FY-3D/MERSI Global Surface Water Extraction Based on DNN Method

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

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Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

Scientific Reports ◽

10.1038/s41598-021-04754-y ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Jianfeng Li ◽

Jiawei Wang ◽

Liangyan Yang ◽

Huping Ye

Keyword(s):

Remote Sensing ◽

Cloud Computing ◽

Surface Water ◽

Water Resources ◽

Sri Lanka ◽

Water Resource Management ◽

Google Earth ◽

Water Extraction ◽

Inland Lakes ◽

Lakes And Reservoirs

AbstractSri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

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Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽

10.1371/journal.pone.0253209 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253209

Author(s):

Jianfeng Li ◽

Biao Peng ◽

Yulu Wei ◽

Huping Ye

Keyword(s):

Surface Water ◽

Google Earth ◽

Water Extraction ◽

Practical Significance ◽

Commission Error ◽

Complex Environment ◽

Visual Interpretation ◽

Water Index ◽

Google Earth Engine ◽

Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

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Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽

10.3390/s18082580 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2580 ◽

Cited By ~ 42

Author(s):

Tri Acharya ◽

Anoj Subedi ◽

Dong Lee

Keyword(s):

Surface Water ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Pure Water ◽

Confusion Matrix ◽

Threshold Value ◽

Environmental Noise ◽

Water Extraction ◽

Landsat 8 ◽

Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

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Observing Global Surface Water Flood Dynamics

The Earth's Hydrological Cycle - Space Sciences Series of ISSI ◽

10.1007/978-94-017-8789-5_16 ◽

2013 ◽

pp. 839-852

Author(s):

Paul D. Bates ◽

Jefferey C. Neal ◽

Douglas Alsdorf ◽

Guy J.-P. Schumann

Keyword(s):

Surface Water ◽

Flood Dynamics ◽

Global Surface

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Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

Remote Sensing ◽

10.3390/rs12172675 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2675

Author(s):

Qianqian Han ◽

Zhenguo Niu

Keyword(s):

Surface Water ◽

Water Area ◽

Temporal Parameter ◽

Modis Ndvi ◽

Water Index ◽

Normalized Difference Water Index ◽

Spatio Temporal ◽

Global Surface ◽

Global Water

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

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Changes in Net Global Surface Water Area Since 1985

Proceedings ◽

10.3390/proceedings2019030069 ◽

2020 ◽

Vol 30 (1) ◽

pp. 69

Author(s):

Zahra Kalantari ◽

Sonia Borja ◽

Georgia Destouni

Keyword(s):

Surface Water ◽

Ecosystem Functioning ◽

Water Allocation ◽

Land Use Changes ◽

Water Area ◽

Aral Sea ◽

Surface Water Resources ◽

Global Surface ◽

Spatial And Temporal Characteristics

Spatial and temporal characteristics of surface water resources (e.g., extension, connectivity, seasonality) are key elements in water allocation, climate and hydrological regulation, ecosystem functioning, and the food-energy-water nexus. Changes in surface water area due to losses/gains to land could strongly affect these processes on different scales. Previous findings on changes in the Earth’s surface water area are contradictory. Based on water–land year classification datasets, we estimated global surface water area changes between 1985–2000 and 2001–2015. We found a net global gain in surface water of 100,454 km2, attributable to a large net gain in seasonal water (83,329 km2) and a small net gain in permanent water (17,125 km2). In general, net changes were highly heterogeneous in space, with local exceptions of clear drying and wetting trends, e.g., the Aral Sea and Quill Lakes, respectively. These findings raise multiple questions as to why seasonal water gains dominate and how different intertwined drivers (e.g., hydroclimate and human-induced water–land use changes) shape the distribution of the Earth’s surface water. Understanding these long-term changes is essential to predicting water-related pressures and prioritizing management decisions.

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Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9070424 ◽

2020 ◽

Vol 9 (7) ◽

pp. 424 ◽

Cited By ~ 2

Author(s):

Sulong Zhou ◽

Pengyu Kan ◽

Janet Silbernagel ◽

Jiefeng Jin

Keyword(s):

Image Segmentation ◽

Surface Water ◽

Water Surface ◽

Poyang Lake ◽

Google Earth ◽

Water Extraction ◽

Freshwater Lakes ◽

Essential Information ◽

Advantages And Disadvantages ◽

Otsu Method

Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages of a segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are − 14.88 , − 16.93 , − 16.96 and − 16.87 respectively, and the overall accuracy achieves 92 % after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020.

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