Pub Date : 2022-02-20DOI: 10.1080/20964471.2022.2033149
Mengya Sheng, L. Lei, Z. Zeng, Weiqiang Rao, Hao Song, Changjiang Wu
ABSTRACT A global mapping data of atmospheric carbon dioxide (CO2) concentrations can help us to better understand the spatiotemporal variations of CO2 and the driving factors of the variations to support the actions for emissions reduction and control. Greenhouse gases satellites that measure atmospheric CO2, such as the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO-2), have been providing global observations of the column averaged dry-air mole fractions of CO2 (XCO2) since 2009. However, these XCO2 retrievals are irregular in space and time with many gaps. In this paper, we mapped a global spatiotemporally continuous XCO2 dataset (Mapping-XCO2) using the XCO2 retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps. The dataset covers a geographic range from 56° S to 65° N and 169° W to 180° E for a 1° grid interval in space and 3-day time interval. The uncertainties of the mapped XCO2 values are generally less than 1.5 parts per million (ppm). The spatiotemporal characteristics of global XCO2 that are revealed by the Mapping-XCO2 are similar to the model data obtained from CarbonTracker. Compared to the ground observations, the overall standard bias is 1.13 ppm. The results indicate that this long-term Mapping-XCO2 dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO2 and can support studies related to the carbon cycle and anthropogenic CO2 emissions. The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.
{"title":"Global land 1° mapping dataset of XCO2 from satellite observations of GOSAT and OCO-2 from 2009 to 2020","authors":"Mengya Sheng, L. Lei, Z. Zeng, Weiqiang Rao, Hao Song, Changjiang Wu","doi":"10.1080/20964471.2022.2033149","DOIUrl":"https://doi.org/10.1080/20964471.2022.2033149","url":null,"abstract":"ABSTRACT A global mapping data of atmospheric carbon dioxide (CO2) concentrations can help us to better understand the spatiotemporal variations of CO2 and the driving factors of the variations to support the actions for emissions reduction and control. Greenhouse gases satellites that measure atmospheric CO2, such as the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory (OCO-2), have been providing global observations of the column averaged dry-air mole fractions of CO2 (XCO2) since 2009. However, these XCO2 retrievals are irregular in space and time with many gaps. In this paper, we mapped a global spatiotemporally continuous XCO2 dataset (Mapping-XCO2) using the XCO2 retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps. The dataset covers a geographic range from 56° S to 65° N and 169° W to 180° E for a 1° grid interval in space and 3-day time interval. The uncertainties of the mapped XCO2 values are generally less than 1.5 parts per million (ppm). The spatiotemporal characteristics of global XCO2 that are revealed by the Mapping-XCO2 are similar to the model data obtained from CarbonTracker. Compared to the ground observations, the overall standard bias is 1.13 ppm. The results indicate that this long-term Mapping-XCO2 dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO2 and can support studies related to the carbon cycle and anthropogenic CO2 emissions. The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"21 1","pages":"170 - 190"},"PeriodicalIF":4.0,"publicationDate":"2022-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87079691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-02-20DOI: 10.1080/20964471.2022.2031542
Xiaotian Yuan, L. Jia, M. Menenti, M. Jiang
ABSTRACT Human activities modulate the impact of environmental forcing in general and of climate in particular. Information on the spatial and temporal patterns of human activities is in high demand, but scarce in sparsely populated and data-poor regions such as Northern Africa. The intensity and spatial distribution of nighttime lights provide useful information on human activities and can be observed by space-borne imaging radiometers. Our study helps to bridge the gap between the DMSP-OLS data available until 2013 and the NPP-VIIRS data available since 2013. The approach to calibrate the OLS data includes three steps: a) inter-calibrate the OLS DN data acquired by different sensors in 1992–2013; b) calibrate the OLS DN data using VIIRS data in 2013; c) generate synthetic OLS radiance data by degrading the VIIRS data in 2013–2020. We generated a) a time series of calibrated OLS nighttime light radiance data (1992–2013); b) mean annual VIIRS radiance on stable lights at the OLS spatial resolution for 2013–2020; c) synthetic OLS radiance data generated using VIIRS radiance data degraded to match the radiometric specifications of OLS for 2013–2020. The evaluation of these data products in 2013 documented their accuracy and consistency.
