International audienceWetlands provide a vital resource to ecosystem services and associated rural livelihoods but their extent, geomorphological heterogeneity and flat topography make the representation of their hydrological functioning complex. A semi automated method exploiting 526 MODIS (Moderate Resolution Imaging Spectroradiometer) 8-day 500 m resolution images was developed to study the spatial and temporal dynamics of the annual flood across the Niger Inner Delta over the period 2000–2011. A composite band ratio index exploiting the Modified Normalised Difference Water Index (MNDWI) and Normalised Difference Moisture Index (NDMI) with fixed thresholds provided the most accurate detection of flooded areas out of six commonly used band ratio indices. K-means classified Landsat images were used to calibrate the thresholds. Estimated flooded surface areas were evaluated against additional classified Landsat images, previous studies and field stage data for a range of hydrological units: river stretches, lakes, floodplains and irrigated areas. This method illustrated how large amounts of MODIS images may be exploited to monitor flood dynamics with adequate spatial and temporal resolution and good accuracy, except during the flood rise due to cloud presence. Previous correlations between flow levels and flooded areas were refined to account for the hysteresis as the flood recedes and for the varying amplitude of the flood. Peak flooded areas varied between 10 300 km2 and 20 000 km2, resulting in evaporation losses ranging between 12 km3 and 21 km3. Direct precipitation assessed over flooded areas refined the wetland’s water balance and infiltration estimates. The knowledge gained on the timing, duration and extent of the flood across the wetland and in lakes, floodplains and irrigated plots may assist farmers in agricultural water management. Furthermore insights provided on the wetland’s flood dynamics may be used to develop and calibrate a hydraulic model of the flood in the Niger Inner Delta

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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.