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In recent years, remote sensing data have played an important role in natural resource management studies, especially in water resources research. Among the studies related to water resources, the use of water indicators has been highly considered. With the advancement of technology and the production of satellite images, these indicators have grown and developed more and their accuracy has increased significantly. In this research, Landsat 8 and Sentinel 2A images related to a 9-year period have been used to analyze and identify water areas in the target area. The indices used in the research are NDWI, NDWIPlus, MNDWI, WRI and AWEI indices in two shaded and unshaded versions. The results showed that the AWEIshadow index with an average kappa coefficient of 0.9901 in Sentinel and 0.9692 in Landsat 8 was the best index and the worst result was AWEINoShadow index with an average kappa coefficient of 0.4997 in Sentinel and 0.618 in Landsat 8 for water identification.The results of this research showed that the use of water extraction indicators and high-precision satellite images can be an effective tool for monitoring and sustainable management of water resources. The results can help planners and managers to make more optimal decisions regarding the protection and exploitation of natural resources [ABSTRACT FROM AUTHOR]

Accurately mapping the surface water bodies through remote sensing technology is of great significance for water resources management, flood monitoring, and drought monitoring. At present, many scholars at home and abroad carry out research on deep learning image recognition algorithms based on convolutional neural networks, and a variety of variant-based convolutional neural networks are proposed to be applied to extract water bodies from remote sensing images. However, due to the low depth of convolutional layers employed and underutilization of water spectral feature information, most of the water body extraction methods based on convolutional neural networks (CNNs) for remote sensing images are limited in accuracy. In this study, we propose a novel surface water automatic extraction method based on the convolutional neural network (CRAUnet++) for Sentinel-2 images. The proposed method includes three parts: (1) substituting the feature extractor of the original Unet++ with ResNet34 to enhance the network's complexity by increasing its depth; (2) Embedding the Spatial and Channel 'Squeeze and Excitation' (SCSE) module into the up-sampling stage of the network to suppress background features and amplify water body features; (3) adding the vegetation red edge-based water index (RWI) into the input data to maximize the utilization of water body spectral information of Sentinel-2 images without increasing the data processing time. To verify the performance and accuracy of the proposed algorithm, the ablation experiment under four different strategies and comparison experiment with different algorithms of RWI, FCN, SegNet, Unet, and DeepLab v3+ were conducted on Sentinel-2 images of the Poyang Lake. The experimental result shows that the precision, recall, F1, and IoU of CRAUnet++ are 95.99%, 96.41%, 96.19%, and 92.67%, respectively. CRAUnet++ has a good performance in extracting various types of water bodies and suppressing noises because it introduces SCSE attention mechanisms and combines surface water spectral features from RWI, exceeding that of the other five algorithms. The result demonstrates that CRAUnet++ has high validity and reliability in extracting surface water bodies based on Sentinel-2 images. [ABSTRACT FROM AUTHOR]

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area–water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the Upper Mississippi River. Otsu's automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each predefined polygon around each gauging station. In addition to the commonly used green-band-based water indices, Landsat 8 and 9 OLI's ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadows), and AWEInsv (AWEI without shadows), where v represents the visible light band used in the water index. At each station, about 60–80 Landsat 8 or 9 images during 2013–2023 were used to assess the performances of the twenty water indices by comparing the estimated WSEs with the measured WSEs. The results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices. [ABSTRACT FROM AUTHOR]

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy. [ABSTRACT FROM AUTHOR]

SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030 Agenda for Sustainable Development, brings renewed vigor and opportunities to water resource monitoring and research. This study evaluates the effectiveness of SDGSAT-1 in extracting water bodies in comparison to Sentinel-2 multi-spectral imager (MSI) data. We applied a confidence thresholding method to delineate river water from land, utilizing the Normalized Differential Water Body Index (NDWI), Normalized Difference Water Index (MNDWI), and Shaded Water Body Index (SWI). It was found that the SWI works best for SDGSAT-1 while the NDWI works best for Sentinel-2. Specifically, the NDWI demonstrates proficiency in delineating a broader spectrum of water bodies and the MNDWI effectively mitigates the impact of shadows, while SDGSAT-1's SWI extraction of rivers offers high precision, clear outlines, and shadow exclusion. SDGSAT-1's SWI overall outperforms Sentinel-2's NDWI in water extraction accuracy (overall accuracy: 90% vs. 91%, Kappa coefficient: 0.771 vs. 0.416, and F1 value: 0.844 vs. 0.651), likely due to its deep blue bands. This study highlights the comprehensive advantages of SDGSAT-1 data in extracting river water bodies, providing a theoretical basis for future research. [ABSTRACT FROM AUTHOR]

Detailed knowledge of vegetation patterns allows to evaluate mire ecosystems and their dynamics. The use of hyperspectral information has the benefits of exploring spectral characteristics of species and vegetation modelling. Our study employed multi-scale and multi-source hyperspectral imaging with a handheld camera in the field and an UAV (Unoccupied Aerial Vehicle) sensor covering the wavelengths of 400 – 1000 nm. Plot-level spectra acquired with a UAV and field spectra collected at 1 m height were combined to develop a spectral library for Sphagnum moss species. This library was then used to map dominant Sphagnum species in a Finnish Aapa mire complex using the Spectral Angle Mapper (SAM) classifier. Classification performance assessment was supported by calculating a water index from the UAV-information. Additionally, we examined the transferability of site-specific spectral libraries to an aapa mire with similar vegetation. The results showed little spectral variation in the plot spectrum between the sensors. A fusion of species- and plot-level libraries yielded the highest accuracy of 62 %. For both mires, there was a great variation among the class accuracies. Floating mosses had an accuracy of 86 %, followed by lawn-forming Sphagnum balticum with 77 %. For the test site, the latter species was mapped with an accuracy of 59 %. Red moss species achieved low accuracies of 45 % and 38 %, likely due to effects from sub-pixel and mixed-pixel effects of neighbouring graminoid species and the presence of litter. This might have also enhanced the contrast of adjacent pixels contributing to spectral alterations. Water table depth measurements and the water index revealed a hydrological preference for most species, with classification performance notably improving with higher water index values. We recommend collecting on-site hyperspectral information at varying hydrological circumstances to build a comprehensive spectral library for mire vegetation and modelling.

