{"id":"582663f5-3be7-4f26-bc45-b56a3c4fc3b7","title":"Global Gross Primary Production (GPP) data by Vegetation Photosynthesis Model","title_en":null,"serial":"","user_limit":"To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.","instructions":"The files are named by the following rules. \"GPP.VPM.v20.YYYY.monthly.CMG_0.05.nc\" or \"GPP.VPM.v20.YYYY.8-day.CMG_0.05.nc\" are for the monthly and 8-day temporal resolutions, respectively. \"YYYY\" represents the year. The units for the monthly data is g C m^-2 month^-1, and is g C m^-2 day^-1 for the 8-day data.","description":"Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000–2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.","doi":"https://doi.org/10.6084/m9.figshare.c.3789814.v1","projection":null,"language":"en","share_type":"online","east":180.0,"west":-180.0,"south":-90.0,"north":90.0,"scale":null,"crs":null,"filesize":9500.0,"share_policy":"a","ts_created":"2021-10-16T22:44:44.414584+08:00","doi_type":"沿用数据已有DOI","ts_updated":"2021-10-17T00:25:02.173344+08:00","time_description":"","datas_group":3,"time_description_en":"","data_format":null,"project":[11],"specials":[],"cite":null,"review_status":5,"review_invite":[{"user_name":"郭群","username":"guoq@igsnrr.ac.cn","email":"guoq@igsnrr.ac.cn","is_cancel_expert":false}],"review_editor":{"id":42421,"last_login":"2022-04-08T15:16:51.670544+08:00","username":"任慧霞","email":"510727846@qq.com","is_active":true,"is_staff":true,"user_name":"任慧霞"},"data_sources":null,"temporal_resolution":"Monthly","spatial_resolution":"1km - 10km","license":"Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","fund":[],"subtopics":{"metadata":"582663f5-3be7-4f26-bc45-b56a3c4fc3b7","three_pole":null,"third_pole":null},"username":"张尧","review_time":null,"protected_duration":null,"protected_describe":null,"is_important":false,"hit_count":965}