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

Abstract

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.

Remote sensing applications for reservoir water level monitoring, sustainable water surface management, and environmental risks in Quang Nam province, Vietnam

“…Figure 4 shows the performance of NDVI and mNDVI indices in extracting surface water of Quang Nam province using Landsat 8 OLI/TIRs image on 17 May 2018. Noticeably, a slight difference between the extracted water distribution using two indices can be seen, similar to a study by Herndon et al (2020).…”

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

“…Herndon et al [141] Achieved an overall accuracy of 98% when using Landsat-8 data to identify waterbodies in the Nigerian Sahel. Background noise led to misclassification.…”

Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula

“…GP was originally developed and tested across several water bodies including temporary shallow lakes [20], whereas AWEI was tested by its authors under a wide range of environmental conditions and water body types, such as small freshwater reservoirs, large lakes, harbors, and the sea [35]. In our study, AWEI presented better results for permanent lake/wetland types than for temporary lake/wetland types, which could possibly be explained by the fact that it was developed and tested with permanent water bodies [45][46][47][48]. In general, AWEInsh behave better than AWEIsh, mainly due to the absence of shadows in the scenes [35].…”