High-Resolution Monitoring and Modelling for Water Resources Management: New Sensors, New Approaches and Applications

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 (30 June 2023) | Viewed by 14882

Special Issue Editors

College of Water Science, Beijing Normal University, Beijing, China
Interests: hydrological model; ecohydrological; remote sensing hydrology; remote sensing of environmental; satellite remote sensing and UAV
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Interests: remote sensing of flood & drought; UAV; remote sensing of agricultural irrigation; remote sensing scale problem
Special Issues, Collections and Topics in MDPI journals
Geography and Environment, University of Southampton, Southampton, UK
Interests: soil moisture; data assimilation; microwave remote sensing; precision agriculture

Special Issue Information

Dear Colleagues,

Water resources management at high spatial and temporal resolution calls for data support at the revelant scales, which has long been hindered by the availability of high-resolution data. Thanks to the development of data acquisition, storage and processing techniques, the data acquisition and implementation have been enhanced to an unprecedented level. Data from new platforms, such as the GaoFen series from China, the Sentinel series from the EU and the Landsat series from the US have become available; new approaches, such as AI, machine learning and Web of Things have been developed, and new platforms, such as the Google Earth Engine have been utilized for water resources management at a resolution much higher than that of traditional research. In this Special Issue, we seek to publish studies on the use of new sensors and approaches for water resource management, and ecohydrological modelling at high resolution. The topics covered by this Special Issue will include but not be limited to the following:

  • New sensor data for water resources management;
  • Novel approaches to extracting key ecohydrological variables;
  • High-resolution ecohydrological modelling;
  • AI models and approaches to monitor water disasters.

Prof. Dr. Hezhen Lou
Prof. Dr. Wenlong Song
Dr. Yang Lu
Guest Editors

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Keywords

  • high resolution
  • new sensors
  • intelligent model
  • new approaches
  • water resource
  • remote sensing
  • UAV

Published Papers (13 papers)

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Editorial

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3 pages, 156 KiB  
Editorial
Water Resources Management Using High-Resolution Monitoring and Modelling
by Hezhen Lou, Wenlong Song and Yang Lu
Water 2023, 15(18), 3252; https://doi.org/10.3390/w15183252 - 13 Sep 2023
Viewed by 688
Abstract
Water resources’ management at a high spatial and temporal resolution calls for data support at the relevant scales, which has long been hindered by the availability of high-resolution data [...] Full article

