Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level

Singh, Kanwar Vivek; Setia, Raj; Sahoo, Shashikanta; Prasad, A. C. S. V.; Pateriya, Brijendra

doi:10.1080/10106049.2014.965757

Cited by 160 publications

(57 citation statements)

References 22 publications

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“…To overcome the shortcomings of NDWI, Xu (2006) developed the Modified Normalized Difference Water Index (MNDWI) [22] that uses the Shortwave Infrared (SWIR) band to replace the NIR band used in NDWI. Many previous research works have demonstrated that MNDWI is more suitable to enhance water information and can extract water bodies with greater accuracy than NDWI [12,13,22,23].…”

Section: Introductionmentioning

confidence: 99%

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

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Abstract:Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

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Section: Introductionmentioning

confidence: 99%

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

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“…Research has demonstrated that mNDWI can enhance water information and extract water bodies with a significant accuracy [12,13]. The index is expressed as follows:…”

Section: Flood Damage Spatial Distributionmentioning

confidence: 99%

Redefining Agricultural Insurance Services Using Earth Observation Data. The Case of Beacon Project

Lekakis¹,

Kotsopoulos²,

Mygdakos³

et al. 2020

IFIP Advances in Information and Communication Technology

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BEACON is a market-led project that couples cutting edge Earth Observation (EO) technology with weather intelligence and blockchain to deliver a toolbox for the Agricultural Insurance (AgI) sector with timely cost-efficient and actionable insights for the agri-insurance industry. BEACON enables insurance companies to exploit the untapped market potential of AgI, while contributing to the redefinition of existing AgI products and services. The Damage Assessment Calculator of BEACON employs remote sensing techniques in order to improve the quality and cost-effectiveness of agri-insurance by: i) increasing the objectivity of the experts field inspections; ii) reducing the cost of field visits and iii) increasing farmers' confidence in the estimation results, given the significant economic impact of erroneous estimation. This paper provides an analysis of different type of EO data and remote sensing techniques implemented in the operational workflow of BEACON that can be used by AgI companies to provide safe and reliable results on storms, floods, wildfires and droughts damage on crops.

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“…One band represents the visible wavelength and the second from the near-infrared (NIR) or the micro-wavelengths. The Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) are band ratio algorithms, which have significant application to identify vegetated and waterlogged areas, respectively (Albarakat and Lakshmi 2019;Chu et al 2019;Li et al 2019;Acharya et al 2018;Baniya et al 2018;Sahu 2018;Mehmood et al 2017;Singh et al 2015). NDVI was developed based on the red and near-infrared wavelengths (Rouse et al 1974), while MNDWI was developed based on the green and short-wave-infrared wavelengths (Xu 2006).…”

Section: Introductionmentioning

confidence: 99%

Challenges of Sustainable Land Management in Siwa Oasis: The Waterlogging Problem in the Newly Reclaimed Desert Land

EL-Kawy

2019

Journal of Soil Sciences and Agricultural Engineering

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Accurate and frequent information about waterlogging problems is very necessary for the sustainable land management. The objectives of this research are to detect and mapping the vegetated and waterlogged areas in a newly reclaimed desert land in Siwa oasis, using remote sensing data and techniques; and suggest a future plan to solve the waterlogging problem. The Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) algorithms were applied on four Landsat images (2009, 2012, 2015 and 2018) to detect and map the vegetation and waterlogging states, respectively. The results of NDVI revealed that the cultivated land was increased by 2.3 times in nine years (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) due to private land reclamation process. The results of MNDWI indicated that the waterlogged area was increased remarkably by 21 times during nine years, where it increased remarkably from 19 ha in 2009 to 393 ha in 2018. The development of the waterlogged areas was most likely due to the increase in the surface water level of Lake Aghormy North of the study area, as well as the trapping of the agricultural drainage water and well-water overflow between the sand dune formations. This study recommended a strategy to solve the waterlogging problem, which include the establishment of a surface drainage network covering the study area. This network should be connected with a main drain at the Northern border of the study area. This strategy will enhance the drainage conditions and solve the waterlogging problem in the area.

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Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level

Cited by 160 publications

References 22 publications

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Redefining Agricultural Insurance Services Using Earth Observation Data. The Case of Beacon Project

Challenges of Sustainable Land Management in Siwa Oasis: The Waterlogging Problem in the Newly Reclaimed Desert Land

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