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A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2000-2020)

A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2000-2020)

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Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.
数据使用方法

For 0.5 degree dataset, each file contains one year of data for each month (monthly resolution), or every 4 day (4day resolution). These files are named by the years "OCO2.SIF.clear.daily.yyyy.nc", where yyyy indicate the year. For 0.05 degree dataset, each file contains one day of year (DOY). The filenames include the year and DOY information "OCO2.SIF.clear.inst.yyyydoy.v2.nc", where yyyy indicates the year, and doy indicates the day of year.

For each DOY, clear daily SIF represents the daily averaged SIF (a good proxy of GPP) during this four day, in which the starting date is indicated by the DOY. The clear daily SIF should be more comparable with satellite observed SIF and should have a better relationship with GPP.

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文章的引用

1. Zhang, Y., Joiner, J., Alemohammad, S.H., Zhou, S., & Gentine, P. ( 2018). A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences, 15, 5779-5800, https://doi.org/10.5194/bg-15-5779-2018.(查看)

数据的引用

张尧. (2021). A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2000-2020). 国家青藏高原科学数据中心, 2021. doi: 10.11888/Ecolo.tpdc.271751.
[Zhang, Y. (2021). . National Tibetan Plateau Data Center, 2021]

使用本数据时必须引用“文章的引用”中列出的文献，并进行数据的引用

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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空间位置
East: 180.00 West: -180.00
South: -90.00 North: 90.00
数据细节
  • 时间分辨率：Daily
  • 空间分辨率：1km - 10km
  • 大小：60000.0 MB
  • 浏览：427 次
  • 下载量：31 次
  • 数据时间范围：2000-03-01 至 2020-12-31
  • 共享方式： 开放获取
  • 元数据更新时间：2021-10-19
联系信息

张尧

分发方： 时空三极环境大数据平台

Email： poles@itpcas.ac.cn

导出元数据
Word JSON

联系方式

中国科学院西北生态环境资源研究院 0931-4967287 poles@itpcas.ac.cn

友情链接

国家青藏高原科学数据中心
三江源国家公园星空地一体化生态监测数据平台
寒区旱区科学数据中心
数字黑河

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