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Analyzing the uncertainty of the multisource remote sensing-based vegetation products for drought monitoring

  • role： First author第一作者
  • Affiliation：

    Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

  • Email：liuxuan_610@163.com
  • Introduction：刘轩，研究方向为生态水文遥感。E-mail： liuxuan_610@163.com
LIU Xuan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

  • Email：zhou.j@ccnu.edu.cn
  • Introduction：周杰，研究方向为生态水文遥感。E-mail： zhou.j@ccnu.edu.cn
ZHOU Jie12*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LU Jing3,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

JIA Li3,  
  • Affiliation：

    Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

XIONG Xuqian12,  
  • Affiliation：

    Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China

    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

CUI Yilin12

реферат

In the context of continued global warming, the risks of drought have increased remarkably, causing tremendous impacts on the sustainability of natural ecosystems and socioeconomic 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. VCI calculated from multisource 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 multisource 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. In this study, the uncertainty of multisource 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, on the basis of 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 between VCI time series under different experimental settings were calculated to quantify the uncertainty of multisource vegetation remote sensing products for drought monitoring. The possible explanation for the quantified uncertainty was further attributed in the study. (1) The VCI time series calculated from NDVI products based on different sensors show considerable 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 over specific regions, with strong correlations between the VCI time series calculated on the basis of NDVI and EVI products, NDVI and LAI products, respectively, while the VCI time series calculated on the basis of 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 on the basis of 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. In summary, when using vegetation remote sensing products for drought monitoring, the consistency characteristics among multisource vegetation remote sensing products must 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 quantify the uncertainty systematically.

ключеви́че слова́

Vegetation Condition Index (VCI);drought monitoring;uncertainty analysis;Remote sensing of Vegetation;remote sensing products

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