Spectral vegetation indices are among the most commonly used satellite data products for evaluation, monitoring, and measurement of vegetation cover, condition, biophysical processes, and changes. This recommended practice shows how to apply a multi-temporal analysis of MODIS-based Vegetation Condition Index (VCI) to support drought monitoring and early warning. The method has been developed by the Iranian Space Agency and was firstly applied in Iran using ENVI 4.8.

The recommended practice on drought monitoring and early warning was consecutively adapted and updated and can now be followed using ENVI, RStudio and Python. The versions most up to date now also include a cloud mask, which had not been considered before. Also, an additional R script is provided to be able to compute the VCI for large areas (>200.000 km²). The adapted versions were tested in Central America.

A related recommended practice applies the Standardized Vegetation Index (SVI) instead of the Vegetation Condition Index (VCI) for drought monitoring and can be found here. 

Background

Drought is a recurring phenomenon that can lead to significant losses to societies affecting different aspects of human life such as agriculture, food security, and the environment. Since 1967 droughts have affected 50% of the 2.8 billion people who suffered from all natural disasters [1].

Traditional methods of drought assessment and monitoring rely on rainfall data. However, this approach has two main disadvantages: rainfall data are limited to the region, and they are often inaccurate and difficult to obtain in near-real time. In contrast, satellite-sensor data is continuously available and can be used to detect the onset of a drought, its duration and magnitude [2]. The purpose of this recommended practice is to monitor impacts of meteorological droughts on natural vegetation (rain fed, range land & forest). Availability, simplicity, free of charge data, good research literature and citation, minimum requirements of inputs are main criteria, which have been considered to define the methodology.

Drought definition

Meteorological drought is often defined by a period of substantially diminished precipitation duration and/or intensity. The commonly used definition of meteorological drought is an interval of time, generally on the order of months or years, during which the actual moisture supply at a given place falls below the climatically appropriate moisture supply.

Agricultural drought occurs when there is inadequate soil moisture to meet the needs of a particular crop at a particular time. Agricultural drought usually occurs after or during meteorological drought but before hydrological drought.

Hydrological drought refers to deficiencies in surface and subsurface water supplies. It is measured as stream flow, snowpack, and as lake and groundwater levels. There is usually a delay between lack of rain or snow and less measurable water in streams, lakes and reservoirs. Therefore, hydrological measurements tend to lag other drought indicators.

Socioeconomic drought occurs when physical water shortages start to affect the health, well-being, and quality of life of the people, or when the drought starts to affect the supply and demand of an economic product.

The figure below shows the relations between these types of drought:

Definition of the VCI

Kogan proposed a Vegetation Condition Index (VCI) based on the relative Normalized Difference Vegetation Index (NDVI) change with respect to minimum historical NDVI value. The VCI therefore compares the current Vegetation Index (VI) such as NDVI or Enhanced Vegetation Index (EVI) to the values observed in the same period in previous years within a specific pixel.

The VCI is calculated as shown below,

where VCI_ijk is the VCI value for the pixel i during week/month/DOY j for year k, VI_ijk is the weekly/monthly/DOYs VI value for pixel i in week/month/DOY j for year k whereby both the NDVI or EVI can utilized as VI, VI_{i, min} and VI_i,max are the multiyear minimum and maximum VI, respectively, for pixel i.

The resulting percentage of the observed value is situated between the extreme values (minimum and maximum) in the previous years. Lower and higher values therefore indicate bad and good vegetation state conditions, respectively.

While the first step-by-step instructions for calculating the VCI in ENVI 4.8 and 5.0 as well as the first update in R based the normalized index on the NDVI, the consecutive updates in R as well as the implementation in Python are based on the EVI which has some advantages compared to the NDVI such as

• no distortions in the reflected light caused by the particles in the air,
• no distortions in the reflected light caused by ground cover below the vegetation, and
• the EVI data product does not become saturated as the NDVI when viewing rainforests and large amounts of chlorophyll.

Cloud mask

In order to mask out pixels that are impacted by atmospheric interferences such as clouds as well as snow and ice cover, the Pixel Reliability Quality Assurance (QA) layer of MOD13Q1 is being used. The layer classifies the quality of the vegetation index in the following categories:

"Good" and "marginal" data in the pixel reliability bands are accepted as sufficient quality and will be considered for the analysis. Therefore, other filter values than "0" and "1" are used to mask out the corresponding EVI pixels.

Find more information about the MODIS Land Products Quality Assurance here.