Contiguous solar induced chlorophyll fluorescence (CSIF) dataset of Tibetan Plateau (2000-2018)
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
File name: CSIF is stored in TIF format and named as "CSIF"_ yyyy_ mm.tif ”Where yyyy is the year and MM is the month. Data reading mode: can be directly opened with ArcGIS, MATLAB, or other software that can read TIF files.
[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)
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生态安全屏障功能与优化体系(2019QZKK0405)
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