{"title":"Consistent nighttime light time series in 1992–2020 in Northern Africa by combining DMSP-OLS and NPP-VIIRS data","authors":"Xiaotian Yuan, L. Jia, M. Menenti, M. Jiang","doi":"10.1080/20964471.2022.2031542","DOIUrl":"https://doi.org/10.1080/20964471.2022.2031542","url":null,"abstract":"ABSTRACT Human activities modulate the impact of environmental forcing in general and of climate in particular. Information on the spatial and temporal patterns of human activities is in high demand, but scarce in sparsely populated and data-poor regions such as Northern Africa. The intensity and spatial distribution of nighttime lights provide useful information on human activities and can be observed by space-borne imaging radiometers. Our study helps to bridge the gap between the DMSP-OLS data available until 2013 and the NPP-VIIRS data available since 2013. The approach to calibrate the OLS data includes three steps: a) inter-calibrate the OLS DN data acquired by different sensors in 1992–2013; b) calibrate the OLS DN data using VIIRS data in 2013; c) generate synthetic OLS radiance data by degrading the VIIRS data in 2013–2020. We generated a) a time series of calibrated OLS nighttime light radiance data (1992–2013); b) mean annual VIIRS radiance on stable lights at the OLS spatial resolution for 2013–2020; c) synthetic OLS radiance data generated using VIIRS radiance data degraded to match the radiometric specifications of OLS for 2013–2020. The evaluation of these data products in 2013 documented their accuracy and consistency.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"20 1","pages":"603 - 632"},"PeriodicalIF":4.0,"publicationDate":"2022-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87157252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-02-17DOI: 10.1080/20964471.2022.2032998
Lingmei Jiang, Jianwei Yang, Cheng Zhang, Shengli Wu, Z. Li, L. Dai, Xiaofeng Li, Y. Qiu
ABSTRACT The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. Thus, a long-time snow water equivalent (SWE) dataset is necessary. This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction. The unbiased root-mean-square error of snow depth is about 5–7 cm, corresponding to 10–15 mm for SWE, when compared with stations measurements and field snow course data. The spatial patterns and trends of SWE over China present significant regional differences. The overall slope trend presented an insignificant decreasing pattern during 1980–2020 over China; however, there is an obvious fluctuation, i.e. a significant decrease trend during the period 1980–1990, an upward trend from 2005 to 2009, a significant downward trend from 2009 to 2018. The increase of SWE occurred in the Northeast Plain, with an increase trend of 0.2 mm per year. Whereas in the Hengduan Mountains, it presented a downward trend of SWE, up to −0.3 mm per year. In the North Xinjiang, SWE has an increasing trend in the Junggar Basin, while it shows a decreasing trend in the Tianshan and Altai Mountains.
摘要:季节积雪量及其变化趋势的可靠数据对了解地球气候系统具有重要意义。因此，需要一个长期的雪水当量(SWE)数据集。本文利用SMMR、SSM/I和SSMIS等无源微波数据，经过交叉定标和偏置校正，采用线性解混方法，给出了1980-2020年中国的日SWE产品。积雪深度的无偏均方根误差约为5 ~ 7 cm，相当于SWE与台站测量和现场雪道数据的10 ~ 15 mm。中国SWE的空间格局和趋势存在显著的区域差异。1980—2020年，中国的总体坡度趋势呈不显著的下降趋势;但存在明显的波动，即1980-1990年呈明显下降趋势，2005 - 2009年呈上升趋势，2009 - 2018年呈明显下降趋势。SWE增加主要发生在东北平原，增加趋势为0.2 mm / a。横断山区SWE呈下降趋势，最高可达- 0.3 mm /年。在北疆，准噶尔盆地SWE呈增加趋势，天山和阿尔泰山SWE呈减少趋势。
{"title":"Daily snow water equivalent product with SMMR, SSM/I and SSMIS from 1980 to 2020 over China","authors":"Lingmei Jiang, Jianwei Yang, Cheng Zhang, Shengli Wu, Z. Li, L. Dai, Xiaofeng Li, Y. Qiu","doi":"10.1080/20964471.2022.2032998","DOIUrl":"https://doi.org/10.1080/20964471.2022.2032998","url":null,"abstract":"ABSTRACT The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. Thus, a long-time snow water equivalent (SWE) dataset is necessary. This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction. The unbiased root-mean-square error of snow depth is about 5–7 cm, corresponding to 10–15 mm for SWE, when compared with stations measurements and field snow course data. The spatial patterns and trends of SWE over China present significant regional differences. The overall slope trend presented an insignificant decreasing pattern during 1980–2020 over China; however, there is an obvious fluctuation, i.e. a significant decrease trend during the period 1980–1990, an upward trend from 2005 to 2009, a significant downward trend from 2009 to 2018. The increase of SWE occurred in the Northeast Plain, with an increase trend of 0.2 mm per year. Whereas in the Hengduan Mountains, it presented a downward trend of SWE, up to −0.3 mm per year. In the North Xinjiang, SWE has an increasing trend in the Junggar Basin, while it shows a decreasing trend in the Tianshan and Altai Mountains.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"62 1","pages":"420 - 434"},"PeriodicalIF":4.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88354764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-02-17DOI: 10.1080/20964471.2022.2037203
