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Open AccessArticle

An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

1
NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA
2
Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
3
Department of Atmospheric and Earth Science, The University of Alabama in Huntsville, Huntsville, AL 35899, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 431; https://doi.org/10.3390/s20020431
Received: 30 October 2019 / Revised: 9 January 2020 / Accepted: 10 January 2020 / Published: 12 January 2020
(This article belongs to the Special Issue Applications of Remote Sensing Data in Water Resources Management)
Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs. View Full-Text
Keywords: remote sensing; spectral indices; Landsat 8 OLI; West Africa remote sensing; spectral indices; Landsat 8 OLI; West Africa
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Herndon, K.; Muench, R.; Cherrington, E.; Griffin, R. An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel. Sensors 2020, 20, 431.

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