Improved surface water mapping using satellite remote sensing imagery based on optimization of the Otsu threshold and effective selection of remote-sensing water index

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132771 - 132771

Опубликована: Фев. 1, 2025

Язык: Английский

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Land use and RSEI spatial-temporal changes in Horqin Sandland (Inner Mongolia, China)

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Enhancing Surface Water Monitoring through Multi-Satellite Data-Fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR

Remote Sensing, Год журнала: 2024, Номер 16(17), С. 3329 - 3329

Опубликована: Сен. 8, 2024

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple offers potential for more frequent observations, yet combining different satellite sources, particularly optical SAR satellites, presents complexities. This research explores data-fusion limitations Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), Sentinel-1 Synthetic Aperture (SAR) to enhance monitoring. By focusing on segmented images, we demonstrate that data is generally effective straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 0.95 indicate very strong harmony integration across reservoirs, lakes, river environments. In vegetative environments, with S1SAR shows weak harmony, κ values 0.27 0.45, indicating need further Global interval maps reveal significant improvement median 15.87 22.81 days L8/9 alone, 4.51 7.77 after incorporating S2, 3.48 4.62 adding S1SAR. Even during wet season months, multi-satellite fusion maintained less than week. Maximizing all available open-source integral advancing studies requiring such as flood, inundation, hydrological modeling.

Язык: Английский

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Super-resolution water body mapping with a feature collaborative CNN model by fusing Sentinel-1 and Sentinel-2 images

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 134, С. 104176 - 104176

Опубликована: Сен. 17, 2024

Язык: Английский

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Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1262 - 1262

Опубликована: Апрель 3, 2024

Surface water is a vital component of the Earth’s cycle and characterizing its dynamics essential for understanding managing our resources. Satellite-based remote sensing has been used to monitor surface dynamics, but cloud cover can obscure observations, particularly during flood events, hindering identification. The fusion optical synthetic aperture radar (SAR) data leverages advantages both sensors provide accurate maps while increasing temporal density unobstructed observations monitoring spatial dynamics. This paper presents method generating dense time series using optical–SAR sensor gap filling. We applied this from Copernicus Sentinel-1 Landsat 8 satellite 2019 over six regions spanning different ecological climatological conditions. validated resulting an independent, hand-labeled dataset found overall accuracy 0.9025, with range 0.8656–0.9212 between regions. validation showed false alarm ratio (FAR) 0.0631, probability detection (POD) 0.8394, critical success index (CSI) 0.8073, indicating that generally performs well at identifying areas. However, it slightly underpredicts areas more negatives. fusing SAR mapping increased, on average, number months in 11.46 55.35 64.90 both, 466% 17% increase, respectively. results show effectively fill gaps caused by produce maps. potential improve support sustainable management.

Язык: Английский

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High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

Geophysical Research Letters, Год журнала: 2024, Номер 51(6)

Опубликована: Март 22, 2024

Abstract Characterizing and understanding the changes in flow regimes of rivers have been challenging. Existing global river network data sets are not updated can only identify wider than 30 m. We propose a novel automated method to map networks on monthly basin scale for first time at 10‐m resolution using Sentinel‐1 Synthetic Aperture Radar, Sentinel‐2 multispectral images, AW3D30 Digital Surface Model. This achieved an overall accuracy 95.8%. The total length Yellow River produced is 40,280 km, approximately 3.2 times that Global Widths from Landsat (GRWL) database, more effectively covering small medium rivers. geometry revealed positive correlation between area precipitation. study expected provide cost‐effective alternative accurately mapping advance our drivers systems.

