Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series
<p>Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-08-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/22/4349/2018/hess-22-4349-2018.pdf |
| Summary: | <p>Hydrometric monitoring of small
water bodies (1–10 ha) remains rare, due to their limited size and
large numbers, preventing accurate assessments of their agricultural
potential or their cumulative influence in watershed hydrology. Landsat
imagery has shown its potential to support mapping of small water bodies, but
the influence of their limited surface areas, vegetation growth, and rapid
flood dynamics on long-term surface water monitoring remains unquantified. A
semi-automated method is developed here to assess and
optimize the potential of
multi-sensor Landsat time series to monitor surface water extent and mean
water availability in these small water bodies. Extensive hydrometric field
data (1999–2014) for seven small reservoirs within the Merguellil catchment
in central Tunisia and SPOT imagery are used to calibrate the method and
explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six
commonly used water detection indices to provide high overall accuracy and
threshold stability during high and low floods, leading to a mean surface
area error below 15 %. Applied to 546 Landsat 5, 7, and 8 images over
1999–2014, the method reproduces surface water extent variations across
small lakes with high skill (<i>R</i><sup>2</sup> = 0.9) and a mean root mean square error
(RMSE) of 9300 m<sup>2</sup>. Comparison with published global water datasets
reveals a mean RMSE of 21 800 m<sup>2</sup> (+134 %) on the same lakes
and highlights the value of a tailored MNDWI approach to improve hydrological
monitoring in small lakes and reduce omission errors of flooded vegetation.
The rise in relative errors due to the larger proportion and influence of
mixed pixels restricts surface water monitoring below 3 ha with
Landsat (Normalised RMSE  =  27 %). Interferences from
clouds and scan line corrector failure on ETM+ after 2003 also decrease the
number of operational images by 51 %, reducing performance on lakes with
rapid flood declines. Combining Landsat observations with 10 m
pansharpened Sentinel-2 imagery further reduces RMSE to 5200 m<sup>2</sup>,
displaying the increased opportunities for surface water monitoring in small
water bodies after 2015.</p> |
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| ISSN: | 1027-5606 1607-7938 |