Remote Sensing

Remote sensing applications offer unique opportunities for augmenting and/or improving drought monitoring efforts that complement the traditionally used climatological and hydrological drought indicators and i ndices. Satellite-derived remote sensing information is particularly useful in assisting with drought monitoring over larger spatial scales. Satellites provide synoptic, repeat coverage of spatially continuous information in a consistent, systematic, and objective manner (Hayes et al. 2012). This information can supplement or simulate data from regions by utilizing existing observation networks having abundant ground-based data, or where ground-based observational networks and monitoring data are sparse.

During the past decade, new satellite-based instruments and major advancements in computing, analyses, and modeling techniques have resulted in the rapid development of many remote sensing tools and products with drought monitoring applications (Hayes et al. 2012). These new tools and products, described in more detail in Chapter 10 of this book, cover a suite of environmental variables that are useful in drought monitoring, including vegetation health, precipitation, evapotranspiration, soil moisture, terrestrial water resources, and snow cover.

The specific advantages satellite remote sensing can provide within a drought early warning system, as described by Hayes et al. (2012, 5), include:

  • 1. Provide information at spatial scales required for local-scale drought monitoring and decision-making, that cannot be adequately supported from information derived from traditional, point-based data sources (e.g., single area-based value over administrative geographic unit or spatially interpolated climate index grids).
  • 2. Fill in informational gaps on drought conditions for locations between in situ observations and in areas that lack (or have very sparse) ground-based observational networks.
  • 3. Enable earlier drought detection in comparison to traditional climatic indices.
  • 4. Collectively provide a suite of tools and data sets geared to meet the observational needs (e.g., spatial scale, update frequency, and data type) for a broad range of decision support activities related to drought.
 
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