Globally, water scarcity threatens food security, especially in arid and semi-arid food-producing regions, such as the Hetao Irrigation District (HID), where the water consumption during the non-growth season (spring irrigation, autumn irrigation) accounts for more than 30 % of the total irrigation volume. Meanwhile, mapping the characteristics of spatial and temporal evolution of the area of cropland irrigated in the spring and autumn and the pattern of water consumption is crucial for strengthening the management of agricultural water use. Evaporation and infiltration of irrigation water are difficult to capture in time due to insufficient timeliness or accuracy of single remote sensing data. This study integrates seven remote sensing datasets (Landsat5, Landsat7, Landsat8, HJ-1A/B, GF-1, Sentinel-2 and MODIS) for the first time to build a high spatial-temporal resolution dataset, and combines the water index NDWI and MNDWI to extract the maximum water range formed after irrigation by manually adjusting the threshold method. In addition to the global land use/cover dataset (GLC FCS30–1985_2020), the cropland layer was superimposed to accurately identify the cropland spring irrigation and autumn irrigation range from 2000 to 2021 in the HID. In the past 20 years, spring-irrigated (SI) areas increased by 1012.60 km2 (+49.96 %), and the autumn-irrigated (AI) areas decreased by 512.63 km2 (-11.02 %). Combined with the quantity of water diversion and displacement, the characteristics of irrigation water consumption in the non-growth season were further analysed. The results showed that the water consumption of spring irrigation increased by 0.67×108 m3 (+22.26 %), and that of autumn irrigation increased by 4.34×108 m3 (+38.82 %). The decrease of autumn- irrigated area and the increase of water consumption were mainly related to the stability of autumn irrigation water, imperfect irrigation and drainage conditions and insufficient field water management. In 2020­2021, the area of non-growth “autumn irrigation - spring irrigation” repeated irrigation is about 1271.52 km2, which can be used as a key control area. This study provides a theoretical basis and direction for rational water use in non-growth seasons of irrigation districts.

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes. [ABSTRACT FROM AUTHOR]

Wetlands are an integral part of our global ecosystems and play crucial roles in ecological functions such as carbon sequestration, flood mitigation, water purification, and recreational activities. The Ramsar Convention is the most significant wetland protection pact and is doing tremendous work in conserving wetlands worldwide. However, the wetlands area is still under threat due to anthropogenic activity.The current study utilized drone images, chlorophyll measurements and machine leaning to discriminate and map vegetation at marsh wetland area—the Ramsar site. The high‐resolution, multispectral imagery is acquired using a drone‐mounted MICAsense sensor.Eight spectral indices such as Normalized Difference Water Index (NDWI), Two‐Band Algorithm (2BDA), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Vegetation Index (NDVI) Enhanced Normalized Difference Vegetation Index (ENDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalised Difference RedEdge (NDRE) were calculated on the acquired imagery in order to discriminate the different vegetation covers such as floating aquatic vegetation (FAV), open water, and other vegetations types. These include the following: Eichhornia, Nymphea, Oleracea, Paspalam, and Oryza from agriculture land at the study site.Two models (viz., the Taylor plot and the Lek Profile methods) were employed to assess the sensitivity of the spectral indices for prediction of chlorophyll and vegetation discrimination. It is inferred from both methods that NDCI was most sensitive for chlorophyll prediction of vegetation followed by NGRDI/ ENDVI/ 2BDA and NDVI for chlorophyll prediction in wetland ecosystems. Further, three machine learning algorithms, support vector machine (SVM), random forest (RF), and gradient tree boost (GTB), were utilized for classification, and the performance accuracy of GTB was found to be the highest (0.893), followed by RF (0.851) and SVM (0.723). The GTB algorithm was applied over NDCI for vegetation discrimination.The study revealed that Eichhronia sp. is abundantly present at the study site; hence, strategic management plans should be carried out for the eradication of invasive species and proper management of wetland vegetation. [ABSTRACT FROM AUTHOR]

Water-free oil production period is an important stage for oil production, prediction and water cut prediction. Because there are many factors affecting the water-free oil production period and the influencing mechanism is complex, it is very difficult to calculate the water-free oil production period. In this paper, the main influencing factors of water-free oil production period are analyzed by the stream tube approach method. The influence law of four influencing factors on water-free oil production period was analyzed and the quantitative relationship between four influencing factors and water-free oil production period were established. The average water saturation of reservoir and water saturation near producers are different which is important for the calculation of theoretical water cut. The quantitative relationship between average water saturation of reservoir and water saturation near producers is established. On this basis, a new method for calculating theoretical water cut is built and is applied to BZ reservoir. The result shows the theoretical water cut calculated by this method is consistent with field data. [ABSTRACT FROM AUTHOR]

Multispectral satellite imageries (MSIs) are strong enough to delineate features of interest by suppressing others. The water index is a method applied to delineate the water features by combining the multispectral band images. This research proposes a novel index for extracting water features from multisensor MSIs named multisensor water index. The proposed index, along with existing indices, is applied on Landsat-8 OLI imageries, a comparative analysis is done, and it is found that the proposed index is outperforming with 99.77% accuracy. The proposed index was validated on other sensors, such as a series of Landsat (5, 7, 8, and 9), Sentinel-2, Resourcesat, and Modis satellite imagery, by mapping water features successfully. Furthermore, the proposed index maps marine pollution by discriminating phytoplankton and algal bloom from the water feature. It is observed that the increase in phytoplankton or algal bloom is the result of eutrophication. The blooming of phytoplankton and algae is on the surface of the water. Thus, it causes shading at the bottom of the marine ecosystem and negatively impacts the growth of seagrasses, dissolved oxygen levels, fish suffocation, water properties, and other bottom habitats. The three test sites, viz., lake Villarrica, lake Okeechobee, and the Atlantic Ocean, have been considered to study increasing phytoplankton and algal bloom. The proposed index successfully discriminates the phytoplankton and algal bloom from the water features.