Язык: Английский

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Reply on RC1

Опубликована: Янв. 16, 2025

Abstract. Rivers play important roles in ecological biodiversity, shipping trade and the carbon cycle. Owing to human disturbances extreme climates recent decades, river extents have altered frequently dramatically. The development of sequential fine-scale extent datasets, which could offer strong data support for protection, management sustainable use, is urgently needed. A literature review revealed that annual datasets with fine spatial resolutions are generally unavailable China. To address this issue, first Sentinel-derived China dataset (CRED) from 2016 2023 was produced our study. We water maps by combining dynamic world (DW), ESRI global land cover (EGLC) multiple index detection rule (MIWDR). For DW MIWDR time series, mode algorithm, calculates most common values, used generate yearly maps. Then, an object-based hierarchical decision tree based on geometric features auxiliary developed extract rivers data. results indicated overall accuracies (OAs) CRED were greater than 96.0 % 2023. user (UAs), producer (PAs) F1 scores exceeded 95.3 %, 91.3 93.7 respectively. further intercomparison shared similar patterns wetland map East Asia (EA_Wetlands), use/cover change (CNLUCC) covers (CWaC) correlation coefficients (R) 0.75. Moreover, outperformed three terms small mapping misclassification reduction. area statistics 44,948.78 km² 2023, mostly distributed coastal provinces From areas characterized initial increase, followed a decrease then slight increase. Spatially, decreased located mainly Southeast China, whereas increased Central Northeast In general, explicitly delineated dynamics provide good foundation improving ecology management. publicly available at https://doi.org/10.5281/zenodo.13841910 (Peng et al., 2024a).

Язык: Английский

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Reply on RC2

Опубликована: Янв. 16, 2025

Abstract. Rivers play important roles in ecological biodiversity, shipping trade and the carbon cycle. Owing to human disturbances extreme climates recent decades, river extents have altered frequently dramatically. The development of sequential fine-scale extent datasets, which could offer strong data support for protection, management sustainable use, is urgently needed. A literature review revealed that annual datasets with fine spatial resolutions are generally unavailable China. To address this issue, first Sentinel-derived China dataset (CRED) from 2016 2023 was produced our study. We water maps by combining dynamic world (DW), ESRI global land cover (EGLC) multiple index detection rule (MIWDR). For DW MIWDR time series, mode algorithm, calculates most common values, used generate yearly maps. Then, an object-based hierarchical decision tree based on geometric features auxiliary developed extract rivers data. results indicated overall accuracies (OAs) CRED were greater than 96.0 % 2023. user (UAs), producer (PAs) F1 scores exceeded 95.3 %, 91.3 93.7 respectively. further intercomparison shared similar patterns wetland map East Asia (EA_Wetlands), use/cover change (CNLUCC) covers (CWaC) correlation coefficients (R) 0.75. Moreover, outperformed three terms small mapping misclassification reduction. area statistics 44,948.78 km² 2023, mostly distributed coastal provinces From areas characterized initial increase, followed a decrease then slight increase. Spatially, decreased located mainly Southeast China, whereas increased Central Northeast In general, explicitly delineated dynamics provide good foundation improving ecology management. publicly available at https://doi.org/10.5281/zenodo.13841910 (Peng et al., 2024a).

Язык: Английский

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State-of-the-Art Status of Google Earth Engine (GEE) Application in Land and Water Resource Management: A Scientometric Analysis

Journal of Geovisualization and Spatial Analysis, Год журнала: 2025, Номер 9(1)

Опубликована: Фев. 26, 2025

Язык: Английский

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Remote Sensing for Identification and Mapping of Thermokarst Landforms: A Review

Permafrost and Periglacial Processes, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

ABSTRACT Permafrost is deeply involved in a series of geophysical processes, and it plays an important role the hydrology cycle, vegetation evolution, greenhouse gas emission. As one most sensitive indicators global climate warming, dynamic changes permafrost distribution its thermal state have been focus cryospheric change research. The highly developed remote sensing technology can provide abundant earth observation data over wide spatiotemporal range, has become powerful approach to detecting variations their related landforms. In this review, we summarize applications technologies identifying mapping typical thermokarst landforms that are closely degradation, namely, lakes, thaw slumps, bogs. We emphasize great potential using automated methods on high‐resolution optical images extraction multi‐temporal kinematic information from laser scanning interferometric synthetic aperture radar (InSAR). not only show usefulness identification landforms, but also point out several limitations future directions for further improvement.

Язык: Английский

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