Research

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25 pages, 7373 KiB  
Article
Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling
by Madeleine M. Bolick, Christopher J. Post, M. Z. Naser, Farhang Forghanparast and Elena A. Mikhailova
Water 2023, 15(14), 2581; https://doi.org/10.3390/w15142581 - 14 Jul 2023
Cited by 3 | Viewed by 1555
Abstract
Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing [...] Read more.
Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing urbanization, it is critical to understand how urban stream channels will respond to precipitation events to prevent catastrophic flooding. This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine learning was highly accurate in predicting changes in water level for five locations along the stream with R2 values greater than 0.9. Yet, it can be challenging to understand how these water level prediction values will translate to water volume in the stream channel. Therefore, this study collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel. The predicted water levels were also used to calculate upstream flood volumes to provide further context for how small changes in the water level correspond to changes in the stream channel. Overall, the methodology determined that the areas of Hunnicutt Creek with more urban impacts experience larger rises in stream levels and greater volumes of upstream water during storm events. Together, this innovative methodology combining machine learning, terrestrial LiDAR, 3D modeling, and volume calculations provides new techniques to understand flood-prone areas in urban stream environments. Full article
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16 pages, 5156 KiB  
Article
Spatio-Temporal Heterogeneity of Soil Moisture on Shrub–Grass Hillslope in Karst Region
by Juncai Li, Xiaorong Meng, Hua Li, Xiaoxiao Gu, Xiaojun Cai, Yuanlong Li and Qiuwen Zhou
Water 2023, 15(10), 1868; https://doi.org/10.3390/w15101868 - 15 May 2023
Cited by 1 | Viewed by 921
Abstract
Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. [...] Read more.
Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture. Full article
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17 pages, 3140 KiB  
Article
A Pixel-Scale Measurement Method of Soil Moisture Using Ground-Penetrating Radar
by Wenlong Song, Yizhu Lu, Yu Wang, Jingxuan Lu and Haixian Shi
Water 2023, 15(7), 1318; https://doi.org/10.3390/w15071318 - 28 Mar 2023
Cited by 1 | Viewed by 1342
Abstract
Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In [...] Read more.
Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m2 plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation. Full article
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13 pages, 12743 KiB  
Article
Characteristics of Soil Calcium Content Distribution in Karst Dry-Hot Valley and Its Influencing Factors
by Ya Luo, Chunmao Shi, Shengtian Yang, Yang Liu, Shuang Zhao and Chunchang Zhang
Water 2023, 15(6), 1119; https://doi.org/10.3390/w15061119 - 14 Mar 2023
Cited by 3 | Viewed by 2281
Abstract
Calcium is an essential macronutrient in soils and plays an important role in the structure and function of an ecosystem. In this study, we selected the Huajiang dry-hot valley region in southwest China as our research object, aiming to comprehend the soil calcium [...] Read more.
Calcium is an essential macronutrient in soils and plays an important role in the structure and function of an ecosystem. In this study, we selected the Huajiang dry-hot valley region in southwest China as our research object, aiming to comprehend the soil calcium distribution characteristics of different altitudes and vegetation types in this karst dry-hot valley region. The results showed that the mean value of total soil calcium content in the karst dry-hot valley was 13.00 ± 3.28 g·kg−1, and the mean value of the proportion of exchangeable calcium content to total calcium was 50.31%. In the vertical profile, total soil and exchangeable calcium contents decreased with increasing soil depth. With increasing altitude, total soil and exchangeable calcium contents increased. Among the different vegetation types, the total and exchangeable calcium contents of crops were higher than the three natural vegetation types of forest, scrub, and grassland, and the soil calcium content of forest was the lowest. Total soil and exchangeable calcium content in the karst dry-hot valley were affected by rock exposure rate, vegetation coverage, soil thickness, soil organic matter and soil pH. In addition, the unique environmental gradient characteristics at different elevations in the dry-hot valley area may redistribute soil calcium, and the positive vegetation succession may cause a gradual decrease of soil calcium content in the area. Full article
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28 pages, 3326 KiB  
Article
Assessment of Aquifer Recharge Potential Using Remote Sensing, GIS and the Analytical Hierarchy Process (AHP) Combined with Hydrochemical and Isotope Data (Tamassari Basin, Burkina Faso)
by Issan Ki, Hedia Chakroun, Youssouf Koussoube and Kamel Zouari
Water 2023, 15(4), 650; https://doi.org/10.3390/w15040650 - 07 Feb 2023
Cited by 2 | Viewed by 1872
Abstract
In the Tamassari basin, the agricultural population is highly dependent on groundwater resources for its socioeconomic development. However, the decrease in rainfall in the region since the late 1960s and the demographic pressure on the land are significantly affecting groundwater recharge. In order [...] Read more.