Shoreline management is essential for navigation, coastal resource management, and coastal planning and development. Shoreline change detection is vital for shoreline monitoring; however, traditional methods used for such detection are laborious and have limited accuracy. An approach that integrates remote sensing imagery and geographic information systems (GISs) is proposed herein to simultaneously identify shoreline changes and perform grid-level visualization for updating shoreline data. The integrated approach uses deep learning-based segmentation networks and water indexes to accurately classify land and sea in remote sensing images. Transfer learning was used to address the issue of insufficient data, wherein weights trained on a large open dataset were applied to the target area. The segmentation results were compared with existing shoreline GIS data to identify the areas experiencing shoreline changes. Grid-level visualization enhanced the identification of regions requiring flexible data updates and investigation efficiency by focusing on specific areas. The proposed approach accurately detected shoreline changes, albeit with some errors of commission, predominantly in regions featuring intricate shorelines and small clusters of islands. The proposed approach offers efficient solutions for shoreline change detection, with potential applications in coastal management, environmental science, urban planning, and coastal hazard assessment. [ABSTRACT FROM AUTHOR]

Vietnam is a coastal country with a coastline of more than 3,260 km, stretching from north to south. Coastal change in Vietnam is complicated further by the effects of climate change, including erosion and accretion, causing great impacts on infrastructure and the environment. This article presents the results of assessing coastline changes in Thanh Hoa Province (North Central region of Vietnam) from Sentinel-2 MSI satellite image data for the period 2015-23. Three Sentinel-2 MSI images taken in December 2015, December 2020 and December 2023 were used to calculate the water indices, including Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI) Argumented Normalised Difference Water Index (ANDWI) and Automated Water Extraction Index (AWEIsh), then extract the shoreline using the thresholding method and select the water index with the highest accuracy through comparing the overall accuracy and Kappa coefficient. The coastlines of 2015, 2020 and 2023 years are overlaid to evaluate the coastal changes in the study area. The results received in the study provide objective and timely information, helping managers effectively monitor and respond to coastline changes. [ABSTRACT FROM AUTHOR]

Since the Normalized Difference Water Index (NDWI) was proposed, water indices have served as useful tools for surface water detection. However, existing water indices are highly influenced by atmospheric and other environmental conditions and suffer from limited performance, especially in urban areas. At the core of the limitation is the sole dependency on the spectral distribution of reflectance signals. To overcome this, we propose to utilize topographic data as additional information for better water detection. Accordingly, the new index, namely Combined Water Index (CWI), is developed as the product of the topographic index and the reflectance-based index. These two indices excellently compensate each other: the former is free from noise issues but invariant over time while the latter can capture temporal dynamics of waterbody extents. The CWI is applied to four study areas of different development levels (natural, medium-sized cities, and megalopolis) in the Han River basin, South Korea. The water detection results of the CWI is promising, particularly in the heavily developed urban setting, demonstrated through visual images as well as various statistical measures. [ABSTRACT FROM AUTHOR]

Accurately mapping the surface water bodies through remote sensing technology is of great significance for water resources management, flood monitoring, and drought monitoring. At present, many scholars at home and abroad carry out research on deep learning image recognition algorithms based on convolutional neural networks, and a variety of variant-based convolutional neural networks are proposed to be applied to extract water bodies from remote sensing images. However, due to the low depth of convolutional layers employed and underutilization of water spectral feature information, most of the water body extraction methods based on convolutional neural networks (CNNs) for remote sensing images are limited in accuracy. In this study, we propose a novel surface water automatic extraction method based on the convolutional neural network (CRAUnet++) for Sentinel-2 images. The proposed method includes three parts: (1) substituting the feature extractor of the original Unet++ with ResNet34 to enhance the network’s complexity by increasing its depth; (2) Embedding the Spatial and Channel ‘Squeeze and Excitation’ (SCSE) module into the up-sampling stage of the network to suppress background features and amplify water body features; (3) adding the vegetation red edge-based water index (RWI) into the input data to maximize the utilization of water body spectral information of Sentinel-2 images without increasing the data processing time. To verify the performance and accuracy of the proposed algorithm, the ablation experiment under four different strategies and comparison experiment with different algorithms of RWI, FCN, SegNet, Unet, and DeepLab v3+ were conducted on Sentinel-2 images of the Poyang Lake. The experimental result shows that the precision, recall, F1, and IoU of CRAUnet++ are 95.99%, 96.41%, 96.19%, and 92.67%, respectively. CRAUnet++ has a good performance in extracting various types of water bodies and suppressing noises because it introduces SCSE attention mechanisms and combines surface water spectral features from RWI, exceeding that of the other five algorithms. The result demonstrates that CRAUnet++ has high validity and reliability in extracting surface water bodies based on Sentinel-2 images.

SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030 Agenda for Sustainable Development, brings renewed vigor and opportunities to water resource monitoring and research. This study evaluates the effectiveness of SDGSAT-1 in extracting water bodies in comparison to Sentinel-2 multi-spectral imager (MSI) data. We applied a confidence thresholding method to delineate river water from land, utilizing the Normalized Differential Water Body Index (NDWI), Normalized Difference Water Index (MNDWI), and Shaded Water Body Index (SWI). It was found that the SWI works best for SDGSAT-1 while the NDWI works best for Sentinel-2. Specifically, the NDWI demonstrates proficiency in delineating a broader spectrum of water bodies and the MNDWI effectively mitigates the impact of shadows, while SDGSAT-1’s SWI extraction of rivers offers high precision, clear outlines, and shadow exclusion. SDGSAT-1’s SWI overall outperforms Sentinel-2’s NDWI in water extraction accuracy (overall accuracy: 90% vs. 91%, Kappa coefficient: 0.771 vs. 0.416, and F1 value: 0.844 vs. 0.651), likely due to its deep blue bands. This study highlights the comprehensive advantages of SDGSAT-1 data in extracting river water bodies, providing a theoretical basis for future research.

