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Agricultural Drought Indices - US Department of Agriculture

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Conclusions and Discussion<br />

Several authors have applied methodologies based on remote sensing and climate data for<br />

monitoring drought conditions at the regional scale, generally showing that indicators based only<br />

on meteorological data are not enough to describe the spatial patterns <strong>of</strong> droughts (Bhuiyan et al.<br />

2006).<br />

The time monitoring <strong>of</strong> spatial distributions <strong>of</strong> indicators correlated with soil moisture and<br />

vegetation water stress, such as VTCI, TDVI, and WDI, given the strong theoretical base on which<br />

they are founded, has been shown to provide a good approximation for conditions <strong>of</strong> water stress<br />

or drought at the regional level.<br />

In the present work, it has been demonstrated that during dry conditions (less availability <strong>of</strong> soil<br />

moisture), the increase in surface temperature (involving temperature <strong>of</strong> vegetation as well as soil<br />

temperature, according to the cell) serves as a good indicator <strong>of</strong> vegetation water stress preceding<br />

the onset <strong>of</strong> drought. The response <strong>of</strong> vegetation to conditions <strong>of</strong> high temperature can occur even<br />

when the vegetation is green, because <strong>of</strong> stomatal regulation to minimize water loss through<br />

transpiration (Wan et al. 2004). This leads to a reduction in latent heat flux and, consequently, to a<br />

noticeable increase in heat flux, resulting in a rise in leaf temperature with respect to the air<br />

temperature. This increase in leaf temperature is <strong>of</strong>ten used to identify stress in crops.<br />

The increasingly widespread availability <strong>of</strong> satellite images allows evaluation studies to address<br />

drought conditions and impacts from remote sensing, either based on indicators correlated with<br />

soil moisture, or directly on estimates <strong>of</strong> actual evapotranspiration.<br />

Acknowledgments<br />

This work was developed in the framework <strong>of</strong> the agreement between the UPCT and the CHS,<br />

“Development <strong>of</strong> indicators <strong>of</strong> water scarcity and droughts, in the context <strong>of</strong> climate change”, and<br />

the agreement “Development <strong>of</strong> early warning system for Jucar River and Segura River Basins”,<br />

between UPCT and the General Deputy <strong>of</strong> Water, <strong>of</strong> the Ministry for the Environment, Marine and<br />

Rural Affairs. The financial support, as well as the support <strong>of</strong> Project CGL, are acknowledged.<br />

References<br />

Bhuiyan, C., R.P. Singh, and F.N. Kogan. 2006. Monitoring drought dynamics in the Aravalli region<br />

(India) using different indices based on ground and remote sensing data. International<br />

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Cihlar, J., L. St. Laurent, and J. Dyer. 1991. Relation between the Normalized Difference<br />

Vegetation Index and ecological variables. Remote Sensing <strong>of</strong> Environment 35:279-298.<br />

EC. 2009. Common Implementation Strategy for the Water Framework Directive (2000/60/EC).<br />

Technical Report – 2009 – 040, European Communities, 52 pp.<br />

Fensholt, R. and I. Sandholt. 2003. Derivation <strong>of</strong> a shortwave infrared water stress index from<br />

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Española de Teledetección. Hacia un mejor entendimiento de la dinámica global y regional.<br />

Moran, M.S., T.R. Clarke, Y. Inoue, and A. Vidal. 1994. Estimating crop water deficit using the<br />

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Ranjan Parida, B. 2006. Analysing the Effect <strong>of</strong> Severity and Duration <strong>of</strong> <strong>Agricultural</strong> <strong>Drought</strong> on<br />

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International Institute for Geo-Information Science and Earth Observation Enschede, The<br />

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