DOI: 10.57760/sciencedb.36517
title: Global Clumping Index Product Derived from VIIRS BRDF Data (2012–2023)
title: Global Clumping Index Product Derived from VIIRS BRDF Data (2012–2023)

description: Vegetation clumping index (CI), which quantifies the deviation of leaf spatial distribution from a random case, is a vital parameter in canopy radiative transfer, sunlit/shaded leaf separation, photosynthesis estimation, and global carbon cycle modeling. By characterizing the spatial grouping of foliage, CI significantly influences the radiation absorption regime and the partitioning of energy within the canopy. This dataset provides the first global long-term Vegetation Clumping Index (CI) product derived from VIIRS BRDF data (Suomi-NPP) at a spatial resolution of 500 m. The product covers the period from 2012 to 2023, offering a critical medium-resolution successor to the MODIS CI era. The products are recorded in GeoTIFF format using the MODIS Sinusoidal projection. The data are organized into the MODIS sinusoidal tiling system (e.g., hXXvXX), facilitating seamless integration with existing MODIS-based workflows and products.The retrieval of this VIIRS CI product employs several technical advancements to ensure high data quality. Specifically, the NIR band was selected as the primary BRDF data source to minimize CI retrieval uncertainty. The dataset utilizes an updated RossThick-LiSparseReciprocal_Chen (RTLSR_C) model, which incorporates hotspot parameters (C1 and C2) specifically optimized for VIIRS to accurately reconstruct hotspot and dark spot reflectances. This approach effectively overcomes potential scale effects in hotspot signatures that often arise when using prior values from sensors with different pixel resolutions. Furthermore, an enhanced lookup table was established as a backup algorithm to reprocess anomalous values and pixels under snow-covered conditions, ensuring the spatial completeness of the global record. Extensive validation against field measurements confirms the reliability of the dataset, with the main algorithm achieving a RMSE of 0.07 and a bias of -0.01. The backup algorithm also demonstrates robust performance with an RMSE of 0.09. The product successfully captures the complex spatial patterns of vegetation structural hierarchy across diverse land cover types at both regional and global scales.
description: Vegetation clumping index (CI), which quantifies the deviation of leaf spatial distribution from a random case, is a vital parameter in canopy radiative transfer, sunlit/shaded leaf separation, photosynthesis estimation, and global carbon cycle modeling. By characterizing the spatial grouping of foliage, CI significantly influences the radiation absorption regime and the partitioning of energy within the canopy. This dataset provides the first global long-term Vegetation Clumping Index (CI) product derived from VIIRS BRDF data (Suomi-NPP) at a spatial resolution of 500 m. The product covers the period from 2012 to 2023, offering a critical medium-resolution successor to the MODIS CI era. The products are recorded in GeoTIFF format using the MODIS Sinusoidal projection. The data are organized into the MODIS sinusoidal tiling system (e.g., hXXvXX), facilitating seamless integration with existing MODIS-based workflows and products.The retrieval of this VIIRS CI product employs several technical advancements to ensure high data quality. Specifically, the NIR band was selected as the primary BRDF data source to minimize CI retrieval uncertainty. The dataset utilizes an updated RossThick-LiSparseReciprocal_Chen (RTLSR_C) model, which incorporates hotspot parameters (C1 and C2) specifically optimized for VIIRS to accurately reconstruct hotspot and dark spot reflectances. This approach effectively overcomes potential scale effects in hotspot signatures that often arise when using prior values from sensors with different pixel resolutions. Furthermore, an enhanced lookup table was established as a backup algorithm to reprocess anomalous values and pixels under snow-covered conditions, ensuring the spatial completeness of the global record. Extensive validation against field measurements confirms the reliability of the dataset, with the main algorithm achieving a RMSE of 0.07 and a bias of -0.01. The backup algorithm also demonstrates robust performance with an RMSE of 0.09. The product successfully captures the complex spatial patterns of vegetation structural hierarchy across diverse land cover types at both regional and global scales.