Flood inundation mapping using NOAA AVHRR data

Jain, Sanjay K.; Saraf, A. K.; Goswami, Ajanta; Ahmad, Tanvear

doi:10.1007/s11269-006-9016-4

Cited by 93 publications

(57 citation statements)

References 13 publications

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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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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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“…This finding is in accordance with [45], who found that MERIS had higher reflectance values over water surfaces in the green band compared to TM. Most studies [53][54][55] that make use of coarse-resolution optical imagery for flood mapping do not adequately account for the effects of mixed pixels.…”

Section: Discussionmentioning

confidence: 99%

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

et al. 2017

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The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water (γ w ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γ w . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γ w extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.

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“…The uncertainty in the discharge figures derived with this method stems mostly from uncertainty in Manning's n values for the channel and floodplain at the gauging site. commonly used for differentiating flooded area from dry surface (Jain et al, 2006). Likewise, Normalized Difference Water Index (NDWI), developed by McFeeters (1996) and calculated as (Green -Near Infrared) / (Green + Near Infrared), has been used to demarcate flooded areas (Jain et al, 2005).…”

Section: Terrain and Hydrologic Inputsmentioning

confidence: 99%

2D Finite Element Inundation Modelling in Anabranching Channels with Sparse Data: Examination of Uncertainties

Sanyal

Densmore

Carbonneau

2014

Water Resour Manage

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The nal publication is available at Springer via http://dx.doi.org/10.1007/s11269-014-0619-x.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

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Flood inundation mapping using NOAA AVHRR data

Cited by 93 publications

References 13 publications

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

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

A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain

2D Finite Element Inundation Modelling in Anabranching Channels with Sparse Data: Examination of Uncertainties

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