A. Duan, Senfeng Liu, Wenting Hu, Die Hu, Yuzhuo Peng
ABSTRACT As the main components of the atmospheric heat source/sink over the Tibetan Plateau (TP), up-to-date spatiotemporal fields of surface sensible heat flux and latent heat release by precipitation are vital for investigating the local land–atmosphere interaction and the effect of the thermal forcing of the TP on global weather and climate. This study recalculates the long-term daily dataset of surface sensible heat flux and latent heat release of condensation over the TP based on 293 routine meteorological observations, with the latest date being 31 December 2019. Most stations have adequate and valid records during the period 1981–2019, and the results for 1951–1980 are also calculated if the observations are available. Moreover, a brief evaluation of the climatology and long-term variation during 1981–2019 is conducted. By providing the most continuous and longest set of observational surface sensible heat flux and latent heat release of condensation data over the TP with a high degree of credibility, this new dataset will support research concerning the multi-timescale variation of diabatic heating/cooling over the TP and its remote influence. It is openly available on the LASG data-sharing platform (http://data.lasg.ac.cn/TPSHLH/).
{"title":"Long-term daily dataset of surface sensible heat flux and latent heat release over the Tibetan Plateau based on routine meteorological observations","authors":"A. Duan, Senfeng Liu, Wenting Hu, Die Hu, Yuzhuo Peng","doi":"10.1080/20964471.2022.2037203","DOIUrl":"https://doi.org/10.1080/20964471.2022.2037203","url":null,"abstract":"ABSTRACT As the main components of the atmospheric heat source/sink over the Tibetan Plateau (TP), up-to-date spatiotemporal fields of surface sensible heat flux and latent heat release by precipitation are vital for investigating the local land–atmosphere interaction and the effect of the thermal forcing of the TP on global weather and climate. This study recalculates the long-term daily dataset of surface sensible heat flux and latent heat release of condensation over the TP based on 293 routine meteorological observations, with the latest date being 31 December 2019. Most stations have adequate and valid records during the period 1981–2019, and the results for 1951–1980 are also calculated if the observations are available. Moreover, a brief evaluation of the climatology and long-term variation during 1981–2019 is conducted. By providing the most continuous and longest set of observational surface sensible heat flux and latent heat release of condensation data over the TP with a high degree of credibility, this new dataset will support research concerning the multi-timescale variation of diabatic heating/cooling over the TP and its remote influence. It is openly available on the LASG data-sharing platform (http://data.lasg.ac.cn/TPSHLH/).","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"27 1","pages":"480 - 491"},"PeriodicalIF":4.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90553722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-02-14DOI: 10.1080/20964471.2022.2031543
N. Liu, J. Strobl
ABSTRACT The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai, as well as structure and location characteristics. Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018. Six neighborhood features, structural and location characteristics, are selected according to their statistical significance and multicollinearity test results from an OLS model. Regression analysis is performed by OLS, GWR, and MGWR to compare their performance in housing price research at community level. The comparison of the three models also demonstrates that the GWR (66%) and MGWR (68%) models perform much better than OLS model (52%). MGWR is not significantly different from GWR in areas with few sample points, and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area. The regression parameter indicates that building age is the most important factor among all influencing factors. Proximity to schools and factories have positive and negative significant effects on housing resale prices, respectively. The spatial pattern of neighborhood features is also detected at town level. GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market, which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present. The results provide references for local planning departments, helping to reveal the complicated relationship and spatial patterns between housing price and determinants over space.