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

Automatic surface water body mapping using remote sensing technology is greatly meaningful for studying inland water dynamics at regional to global scales. Convolutional neural networks (CNN) have become an efficient semantic segmentation technique for the interpretation of remote sensing images. However, the receptive field value of a CNN is restricted by the convolutional kernel size because the network only focuses on local features. The Swin Transformer has recently demonstrated its outstanding performance in computer vision tasks, and it could be useful for processing multispectral remote sensing images. In this article, a Water Index and Swin Transformer Ensemble (WISTE) method for automatic water body extraction is proposed. First, a dual-branch encoder architecture is designed for the Swin Transformer, aggregating the global semantic information and pixel neighbor relationships captured by fully convolutional networks (FCN) and multihead self-attention. Second, to prevent the Swin Transformer from ignoring multispectral information, we construct a prediction map ensemble module. The predictions of the Swin Transformer and the Normalized Difference Water Index (NDWI) are combined by a Bayesian averaging strategy. Finally, the experimental results obtained on two distinct datasets demonstrate that the WISTE has advantages over other segmentation methods and achieves the best results. The method proposed in this study can be used for improving regional to continental surface water mapping and related hydrological studies.

Lakes are sensitive to both climate change and human activities, especially in the Yungui Plateau zone. As the main supply of water sources, surface water has a significant impact on environment, ecological balance and socioeconomic development. However, it is unclear how the surface water areas (SWAs) of the nine Yunnan Plateau lakes (NYPLs) have changed over previous decades. In this study, the SWAs of the NYPLs were first extracted using the optimal water index (WI) based on Landsat remote sensing images and the Google Earth Engine (GEE). Then, a spatiotemporal variation analysis of the SWAs was carried out for the NYPLs from 1988 to 2021. Finally, the correlations between the SWA changes in each lake, climatic factors and human activities were analysed. We found that the total SWAs of the NYPL have remained stable over the past 30 years, reaching a maximum of 1,012.51 km2 in 2000 and a minimum of 972.94 km2 in 2015. From 1988 to 2015, the overall SWAs of the NYPLs fluctuated steadily within 1%. However, Lakes Qilu and Yilong shrunk significantly in 2015. Generally, the SWAs of the NYPLs during the rainy season were larger than during the dry season; however, there were some differences between each period. The seasonal change in the SWAs was approximately 10 km2 in 1991, 1994, and 2003, but was relatively less in other years. Precipitation was a direct factor, whereas temperature and evaporation were indirect factors influencing the intra- and inter-annual SWAs of the NYPLs. The changes in the SWAs of the NYPLs were consistent with the precipitation trend and were negatively correlated with changes in temperature and evaporation. Evaporation had more of an influence on shallow lakes than on deep-water lakes. Human activities led to significant changes in individual lakes (such as lakes Dianchi, Yilong, and Qilu). The NYPLs had a spatially heterogeneous response to regional climate change caused by both global climate change and human activities. The results of this study can provide a data reference and decision support for the scientific management of the ecological environment of the Yunnan Plateau lakes.

Meandering channel migration is a widespread phenomenon in rivers all around the world. Channel activity, which reflects the rate of change of a meandering channel, is calculated by averaging lateral channel migration. Channel migration can create new channels and abandon old ones, with effects on the natural environment. Floods can even lead to excessive rates of channel migration, which can threaten cities or farmland. Remote sensing can detect the spatial and temporal dynamic characteristics of the river channel, taking into account both spatial and temporal resolution, and can help in planning for the safety of the river channel in advance. Previous studies on river channels have suffered from a low accuracy of data, low level of automation, and subjectivity. To overcome these limitations, we propose a channel activity remote sensing retrieval model (CARSM) in this paper. CARSM extracts water using the modified normalized difference water index (MNDWI) combined with Otsu's method on the Google Earth Engine (GEE) platform, then extracts the channel centerlines via water mask maps using RivWidthCloud, and finally calculates channel activity based on the geometric relationship of the channel centerlines. With more objective extraction results, CARSM can guarantee more than 95% accuracy of channel activity and its high degree of automation can save a lot of labor costs. We use Landsat images to monitor the channel of the Lower Yellow River and calculate the overall and segmental channel activity separately. Our results show that the overall channel activity of the Lower Yellow River has gradually decreased between 1990 and 2020, with decreases of 33.04% and 41.06%, respectively. Analysis of channel activity reveals that the water sediment pattern of the Lower Yellow River changed from siltation to scouring after the completion of Xiaolangdi Reservoir, and the Lower Yellow River is gradually becoming stable. [ABSTRACT FROM AUTHOR]

The extraction of water bodies is essential for monitoring water resources, ecosystem services and the hydrological cycle, so analyzing water bodies from remote sensing images is necessary. The water index is designed to highlight water bodies in remote sensing images. We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images. Firstly, we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction. Subsequently, we apply KT transformation, LBV transformation, AWEInsh, and HIS transformation to the preprocessed image to calculate a new water index. Then, we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately. Meanwhile, we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies. Finally, we combine small and large water bodies to get complete water bodies. Compared with other traditional methods, our method has apparent advantages in water extraction, particularly in the extraction of small water bodies.

