A Global Grassland Drought Index (GDI) Product: Algorithm and Validation

Binbin He,Zhanmang Liao,Xing Li,Xingwen Quan,Junjie Hu

doi:10.3390/rs71012704

Binbin He, Zhanmang Liao + Show 3 more

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https://doi.org/10.3390/rs71012704

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Abstract

Existing drought indices have been widely used to monitor meteorological drought and agricultural drought; however, few of them are focus on drought monitoring for grassland regions. This study presented a new drought index, the Grassland Drought Index (GDI), for monitoring drought conditions in global grassland regions. These regions are vital for the environment and human society but susceptible to drought. The GDI was constructed based on three measures of water content: precipitation, soil moisture (SM), and canopy water content (CWC). The precipitation information was extracted from the available precipitation datasets, and SM was estimated by downscaling exiting soil moisture data to a 1 km resolution, and CWC was retrieved based on the PROSAIL (PROSPECT + SAIL) model. Each variable was scaled from 0 to 1 for each pixel based on absolute minimum and maximum values over time, and these scaled variables were combined with the selected weights to construct the GDI. According to validation at the regional scale, the GDI was correlated with the Standardized Precipitation Index (SPI) to some extent, and captured most of the drought area identified by the United States Drought Monitor (USDM) maps. In addition, the global GDI product at a 1 km spatial resolution substantially agreed with the global Standardized Precipitation Evapotranspiration Index (SPEI) product throughout the period 2005–2010, and it provided detailed and accurate information about the location and the duration of drought based on the evaluation using the known drought events.

Highlights

Drought is a major environmental disaster, and affects numerous people and various trades [1].Drought monitoring and forecasting techniques are crucial for reducing society’s vulnerability to drought and its subsequent impacts and will lead to better drought-management practices [2]
To simplify the retrieval of canopy water content (CWC), we developed a multiple regression model between Normalized Difference Infrared Index (NDII) and the Leaf Area Index (LAI) and Equivalent Water Thickness (EWT) variables based on the look-up table
The Scaled Drought Condition Index (SDCI), which is constructed by integrating the scaled Normalized Difference Vegetation Index (NDVI), LST and precipitation data, and the Vegetation Health Index (VHI) was chosen as the comparison model

Summary

Introduction

Drought monitoring and forecasting techniques are crucial for reducing society’s vulnerability to drought and its subsequent impacts and will lead to better drought-management practices [2]. Grasslands are among the world’s most widely distributed vegetation types. The importance of the grasslands lies in their vast coverage and in the diverse benefits they produce [3]. They provide valuable ecological services, such as nutrient cycling and the storage of carbon, and improve local economies by supporting the livestock industry and recreational activities such as tourist. Because grasslands are generally located in arid and semi-arid regions, they are highly susceptible to drought. Developing an effective drought monitoring method for grasslands is imperative

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