Remote Sensing-Based Study on Surface Water Environment

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 19334

Special Issue Editors


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Guest Editor
College of Geography and Remote sensing Sciences, Xinjiang University, Urumqi, China
Interests: remote sensing; water environment; landscape pattern; land use/land cover; modelling building; data reconstruction; image fusion
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Natural Resources and Environment, Northwest A&F University, Yangling, China
Interests: terrestrial water–carbon cycle; water quality monitoring and management
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Natural Resources and Environment, Northwest A&F University, Yangling, China
Interests: soil erosion dynamic processes; coupling effect of hydrology, sediment and pollutants

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Guest Editor
GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang, Malaysia
Interests: climate change impact assessment; hydrological modeling; analysis of hydroclimatic extremes; remote sensing; geographical information system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water resources play an irreplaceable role in human survival and development. The deterioration of the water environment caused by human activities and climate change has caused significant harm to human health. Water environment monitoring is very important for formulating security management measures for water resources. Therefore, the application of advanced technology and methods in water environment monitoring is a current focus of research. In recent years, remote sensing has played an increasingly important role in surface water environment monitoring. Remote sensing covers a variety of applications, such as water storage, water quality, water level, hydrodynamics, flooding, and soil erosion. This has paved the way for an explosion in the use of remote sensing data, especially through the use of multisource remote sensing and novel modeling techniques. This Special Issue aims to present reviews and recent advances in research on the use of remote sensing and GIS in the water environment. Submitted manuscripts can be related to any use of remote sensing and/or GIS for any on surface water environment application. These manuscripts can be focused on the monitoring of surface water (e.g., water storage, water quality, water level, hydrodynamics, flooding and soil erosion) or flux (e.g., CO2, evapotranspiration) at any scale, as well as on the management of the water environment. Observations taking into account spatial and temporal variability are needed to calibrate the models and control their forecasts. Remote sensing currently provides the possibility of monitoring useful factors in the water environment.

Prof. Dr. Fei Zhang
Dr. Xiaoping Wang
Dr. Chenfeng Wang
Dr. Mou Leong Tan
Guest Editors

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Keywords

  • water environment
  • remote sensing
  • modeling
  • lake
  • soil erosion
  • water resources management

Published Papers (12 papers)