Anthropogenic activities can greatly influence the lake ecosystems across the globe. Within these ecosystems, the impacts of human activities are most evident on sedimentation, light and nutrient availability, and disturbance frequency. There have been times of natural environmental healing of reservoirs and the present research aims to explore the variations in the water quality of Tarbela reservoir, Pakistan the largest rock-filled dam of the world, from 1990 to 2020. Landsat imagery (Landsat 4-5, 5, 7 and 8) was used to monitor Land Use Land Cover (LULC), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Turbidity Index (NDTI) and Normalized Difference Water Index (NDWI) in Tarbela reservoir, and its surrounding area from 1990−2020, on decadal interval. The results indicated a significant increase in built-up area, of about 630 km2 , in the western and eastern parts of the reservoir, whereas turbidity level, revealed a substantial decline with 4% decrease observed in the last decade, 2010-2020 thus confirming improved water quality. The study also presented expanse in the spatial coverage of chlorophyll index and water index, indicating increase in residence time of the water. It is concluded that the water quality continued to deteriorate with time, however, 2020 was a year of environmental healing and there was an overall water quality improvement of the reservoir observed. The study recommends policies to be formulated for sediment flushing and turbidity reduction for longer time duration to enhance the life of this mega reservoir.

Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling.

Qilu Lake is one of the nine plateau lakes in Yunnan Province, China. In recent years, under the influence of extreme climate and human activities, the area of Qilu Lake has shrunk significantly, the water level has dropped, and the problem of water shortage has become increasingly serious. Based on the Landsat and MODIS image data from 2000 to 2020, this study applied the ESTARFM spatiotemporal fusion model to unify the data images used in the study to February, used three kinds of water body indexes, selected the water body index most suitable for the study area to extract relevant information, and analyzed the spatiotemporal change characteristics of Qilu Lake area in the last 20 years. The results showed that: (1) Based on the ESTARFM model, the Landsat and MODIS data on 18 January 2020, the Landsat and MODIS data on 9 May 2020, and the MODIS data on the date to be predicted (February 13) were fused to obtain the Landsat image data of the predicted date, which met the accuracy requirements; (2) Taking 2005 as an example, the NDWI, MNDWI, and AWEIsh indexes were used to extract the water body with the precisions of 99.0%, 99.6% and 98.6%, respectively, and then the MNDWI water body index was selected to extract the lake area; (3) In the past 20 years, the overall area of Qilu Lake has shown a downward trend, with the area reduced by 0.7132 km2. From 2000 to 2010, the lake area was relatively stable, fluctuating up and down around 36 km2. From 2010 to 2015, the lake area decreased sharply, with a change rate of −40%. After 2015, the lake area gradually increased; (4) The spatial change of Qilu Lake area mainly occurred in the southwest and west, which decreased by 0.44 km2 and 0.49 km2, respectively, and there were small fluctuations in other directions. In the past two decades, the shape index of Qilu Lake has shown a downward trend as a whole; the contour of the lake tends to be simplified, the contour is complex and stable from 2000 to 2010, and the shape index decreases from 2.17 to 1.74 from 2010 to 2020; (5) The change in the Qilu Lake area is positively correlated with the change in the water level. Polynomial models with different times were selected as the model for retrieving water level elevation from the Qilu Lake water surface area, with a highest correlation coefficient of 0.9259. The temporal and spatial changes of the Qilu Lake area in the last 20 years are the result of the joint action of natural factors and socio-economic factors. According to the analysis, the annual average temperature, annual precipitation, annual average sunshine hours, and population density are the main driving forces leading to the change. In the future, the government and relevant researchers should strengthen real-time monitoring and regular research, formulate and optimize emergency plans to deal with changes in the ecological environment of lakes, and promote the sustainable development of the ecological environment and social economy of the basin. [ABSTRACT FROM AUTHOR]

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions. [ABSTRACT FROM AUTHOR]

The extraction of water bodies is essential for monitoring water resources, ecosystem services and the hydrological cycle, so analyzing water bodies from remote sensing images is necessary. The water index is designed to highlight water bodies in remote sensing images. We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images. Firstly, we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction. Subsequently, we apply KT transformation, LBV transformation, AWEInsh, and HIS transformation to the preprocessed image to calculate a new water index. Then, we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately. Meanwhile, we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies. Finally, we combine small and large water bodies to get complete water bodies. Compared with other traditional methods, our method has apparent advantages in water extraction, particularly in the extraction of small water bodies. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is one of the most common joint diseases worldwide. Unfortunately, clinical methods lack the ability to detect OA in the early stages. Timely detection of the knee joint degradation at the level of tissue changes can prevent its progressive damage. Here, diffuse reflectance spectroscopy (DRS) in the NIR range was used to obtain optical markers of the cartilage damage grades and to assess its mechanical properties. It was observed that the water content obtained by DRS strongly correlates with the cartilage thickness (R =.82) and viscoelastic relaxation time (R =.7). Moreover, the spectral parameters, including water content (OH‐band), protein content (CH‐band), and scattering parameters allowed for discrimination between the cartilage damage grades (10−4 < P ≤ 10−3). The developed approach may become a valuable addition to arthroscopy, helping to identify lesions at the microscopic level in the early stages of OA and complement the surgical analysis. [ABSTRACT FROM AUTHOR]

Marine mucilage that threatens marine habitats is one of the natural disasters, mainly resulting from global warming and marine pollution. Monitoring sea surface mucilage formations and mapping their spatial distributions provide valuable information to the local authorities and decision-makers in developing prevention and rehabilitation strategies. This study proposes a new spectral index called Automated Mucilage Extraction Index (AMEI) that allows effective and accurate detection of surface mucilage aggregates using Sentinel-2 satellite imagery. The index uses four bands of Sentinel-2 Level-2A imagery (Bands 3, 4, 8, and 12) covering visible, near-infrared, and shortwave infrared regions. The index was formulated considering the image acquired on 19 May 2021, when mucilage formations were most intensively observed in the Sea of Marmara. The performance of the developed index was then evaluated using the images acquired on 14 and 24 May and 13 June 2021. The Jenks Natural Breaks (JNB) algorithm was applied to estimate the threshold values for separating mucilage formations from the water surface background and classify index maps into two classes: 'mucilage' and 'others'. To statistically analyse the effectiveness of the indices in distinguishing water background and mucilage formations, the proposed index was compared with 21 widely used water indices by applying Bhattacharyya Distance, Jeffries-Matusita Distance, and M-Statistic measures. Results confirm the robustness of the proposed spectral index, offering superior separation performance (above 1.5 in terms of M-Statistic) compared to other water indices on both cloud-free images and images with cumulus clouds. Visual interpretations also verified that boundaries of the mucilage formations in the cloudless and thin cloudy images were accurately identified by the proposed index, and different types of mucilage (i.e. yellow and white) can be identified when an appropriate histogram thresholding is applied. [ABSTRACT FROM AUTHOR]

