph construction, based on books and websites of dairy cow disease types, we carried out data collecting, cleansing, organizing and merging to obtain 261 data of 5 types of dairy cow disease, including 42 infectious diseases, 16 kinds of parasitic diseases, 111 kinds of internal diseases, 54 kinds of surgical diseases, and 38 kinds of obstetric diseases. This dataset can be used to construct a knowledge graph of dairy cow diseases, and provide basic data support for the construction of dairy cow disease prevention and control in China.
{"title":"A dataset of dairy cow diseases for knowledge graph construction in China","authors":"Pengpeng Zhang, Quansheng Li, Fantao Kong, Rui Man, Shanshan Cao, Wei Sun","doi":"10.11922/11-6035.nasdc.2022.0011.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2022.0011.zh","url":null,"abstract":"As a key area of China’s animal husbandry development, dairy farming plays a pivotal role in promoting agricultural structural adjustment and driving social and economic development. The occurrence of dairy cow disease not only brings great economic losses to animal husbandry, but also seriously threatens food safety and human health. The task of disease prevention and control is important and arduous. The construction of dairy cow disease knowledge graph is the basis of disease prevention and control, which is of great significance to the development of animal husbandry. In the dataset of dairy cow diseases in China for knowledge graph construction, based on books and websites of dairy cow disease types, we carried out data collecting, cleansing, organizing and merging to obtain 261 data of 5 types of dairy cow disease, including 42 infectious diseases, 16 kinds of parasitic diseases, 111 kinds of internal diseases, 54 kinds of surgical diseases, and 38 kinds of obstetric diseases. This dataset can be used to construct a knowledge graph of dairy cow diseases, and provide basic data support for the construction of dairy cow disease prevention and control in China.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46424638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0019.zh
Yujie Yan, Guangsheng Zhou, B. Jia, Jiaxin Song, Sen Zhang
As an important component of the global carbon pool, boreal forests have attracted much attention in the research on climate change and carbon cycle due to their extreme sensitivity to climate warming. Located in the largest cold-temperate coniferous forest ecosystem nature reserve in China, China Boreal Forest Ecosystem Research Station (Huzhong Station) is one of the member stations of Chinese FLUX Observation and Research Network (ChinaFLUX), which has conducted long-term carbon and water flux observations of boreal forest ecosystems based on eddy covariance techniques since 2006. Following the ChinaFLUX data processing protocols, we collected the carbon and water fluxes and auxiliary meteorological environment observations of the boreal forest ecosystem at Huzhong Station from 2014 to 2018. Processing the data in a standard process, we formed a standardized dataset with four time scales, namely half-hourly, daily, monthly and yearly. The dataset is expected to provide data support for the studies on climate change, boreal forest ecosystem carbon, as well as water and energy balance.
{"title":"A dataset of carbon and water fluxes of the boreal forest ecosystem in Huzhong (2014 – 2018)","authors":"Yujie Yan, Guangsheng Zhou, B. Jia, Jiaxin Song, Sen Zhang","doi":"10.11922/11-6035.csd.2023.0019.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0019.zh","url":null,"abstract":"As an important component of the global carbon pool, boreal forests have attracted much attention in the research on climate change and carbon cycle due to their extreme sensitivity to climate warming. Located in the largest cold-temperate coniferous forest ecosystem nature reserve in China, China Boreal Forest Ecosystem Research Station (Huzhong Station) is one of the member stations of Chinese FLUX Observation and Research Network (ChinaFLUX), which has conducted long-term carbon and water flux observations of boreal forest ecosystems based on eddy covariance techniques since 2006. Following the ChinaFLUX data processing protocols, we collected the carbon and water fluxes and auxiliary meteorological environment observations of the boreal forest ecosystem at Huzhong Station from 2014 to 2018. Processing the data in a standard process, we formed a standardized dataset with four time scales, namely half-hourly, daily, monthly and yearly. The dataset is expected to provide data support for the studies on climate change, boreal forest ecosystem carbon, as well as water and energy balance.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42977432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0127.zh
Leiming Zhang, Zhi Chen, Qiufeng Wang, Guirui Yu
{"title":"Preface to the special issue: in Celebration of ChinaFlux's 20th Anniversary","authors":"Leiming Zhang, Zhi Chen, Qiufeng Wang, Guirui Yu","doi":"10.11922/11-6035.csd.2023.0127.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0127.zh","url":null,"abstract":"","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48316126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0007.zh
Sen Zhang, Li Zhou, Guangsheng Zhou, Q. Jia, Rongping Li, Yu Wang
The observation of carbon and water fluxes in agricultural ecosystems based on eddy covariance technique (EC) is of great significance for improving crop yield and utilization efficiency of water resources in cropland, and promoting the realization of carbon peaking and carbon neutrality goals. Jinzhou agricultural Ecosystem Field Observation Station (Jinzhou Station) is one of the member stations of Chinese FLUX Observation and Research Network (ChinaFLUX). It is located in the main spring maize-producing area in Jinzhou City, Liaoning Province, China. Jinzhou Station has been carrying out the EC-based observation of carbon and water fluxes of the spring maize agricultural ecosystem since May 2004. In line with the ChinaFLUX data processing protocols, we statistically collected the carbon and water fluxes and auxiliary meteorological environment observations of the spring maize agricultural ecosystem in Jinzhou from 2005 to 2014, so as to obtain a standardized dataset with four-time scales (i.e. half-hourly, daily, monthly, and yearly). This dataset is expected to provide data support for the study of climate change and the process and mechanism of carbon and water cycle in farmland ecosystems.