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Research

40 pages, 37454 KiB  
Article
Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia
by Ahmed M. Helmi, Mohamed I. Farouk, Raouf Hassan, Mohd Aamir Mumtaz, Lotfi Chaouachi and Mohamed H. Elgamal
Water 2024, 16(7), 925; https://doi.org/10.3390/w16070925 - 22 Mar 2024
Viewed by 785
Abstract
Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation [...] Read more.
Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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23 pages, 3632 KiB  
Article
The Impact of Vegetation Types on Soil Hydrological and Mechanical Properties in the Hilly Regions of Southern China: A Comparative Analysis
by Bofu Zheng, Dan Wang, Yuxin Chen, Yihui Jiang, Fangqing Hu, Liliang Xu, Jihong Zhang and Jinqi Zhu
Water 2024, 16(2), 350; https://doi.org/10.3390/w16020350 - 20 Jan 2024
Viewed by 1164
Abstract
Background: Vegetation roots are considered to play an effective role in controlling soil erosion by benefiting soil hydrology and mechanical properties. However, the correlation between soil hydrology and the mechanical features associated with the variation root system under different vegetation types remains poorly [...] Read more.
Background: Vegetation roots are considered to play an effective role in controlling soil erosion by benefiting soil hydrology and mechanical properties. However, the correlation between soil hydrology and the mechanical features associated with the variation root system under different vegetation types remains poorly understood. Methods: We conducted dye-tracer infiltration to classify water flow behavior and indoor experiments (including tests on soil bulk density, soil organic carbon, mean weight diameter, soil cohesion, root density, etc.) to interpret variation patterns in three forest systems (coniferous and broad-leaved mixed forest, CBF; coniferous forest, CF; Phyllostachys edulis, PF) and fallow land (FL). Results: Based on the soil dye-tracer infiltration results, the largest dyeing area was observed in CF (36.96%), but CF also had the lowest infiltration rate (60.3 mm·min−1). The soil under CBF had the highest shear strength, approximately 25% higher than other vegetation types. CF exhibited the highest aggregate stability, surpassing CBF by 98.55%, PF by 34.31%, and FL by 407.41%, respectively. Additionally, PF forests showed the greatest root biomass and length. The results of correlation analysis and PCA reveal complex relationships among hydrological and mechanical soil traits. Specifically, soil cohesion does not exhibit significant correlations with hydrological traits such as the dyeing area, while traits like MWD and PAD show either positive or negative associations with hydrological traits. Root traits generally exhibit positive relationships with soil mechanical traits, with limited significant correlations observed with hydrological traits. Conversely, we found that root biomass contributes significantly to the dyeing area (accounting for 51.48%). Conclusions: Our findings indicate that the reforestation system is a successful approach for conserving water and reducing erosion by increasing soil-aggregated stability and shear strength, causing water redistribution to be more homogenized across the whole soil profile. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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14 pages, 5663 KiB  
Article
An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges
by Yue Yin and Malaquias Peña
Water 2024, 16(2), 250; https://doi.org/10.3390/w16020250 - 11 Jan 2024
Viewed by 751
Abstract
A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial [...] Read more.
A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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14 pages, 15521 KiB  
Article
An Extended Quasi−Analytical Algorithm for Retrieving Absorption Coefficient Using 510–620 nm Bands from OLCI and MERIS Satellite Data
by Liangliang Shi, Zhihua Mao, Yiwei Zhang, Zheng Wang and Qianguang Tu
Water 2024, 16(1), 67; https://doi.org/10.3390/w16010067 - 23 Dec 2023
Viewed by 803
Abstract
This study focuses on deriving the total absorption coefficients based on field measurements and satellite data. An extended quasi−analytical algorithm (QAA−GRI) was developed based on the two in situ datasets collected from inland waters of Lake Qiandaohu (QDH) and oceanic waters of the [...] Read more.
This study focuses on deriving the total absorption coefficients based on field measurements and satellite data. An extended quasi−analytical algorithm (QAA−GRI) was developed based on the two in situ datasets collected from inland waters of Lake Qiandaohu (QDH) and oceanic waters of the East China Sea (ECS). The key model between absorption coefficients at 510 nm (a(510)) and green red index (GRI) was established using power function in the extended QAA−GRI algorithm. The results reveal that the extended QAA−GRI algorithm performs better than the original quasi−analytical algorithm (QAA−v5) and Garver–Siegel–Maritorena’s algorithm (GSM), and the red–green quasi−analytical algorithm (QAA−RGR), at least for the two in situ datasets from the ECS and QDH. For QAA−GRI, the averaged mean absolute percentage error (MAPE) value of retrieved versus in situ total absorption coefficients is approximately 20%. Subsequently, the extended QAA−GRI algorithm was applied to the OLCI satellite imagery, which is the new successor of MERIS with three specific bands (510, 560, and 620 nm). The implementation of the extended QAA−GRI algorithm on OLCI imagery yielded similar results comparable to that of the QAA−v5 in the ECS region. Furthermore, the application of the algorithm on seasonal and annual MERIS satellite imagery help clarify the combined influences from Yangtze River discharge and coastal currents on the distribution of total absorption in the ECS waters. This study suggests that the extended QAA−GRI algorithm is an alternative for retrieving total absorption coefficient, although it is not recommended for highly turbid waters. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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14 pages, 4384 KiB  
