Time-series surface water gap filling based on spatiotemporal neighbourhood similarity

Bai, Bingxin; Tan, Yumin; Zhou, Kailei; Donchyts, Gennadii; Haag, Arjen; Weerts, Albrecht

doi:10.1016/j.jag.2022.102882

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…In the case of false negatives, this can be caused by issues with the satellite processing chain (e.g., speckle filter in SAR imagery or cloud/object shadows in optical imagery), the flood mapping algorithm, or the presence of a temporary object (e.g., boats and floating debris) blocking the water/flood signal to the satellite's sensor (which is more of a grey area between a false and true positive). These issues could potentially be solved within the flood map, for example, by gap filling (e.g., [25,26]), although that does require additional processing steps. Errors associated with true negatives can be caused by the fact that rivers are dynamic and can move over time, while the DEM is static and based on the period over which its data were acquired.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

Cohen

Peter²,

Haag³

et al. 2022

Remote Sensing

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The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used.

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Section: Discussionmentioning

confidence: 99%

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

Cohen

Peter²,

Haag³

et al. 2022

Remote Sensing

Self Cite

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“…MODIS provides daily observations, yet omits local features such as hotspot gulfs, complex shorelines, and even narrow rivers [24][25][26]. In contrast, Landsat imagery offers relatively high spatial resolution (30 m), yet the 16-day revisit time reduces the possibility of cloud-free images over tropical regions like Lake Victoria [27,28]. These limitations pose a significant challenge to capturing rapid-changing lake surface dynamics, especially during flood/drought events [20,28,29].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, Landsat imagery offers relatively high spatial resolution (30 m), yet the 16-day revisit time reduces the possibility of cloud-free images over tropical regions like Lake Victoria [27,28]. These limitations pose a significant challenge to capturing rapid-changing lake surface dynamics, especially during flood/drought events [20,28,29]. To our knowledge, even the widely used GSW dataset still has gaps and temporal discontinuity issues due to cloud contamination [30,31].…”

Section: Introductionmentioning

confidence: 99%

Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data

Chen

Fan

et al. 2023

Remote Sensing

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To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In the proposed methodology, daily normalized difference vegetation index (NDVI) time series data with 30 m resolution were first generated based on the constructed pixel-by-pixel downscaling models between the simultaneously acquired MODIS-NDVI and Landsat-NDVI data. In the compositing process, a Minimum Value Composite (MinVC) algorithm was used to generate monthly minimum NDVI time series, which were then segmented into a seamless SWE time series of the years 2000–2020 with 30 m resolution from the cloud background. A comparison with the existing Landsat-derived JRC (European Joint Research Centre) monthly surface water products and altimetry-derived water level series revealed that the proposed methodology effectively provides reliable descriptions of spatio–temporal SWE dynamics. Over Lake Victoria, the average percentage of valid observations made using the JRC’s products was only about 70% due to persistent cloud cover or linear strips, and the correlation with the water level series was poor (R2 = 0.13). In contrast, our derived results strongly correlated with the water level series (R2 = 0.54) and efficiently outperformed the JRC’s surface water products in terms of both space and time. Using the derived SWE data, the long-term and seasonal characteristics of lake area dynamics were studied. During the past 20 years, a significant changing pattern of an initial decline followed by an increase was found for the annual mean SWE, with the lowest area of 66,386.57 km2 in 2006. A general seasonal variation in the monthly mean lake area was also observed, with the largest SWE obtained during June–August and the smallest SWE observed during September–November. Particularly in the spring of 2006 and the autumn of 2020, Lake Victoria experienced intense episodes of drought and flooding, respectively. These results demonstrate that our proposed methodology is more robust with respect to capturing spatially and temporally continuous SWE data in cloudy conditions, which could also be further extended to other regions for the optimal management of water resources.

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Image similarity-based gap filling method can effectively enrich surface water mapping information

Huang

Wang

Duan

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

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Time-series surface water gap filling based on spatiotemporal neighbourhood similarity

Cited by 4 publications

References 29 publications

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data

Image similarity-based gap filling method can effectively enrich surface water mapping information

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