Downscaling MODIS Surface Reflectance to Improve Water Body Extraction

Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

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Extraction of Water-body Area from High-resolution Landsat Imagery

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i6.pp4111-4119 ◽

2018 ◽

Vol 8 (6) ◽

pp. 4111

Author(s):

B. Chandrababu Naik ◽

B. Anuradha

Keyword(s):

Surface Water ◽

Satellite Data ◽

Water Body ◽

Water Area ◽

Water Bodies ◽

Landsat 8 ◽

Indexing Methods ◽

Water Index ◽

Surface Water Body ◽

Normalized Difference Water Index

Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.

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Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform

Water ◽

10.3390/w12102822 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2822

Author(s):

Jiahao Chen ◽

Tingting Kang ◽

Shuai Yang ◽

Jingyi Bu ◽

Kexin Cao ◽

...

Keyword(s):

Surface Water ◽

Water Body ◽

Water Area ◽

Google Earth ◽

Water Bodies ◽

Tarim River ◽

Tarim River Basin ◽

Open Surface ◽

Body Area ◽

The Tarim River Basin

The Tarim River Basin (TRB), located in an arid region, is facing the challenge of increasing water pressure and uncertain impacts of climate change. Many water body identification methods have achieved good results in different application scenarios, but only a few for arid areas. An arid region water detection rule (ARWDR) was proposed by combining vegetation index and water index. Taking computing advantages of the Google Earth Engine (GEE) cloud platform, 56,284 Landsat 5/7/8 optical images in the TRB were used to detect open-surface water bodies and generated a 30-m annual water frequency map from 1992 to 2019. The interannual changes and trends of the water body area were analyzed and the impacts of climatic and anthropogenic drivers on open-surface water body area dynamics were examined. The results show that: (1) ARWDR is suitable for long-term and large-scale water body identification, especially suitable for arid areas lacking vegetation. (2) The permanent water area was 2093.63 km2 and the seasonal water area was 44,242.80 km2, accounting for 4.52% and 95.48% of the total open-surface water area of he TRB, respectively. (3) From 1992 to 2019, the permanent and seasonal water bodies of the TRB all showed an increasing trend, with obvious spatial heterogeneity. (4) Among the effects of human activities and climate change, precipitation has the largest impact on the water area, which can explain 65.3% of the change of water body area. Our findings provide valuable information for the entire TRB’s open-surface water resources planning and management.

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STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-641-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 641-648

Author(s):

J. Y. Sun ◽

G. Z. Wang ◽

G. J. He ◽

D. C. Pu ◽

W. Jiang ◽

...

Keyword(s):

Surface Water ◽

Water Body ◽

Rapid Development ◽

Kappa Coefficient ◽

Water Bodies ◽

Integrated Learning ◽

Visual Interpretation ◽

Adaboost Algorithm ◽

Classification Time ◽

Better Than

Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).

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Remote Sensing of Surface Water Dynamics Between 2015 and 2020 in the Prairie Pothole Region

10.5194/egusphere-egu21-8737 ◽

2021 ◽

Author(s):

Stefan Schlaffer ◽

Marco Chini ◽

Wouter Dorigo

Keyword(s):

Surface Water ◽

Prairie Pothole Region ◽

Water Area ◽

Water Bodies ◽

Water Dynamics ◽

Drought Indices ◽

Prairie Pothole ◽

Small Water ◽

Wide Range ◽

Small Water Bodies

The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (<&#160;1&#160;ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.

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Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain

Remote Sensing ◽

10.3390/rs12193157 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3157

Author(s):

Andrew Ogilvie ◽

Jean-Christophe Poussin ◽

Jean-Claude Bader ◽

Finda Bayo ◽

Ansoumana Bodian ◽

...

Keyword(s):

Surface Water ◽

Water Bodies ◽

Landsat 8 ◽

Site Specific ◽

Senegal River ◽

Landsat 7 ◽

Long Term Monitoring ◽

Term Monitoring ◽

Sentinel 2

Accurate monitoring of surface water bodies is essential in numerous hydrological and agricultural applications. Combining imagery from multiple sensors can improve long-term monitoring; however, the benefits derived from each sensor and the methods to automate long-term water mapping must be better understood across varying periods and in heterogeneous water environments. All available observations from Landsat 7, Landsat 8, Sentinel-2 and MODIS over 1999–2019 are processed in Google Earth Engines to evaluate and compare the benefits of single and multi-sensor approaches in long-term water monitoring of temporary water bodies, against extensive ground truth data from the Senegal River floodplain. Otsu automatic thresholding is compared with default thresholds and site-specific calibrated thresholds to improve Modified Normalized Difference Water Index (MNDWI) classification accuracy. Otsu thresholding leads to the lowest Root Mean Squared Error (RMSE) and high overall accuracies on selected Sentinel-2 and Landsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. On MODIS imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km2) water bodies despite the 500 m spatial resolution. Over 1999–2019, MODIS observations reduce average daily RMSE by 48% compared to the full Landsat 7 and 8 archive and by 51% compared to the published Global Surface Water datasets. Results reveal the need to integrate coarser MODIS observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full Landsat time series before 2013. From 2013, the Landsat 7 and Landsat 8 constellation becomes sufficient, and integrating MODIS observations degrades performance marginally. Combining Landsat and Sentinel-2 yields modest improvements after 2015. These results have important implications to guide the development of multi-sensor products and for applications across large wetlands and floodplains.