Article
Soil Moisture Distribution and Time Stability of Aerially Sown Shrubland in the Northeastern Margin of Tengger Desert (China)
by Zhenyu Zhao, Guodong Tang, Jian Wang, Yanping Liu and Yong Gao
Water 2023, 15(20), 3562; https://doi.org/10.3390/w15203562 - 12 Oct 2023
Cited by 1 | Viewed by 702
Abstract
Considering the importance of soil moisture in hydrological processes, it is crucial to understand the water distribution and time stability of different aerial shrub soils. There are few studies on the soil moisture of aerial vegetation in the northeastern margin of the Tengger [...] Read more.
Considering the importance of soil moisture in hydrological processes, it is crucial to understand the water distribution and time stability of different aerial shrub soils. There are few studies on the soil moisture of aerial vegetation in the northeastern margin of the Tengger Desert. Based on long-term monitoring data from the aerial seeding area in the northeastern margin of the Tengger Desert, the distribution characteristics of soil moisture and the temporal stability of soil moisture were studied. From June to October 2022, the soil moisture monitoring instrument WatchDog was used to monitor the long-term soil moisture changes (0–200 cm) in the four aerial afforestation plots of Hedysarum scoparium, mixed forest land (Hedysarum scoparium dominant species), mixed forest land (Calligonum mongolicum dominant species), and Calligonum mongolicum. The Spearman rank correlation coefficient was used to study the temporal stability of soil moisture in the four plots. Rainfall data were collected through small weather stations. The results show that the average soil water storage of four kinds of aerial shrub land in the study area was the highest in August, and the average soil water storage of different forest lands was different. The soil water content of the surface layer (0–30 cm) fluctuated the most in different months. The variation in soil water content in the shallow layer (30–100 cm) was smaller than that in the surface layer. The fluctuation of soil water content in the middle layer (100–150 cm) and deep layer (150–200 cm) was relatively stable. There was no strong variability in soil moisture content, and the temporal variation coefficient of surface soil moisture was the highest (31.44–39.8%), which showed moderate variability. The temporal variation coefficient of soil moisture in the shallow, middle and deep layers of all kinds of plots was significantly reduced, and the soil moisture stability of different aerial shrub land was the same. Spearman rank correlation analysis showed that the spatial pattern of soil water content in the surface layer (0–30 cm) and deep layer (150–200 cm) was more stable over time, that is, the temporal stability of soil water content was higher, and the temporal stability of soil water content in the middle and shallow layers of different types of shrub land was different. The research results help us to understand the soil hydrological process in the aerial seeding afforestation area in the northeastern margin of Tengger Desert, rationally arrange soil moisture monitoring points, efficiently manage and utilize water resources in the aerial seeding area, and provide a theoretical basis for local vegetation restoration and the optimization of the ecological environment. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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19 pages, 3084 KiB  
Article
Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression
by Juan G. Loaiza, Jesús Gabriel Rangel-Peraza, Sergio Alberto Monjardín-Armenta, Yaneth A. Bustos-Terrones, Erick R. Bandala, Antonio J. Sanhouse-García and Sergio A. Rentería-Guevara
Water 2023, 15(14), 2606; https://doi.org/10.3390/w15142606 - 18 Jul 2023
Cited by 7 | Viewed by 2346
Abstract
A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses [...] Read more.
A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transformation model to normalize data on three water quality parameters: total organic carbon (TOC), total dissolved solids (TDS), and chlorophyll a (Chl-a). After the Box–Cox transformation, a mathematical model was generated for every parameter using multiple linear regression to correlate normalized data and spectral reflectance from Landsat 8 imagery. Then, significant testing was conducted to discard spectral bands that did not show a statistically significant response (α = 0.05) from the different water quality models. The r2 values achieved for TOC, TDS, and Chl-a water quality models after the band discrimination process were found 0.926, 0.875, and 0.810, respectively, achieving a fair fitting to real water quality data measurements. Finally, a comparison between estimated and measured water quality values not previously used for model development was carried out to validate these models. In this validation process, a good fit of 98% and 93% was obtained for TDS and TOC, respectively, whereas an acceptable fit of 81% was obtained for Chl-a. This study proposes an interesting alternative for ordered and standardized steps applied to generate mathematical models for the estimation of TOC, TDS, and Chl-a based on water quality parameters measured in the field and using satellite images. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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29 pages, 6272 KiB  
