LAI Validation Dataset Based on 28 Ground Stations in Asia and North American (1km Grid, 2001-2011)
LI Jing1LIU Qinhuo1XU Baodong1ZHAO Jing1
1 State Key Laboratory of Remote Sensing Science，Institute of Remote Sensing and Digital Earth，Chinese Academy of Sciences，Beijing 100101，China
DOI:10.3974/geodb.2018.03.15.V1
Published:May 2018
Visitors：10241 Data Files Downloaded：176
Data Downloaded：8.20 MB Citations：
Key Words：
leaf area index,data validation,GUGM model,Spatial upscaling,spatial representativeness evaluation,Journal of Remote Sensing
Abstract：
For evaluating the LAI (Leaf Area Index) data quality, the data validation should be necessary. The ground station observation data plays critical role for the IAI data validation. For doing so, two steps for the data pre-processing should be taken in the LAI data validation progress. One is the spatial representativeness evaluation and the other one is the spatiotemporal upscaling. Based on the 28 ground station data from FLUXET and the Chinese Ecosystem Research Network (CERN) in Asia and North American, the GUGM (Grading and Upscaling of Ground Measurements) model was applied in the data pre-processing. The LAI Validation Dataset based on 28 Ground Stations in Asia and North American (1km Grid, 2001-2011) is consisted of 924 validation records in 28 stations in Asian and North American. In which, 508 records from 16 stations are for forest (55.0%), 341 Records from 11 stations for agriculture (36.9%), 75 records from one station for grassland (8.1%). The dataset is archived in .xlsx and .kmz data format with the data size of 92.4 KB.
Foundation Item：
Ministry of Science and Technology of P. R. China (2018YFA0605503)
Data Citation：
LI Jing, LIU Qinhuo, XU Baodong, ZHAO Jing. LAI Validation Dataset Based on 28 Ground Stations in Asia and North American (1km Grid, 2001-2011)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2018. https://doi.org/10.3974/geodb.2018.03.15.V1.
Related Publication：
XU Baodong, LI Jing, LIU Qinhuo, et al. Spatial representativeness estimation of station observation in validation of LAI products:A case study with CERN insitu data. Journal of Remote Sensing. 2015, 19(6): 910-927.
Baodong Xu, Jing Li, Taejin Park, Qinhuo Liu, Yelu Zeng, Gaofei Yin, Jing Zhao, Weiliang Fan, Le Yang, Yuri Knyazikhin, Ranga B. Myneni. (2018). An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209: 134-151.
Baodong Xu, Jing Li, Qinhuo Liu, Alfredo R. Huete, Qiang Yu, Yelu Zeng, Gaofei Yin, Jing Zhao, Le Yang. (2016). Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7): 3267-3282.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 1 |
LAIValidationDataset1km28S_2001-2011.kmz |
30.06KB |
|
| 2 |
LAIValidationDataset1km28S_2001-2011.xlsx |
62.39KB |
|