Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

Abstract

Abstract. 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 (R2=0.9) and a mean root mean square error (RMSE) of 9300 m2. Comparison with published global water datasets reveals a mean RMSE of 21 800 m2 (+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 m2, displaying the increased opportunities for surface water monitoring in small water bodies after 2015.

Potential of SWOT for Monitoring Water Volumes in Sahelian Ponds and Lakes

“…Alternative approaches have been developed to derive information on Sahelian and west African water bodies. They combine water areas derived by optical or radar remote sensing (spanning few hectares to several square kilometers) to area-volumes relationships available from in-situ bathymetry or a priori assumptions on the shape of these water bodies [2,14,15,16,17,18]. Despite the good results obtained, it remains difficult to extrapolate this kind of approaches at a regional scale, given that volume-area relationships can vary significantly among different lakes (see for example fig.…”

On the Potential of RST-FLOOD on Visible Infrared Imaging Radiometer Suite Data for Flooded Areas Detection

“…Finally, the achieved results demonstrated the potential of medium-resolution optical sensors, like VIIRS, in detecting floods that affect small watersheds, usually investigable only by using data (opticalor microwave-based) at higher spatial resolution [17]. Considering the high temporal resolution offered by VIIRS sensor (that thanks to the recent launch of JPSS-1 has further improved), the relevance of the achieved results become quite evident.…”

Detecção e delimitação de pequenos reservatórios na Bacia Hidrográfica do Rio Cachoeira, Bahia, utilizando imagens multiespectrais

“…Em razão da vasta quantidade, dimensões reduzidas e dispersão geográfica, o monitoramento hidrológico desses reservatórios requer investimentos incompatíveis com sua importância regional e com os recursos financeiros disponíveis (LIEBE et al, 2005;OGILVIE et al, 2018OGILVIE et al, , 2019. Por consequência, pouca ou nenhuma informação está disponível acerca dos processos hidrológicos atuantes e do volume de água armazenado nestes reservatórios (OGILVIE et al, 2016).…”