Lakes are sensitive to both climate change and human activities, especially in the Yungui Plateau zone. As the main supply of water sources, surface water has a significant impact on environment, ecological balance and socioeconomic development. However, it is unclear how the surface water areas (SWAs) of the nine Yunnan Plateau lakes (NYPLs) have changed over previous decades. In this study, the SWAs of the NYPLs were first extracted using the optimal water index (WI) based on Landsat remote sensing images and the Google Earth Engine (GEE). Then, a spatiotemporal variation analysis of the SWAs was carried out for the NYPLs from 1988 to 2021. Finally, the correlations between the SWA changes in each lake, climatic factors and human activities were analysed. We found that the total SWAs of the NYPL have remained stable over the past 30 years, reaching a maximum of 1,012.51 km2 in 2000 and a minimum of 972.94 km2 in 2015. From 1988 to 2015, the overall SWAs of the NYPLs fluctuated steadily within 1%. However, Lakes Qilu and Yilong shrunk significantly in 2015. Generally, the SWAs of the NYPLs during the rainy season were larger than during the dry season; however, there were some differences between each period. The seasonal change in the SWAs was approximately 10 km2 in 1991, 1994, and 2003, but was relatively less in other years. Precipitation was a direct factor, whereas temperature and evaporation were indirect factors influencing the intra- and inter-annual SWAs of the NYPLs. The changes in the SWAs of the NYPLs were consistent with the precipitation trend and were negatively correlated with changes in temperature and evaporation. Evaporation had more of an influence on shallow lakes than on deep-water lakes. Human activities led to significant changes in individual lakes (such as lakes Dianchi, Yilong, and Qilu). The NYPLs had a spatially heterogeneous response to regional climate change caused by both global climate change and human activities. The results of this study can provide a data reference and decision support for the scientific management of the ecological environment of the Yunnan Plateau lakes. [ABSTRACT FROM AUTHOR]

Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z > 1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction. [ABSTRACT FROM AUTHOR]

The Shapotou National Nature Reserve in the Ningxia Hui Autonomous Region is a typical arid region in China. There is an exceptionally serious problem of surface water resource conservation, and dynamic monitoring of surface water with the help of water indices can help to elucidate its change patterns and impact mechanisms. Here, we analysed the characteristics of interannual variation in surface water area in the study area from 1992–2021. The correlation coefficients of the surface water area in the previous year and the contemporaneous water bodies of the Yellow River with the total surface water area (TSWA) were calculated. The results show the following: ① In terms of the classification accuracy of the two methods, water indices and support vector machine classification, water indices are more suitable for water body extraction in the study area. In particular, the three water indices, NDWI, MNDWI and AWEIsh, were more effective, with average overall accuracies of 90.38%, 90.33% and 90.36% over the 30-year period, respectively. ② From the TSWA extraction results from the last 30 years, the TSWA showed an increasing trend with an increase of 368.28 hm2. Among the areas, Tenggeli Lake contributed the most to the increase in TSWA. ③ The highest correlation between the TSWA and the previous year's TSWA was 0.89, indicating that the better way to protect the water body is to maintain water surface stability year-round. The surface water area of the Yellow River and TSWA also showed a strong correlation, indicating that the rational use of Yellow River water is also an important direction for the future conservation of water resources in the study area. [ABSTRACT FROM AUTHOR]

Sopron mountains were affected by bark beetle (Ips typographus) damage between 2017 and 2020, which was surveyed on high-resolution ESA Sentinel-2 satellite images for the period 2017 and 2020 using Mosaic Hub, Anaconda, and Jupyter Notebook web-based computing environments. Biotic forest damage was detected based on vegetation (NDVI) and moisture (MSI, NDWI) indices derived from satellite images. The spatial and temporal change of damage was observed in the image series, resulting in information about the level of degradation and regeneration. In pursuance of GIS processing, 84 forest compartments were compared, which showed in most of the cases (97%) negative interannual change in the index mean values (MSI = - 0.14, NDWI = - 0.2, NDVI= - 0.19) when years compared to each other. The remote sensing-based survey was marked out and validated based on the forest database of the Hungarian Division of Forest of National Land Centre and forest protection damage reports of the Hungarian National Forest Damage Registration System. [ABSTRACT FROM AUTHOR]

