全球逐日0.25度C波段植被光学厚度数据集（2002-2022）

基于微波遥感的植被光学厚度（Vegetation Optical Depth，VOD）是反映植被含水量、冠层结构和生物量的重要指标。与光学植被指数相比，VOD不受云雾等影响，且不易饱和，因此在植被碳循环监测、植被生物量变化评估及干旱识别等研究中具有重要应用价值。本数据集通过跨传感器组合校准方法（在稀疏植被区采用线性回归，在密集植被区采用线性放缩）融合了 AMSR-E（2002–2011）、WindSat（2003–2020）及 AMSR2（2012–2022）三个传感器的 C 波段亮温（TB）观测数据，有效消除了传感器间的系统偏差。利用 C-MEB 模型反演，生成了全球长时序（2002–2022）逐日降轨 C-VOD 数据集，空间分辨率为 0.25°，并提供逐像元质量控制以满足不同研究需求。验证结果表明，合并后的 C-VOD在保留原始植被动态特征的同时，其跨传感器时空一致性显著提升；在空间上与多种植被变量（包括地上生物量 AGB、冠层高度、NDVI 和 EVI）具有高度一致的跨传感器拟合关系。与现有 VODCA C-VOD 产品相比，合并 C-VOD在全球 54.44% 和 50.11% 的植被覆盖区域与光学植被指数 NDVI 和 EVI 的时间相关性优于 VODCA C-VOD（分别为 29.22% 和 36.53%）；基于 FLUXNET GPP 站点的分析显示，合并 C-VOD 与 GPP 的时间相关性在 51.02% 的站点优于 VODCA C-VOD（仅为 36.73%）。这些结果表明，本数据集能够更可靠地反映植被动态生长，为全球植被监测、生态系统碳循环研究及气候变化影响评估提供长期连续的高质量基础数据资源。

Vegetation Optical Depth (VOD) based on microwave remote sensing is an important indicator reflecting vegetation water content, canopy structure and biomass. Compared with optical vegetation indices, VOD is unaffected by clouds and fog and less prone to saturation, thus holding important application value in studies such as vegetation carbon cycle monitoring, assessment of vegetation biomass changes and drought identification. This dataset fuses C-band brightness temperature (TB) observations from three sensors: AMSR-E (2002–2011), WindSat (2003–2020) and AMSR2 (2012–2022) via a cross-sensor combined calibration method (linear regression for sparse vegetation areas and linear scaling for dense vegetation areas), effectively eliminating systematic biases between sensors. Using C-MEB model retrieval, a long-term global daily descending orbit C-VOD dataset (2002–2022) is generated, with a spatial resolution of 0.25°, and per-pixel quality control is provided to meet different research needs. Validation results show that while retaining the original vegetation dynamic characteristics, the merged C-VOD has significantly improved spatiotemporal consistency across sensors; spatially, it has highly consistent cross-sensor fitting relationships with multiple vegetation variables, including aboveground biomass (AGB), canopy height, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Compared with the existing VODCA C-VOD product, the merged C-VOD outperforms VODCA C-VOD (with temporal correlations of 29.22% and 36.53% respectively) in temporal correlations with optical vegetation indices NDVI and EVI across 54.44% and 50.11% of global vegetated areas, respectively. Analysis based on FLUXNET GPP sites shows that the merged C-VOD has better temporal correlation with GPP than VODCA C-VOD (only 36.73%) at 51.02% of the sites. These results demonstrate that this dataset can more reliably reflect vegetation dynamic growth, providing long-term continuous high-quality basic data resources for global vegetation monitoring, ecosystem carbon cycle research and climate change impact assessment.

搜集汇总

背景与挑战

背景概述

该数据集是一个全球范围的C波段植被光学厚度（VOD）产品，时间跨度为2002年至2022年，空间分辨率为0.25度，逐日更新。它通过融合AMSR-E、WindSat和AMSR2三个传感器的观测数据，采用跨传感器校准方法生成，有效消除了系统偏差，提供了长期连续的高质量植被含水量、冠层结构和生物量信息。与现有产品相比，该数据集在时空一致性和与植被指数、总初级生产力的相关性方面表现更优，适用于全球植被动态监测、生态系统碳循环研究和气候变化影响评估。