Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

Lin, Li; Di, Liping; Tang, Jinshan; Yu, Genong; Zhang, Chen; Rahman, Md. Shahinoor; Shrestha, Ranjay; Kang, Lingjun

doi:10.3390/rs11020205

Cited by 61 publications

(27 citation statements)

References 46 publications

(61 reference statements)

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“…Widespread frequent flooding is not only a profound problem for Bangladesh. Between 1995-2015, there was at least a $166 billion economic loss caused by different floods events around the world [13].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, a sufficiently high accurate DEM and infrastructure data are often not available [17]. Flood management based on water level forecasting is not effective in providing a spatially distributed flood area for the timely monitoring of flood events [13,18]. Satellite-based monitoring of flood extent overcomes the limitations of the hydrological model-based approach [19].…”

Section: Introductionmentioning

confidence: 99%

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Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

2019

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Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

2019

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“…Despite the spatial resolution of the MODIS-based NRT product being comparatively coarse (~250 m), it is still one of the most significant sources to assist flood assessment all over the world. Prior studies also applied the NRT product in flood mapping and discussed its application and underlying uncertainties [26,57,58].…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar during the August 2017 Flood Event

2019

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Flooding is one of the most common natural disasters in India. Typically, the Kosi and Gandak river basins are well-known for lingering flood affected basins in North Bihar every year, which lies in the eastern part of India. There were no such comprehensive studies available in North Bihar that discussed flood progression and regression at shorter time-scales like two day intervals. So in this study, we employed high temporal resolution data to capture inundation extent and further, the flood extent has been validated with high spatial resolution data. The specific objective of this study was to analyze the satellite-derived Near Real Time (NRT) MODIS flood product for spatiotemporal mapping of flood progression and regression over the North Bihar. The synthetic aperture RADAR (SAR) data were also used to validate the MODIS NRT Flood data. As a case study, we selected a recent flood event of August–September 2017 and captured the flood inundation spatial extent at two day intervals using the 2 day composite NRT flood data. The flood prognosis analysis has revealed that during the peak flooding period, 12% to 17% of the area was inundated and the most adversely affected districts were Darbhanga and Katihar in North Bihar. We estimated that in total nearly 6.5% area of the North Bihar was submerged. The method applied was simple, but it can still be suitable to be applied by the community involved in flood hazard management, not necessarily experts in hydrological modeling. It can be concluded that the NRT MODIS flood product was beneficial to monitor flood prognosis over a larger geographical area where observational data are limited. Nevertheless, it was noticed that the flood extent area derived from MODIS NRT data has overestimated areal extent, but preserved the spatial pattern of flood. Apparently, the present flood prognosis analysis can be improved by integrating microwave remote sensing data (SAR) and hydrological models.

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“…Similarly, other derived products such as MODIS leaf area index (LAI) were also be utilized in crop condition profile monitoring. MODIS-derived flood products such as the National Aeronautics and Space Administration (NASA)/MODIS near real-time (NRT) flood products were directly used in the crop loss assessment models [17,55]. Flood frequency and duration were also be calculated from MODIS-derived daily flood maps [56].…”

Section: Remote Sensing Data Utilizationmentioning

confidence: 99%

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Rahman

2020

Agriculture

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This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.

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Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

Cited by 61 publications

References 46 publications

Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar during the August 2017 Flood Event

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

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