{"title":"Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model","authors":"N. Liu, J. Strobl","doi":"10.1080/20964471.2022.2031543","DOIUrl":"https://doi.org/10.1080/20964471.2022.2031543","url":null,"abstract":"ABSTRACT The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai, as well as structure and location characteristics. Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018. Six neighborhood features, structural and location characteristics, are selected according to their statistical significance and multicollinearity test results from an OLS model. Regression analysis is performed by OLS, GWR, and MGWR to compare their performance in housing price research at community level. The comparison of the three models also demonstrates that the GWR (66%) and MGWR (68%) models perform much better than OLS model (52%). MGWR is not significantly different from GWR in areas with few sample points, and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area. The regression parameter indicates that building age is the most important factor among all influencing factors. Proximity to schools and factories have positive and negative significant effects on housing resale prices, respectively. The spatial pattern of neighborhood features is also detected at town level. GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market, which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present. The results provide references for local planning departments, helping to reveal the complicated relationship and spatial patterns between housing price and determinants over space.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"35 1","pages":"146 - 169"},"PeriodicalIF":4.0,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77413864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-02-02DOI: 10.1080/20964471.2022.2025663
Xiaoqing Xu, C. Liu, Caixia Liu, F. Hui, Xiao Cheng, Huabing Huang
ABSTRACT Man-made impervious areas map is of great demand in environmental and urbanization studies since impervious areas are considered as a key indication of urbanization, especially for circumpolar Arctic. However, to date, finer resolution and spatially continuous impervious areas information remains scarce in the Arctic. In this study, we developed an accurate and complete circumpolar Arctic Man-made impervious areas (CAMI) map at a resolution of 10 m by combining Sentinel-1 C-band Synthetic Aperture Radar, Sentinel-2 multispectral images, OpenStreetMap, and ArcticDEM via Google Earth Engine platform. A random forest classifier model was trained and used to generate corresponding impervious areas map for the year 2020. The evaluation results suggested that CAMI was the most accurate with an overall accuracy of 86.36% and kappa coefficient of 70.73% as against the three existing impervious areas products. Based on the generated map and OpenStreetMap, we estimated that total impervious areas area in the Arctic has achieved 807.80 , of which roads, industrial and resident land were three major land use types, accounting for 54.08%, 17.85% and 10.34%, respectively. The CAMI map will support for new application and provide advanced insight into the infrastructure vulnerability evaluation and environmental sustainability in the Arctic.
{"title":"Fine-resolution mapping of the circumpolar Arctic Man-made impervious areas (CAMI) using sentinels, OpenStreetMap and ArcticDEM","authors":"Xiaoqing Xu, C. Liu, Caixia Liu, F. Hui, Xiao Cheng, Huabing Huang","doi":"10.1080/20964471.2022.2025663","DOIUrl":"https://doi.org/10.1080/20964471.2022.2025663","url":null,"abstract":"ABSTRACT Man-made impervious areas map is of great demand in environmental and urbanization studies since impervious areas are considered as a key indication of urbanization, especially for circumpolar Arctic. However, to date, finer resolution and spatially continuous impervious areas information remains scarce in the Arctic. In this study, we developed an accurate and complete circumpolar Arctic Man-made impervious areas (CAMI) map at a resolution of 10 m by combining Sentinel-1 C-band Synthetic Aperture Radar, Sentinel-2 multispectral images, OpenStreetMap, and ArcticDEM via Google Earth Engine platform. A random forest classifier model was trained and used to generate corresponding impervious areas map for the year 2020. The evaluation results suggested that CAMI was the most accurate with an overall accuracy of 86.36% and kappa coefficient of 70.73% as against the three existing impervious areas products. Based on the generated map and OpenStreetMap, we estimated that total impervious areas area in the Arctic has achieved 807.80 , of which roads, industrial and resident land were three major land use types, accounting for 54.08%, 17.85% and 10.34%, respectively. The CAMI map will support for new application and provide advanced insight into the infrastructure vulnerability evaluation and environmental sustainability in the Arctic.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"8 1","pages":"196 - 218"},"PeriodicalIF":4.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83618784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-02-01DOI: 10.1080/20964471.2021.2017539
Jeffery A. Thompson, M. Brodzik, K. Silverstein, M. Hurley, Nathan L. Carlson
ABSTRACT Although we live in an era of unprecedented quantities and access to data, deriving actionable information from raw data is a hard problem. Earth observation systems (EOS) have experienced rapid growth and uptake in recent decades, and the rate at which we obtain remotely sensed images is increasing. While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster, less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis. Discrete global grid systems (DGGS) have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability. Here, we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets. We describe the extensions as well as present implementation as an application programming interface (API), which forms part of the University of Minnesota’s GEMS (Genetic x Environment x Management x Socioeconomic) Informatics Center’s API portfolio.
