Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Wenzhe Jiao,Lifu Zhang,Dongjie Fu,Yi Cen,Qingxi Tong,Qing Chang

doi:10.3390/rs8030224

Abstract

Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

Highlights

Drought is the most costly disaster that can affect natural habitats, ecosystems, agricultural systems, and urban water supplies [1,2]
precipitation condition index (PCI) is directly based on the precipitation data of Tropical Rainfall Measuring Mission (TRMM) and VUA-based
We evaluated the utility of VIUPD-derived vegetation condition index (VCI) using the United States drought monitor (USDM), different time scale standardized precipitation index (SPI), Palmer drought severity index (PDSI), Z-index, normalized difference vegetation index (NDVI)-derived VCI, VUA derived soil moisture condition index (SMCI), NSIDC derived SMCI, VUA

Summary

Introduction

Drought is the most costly disaster that can affect natural habitats, ecosystems, agricultural systems, and urban water supplies [1,2]. Many techniques for monitoring drought conditions have been developed. A variety of drought indices have been developed [1]. Many were derived using station-based measurements of temperature and precipitation, such as the Palmer drought severity index (PDSI), the moisture anomaly index (Z-index) [4], and the standardized precipitation index (SPI) [5]. These drought indices can effectively evaluate drought conditions around meteorological stations, but the lack of continuous spatial coverage limits the ability to characterize and monitor the detailed spatial pattern of drought

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Discussion

Conclusion