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Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽

10.3390/w9040256 ◽

2017 ◽

Vol 9 (4) ◽

pp. 256 ◽

Cited By ~ 50

Author(s):

Yan Zhou ◽

Jinwei Dong ◽

Xiangming Xiao ◽

Tong Xiao ◽

Zhiqi Yang ◽

...

Keyword(s):

Surface Water ◽

Water Body ◽

Landsat 8 ◽

Open Surface ◽

Body Mapping ◽

Water Index ◽

Landsat 7 ◽

Surface Water Bodies ◽

Water Indices ◽

Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

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WATER MONITORING IN UKRAINE: METHODS FOR ASSESSING WATER QUALITY FOR VARIOUS PURPOSES IN CONNECTION WITH CHANGES IN THE REGULATORY FRAMEWORK (2014-2021)

Hydrology, hydrochemistry and hydroecology ◽

10.17721/2306-5680.2021.3.1 ◽

2021 ◽

pp. 6-19

Author(s):

V.K. Khilchevskyi

Keyword(s):

Water Quality ◽

Surface Water ◽

Water Use ◽

Water Body ◽

Water Monitoring ◽

Chemical State ◽

Water Bodies ◽

State Monitoring ◽

Altered Surface ◽

Surface Water Bodies

Over the past five years (2014-2021), there have been significant changes in regulatory methods for assessing water quality for various purposes, which is due to Ukraine’s course towards European integration. An important feature was the cancellation of the acts of sanitary legislation of the Ukrainian SSR and the USSR (from 01.01.2017), which were applied in Ukraine for a long time (order of the Cabinet of Ministers of Ukraine of 2016). The Law of Ukraine “On Amendments to Certain Legislative Acts of Ukraine Concerning the Implementation of Integrated Approaches in Water Resources Management Based on the Basin Principle” (2016) amended the Water Code of Ukraine regarding hydrographic zoning and water monitoring in accordance with the provisions of the EU Water Framework Directive. In 2018, by a resolution of the Cabinet of Ministers of Ukraine, the “Procedure for the implementation of state monitoring of waters” was approved. In 2019, the Ministry of Natural Resources of Ukraine approved the normative “Methodology for assigning a surface water array to one of the classes of the ecological and chemical states of a surface water array, as well as assigning an artificial or significantly altered surface water array to one of the classes of the ecological potential of an artificial or significantly altered surface water array” The objects of state monitoring of waters are land and ground water bodies and sea waters. Surface water body – a specially defined surface water body or part of it. The body of surface waters can be classified into one of five categories: 1) rivers; 2) lakes; 3) transitional waters; 4) coastal waters; 5) artificial or substantially altered surface water bodies. The program of state monitoring of waters provides for control over four groups of indicators: 1) biological; 2) physical and chemical; 3) chemical; 4) hydromorphological. Based on the data and information obtained as a result of the state monitoring of the waters of surface and groundwater bodies, the ecological and chemical state of the surface water bodies, the ecological potential of artificial or significantly altered surface water bodies, the quantitative and chemical state of the groundwater bodies are determined, taking into account which river basin management plans and assess the level of achievement of environmental objectives. The purpose of this study is to highlight the approaches that have developed at the present stage to the regulation of water quality for various purposes, the main of which are: environmental; hygienic (household and drinking and cultural and household or recreational water use), fishery. If, when assessing the quality of water for environmental purposes, a deviation from the maximum permissible concentrations (MPC) was made, then in other areas of water use, the MPC standards remain relevant. The importance of this study also lies in the need to convey generalized information to a wide range of authors who are interested in water quality issues.

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Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

Remote Sensing ◽

10.3390/rs12223701 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3701

Author(s):

Deepakrishna Somasundaram ◽

Fangfang Zhang ◽

Sisira Ediriweera ◽

Shenglei Wang ◽

Junsheng Li ◽

...

Keyword(s):

Climate Change ◽

Surface Water ◽

Sri Lanka ◽

Anthropogenic Activities ◽

Water Area ◽

Temporal Changes ◽

Water Bodies ◽

Water Dynamics ◽

Annual Growth Rate ◽

Spatial And Temporal Changes

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

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Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Water ◽

10.3390/w12113010 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3010 ◽

Cited By ~ 1

Author(s):

Ruimeng Wang ◽

Haoming Xia ◽

Yaochen Qin ◽

Wenhui Niu ◽

Li Pan ◽

...

Keyword(s):

Surface Water ◽

Water Resources ◽

Water Body ◽

Vegetation Index ◽

Water Area ◽

Google Earth ◽

Body Area ◽

Hetao Plain ◽

Google Earth Engine

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

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Constructing Mediterranean wetland open water dynamics using a new 18-year MODIS-derived surface water fraction dataset

10.5194/egusphere-egu2020-19945 ◽

2020 ◽

Author(s):

Linlin Li ◽

Anton Vrieling ◽

Andrew Skidmore ◽

Tiejun Wang

Keyword(s):

Time Series ◽

Surface Water ◽

Temporal Dynamics ◽

Open Water ◽

Water Area ◽

Water Bodies ◽

Water Dynamics ◽

Water Fraction ◽

Wide Range

Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies despite the ecological importance of smaller-sized wetland systems. In this paper, we constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. These water bodies has a wide range of size, specifically 0.01 km2 and larger. We then compared the water extent time series derived from MODIS SWF with those derived from a Landsat-based dataset. Results showed that MODIS- and Landsat-derived water extent time series showed a high correlation (r = 0.81) for more dynamic water bodies. Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (<1 km2) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.

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