Globally, water scarcity threatens food security, especially in arid and semi-arid food-producing regions, such as the Hetao Irrigation District (HID), where the water consumption during the non-growth season (spring irrigation, autumn irrigation) accounts for more than 30 % of the total irrigation volume. Meanwhile, mapping the characteristics of spatial and temporal evolution of the area of cropland irrigated in the spring and autumn and the pattern of water consumption is crucial for strengthening the management of agricultural water use. Evaporation and infiltration of irrigation water are difficult to capture in time due to insufficient timeliness or accuracy of single remote sensing data. This study integrates seven remote sensing datasets (Landsat5, Landsat7, Landsat8, HJ-1A/B, GF-1, Sentinel-2 and MODIS) for the first time to build a high spatial-temporal resolution dataset, and combines the water index NDWI and MNDWI to extract the maximum water range formed after irrigation by manually adjusting the threshold method. In addition to the global land use/cover dataset (GLC FCS30–1985_2020), the cropland layer was superimposed to accurately identify the cropland spring irrigation and autumn irrigation range from 2000 to 2021 in the HID. In the past 20 years, spring-irrigated (SI) areas increased by 1012.60 km2 (+49.96 %), and the autumn-irrigated (AI) areas decreased by 512.63 km2 (-11.02 %). Combined with the quantity of water diversion and displacement, the characteristics of irrigation water consumption in the non-growth season were further analysed. The results showed that the water consumption of spring irrigation increased by 0.67×108 m3 (+22.26 %), and that of autumn irrigation increased by 4.34×108 m3 (+38.82 %). The decrease of autumn- irrigated area and the increase of water consumption were mainly related to the stability of autumn irrigation water, imperfect irrigation and drainage conditions and insufficient field water management. In 2020-2021, the area of non-growth "autumn irrigation - spring irrigation" repeated irrigation is about 1271.52 km2, which can be used as a key control area. This study provides a theoretical basis and direction for rational water use in non-growth seasons of irrigation districts. ● Multi-source remote sensing integration enhances irrigation monitoring accuracy and timeliness. ● Innovative water index application precisely identifies irrigated areas. ● Two decades of data reveal seasonal dynamics in irrigated areas. ● Despite fall in autumn-irrigated area, water consumption rose, indicating management gaps. [ABSTRACT FROM AUTHOR]

Urban water bodies are critical for sustainable urban ecological and social development. However, the complex compositions of urban land cover and small water bodies pose considerable challenges to urban water surface delineation. Here, we propose a novel urban water extraction algorithm (UWEA) that is efficient in distinguishing water and other low-reflective objects by combining the modified normalized difference water index (mNDWI) and HSV transformation. The spectral properties of urban land covers were analyzed and the separability of objects in different color spaces was compared before applying the HSV transformation. The accuracy and robustness of the UWEA were validated in six highly urbanized subregions of Beijing, Tokyo, and New York, and compared with the mNDWI and HIS methods. The results show that the UWEA had the fewest total errors (sum of omission and commission errors) for all the validation sites, which was approximately 3% fewer errors than those of the mNDWI and 17% fewer errors than those of the HIS method. The UWEA performed best because it was good at identifying small water bodies and suppressing reflective surfaces. The UWEA is effective in urban water monitoring and its thresholds are also robust in various situations. The resulting highly accurate water map could support water-related analyses. This method is also useful for scientists, managers, and planners in water resource management, urban hydrological applications, and sustainable urban development. [ABSTRACT FROM AUTHOR]

Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.

Meandering channel migration is a widespread phenomenon in rivers all around the world. Channel activity, which reflects the rate of change of a meandering channel, is calculated by averaging lateral channel migration. Channel migration can create new channels and abandon old ones, with effects on the natural environment. Floods can even lead to excessive rates of channel migration, which can threaten cities or farmland. Remote sensing can detect the spatial and temporal dynamic characteristics of the river channel, taking into account both spatial and temporal resolution, and can help in planning for the safety of the river channel in advance. Previous studies on river channels have suffered from a low accuracy of data, low level of automation, and subjectivity. To overcome these limitations, we propose a channel activity remote sensing retrieval model (CARSM) in this paper. CARSM extracts water using the modified normalized difference water index (MNDWI) combined with Otsu’s method on the Google Earth Engine (GEE) platform, then extracts the channel centerlines via water mask maps using RivWidthCloud, and finally calculates channel activity based on the geometric relationship of the channel centerlines. With more objective extraction results, CARSM can guarantee more than 95% accuracy of channel activity and its high degree of automation can save a lot of labor costs. We use Landsat images to monitor the channel of the Lower Yellow River and calculate the overall and segmental channel activity separately. Our results show that the overall channel activity of the Lower Yellow River has gradually decreased between 1990 and 2020, with decreases of 33.04% and 41.06%, respectively. Analysis of channel activity reveals that the water sediment pattern of the Lower Yellow River changed from siltation to scouring after the completion of Xiaolangdi Reservoir, and the Lower Yellow River is gradually becoming stable.

Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann–Kendall test was used. San's slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake's water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions. [ABSTRACT FROM AUTHOR]

The surface water extraction algorithm based on satellite remote sensing images is advantageous as it is able to obtain surface water information in a relatively short time. However, when it is used to extract information on surface water in large-scale, long-time series and complex terrain areas, there will be a large number of misclassified pixels, and a large amount of image preprocessing work is required. The accuracy verification is time-consuming and laborious, and the results may not be accurate. The complex climatic and topographic conditions in Bosten Lake Basin make it more difficult to monitor and control surface water bodies. Therefore, based on the GEE (Google Earth Engine) cloud platform, and the studies of the effect of nine kinds of water indexes on the surface water extraction in Bosten Lake Basin, this paper adds a slope mask to remove misclassified pixels and finds the best extraction method of surface water extraction in the basin by means of accuracy verification and visual discrimination through continuous iteration of index threshold and slope mask threshold. The results show that when the threshold value is −0.25 and the slope mask is 8 degrees, the index WI2019 has the best effect on the surface water information extraction of Bosten Lake Basin, effectively eliminating the interference of shadow and snow. The effect of water extraction in the long-time series is discussed and it was found that the precision of water extraction in the long-time series is also better than other indexes. The effects of various indexes on surface water extraction under complex terrain are compared. It can quickly and accurately realize the long-time series of surface water extraction under large-area complex terrain and provides useful guiding significance for water resources management and allocation as well as a water resources ecological assessment of Bosten Lake Basin. [ABSTRACT FROM AUTHOR]

