Résumé
Freshwater wetlands ensure fundamental functions such as flood mitigation,groundwater recharge, water purification, and nutrient and sediment retention, as well assupporting high levels of biodiversity. Amazonian floodplains support one of Earth´s largestreservoirs of biodiversity, yet are increasingly threatened by land cover and land use changesinduced by large-scale agriculture expansion, waterway network development, andhydroelectric dam construction. These pressures conjointly with climate change may havedramatic impacts on floodplain biodiversity and endemic plant and animal species. Becauseflooding dynamics is an important driver of floodplain biodiversity and productivity,characterizing and monitoring floodplain hydrology are important to supporting biodiversityconservation. Several global- or regional-scale wetland or flood extent maps have beenproduced. Until recently, these maps were of coarse spatial resolution, inadequate to supportwetlands biodiversity conservation. Recently, based on Landsat imagery, Pekel et al. (2016)produced a global surface water (GSW) map at 30 m and analyzed changes inminimum/maximum flood extent and flood duration over the past three decades. However, intropical regions, clouds and vegetation may significantly impact the accuracy of surface watermapping based on optical data. On the other hand, SAR sensors acquire data regardless ofweather conditions and SAR imagery has been widely used over the past decades to monitorand map wetland inundation and vegetation worldwide, including in the Amazon region.In this study, we use Sentinel-1 Synthetic Aperture Radar (SAR) time series (12-dayrepeat cycle at this latitude, 10 m resolution) to monitor the flood dynamics of a segment ofthe Solimões/Amazon river encompassing the Curuai floodplain (eastward) and the Janauacáfloodplain (westward) for the year 2017 (covering 6 S1 tiles). The Curuai floodplain (4000km2, including the local watershed) forms a vast complex system of temporally connectedlakes, flooded forest and fringing wetlands along the Amazon river right margin. Severalperennial or intermittent channels of various size link the floodplain lakes system with theAmazon River. The Janauacá floodplain is a medium size system (786 km2, including thelocal watershed), composed by a lake and associated flooded forest and other wetlands, linkedto the Solimões River by a single channel.Images from the S1 time series were stacked (around 30 images per tile), a meanimage was calculated and a thresholding classification was applied on the basis of optical data(Sentinel 2). In this mean classification, areas always flooded will appear in black, areas neverflooded in light grey and areas occasionally flooded in shades of grey on flood duration. Foreach date of the stack, the same thresholding classification is performed and compared to themean classification in order to produce 4 land cover classes: open water, potentially floodedvegetation, low vegetation and forest. Classification results are then refined applying posttreatments: we used the HAND index combined with spatial and temporal rules to avoidoverestimation of water in areas that are not compatible with the hydrodynamics of the area.We compare our results with the GSW products in terms of maximum water extent andinundation duration in order to assess the reliability of GSW for large Amazonian floodplains.Maximum open water extentBoth studies are in good agreement, with an estimated open water maximal extent of27 000 km2 (our study) and 30 000 km2 (Pekel et al., 2016). Most of the discrepancies areobserved along floodplain and mainstream margins, and differences are greater at Curuai thanat Janauacá. The lengths of the time series used to construct our product and GSW productsare very different. Pekel et al. (2016) used a Landsat chronology over the 32 last years, whilewe used only the year 2017. Consequently, the maximum water level recorded at Óbidosgauge for our study was 760 cm while it was 860 cm over the last 32 years included in Pekelet al.’s study. As reported in Sippel et al. (1998), flood extent and main stream water level aredirectly related. According to their relationship between water level and flood extent, a 1 mwater level variation induces an increase of roughly 11% of the flood extent, comparable withthe expected flood extent increase for the same water level variation in the Curuai floodplain(Bonnet et al, 2008). Applying this percentage to our results leads to a maximal flood extentof circa 30 000 km2, similar to the extension found by Pekel et al. (2016).Flood durationDiscrepancies between the GSW and Sentinel-based products are larger in the case offlood duration. These differences are not related to upstream or downstream position but tolateral flow propagation across the floodplain. Throughout the entire study area, strongheterogeneities are observed with variations between both results of several months.At the level of the floodplains, we evidence smaller water residence duration in themain lake of the Janauacá floodplain (between 0 and 2 months). In Curuai, we observe longerwater residence duration throughout the floodplain (up to 8 months). Part of the discrepanciesmight be explained by water level differences between the time series used to build theproduct (2014-2015 for GSW vs 2017 in this study). Thanks to the repetitivity of cloud-freeSentinel 1, we provide a finer quantification of the temporal dynamics of floods in thefloodplains and explain the over-estimation and under-estimation of flood duration inJanauacá and Curuai respectively by the GSW product. Therefore, compared with the GSWproduct, the flood duration dynamics of our product correspond more closely withhydrodynamic modelling results obtained by Bonnet et al. (2017) for the Janauacá floodplainand Rudorff et al. (2014) and Bonnet et al. (2008) for the Curuai floodplain.We conclude that GSW provides realistic maximum open water extents even at localscale and with accuracies suitable for supporting hydrologic applications, for example modelcalibration or validation. On the other hand, GSW should be used cautiously when looking atflood duration and subsequent hydrological connectivity analysis, which are fundamentalproperties to support biodiversity conservation. The Sentinel 1 and Sentinel 2 constellationshould provide improved mapping of flood duration at global scale.