Article
Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process
by Nurul Syakira, Mou Leong Tan, Zed Zulkafli, Fei Zhang, Fredolin Tangang, Chun Kiat Chang, Wan Mohd Muhiyuddin Wan Ibrahim and Mohd Hilmi P. Ramli
Water 2023, 15(12), 2195; https://doi.org/10.3390/w15122195 - 11 Jun 2023
Cited by 1 | Viewed by 2084
Abstract
The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical [...] Read more.
The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of “very high” and “high” hazard levels that require particular attention from local stakeholders. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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20 pages, 11331 KiB  
Article
High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI
by Xin Hang, Xinyi Li, Yachun Li, Shihua Zhu, Shengqi Li, Xiuzhen Han and Liangxiao Sun
Water 2023, 15(12), 2165; https://doi.org/10.3390/w15122165 - 8 Jun 2023
Cited by 2 | Viewed by 1283
Abstract
China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, [...] Read more.
China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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15 pages, 6984 KiB  
Article
Reflection Spectra Coupling Analysis and Polarized Modeling of Optically Active Particles in Lakes
by Banglong Pan, Hongwei Cheng, Shuhua Du, Hanming Yu, Yi Tang, Ying Shu, Juan Du and Huaming Xie
Water 2023, 15(9), 1706; https://doi.org/10.3390/w15091706 - 27 Apr 2023
Viewed by 1267
Abstract
The coupling between optically active substances of algae particles and inorganic suspended solids of water makes the characteristics of reflection spectra of water complex and changeable. This makes modeling and inversion of polarization remote sensing in class II water difficult. In our study, [...] Read more.
The coupling between optically active substances of algae particles and inorganic suspended solids of water makes the characteristics of reflection spectra of water complex and changeable. This makes modeling and inversion of polarization remote sensing in class II water difficult. In our study, considering the influence of the mixing ratio of algae particles and inorganic suspended solids, the sensor incidence angle, and the solar zenith angle on the polarization reflection spectrum, we analyzed the coupling characteristics of the polarized bidirectional reflectance of particulate matter through control experiments of mixed components of water particles in the laboratory. With Chaohu Lake in China as an example, the polarized reflectance coupling characteristics of water particles was investigated by the water-leaving radiation. The results showed that in the characteristic bands of 570, 675, and 705 nm, the degree of linear polarization (DOLP) was sensitive to the water-leaving radiation of the particles rather than to the reflectance. With the variation of observation angle, the reflection spectra were strongly interfered with by solar flare when the sensor zenith angle was close to 50° on the meridian plane with an azimuth angle of 180°, but DOLP was less affected, while also having a low correlation in the high concentration region. Combined with the coupling characteristics of particles at 675 and 705 nm, the model of DOLP ratio was established by partial least squares regression (PLSR) with a determination coefficient (R2) of 0.91, root mean square error (RMSE) 0.035, and a verification accuracy of 0.959. This shows that the model has better prediction ability for the coupling characteristics of water particles by the polarization reflection spectra and provides good support for mixed spectral unmixing of class II water. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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18 pages, 8612 KiB  
Article
Remote-Sensing Extraction of Small Water Bodies on the Loess Plateau
by Jia Guo, Xiaoping Wang, Bin Liu, Ke Liu, Yong Zhang and Chenfeng Wang
Water 2023, 15(5), 866; https://doi.org/10.3390/w15050866 - 23 Feb 2023
Cited by 1 | Viewed by 2355
Abstract
The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for [...] Read more.
The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for a large-scale small water body extraction algorithm based on GEE (Google Earth Engine). MFTSA uses the AWEI (automated water extraction index), MNDWI (modified normalized difference water index), NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) for multi-index synergy to extract small water bodies. It also uses slope data generated by the SRTM (Shuttle Radar Topography Mission digital elevation model) and NIR band reflectance to eliminate suppressing high reflectivity noise and shadow noise. An MFTSA algorithm was proposed and the results showed that: (1) The overall extraction accuracy of the MFTSA algorithm on the Loess Plateau was 98.14%, and the correct extraction rate of small water bodies was 92.82%. (2) Compared with traditional water index methods and classification methods, the MFTSA algorithm could extract small water bodies with higher integrity and clearer and more accurate boundaries. (3) The MFTSA algorithm was used to extract a total of 69,900 small water bodies on the Loess Plateau, accounting for 97.63% of the total water bodies, and the area was 482.11 square kilometers, accounting for 16.50% of the total water bodies. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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31 pages, 20018 KiB  
