Abstract
Accurate monitoring of surface water bodies is essential in numerous hydrological and agricultural applications. Combining imagery from multiple sensors can improve long-term monitoring; however, the benefits derived from each sensor and the methods to automate long-term water mapping must be better understood across varying periods and in heterogeneous water environments. All available observations from Landsat 7, Landsat 8, Sentinel-2 and MODIS over 1999–2019 are processed in Google Earth Engines to evaluate and compare the benefits of single and multi-sensor approaches in long-term water monitoring of temporary water bodies, against extensive ground truth data from the Senegal River floodplain. Otsu automatic thresholding is compared with default thresholds and site-specific calibrated thresholds to improve Modified Normalized Difference Water Index (MNDWI) classification accuracy. Otsu thresholding leads to the lowest Root Mean Squared Error (RMSE) and high overall accuracies on selected Sentinel-2 and Landsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. On MODIS imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km2) water bodies despite the 500 m spatial resolution. Over 1999–2019, MODIS observations reduce average daily RMSE by 48% compared to the full Landsat 7 and 8 archive and by 51% compared to the published Global Surface Water datasets. Results reveal the need to integrate coarser MODIS observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full Landsat time series before 2013. From 2013, the Landsat 7 and Landsat 8 constellation becomes sufficient, and integrating MODIS observations degrades performance marginally. Combining Landsat and Sentinel-2 yields modest improvements after 2015. These results have important implications to guide the development of multi-sensor products and for applications across large wetlands and floodplains.
Highlights
Accelerating climate and human changes have significant influences on hydrological systems and notably on surface water dynamics [1]
The distribution of thresholds between images reveals large differences, with the optimal threshold being relatively stable for Landsat 8 and Sentinel-2 compared to Moderate Resolution Imaging Spectroradiometer (MODIS)
Khandelwal et al [17] using a supervised classification algorithm based on Support Vector Machines (SVMs) showed that MODIS imagery could be used to study water trends on large lakes ranging between 240 km2 and 5380 km2
Summary
Accelerating climate and human changes have significant influences on hydrological systems and notably on surface water dynamics [1]. Across large wetlands and multiple dispersed water bodies, remote sensing provides rising opportunities to monitor surface water variations, which can be difficult to capture by localised hydrological monitoring or modelling [2]. These notably face stark difficulties in representing flooded areas due to data scarcity and inaccuracies in global digital elevation models to represent and account for the flat, yet complex topography of large floodplains [3]. Khandelwal et al [17] developed an approach for global eight day monitoring of surface water extent using 500 m MODIS imagery, while D’Andrimont and Defourny [18] used twice daily observations to characterise surface water at a 10 day time step over 2004–2010 across Africa
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