Global Monthly Vegetation Clumping Index (CI) Dataset (2000–2020) at 0.05° Resolution

Global Monthly Vegetation Clumping Index (CI) Dataset (2000–2020) at 0.05° Resolution 1. Data Information Spatial resolution: 0.05° (~5.5 km) Temporal resolution: monthly Temporal Coverage: March 2000 – December 2020 Spatial Extent: Global (60°S–90°N, 180°W–180°E) Format: GeoTIFF 2. Data Generation Methodology Input Datasets: MODIS MCD43A1 (BRDF parameters) MODIS MCD43A2 (quality flags) GLC2000 (land cover classification) Key Processing Steps: (1) Calculate NDHD (Normalized Difference between Hotspot and Darkspot reflectance) (2) Invert CI using the linear relationship: CI = A(θₛ) × NDHD + B(θₛ), where θₛ is the solar zenith angle (SZA), A(θₛ) and B(θₛ) are coefficients jointly determined by solar zenith and the shape of the vegetation canopy. (3) Apply Savitzky-Golay filtering to smooth temporal noise (4) Monthly mean data based on pixel screening with quality assurance. Valid Range: CI ∈ [0.3, 1.0] CI < 1: Indicates clustered leaf distribution (non-random) CI = 1: Indicates random leaf distribution 3. Data Organization Folder Structure Overview Level Type Example Name Description 1 Folder 2001 Organized by year 2 File Full_Extend_Global_MODIS_Clumping_Index_A200101_pro_0.05D_flo_monthly.tif Naming convention explained below Filename Format: Full_Extend_Global_MODIS_Clumping_Index_A[YYYYMM]_pro_0.05D_flo_monthly.tif YYYYMM:Year and month (e.g., 200101 represents January 2001) 4. Point of Contact Prof. Hongliang Fang LREIS, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences (CAS) 11A Datun Road, Beijing, 100101, China Email: [e-mail] 5. Relate to Wei, S., Fang, H., Schaaf, C. B., He, L., and J. M. Chen, 2019. Global 500 m clumping index product derived from MODIS BRDF data (2001-2017). Remote Sensing of Environment. 232, 111296. https://doi.org/10.1016/j.rse.2019.111296. [Global product] Wei, S., and Fang, H., 2016. Estimation of canopy clumping index from MISR and MODIS sensors using the normalized difference hotspot and darkspot (NDHD) method: The influence of BRDF models and solar zenith angle. Remote Sensing of Environment. 187: 476-491. https://www.doi.org/10.1016/j.rse.2016.10.039. [Core Methodology] Li, Y. and Fang, H., 2022. Real-time software for the efficient generation of the clumping index and its application based on the Google Earth Engine. Remote Sensing, 14(15), 3837. https://doi.org/10.3390/rs14153837. [GEE Implementation] Fang, H., Li, S., Zhang, Y., Wei, S., and Wang Y., 2021. New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and leaf area index. Science of Remote Sensing, 4, 100027. https://doi.org/10.1016/j.srs.2021.100027.[Data application] Fang, H., Li, S., Zhang, Y., Wei, S., and Wang Y., 2022. Comprehensive evaluation of global CI, FVC, and LAI products and their relationships using high-resolution reference data. Science of Remote Sensing 6, 100066. https://doi.org/10.1016/j.srs.2022.100066. [Data application] 6. Vertion History Version v1 (2025-09-29) Version v2 (2025-10-07)------------------ * Fixed: Data ghosting in 2018-2019