The improved normalized difference water body index (MNDWI) proposed by Xu Hanqiu is further studied, and the improved eutrophic water body index (MEWI) is proposed. The experimental results show that the MEWI index has not only excellent extraction effect on the boundary of ultra green algae eutrophic water body but also an overall extraction accuracy of 89.5%, which is better than NDWI (85.5%), ESWI (87.5%)and NEWI (87%) the overall accuracy of the water body index, and the MEWI index for the shadow distinction between water body and non water body is 0.38, which is better than NDWI (0.14), ESWI (0.16) and NEWI (0.10) Discrimination of water body index. NDWI, ESWI, NEWI and other water body indexes cannot eliminate the interference of algae containing eutrophic water body, and their extraction accuracy is reduced due to the mixing of building and other information. The experiment shows that MEWI index is less affected by sediment factors than other indexes, which is very helpful to water body monitoring in the flood season. At present, most of the research on the information extraction of this kind of ultra green eutrophic water body is only aimed at extracting the water body itself, ignoring its impact on the extraction of the overall water area. The proposal of MEWI index can effectively solve this problem. [ABSTRACT FROM AUTHOR]

To achieve informed integrated water resource management and sustainability, an understanding of the quantity of water available for use within a spatial and temporal context is needed. This study was consequently focused on the estimation of water levels with the use of geospatial techniques. The availability of water data is a significant challenge, especially for smaller dams used by farmers. The lack of consistent water data in turn poses a problem by limiting the estimation of the overall water availability in water strategy models. This challenge is attributed to the cost of modeling all available water resources and the lack of complete records of all available water resources, as some small dams are not officially registered. This paper provides a simple protocol that can be implemented to reliably derive water levels for dams that are yet to be registered or accounted for, using the Katrivier Dam as a case study. Three main datasets were used which enabled the calculation of water levels -- a 12.5 m digital elevation model, Sentinel-2 optical images, and water data from the Department of Water and Sanitation (DWS), as in-situ data. The resulting water level values were derived using a proposed model that includes two correction factors, k and s. The results obtained showed that the estimated water levels from the model proposed in this paper are analogous with those observed by the DWS. Therefore, the proposed method can serve as an additional cost-effective method in water accounting procedures as it requires less expensive equipment than alternatives such as bathymetric methods. [ABSTRACT FROM AUTHOR]

Despite significant advances in geospatial technologies, planners, policy, and decision-makers seldom integrate modern geospatial technologies to serve citizens with adequate water supply services. Several researchers have evaluated water supply services' performance at the national, state, regional and city level; however, challenges remain to solve inequitable distribution of water supply services at the intra-city level. The current research evaluated water supply services' performance through geospatial techniques in 141 water supply zones in Pune city. We computed performance indicators and water indexes for each water supply zone, analyzed spatially, and compared each zone based on the performance for water supply services. The results found that 50% of the city's total water supply zones are high-performing, 26% medium-performing, and 24% low-performing zones for their water supply services. The study provides greater efficiencies in problem-solving, identifies areas of improvement, and enables decision-makers to allocate resources to achieve equitable water supply services to their citizens. [ABSTRACT FROM AUTHOR]

Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.

【Background】 Inner Mongolia, located in the arid and semi-arid region of central Eurasia, is the home to numerous lakes, rivers and reservoirs. These surface water resources are of great significance to ecosystem functions but have dwindled significantly over the past few decades due to the combined impacts of anthropogenic activities and climate change, leading to environmental deterioration. Understanding spatiotemporal change in these surface waters and their determinants is hence indispensable for improving water management and restoring functions of ecosystems in this region. 【Objective】 This paper is to analyze the spatiotemporal changes in surface water in Inner Mongolia from 1990 to 2015, and the impact of climate change and anthropogenic activities. 【Method】 We divided the whole region into four sub-regions and compared the performance and stability of six spectral indexes: MNDWI, NDWI, AWEI, MBWI, WI2015 and TCW, from which we derived an optimal water-body index most suitable for identifying and extracting surface water bodies for evaluation. 【Result】 The surface water in the region had dwindled steadily from 1990 to 2010 and increased from 2010 to 2015. Except Xing’an League, Hohhot and Wuhai which had seen an increase in surface water resources, surface water in other areas had been in decline during this period. The AWEI water body index threshold was stable and had a good stability and practicality; it was more suitable for identifying and monitoring surface water in this region. 【Conclusion】 Factors impacting surface waters in Inner Mongolia are complex and diverse, but overall, they can be classified as climatic factors and anthropogenic factors, especially the latter which were the main forces driving down the waters in both rivers and lakes.

Monitoring activities on the dynamics of water shrinkage at Lake Limboto are essential to the lake's ecosystem's recovery. A remote sensing technology functions to monitor the dynamics of lake inundation area; this allows one to produce a comprehensive set of spatial and temporal data. Such complex satellite dataset demands extra time, greater storage resources, and greater computing capacity. The Google Earth Engine platform emerges as the alternative to tackle such problems. The present study aims to explore the capability of Google Earth Engine in formulating spatial and temporal maps of the inundation area at Lake Limboto. A total of 345 scenes of Landsat image on the study area (available during the period of 1989-2019) were involved in generating a quick inundation area map of the lake. The whole processes (pre-processing, processing, analysing, and evaluating) were automatized by using the Google Earth Engine interface. The evaluation of mapping result accuracy indicated that the average score of F1-score and Intersection over Union (IoU) was at 0.88 and 0.91, respectively. Moreover, the mapping results of the lake's inundation area from 1989 to 2019 showed that the inundation area tended to decrease significantly in size over time. During the period, the lake's area also shrank from 3023.8 ha in 1989 to 1275.0 ha in 2019. All in all, the spatiotemporal information about the changes in lake area may be treated as a reference for decision-making processes of lake management in the future. [ABSTRACT FROM AUTHOR]

The airport is one of the best transportation systems capable of moving people quickly as fast economic growth is followed by rapid population growth. Also, rapid population growth leads to massive exploitation of natural resources which causes environmental degradation such as illegal logging. Seko Airport, located in North Luwu is indicated to have caused environmental pollution, especially in watersheds. Therefore, this study aims to identify water pollution through physical and chemical parameters. Data were analyzed using the pollution index method to find the index value. The results showed that the waters around Seko Airport had an index value of 0.78 which was categorized as not polluted. This happens because the airport has a good water treatment system. Besides, there are strict regulations and guidance for all visitors and the local community to not throw rubbish into water bodies.