Article
Monitoring Shoreline and Land Use/Land Cover Changes in Sandbanks Provincial Park Using Remote Sensing and Climate Data
by Esmaeil Kouhgardi, Mohammadali Hemati, Elaheh Shakerdargah, Hodjat Shiri and Masoud Mahdianpari
Water 2022, 14(22), 3593; https://doi.org/10.3390/w14223593 - 8 Nov 2022
Cited by 6 | Viewed by 2250
Abstract
Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters [...] Read more.
Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-use/land-cover (LULC) change should be considered. This paper presents a remotely sensed shoreline monitoring in Sandbanks Provincial Park, Ontario, Canada, from 1984 to 2021. The CoastSat toolkit for Python and a multilayer perceptron (MLP) neural network classifier were used for shoreline detection, and an unsupervised change detection framework followed by a postclassification change detection method was implemented for LULC classification and change detection. The study assessed the recent coastal erosion and accretion trends in the region in association with spatiotemporal changes in the total area of the West and East Lakes, the transition between LULC classes, extreme climate events, population growth, and future climate projection scenarios. The results of the study illustrate that the accretion trend apparently can be seen in most parts of the study area since 1984 and is affected by several factors, including lake water-level changes, total annual precipitations, sand movements, and other hydrologic/climatic parameters. Furthermore, the observed LULC changes could be in line with climate change-driven forces and population growth to accelerate the detected accretion trend in the East and West Lakes. In total, the synergistic interaction of the investigated parameters would result in a greater accretion trend along with a lower groundwater table amid even a low carbon scenario. The discussed findings could be beneficial to regional/provincial authorities, policymakers, and environmental advocates for the sustainable development of coastal communities. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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20 pages, 3091 KiB  
Article
Characteristics of Dissolved Organic Matter and Its Relationship with Water Quality along the Downstream of the Kaidu River in China
by Chunyan Cheng, Fei Zhang, Mou Leong Tan, Hsiang-Te Kung, Jingchao Shi, Qi Zhao, Weiwei Wang, Pan Duan, Changjiang An, Yunfei Cai and Xingyou Li
Water 2022, 14(21), 3544; https://doi.org/10.3390/w14213544 - 4 Nov 2022
Cited by 5 | Viewed by 2211
Abstract
The variability in the quality of water that runs along the course of a river, flowing out of a mountain pass, through an agricultural oasis and into a lake, has been a key topic of research in recent years. In this study, the [...] Read more.
The variability in the quality of water that runs along the course of a river, flowing out of a mountain pass, through an agricultural oasis and into a lake, has been a key topic of research in recent years. In this study, the characteristics of dissolved organic matter (DOM) along the river flow, and its relationship with water quality, were analyzed using the Canadian water quality index (CWQI), parallel factor (PARAFAC) and self-organizing map (SOM). The study results include: (1) The conclusion of field sampling along the lower reaches of the Kaidu River and laboratory measurements of water quality parameters, using CWQI to determine the water quality index of the lower Kaidu River, ranging between 59.58 and 93.47. The water quality of the lower reaches of the Kaidu River generally ranges between moderate and good, and can meet the water use requirements of Class II water function standards. (2) The DOM composition of the river predominantly contained three fluorescence components, while the three fluorescence indices of the water body varied less in different river sections. Based on the SOM training model, the fluorescence intensity of the C1 component was larger among the three fluorescence components, followed by the C2 component, and the smallest fluorescence intensity of the C3, which was dominated by humic-like substances, with a high authigenic origin and humification degree. (3) The fluorescence index and fluorescence components were correlated with water quality parameters, and it was found that C1, C2 and C3 were negative and correlated significantly with SO42- and Total-dissolved solids (TDS) concentrations; FI, HIX and BIX showed strong positive correlations with SAL and Cu and negative correlations with dissolved oxygen (DO). This study provides a scientific basis for surface water quality monitoring and water quality pollution management in the Kaidu River. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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