Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone?

Tulbure, Mirela G.; Broich, Mark; Perin, Vinicius; Gaines, Mollie D.; Ju, Junchang; Stehman, Stephen V.; Pavelsky, T.; Masek, Jeffrey G.; Yin, Simon; Mai, J.; Betbeder-Matibet, Luc

doi:10.1016/j.isprsjprs.2022.01.021

Cited by 36 publications

(33 citation statements)

References 86 publications

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“…With ongoing effort to harmonise data from Landsat and Sentinel-2 sensors (Claverie et al, 2018;Shao et al, 2019), the problem of low revisit time will be addressed. The benefit of using harmonised Landsat and Sentinel-2 dataset for surface water mapping has been demonstrated recently (Tulbure et al, 2022a). Secondly, water bodies smaller than 0.09 ha were possibly not mapped accurately by 30 m resolution Landsat data due to mixed pixel problems, and very shallow water might not have been detected, especially given that we relied on visual interpretation of 30 m resolution Landsat imagery for reference data.…”

Section: Discussionmentioning

confidence: 95%

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Hamunyela

Hipondoka

Persendt

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Discussionmentioning

confidence: 95%

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Hamunyela

Hipondoka

Persendt

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Because the number of pixels with viable pixel values (i.e., values ≤ 4) in the stacked raster varies across the raster and across tiles, we set our threshold as a percentage rather than a specific number of pixels classified as high confidence water. The 25% threshold likely omitted possible short‐term flood extents, which already have a low chance of being captured by the DSWE data due to the temporal frequency of the Landsat‐derived product (Heimhuber et al., 2018; Tulbure et al., 2022). However, we found from visual inspection that the threshold reduced some artifacts—likely from unmasked cloud shadows misclassified as high confidence water—that are present in the DSWE high confidence water class, while maximizing the extent of seasonal surface water detected in the DSWE data set.…”

Section: Methodsmentioning

confidence: 99%

Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States

Gaines

Tulbure

Perin

2022

Water Resources Research

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Surface water is the most readily accessible water resource and provides an array of ecosystem services, but its availability and access are stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000–2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the southeastern United States. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean −0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land‐use policies can be highly effective tools in controlling surface water variations in the Southeast.

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“…This might be due to finer information lost during the speckle filtering or elevation delineation. This work may be improved in the future by considering more polarization [78] or data fusion, as suggested by Tulbure et al [60]. They demonstrated the improvement of flood mapping via fusion of different datasets, but the study found that mapping of open water surfaces in terms of flooding is still challenging as the spectral signatures may be affected by sediment load, turbidity, dissolved matters, algae content depth and a bottom reflectance signal [79].…”

Section: Limitations Of the Studymentioning

confidence: 98%

“…This precipitation analysis is conducted to identify the amount of the daily precipitation that has led to the flood in each state of Peninsular Malaysia. IMERG-Late was used to study the spatial and temporal changes of daily precipitation over Malaysia from 17 December 2021 to 10 January 2022 as the explanatory variable to further illustrate the relationship between the climate-induced surface water and flooding extent [60].…”

Section: Precipitation and Flood Victim Analysismentioning

confidence: 99%

“…This process was conducted for the Pahang and Selangor states only due to the availability of the cloudless data from the Sentinel-2 sensor during the flood period as shown in Figure 3. The accuracy was assessed on a pixel-by pixel basis for the "flooded" pixel ("0" for non-flooded pixels and "1" for flooded pixels [60]). However, the background class "0" is the majority in a flood mapping and it might cause overestimation in the accuracy of the correctly mapped non-flooded pixels [62]; thus, only the flooded pixels were considered in the accuracy assessment.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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Rapid Extreme Tropical Precipitation and Flood Inundation Mapping Framework (RETRACE): Initial Testing for the 2021–2022 Malaysia Flood

Tew

Tan

Juneng

et al. 2022

IJGI

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The 2021–2022 flood is one of the most serious flood events in Malaysian history, with approximately 70,000 victims evacuated daily, 54 killed and total losses up to MYR 6.1 billion. From this devastating event, we realized the lack of extreme precipitation and flood inundation information, which is a common problem in tropical regions. Therefore, we developed a Rapid Extreme TRopicAl preCipitation and flood inundation mapping framEwork (RETRACE) by utilizing: (1) a cloud computing platform, the Google Earth Engine (GEE); (2) open-source satellite images from missions such as Global Precipitation Measurement (GPM), Sentinel-1 SAR and Sentinel-2 optical satellites; and (3) flood victim information. The framework was demonstrated with the 2021–2022 Malaysia flood. The preliminary results were satisfactory with an optimal threshold of five for flood inundation mapping using the Sentinel-1 SAR data, as the accuracy of inundated floods was up to 70%. Extreme daily precipitation of up to 230 mm/day was observed and resulted in an inundated area of 77.43 km2 in Peninsular Malaysia. This framework can act as a useful tool for local authorities and scientists to retrace the extreme precipitation and flood information in a relatively short period for flood management and mitigation strategy development.

show abstract

Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone?

Cited by 36 publications

References 86 publications

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States

Rapid Extreme Tropical Precipitation and Flood Inundation Mapping Framework (RETRACE): Initial Testing for the 2021–2022 Malaysia Flood

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