Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine

Singha, Mrinal; Dong, Jinwei; Sarmah, Sangeeta; You, Nanshan; Zhou, Yan; Zhang, Geli; Doughty, Russell

doi:10.1016/j.isprsjprs.2020.06.011

Cited by 122 publications

(85 citation statements)

References 47 publications

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“…The Sentinel-1 satellites are the important part of the Global Monitoring for Environment and Security program (GMES, a set of earth observation missions by ESA). Two additional satellites of Sentinel-1C and Sentinel-1D will be launched to provide better all-weather, day-and-night, and continuous Earth observation services [53,61]. With a high spatial resolution (10 m) and short revisit period (12 days for one satellite, and 6 days for two satellites), Sentinel-1 is an attractive option for frequent, effective mapping of rice paddies at a large regional level.…”

Section: Time-series Sentinel-1 Sar Datamentioning

confidence: 99%

“…The Sentinel-1 data ultimately used in the study had a high spatial resolution of 10 m for the study area with dual-polarization of VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) in the mode of "Interferometric Wide Swath (IW)". The VV polarized and VH polarized data were selected for potential water information and rice mapping, respectively, because of their accuracy in detecting water and rice paddies [52,53]. Finally, 402 available Sentinel-1 SAR images were acquired over the study area in 2017.…”

Section: Time-series Sentinel-1 Sar Datamentioning

confidence: 99%

“…However, the planting area of upland rice is very small, accounting for less than 0.3% of the whole of Thailand, which can be ignored in this study. When there are land cover types similar to the temporal SAR signature of rice, such as wetlands or other areas with water and vegetation cover, additional auxiliary information is needed to further distinguish them [53,64,68]. In practical applications, wetlands and similar areas are sparsely distributed in rice cultivation areas, so they could be ignored in our study.…”

Section: Potential Extensions For Producing Large-area Mapsmentioning

confidence: 99%

“…Furthermore, the cost of using radar datasets is too high, which leads to the limitation of large-scale practical applications [19,23,40,45,46,[48][49][50][51]. With the appearance of free data from Sentinel-1, it has become easier to use SAR datasets for the research of rice paddies identification [19,41,44,[52][53][54].…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods commonly require local prior knowledge of rice paddies at local scales. The second strategy mainly relies on a time-series analysis of SAR backscatter to capture the dynamic water signals and gradually increasingly backscattering characteristics during the rice growth process [45,48,50,53,57,58]. Although these rice paddies identification methods have been implemented using SAR data with the medium and high spatial resolution of less than 50 m, and have been proven effective at small regional scales [40], the exorbitant requirements of SAR data storage and computing should be considered when developing rice maps, especially for higher spatial resolutions of around 10 m and more frequent mapping over larger areas (regional to continental scales).…”

Section: Introductionmentioning

confidence: 99%

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An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand

Huang

et al. 2020

Remote Sensing

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Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.

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Section: Time-series Sentinel-1 Sar Datamentioning

confidence: 99%

Section: Time-series Sentinel-1 Sar Datamentioning

confidence: 99%

Section: Potential Extensions For Producing Large-area Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand

Huang

et al. 2020

Remote Sensing

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Increased population exposure to Amphan‐scale cyclones under future climates

et al. 2022

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Southern Asia experiences some of the most damaging climate events in the world, with loss of life from some cyclones in the hundreds of thousands. Despite this, research on climate extremes in the region is substantially lacking compared to other parts of the world. To understand the narrative of how an extreme event in the region may change in the future, we consider Super Cyclone Amphan, which made landfall in May 2020, bringing storm surges of 2–4 m to coastlines of India and Bangladesh. Using the latest CMIP6 climate model projections, coupled with storm surge, hydrological, and socio‐economic models, we consider how the population exposure to a storm surge of Amphan's scale changes in the future. We vary future sea level rise and population changes consistent with projections out to 2100, but keep other factors constant. Both India and Bangladesh will be negatively impacted, with India showing >200% increased exposure to extreme storm surge flooding (>3 m) under a high emissions scenario and Bangladesh showing an increase in exposure of >80% for low‐level flooding (>0.1 m). It is only when we follow a low‐emission scenario, consistent with the 2°C Paris Agreement Goal, that we see no real change in Bangladesh's storm surge exposure, mainly due to the population and climate signals cancelling each other out. For India, even with this low‐emission scenario, increases in flood exposure are still substantial (>50%). While here we attribute only the storm surge flooding component of the event to climate change, we highlight that tropical cyclones are multifaceted, and damages are often an integration of physical and social components. We recommend that future climate risk assessments explicitly account for potential compounding factors.

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Application of Active Space-Borne Microwave Remote Sensing in Flood Hazard Management

Bhatt

Thakur

Singh

et al. 2021

Water Science and Technology Library

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Globally, floods are attributed to be one of the leading natural hazards responsible for recurrent major economic losses, population affected, and mortality. The rapid assessment of flood hazard dynamics at regional scale during flood crisis is one of the few elements which is required by the agencies involved on ground for relief and rescue operations. Due to the weather-independent and day and night acquisition capability offered through microwave sensors, space-borne remote sensing for flood hazard management has undergone a paradigm change. Today, globally data from synthetic aperture radar (SAR) has emerged as invaluable source for monitoring flood hazard. From demonstrating the proof of concept in its initial launch campaigns, the SAR technology has matured to be competent enough to provide operational support for major flood disasters. In recent times, the continuously streaming of free SAR datasets from Sentinel-1 mission and together with emergence of advanced cloudbased computing and processing technologies like the Google Earth Engine (GEE), automated, and quasi-real-time flood mapping services have evolved. The future missions like the NISAR in conjunction with Sentinel-1 C-band data will help in providing more accurate and faster response during flood crisis and see application of SAR data grow multi-fold in coming years for flood hazard mitigation. This chapter attempts to provide a broad overview of the active microwave remote sensing for flood hazard studies. The first part of the chapter discusses about the interaction of the SAR signal for flooding in open, vegetated, and urbanized areas, followed by the role of the sensor parameters like the wavelength, polarization, and incidence angle on the backscattering of SAR signal. The latter half of the chapter discusses about the flood mapping techniques, SAR satellite mission contributing to flood hazard mapping, various applications of SAR derived flood hazard information, the Indian nationwide near-real-time (NRT) flood hazard mapping under ISRO DMS program, and the emergence of Web-based cloud computing techniques and open-source data policies revolutionizing the flood hazard mitigation activities.

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Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine

Cited by 122 publications

References 47 publications

An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand

An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand

Increased population exposure to Amphan‐scale cyclones under future climates

Application of Active Space-Borne Microwave Remote Sensing in Flood Hazard Management

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