在全球气候持续变暖的背景下,干旱风险持续增大,对自然生态系统和社会经济系统的可持续发展带来巨大威胁。植被遥感产品能够量化地表植被状况,表征生态系统对受气候及人类活动影响的响应。基于长时序植被遥感产品定义的植被状态指数(Vegetation Condition Index,VCI)被广泛应用于大尺度快速干旱监测。当前植被遥感产品众多,产品之间时空一致性各异,由此导致所表征的地表植被扰动特征的不一致性目前尚不明晰。本文以长江中游地区为例,考虑传感器(MODIS、AVHRR)、生物物理定义(NDVI、EVI、LAI、VOD)和时间跨度(5年、10年、20年)等产品特征因素的影响,定量分析了多源植被遥感产品应用于VCI计算的不确定性,并对产品之间存在的差异进行了归因分析。结果表明：(1)由传感器导致的差异较大,基于不同传感器的NDVI产品计算得到的VCI时间序列,在大部分区域上显示出显著的不一致性,二者计算所得的VCI时间序列之间存在微弱相关性、总体偏差较大；(2)不同的植被参数产品导致的差异远低于不同传感器导致的差异,但在特定区域上的差异仍然非常显著,基于NDVI和EVI产品、NDVI和LAI产品分别计算得到的VCI时间序列之间具有较强的相关性,而基于NDVI和VOD产品计算得到的VCI时间序列在大部分区域均呈现出显著性差异；(3)由时间跨度导致的差异较小,基于不同时间跨度计算所得的VCI时间序列之间具有较好的一致性,产品的时间跨度越大、VCI变化差异越小。总之,在使用植被遥感产品进行干旱监测时,需要全面地分析和评估多源植被遥感产品之间的不确定性特征,对监测结果作出合理性的解释,从而保证旱情评价结果的准确性和有效性。此外,对于短时的遥感干旱监测应用,建议先剔除产品中的长期趋势项再进行干旱指数计算。

In the context of continued global warming, the risks of drought have increased significantly, causing tremendous impacts on the sustainability of natural ecosystems and socio-economic systems. Remote sensing-based vegetation products are widely used to capture terrestrial vegetation dynamic and reflect the response of ecosystem to drought events. The Vegetation Condition Index (VCI) derived from long-term vegetation products is one of the most popular indices for drought monitoring. Objective: VCI calculated from multi-source vegetation products had been applied in applications for drought monitoring. However, little attention has been paid to quantify the uncertainty of the drought monitoring result caused by these products. This study aims to explore the uncertainty of drought monitoring using VCI derived from multi-source remote-sensed vegetation products by considering the impact of difference in sensors, physical definition of vegetation parameters, and historical time-span of products on VCI result. Method: In this study, the uncertainty of multi-source Remote sensing-based vegetation products applied to drought monitoring was analyzed and evaluated by taking the middle reaches of Yangtze River as an example. Specifically, based on the experimental settings of different sensors (MODIS, AVHRR), different vegetation parameters (NDVI, EVI, LAI, VOD), and different time spans (5, 10, and 20 years), the corresponding VCI time series were calculated. Then the correlation coefficient (r) and Root Mean Square Deviation (RMSD) between VCI time series under different experimental settings were calculated to quantify the uncertainty of multi-source vegetation remote sensing products for drought monitoring. The possible explanation for the quantified uncertainty was further attributed in the paper. Result: (1) The VCI time series calculated from NDVI products based on different sensors show significant inconsistencies over most of the study area, with weak correlation and large overall deviations. The inconsistent long-term trend pattern between MODIS-NDVI and AVHRR-NDVI over the study area might account for the large uncertainty. (2) The differences due to different vegetation parameter products are much lower than those due to different sensors, but the differences are still significant over specific regions, with strong correlations between the VCI time series calculated based on NDVI and EVI products, NDVI and LAI products, respectively, while the VCI time series calculated based on NDVI and VOD products show significant differences in most regions. The saturation effect of NDVI still effect VCI calculation over highly vegetated area. (3) The VCI time series calculated based on different time spans keep well consistency with each other, and the larger the time span of the products, the smaller the differences in VCI changes. Conclusion: In summary, when using vegetation remote sensing products for drought monitoring, the consistency characteristics among multi-source vegetation remote sensing products need to be carefully addressed to ensure the validity of the monitoring results. Similar analysis should be further expanded to global scale and more vegetation products to systematically quantify the uncertainty.