Vegetation index based technique for global agricultural drought monitoring

Abstract

Droughts occurring every year all over the world have great impacts on human society, nature, and the global economy for example in declining crop yields, reduction of water supplies, and distressed vegetation. Satellite data have been widely used in drought monitoring. Vegetation condition is an excellent indicator of agricultural drought and can be quantified by the Normalized Difference Vegetation Index (NDVI). One way to detect agricultural drought is to quantify it through calculation of drought indices, such as the Vegetation Condition Index (VCI). For this purpose, we have developed an agricultural drought portal under the name of Global Agricultural Drought Information Services System (GADISS), which is interoperable with the Global Earth Observation System of Systems (GOESS) and follows Web standards for geospatial data recommended by the Open Geospatial Consortium (OGC), on the top of remote sensing drought monitoring. This study investigates the performance of VCI drought maps, which is also scientific base of GADISS, against annual grape production. Taking severe drought in the Aegean region, Turkey in 2007 as an example, the VCI drought index derived from 8-day NDVI satisfactorily detect drought. It is also validated with the fluctuations of annual grape production capacity. The results suggest that agricultural droughts have negative impacts on grape production rate per tree, and they can be operationally monitored by VCI over a geographic region for an extended period of time.

Monitoring vegetative drought dynamics in the Brazilian semiarid region

“…Drought indices are particularly useful for monitoring the impact of climate variability on vegetation because the spatial and temporal identification of drought episodes is extremely complex. A number of drought indices, including meteorological (Wilhite and Glantz, 1985), remote sensed, hydrological and other indicators, have been used to measure drought impacts (Palmer, 1965(Palmer, , 1968Gibbs and Maher, 1967;Shafer and Dezman, 1982;Kogan, 1990Kogan, , 2002McKee et al, 1993;Keyantash and Dracup, 2004;Bhuiyan et al, 2006;Yagci et al, 2011;Zhou et al, 2012;Du et al, 2013;Yang et al, 2013;Abbas et al, 2014;Nichol and Abbas, 2015). Traditional methods of drought assessment and monitoring rely on rainfall data (e.g., the Palmer drought severity index (PDSI) and Standardized Precipitation Index (SPI)).…”

Governing agricultural drought: Monitoring using the vegetation condition index

“…Agricultural drought monitoring strictly requires spatially contiguous time series data to predict the surface temperature and vegetation condition, which climate-based indices have main drawbacks (Yagci et al, 2011;Tonini et al, 2012). Hence, data from remote sensing image at high temporal even at low spatial resolution can aid to assist in monitoring crop failures as they provide crucial information in near-real time over large areas as opposed to a single point measurement.…”

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana, USA

“…The VCI value covers 16 days after the ith day. Numerous studies have demonstrated its ability to identify droughts at the regional (Liu and Kogan 1996;Yagci et al 2011aYagci et al , 2011b and global scale (Kogan 1993(Kogan , 1997(Kogan , 2001Unganai and Kogan 1998;Yagci et al 2012). …”