Dataset List


Data Details

Leaf Area Index Daily Dataset from Observation Nodes in Yucheng of Shandong Province, China (2020)

LI Ruoxi1,2ZHOU Xiang*1LV Tingting1TAO Zui1WANG Jin1XIE Futai1,2
1 Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China2 School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China


Published:Mar. 2021

Visitors:8161       Data Files Downloaded:68      
Data Downloaded:2.83 MB      Citations:

Key Words:

Yucheng of Shandong,Leaf Area Index,node,daily,ground observation


From May to November in 2020, 3 nodes were set up at Yucheng station in Shandong Province of China for observing the leaf area index (LAI) measured by SBLX-034 Sensor Network System. First, the authors chose the data observed between 10 am and 15 pm, and filter out the time when there were valid data. Then, according to the temporal and spatial correlation between the nodes, the time-series neural network NARX was used to build the model, and the abnormal time data with the model prediction error greater than 1 was eliminated. The LSTM neural network was used to test the rule of the processed data. Finally, the daily data was averaged to obtain the leaf area index daily dataset from observation nodes in Yucheng of Shandong Province, China (2020). The dataset is consisted of: (1) geo-location of 3 wireless sensor network nodes at the Yucheng Station; (2) leaf area index daily data at three nodes from May to November in 2020. The dataset is archived in .xlsx, .shp and .kmz data format with data size of 49.1 KB (compressed to one single file with 42.5 KB).Browse

Foundation Item:

Ministry of Science and Technology of P. R. China (2018YFE0124200); Chinese Academy of Sciences (2020)

Data Citation:

LI Ruoxi, ZHOU Xiang*, LV Tingting, TAO Zui, WANG Jin, XIE Futai. Leaf Area Index Daily Dataset from Observation Nodes in Yucheng of Shandong Province, China (2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2021.

LI Ruoxi, ZHOU Xiang, LV Tingting, et al. Development and validation of the wireless sensor network dataset of leaf area index in Shandong Yucheng of China (2020) [J]. Journal of Global Change Data & Discovery, 2021, 5(2): 135-142.


[1] Chen, J. M., Black, T. A. Defining leaf area index for non-flat leaves [J]. Plant, Cell & Environment, 1992, 15: 421-429.
     [2] Garrigues, S., Lacaze, R., Baret, F., et al. Validation and intercomparison of global Leaf Area Index products derived from remote sensing data [J]. Journal of Geophysical Research, 2008, 113, G02028. DOI: 10.1029/2007JG000635.
     [3] Liu, J., Chen, J. M., Cihlar, J., et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs [J]. Remote Sensing of Environment, 1997, 62: 158-175.
     [4] Andrew, D., Richardson, R. S., Anderson, M., et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis [J]. Global Change Biology, 2012, 18(2): 566-584.
     [5] Sellers, P. J., Dickinson, R. E., Randall, D. A., et al. Modeling the exchanges of energy, water, and Carbon between continents and the atmosphere [J]. Science, 1997, 275: 502-509.
     [6] Bonan, G. B. Land-atmosphere interactions for climate system models: coupling biophysical, biogeochemical, and ecosystem dynamical processes [J]. Remote Sensing of Environment, 1995, 51: 57-73.
     [7] Friedl, M. A., Davis, F. W., Michaelsen, J., et al. Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE [J]. Remote Sensing of Environment, 1995, 54: 233-246.
     [8] Ding, Y. L. Remote sensing estimation of vegetation coverage and its authenticity test [D]. Changchun: Graduate School of Chinese Academy of Sciences (Northeast Institute of geography and agricultural ecology), 2015.
     [9] Shi, Y. C., Wang, J. D., Qin, J., et al. An upscaling algorithm to obtain the representative ground truth of LAI time series in heterogeneous land surface [J]. Remote Sensing, 2015, 7(10): 12887-12908.
     [10] Zhang, J. L., Liu, Q., Li, X. H., et al. Calibration and data validation of wireless sensor network [P]. Intelligent Earth Observing Systems, 2015.
     [11] Dou, B. C., Wen, J. G., Li, X. H., et al. Wireless sensor network of typical land surface parameters and its preliminary applications for coarse-resolution remote sensing pixel [J]. International Journal of Distributed Sensor Networks, 2016, 12(4): 55-60.
     [12] Zhou, Y. S. Spatiotemporal variation of soil moisture based on improved thermal inertia model [D]. Kaifeng: Henan University, 2018.
     [13] Qu, Y., Zhu, Y., Han, W., et al. Crop leaf area index observations with a wireless sensor network and its potential for validating remote sensing products [J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(2): 431-444.

Data Product:

ID Data Name Data Size Operation
1 LAI_YuCheng_2020_0501-1108.rar 42.57KB