{"title":"A dataset of carbon and water flux observations in the agricultural ecosystem of spring maize in Jinzhou (2005–2014)","authors":"Sen Zhang, Li Zhou, Guangsheng Zhou, Q. Jia, Rongping Li, Yu Wang","doi":"10.11922/11-6035.csd.2023.0007.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0007.zh","url":null,"abstract":"The observation of carbon and water fluxes in agricultural ecosystems based on eddy covariance technique (EC) is of great significance for improving crop yield and utilization efficiency of water resources in cropland, and promoting the realization of carbon peaking and carbon neutrality goals. Jinzhou agricultural Ecosystem Field Observation Station (Jinzhou Station) is one of the member stations of Chinese FLUX Observation and Research Network (ChinaFLUX). It is located in the main spring maize-producing area in Jinzhou City, Liaoning Province, China. Jinzhou Station has been carrying out the EC-based observation of carbon and water fluxes of the spring maize agricultural ecosystem since May 2004. In line with the ChinaFLUX data processing protocols, we statistically collected the carbon and water fluxes and auxiliary meteorological environment observations of the spring maize agricultural ecosystem in Jinzhou from 2005 to 2014, so as to obtain a standardized dataset with four-time scales (i.e. half-hourly, daily, monthly, and yearly). This dataset is expected to provide data support for the study of climate change and the process and mechanism of carbon and water cycle in farmland ecosystems.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47639163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.11922/11-6035.csd.2023.0010.zh
Yiming Feng, Kun Qiao, Shiang Feng, Lei Xi, Zhao Qi, Lan Lan
The Tarim Basin is an area with extremely fragile ecology and severe desertification subject to the ravages of human activities. As an important index of desertification monitoring, vegetation coverage can well reflect the luxuriant degree of surface vegetation. The monitoring of regional vegetation coverage is the basis of mastering the dynamic change of desertification and analyzing the causes of desertification. Using LANDSAT vegetation growth season (April-October) images from 1990 to 2021 as data sources, we obtained seven vegetation coverage data sets from 1990 to 2021 in the Ring Tarim Basin based on GEE remote sensing cloud platform. We intercepted the upper and lower thresholds of NDVI by adopting 0.5% confidence level to get the NDVI values of pure vegetation cover pixels and pure soil cover pixels, so as to remove the effect of the interannual climate differences on vegetation coverage calculation, and ensure the consistency in the calculation of vegetation coverage for each year. The observation work was carried out in 109 UAV orthorectified sample plots. In the following of data pre-processing, we obtained FVC values as validation samples by using a combined algorithm (vegetation index method and Otsu algorithm). The precision of the dataset is R2 = 0.79 and the linear expression is y = 0.8126x - 0.0267. This dataset can provide data support for the research of desertification change and driving mechanism.
{"title":"A dataset of temporal-spatial FVC in the Ring Tarim Basin from 1990 to 2021","authors":"Yiming Feng, Kun Qiao, Shiang Feng, Lei Xi, Zhao Qi, Lan Lan","doi":"10.11922/11-6035.csd.2023.0010.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0010.zh","url":null,"abstract":"The Tarim Basin is an area with extremely fragile ecology and severe desertification subject to the ravages of human activities. As an important index of desertification monitoring, vegetation coverage can well reflect the luxuriant degree of surface vegetation. The monitoring of regional vegetation coverage is the basis of mastering the dynamic change of desertification and analyzing the causes of desertification. Using LANDSAT vegetation growth season (April-October) images from 1990 to 2021 as data sources, we obtained seven vegetation coverage data sets from 1990 to 2021 in the Ring Tarim Basin based on GEE remote sensing cloud platform. We intercepted the upper and lower thresholds of NDVI by adopting 0.5% confidence level to get the NDVI values of pure vegetation cover pixels and pure soil cover pixels, so as to remove the effect of the interannual climate differences on vegetation coverage calculation, and ensure the consistency in the calculation of vegetation coverage for each year. The observation work was carried out in 109 UAV orthorectified sample plots. In the following of data pre-processing, we obtained FVC values as validation samples by using a combined algorithm (vegetation index method and Otsu algorithm). The precision of the dataset is R2 = 0.79 and the linear expression is y = 0.8126x - 0.0267. This dataset can provide data support for the research of desertification change and driving mechanism.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43954244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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