Surface water maps de-noising and missing-data filling using determinist spatial filters based on several a priori information

Aires, Filipe

doi:10.1016/j.rse.2019.111481

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…However, L-band-based approaches tend to overestimate surface water. Therefore, so far, they have been mostly applied locally and under inclusion of a-priori data to reduce misclassifications [141,142]. LIDAR systems as well as hyperspectral optical sensors are not used for water surface delineation in any of the studies reviewed.…”

Section: Sensors and Sensor Typesmentioning

confidence: 99%

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

2022

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Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

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Section: Sensors and Sensor Typesmentioning

confidence: 99%

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

2022

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“…Remote sensing plays a role in providing information on large-scale monitoring of surface waters, with the advantages of high spatiotemporal resolution, multisensors, and nearreal-time operational data [1][2][3]. Monitoring can be performed faster when compared with direct measurements in the field and can assist in strategic planning to cost reduction measures, with limited human and scientific resources [4,5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data

Yulianto

Kushardono

Budhiman

et al. 2022

The Scientific World Journal

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In this study, we proposed an automatic water extraction index (AWEI) threshold improvement model that can be used to detect lake surface water based on optical remote sensing data. An annual Landsat 8 mosaic was created using the Google Earth Engine (GEE) platform to obtain cloud-free satellite image data. The challenge of this study was to determine the threshold value, which is essential to show the boundary between water and nonwater. The AWEI was selected for the study to address this challenge. The AWEI approach was developed by adding a threshold water value based on the split-based approach (SBA) calculation analysis for Landsat 8 satellite images. The SBA was used to determine local threshold variations in data scenes that were used to classify water and nonwater. The class threshold between water and nonwater in each selected subscene image can be determined based on the calculation of class intervals generated by geostatistical analysis, initially referred to as smart quantiles. It was used to determine the class separation between water and nonwater in the resulting subscene images. The objectives of this study were (a) to increase the accuracy of automatic lake surface water detection by improvising the determination of threshold values based on analysis and calculations using the SBA and (b) to conduct a test case study of AWEI threshold improvement on several lakes’ surface water, which has a variety of different or heterogeneous characteristics. The results show that the threshold value obtained based on the smart quantile calculation from the natural break approach (AWEI ≥ −0.23) gave an overall accuracy of close to 100%. Those results were better than the normal threshold (AWEI ≥ 0.00), with an overall accuracy of 98%. It shows that there has been an increase of 2% in the accuracy based on the confusion matrix calculation. In addition to that, the results obtained when classifying water and nonwater classes for the different national priority lakes in Indonesia vary in overall accuracy from 94% to 100%.

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Seasonally inundated area extraction based on long time-series surface water dynamics for improved flood mapping

Zhao,

Wu,

Chen

et al. 2024

ISPRS Journal of Photogrammetry and Remote Sensing

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Surface water maps de-noising and missing-data filling using determinist spatial filters based on several a priori information

Cited by 7 publications

References 32 publications

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data

Seasonally inundated area extraction based on long time-series surface water dynamics for improved flood mapping

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