Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

Bergé-Nguyen, Muriel; Crétaux, Jean-François

doi:10.3390/rs70202127

Cited by 46 publications

(31 citation statements)

References 42 publications

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“…With the large wetland present in this subbasin, signal components related to SW are much larger than in the other areas. More accurate SW estimates are needed to confirm the significance of GW changes for the Inland Delta, although an increase in surface water is reported by Berge‐Nguyen and Cretaux ().…”

Section: Resultsmentioning

confidence: 97%

GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin

2017

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West African regions along the Niger River experience climate and land cover changes that affect hydrological processes and therewith the distribution of fresh water resources (WR). This study provides an investigation of long‐term changes in terrestrial water storages (TWS) of the Niger River basin and its subregions by analyzing a decade of satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) mission. The location of large trends in TWS maps of differently processed GRACE solutions points to rising groundwater stocks. Soil moisture data from a global land surface model allow separating the effect of significantly increasing amount of WR from that of TWS variations. Surface water variations from a global water storage model validated with observations from altimetry data were applied to estimate the groundwater component in WR. For the whole Niger, a rise in groundwater stocks is estimated to be 93 ± 61 km3 between January 2003 and December 2013. A careful analysis of uncertainties in all data sets supports the significance of the groundwater rise. Our results confirm previous observations of rising water tables, indicating that effects of land cover changes on groundwater storage are relevant on basin scales. Areas with rising water storage are stocking a comfortable backup to mitigate possible future droughts and to deliver water to remote areas. This has implications for Niger water management strategies. Increasing groundwater recharges may be accompanied by reduction in water quality. This study helps to inform authority's decision to mitigate its negative impacts on local communities.

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Section: Resultsmentioning

confidence: 97%

GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin

2017

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“…over the Niger and inundated floodplains and in situ measurements (Berge-Nguyen et al 2008). Since these MODIS data are used for the calibration of the downscaling methods using the GIEMS data (25 km 3 25 km), it has been decided here to degrade the MODIS resolution to 500 m (i.e., four MODIS pixels).…”

Section: B Modis Inundation Estimatesmentioning

confidence: 99%

“…A method for wetland mapping and flood event monitoring was developed using surface reflectance measurements from the MODIS Terra instrument in order to detect open water and the spread of aquatic vegetation on a weekly basis with an about 250-m spatial resolution (Berge-Nguyen et al 2008). The methodology uses a combination of surface reflectance measured by different spectral bands, from Band 1 (B1, 620-670 nm) to Band 7 (B7, 2107-2155 nm).…”

Section: B Modis Inundation Estimatesmentioning

confidence: 99%

Characterization and Space–Time Downscaling of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data

Aires

Papa

Prigent

et al. 2014

Journal of Hydrometeorology

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The objective in this work is to develop downscaling methodologies to obtain a long time record of inundation extent at high spatial resolution based on the existing low spatial resolution results of the Global Inundation Extent from Multi-Satellites (GIEMS) dataset. In semiarid regions, high-spatial-resolution a priori information can be provided by visible and infrared observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta where MODIS-derived inundation extent has been estimated at a 500-m resolution. The space-time variability is first analyzed using a principal component analysis (PCA). This is particularly effective to understand the inundation variability, interpolate in time, or fill in missing values. Two innovative methods are developed (linear regression and matrix inversion) both based on the PCA representation. These GIEMS downscaling techniques have been calibrated using the 500-m MODIS data. The downscaled fields show the expected space-time behaviors from MODIS. A 20-yr dataset of the inundation extent at 500 m is derived from this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins and to other variables than inundation, provided enough a priori highspatial-resolution information is available. The derived high-spatial-resolution dataset will be used in the framework of the Surface Water Ocean Topography (SWOT) mission to develop and test the instrument simulator as well as to select the calibration validation sites (with high space-time inundation variability). In addition, once SWOT observations are available, the downscaled methodology will be calibrated on them in order to downscale the GIEMS datasets and to extend the SWOT benefits back in time to 1993.

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“…See [38] for a review of the SAR technique [17] Amazon Basin ENVISAT SAR L-band [39] High latitude regions ENVISAT ASAR C-band [40] Mekong basin ENVISAT ASAR C-band [41] Global scale ENVISAT ASAR C-band [22] Boreal regions Topex-Poseidon C-band Hybrid approaches [42] Global scale SSM/I, ERS, AVHRR Ka-and C-band [32] Global scale SSM/I, ERS, AVHRR Ka-and C-band [43] Global scale SSM/I, ERS, AVHRR Ka-and C-band [37] Global scale SSM/I, SSMI/S, ERS, QSAT, ASCAT Ku-and C-band Optical and infrared [44] Okavango Delta AVHRR - [45] Brahmaputra AVHRR - [46] Inner Niger Delta MODIS - [47] China MODIS - [33] Mekong Delta MODIS - [10] Global scale Landsat -…”

Section: Active Microwavementioning

confidence: 99%

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

Parrens

Bitar

Frappart

et al. 2017

Water

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Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.

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Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets

Cited by 46 publications

References 42 publications

GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin

GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin

Characterization and Space–Time Downscaling of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

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