{"title":"EASE-DGGS: a hybrid discrete global grid system for Earth sciences","authors":"Jeffery A. Thompson, M. Brodzik, K. Silverstein, M. Hurley, Nathan L. Carlson","doi":"10.1080/20964471.2021.2017539","DOIUrl":"https://doi.org/10.1080/20964471.2021.2017539","url":null,"abstract":"ABSTRACT Although we live in an era of unprecedented quantities and access to data, deriving actionable information from raw data is a hard problem. Earth observation systems (EOS) have experienced rapid growth and uptake in recent decades, and the rate at which we obtain remotely sensed images is increasing. While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster, less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis. Discrete global grid systems (DGGS) have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability. Here, we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets. We describe the extensions as well as present implementation as an application programming interface (API), which forms part of the University of Minnesota’s GEMS (Genetic x Environment x Management x Socioeconomic) Informatics Center’s API portfolio.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"64 1","pages":"340 - 357"},"PeriodicalIF":4.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76304619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-01-31DOI: 10.1080/20964471.2021.2017111
B. Xie, S.Y. Zhou, L. Wu, W.F. Mao, Wen Wang
ABSTRACT Spectral data of different rocks and minerals usually show different waveforms and absorption characteristics in visible and infrared wavelengths, which allow identification of mineral species and composition. However, massive spectra of rock/mineral on earth surface were scattered across a variety of spectral libraries worldwide, exhibiting inconsistent data structures and measurement conditions. To advance the data interoperability and the data usability, we collected data and information from six shared libraries with different format and measured field specimen in laboratory to establish an integrated rock spectral library (RockSL). Both the data quality of spectral curves and the integrity of descriptive metadata are considered in the integrated RockSL to be published in GitHub open-source repository. RockSL contains not only the big spectral dataset of rocks and minerals for data service (i.e. data sharing and retrieval) and geological discrimination, but also the characteristics dataset of key parameters/metadata (e.g. particle size, mineral composition and full-band signature, etc.) for exploration of data mining and knowledge discovery. We hope that more researchers will join to improve the availability and practical value of RockSL for remote sensing community. This article introduces the database structure and data processing workflow, and demonstrates a matching service and several examples of characteristic datasets of RockSL.
{"title":"RockSL: an integrated rock spectral library for better global shared services","authors":"B. Xie, S.Y. Zhou, L. Wu, W.F. Mao, Wen Wang","doi":"10.1080/20964471.2021.2017111","DOIUrl":"https://doi.org/10.1080/20964471.2021.2017111","url":null,"abstract":"ABSTRACT Spectral data of different rocks and minerals usually show different waveforms and absorption characteristics in visible and infrared wavelengths, which allow identification of mineral species and composition. However, massive spectra of rock/mineral on earth surface were scattered across a variety of spectral libraries worldwide, exhibiting inconsistent data structures and measurement conditions. To advance the data interoperability and the data usability, we collected data and information from six shared libraries with different format and measured field specimen in laboratory to establish an integrated rock spectral library (RockSL). Both the data quality of spectral curves and the integrity of descriptive metadata are considered in the integrated RockSL to be published in GitHub open-source repository. RockSL contains not only the big spectral dataset of rocks and minerals for data service (i.e. data sharing and retrieval) and geological discrimination, but also the characteristics dataset of key parameters/metadata (e.g. particle size, mineral composition and full-band signature, etc.) for exploration of data mining and knowledge discovery. We hope that more researchers will join to improve the availability and practical value of RockSL for remote sensing community. This article introduces the database structure and data processing workflow, and demonstrates a matching service and several examples of characteristic datasets of RockSL.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"54 2 1","pages":"191 - 211"},"PeriodicalIF":4.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90404714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2022-01-24DOI: 10.1080/20964471.2021.2012912
Majid Hojati, Colin Robertson, S. Roberts, C. Chaudhuri
ABSTRACT Increasing data resources are available for documenting and detecting changes in environmental, ecological, and socioeconomic processes. Currently, data are distributed across a wide variety of sources (e.g. data silos) and published in a variety of formats, scales, and semantic representations. A key issue, therefore, in building systems that can realize a vision of earth system monitoring remains data integration. Discrete global grid systems (DGGSs) have emerged as a key technology that can provide a common multi-resolution spatial fabric in support of Digital Earth monitoring. However, DGGSs remain in their infancy with many technical, conceptual, and operational challenges. With renewed interest in DGGS brought on by a recently proposed standard, the demands of big data, and growing needs for monitoring environmental changes across a variety of scales, we seek to highlight current challenges that we see as central to moving the field(s) and technologies of DGGS forward. For each of the identified challenges, we illustrate the issue and provide a potential solution using a reference DGGS implementation. Through articulation of these challenges, we hope to identify a clear research agenda, expand the DGGS research footprint, and provide some ideas for moving forward towards a scaleable Digital Earth vision. Addressing such challenges helps the GIScience research community to achieve the real benefits of DGGS and provides DGGS an opportunity to play a role in the next generation of GIS.
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