In the Tamassari basin, the agricultural population is highly dependent on groundwater resources for its socioeconomic development. However, the decrease in rainfall in the region since the late 1960s and the demographic pressure on the land are significantly affecting groundwater recharge. In order to exploit this groundwater sustainably, it is necessary to identify potential recharge areas for a better capitalisation of this resource. The objective of this study is to map the recharge potential of the existing aquifers making use of remote sensing and GIS techniques and to make a validation based on chloride and tritium contents in the borehole water. The processing carried out on the Landsat 5 and Landsat 8 images combined with a digital elevation model (ALOS PALSAR), highlight the lithological, linear and topographical characteristics of the study area. In addition, various supervised classification algorithms were used to produce the most accurate land use map. Field campaigns were conducted to validate the thematic maps resulting from the geospatial data processing and to collect water samples for hydrochemical (chloride) and isotopic analysis (tritium). The analytical hierarchy process (AHP) was used to derive recharge factors weights. The resulting recharge map shows a perfect agreement between the recharge classes derived from spatial modelling and the tritium isotope analyses. This was not the case with the chloride contents, which showed a dispersion over all the recharge areas. Full article
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19 pages, 14840 KiB  
Article
Large-Scale Extraction and Mapping of Small Surface Water Bodies Based on Very High-Spatial-Resolution Satellite Images: A Case Study in Beijing, China
by Zhonglin Ji, Yu Zhu, Yaozhong Pan, Xiufang Zhu and Xuechang Zheng
Water 2022, 14(18), 2889; https://doi.org/10.3390/w14182889 - 16 Sep 2022
Cited by 5 | Viewed by 1903
Abstract
Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. [...] Read more.
Surface water is a crucial resource and environmental element for human survival and ecosystem stability; therefore, accurate information on the distribution of surface water bodies is essential. Extracting this information on a large scale is commonly implemented using moderate- and low-resolution satellite images. However, the detection and analysis of more detailed surface water structures and small water bodies necessitate the use of very high-resolution (VHR) satellite images. The large-scale application of VHR images for water extraction requires convenient and accurate methods. In this paper, a method combining a pixel-level water index and image object detection is proposed. The method was tested using 2018/2019 multispectral 4-m resolution images obtained from the Chinese satellite Gaofen-2 across Beijing, China. Results show that the automatic extraction of water body information over large areas using the proposed method and VHR images is feasible. Kappa coefficient and overall accuracy of 0.96 and 99.8% after post-classification improvement were obtained for testing images inside the Beijing area. The Beijing water body dataset obtained included a total of 489.53 km2 of surface water in 2018/2019, 108.01 km2 of which were ponds with an area smaller than 2 km2. This study can be applied for water body extraction and mapping in other large regions and provides a reference for other methods for using VHR images to extract water body information on a large scale. Full article
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18 pages, 4382 KiB  
Article
A New Method to Map Spring Irrigated Areas Using MODIS LST Products and Ancillary Data in an Agricultural District of Northwest China
by Yizhu Lu, Wenlong Song, Linjing Tian, Xiuhua Chen, Rongjie Gui and Long Chen
Water 2022, 14(17), 2628; https://doi.org/10.3390/w14172628 - 26 Aug 2022
Cited by 1 | Viewed by 1309
Abstract
Irrigation alleviates drought in croplands and maintains or increases crop yields. The accurate monitoring of irrigated areas is important to regional water resource management, food security, climate change, drought monitoring, and emergency disaster relief. Based on field experiments that demonstrate the feasibility of [...] Read more.
Irrigation alleviates drought in croplands and maintains or increases crop yields. The accurate monitoring of irrigated areas is important to regional water resource management, food security, climate change, drought monitoring, and emergency disaster relief. Based on field experiments that demonstrate the feasibility of irrigated area mapping using land-surface temperature, we propose a method to map spring irrigation areas using historical meteorological data, main crop phenological characteristics, irrigation regimes, and MODIS land-surface temperature (LST) products. The distribution of irrigation intensity, spring irrigated areas (SIA, considering the irrigation intensity), and total area of spring irrigation (STIA, regardless of irrigation intensity) were monitored by the proposed method for the Donglei Irrigated District (Phase II) in northwestern China from 2011 to 2018. The spring irrigation of the study area was divided into three periods (16 January–23 February, 24 February–24 March, and 25 March–31 May). Then, the temperature threshold of the irrigated area in each period was determined by the diurnal temperature range (DTR) of the rain-fed plots and precipitation data; for the three periods, this was 12 °C, 15 °C, and 11 °C, respectively. The results showed that most of the croplands in the study area were irrigated once or twice. The SIA in most years varied between 55,900 and 73,100 ha, but in 2016, the irrigation area reached 100,200 ha. The STIA accounted for 60–70% of the irrigated area except 2016. The average accuracy of SIA monitoring was satisfactory and above 94% for years when sufficient and reliable data was available. Full article
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16 pages, 6316 KiB  
Article
A River Channel Extraction Method Based on a Digital Elevation Model Retrieved from Satellite Imagery
by Rongjie Gui, Wenlong Song, Xiao Pu, Yizhu Lu, Changjun Liu and Long Chen
Water 2022, 14(15), 2387; https://doi.org/10.3390/w14152387 - 01 Aug 2022
Cited by 4 | Viewed by 2295
Abstract
The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used [...] Read more.
The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used to extract river channels. However, satellite images are insufficiently precise, and field measurement requires significant manpower and cost. In this paper, a new method for river channel extraction is proposed, which is based on the combination of Jenks natural breaks classification method and digital elevation model (DEM), and then the river channel range is complemented by using the water range monitored by GF-1(Gaofen-1 satellite) in flood season. The overall precision is greater than 85%, and the Kappa values achieve moderate stability (0.41–0.60). Using this method, the extraction of river range is practicable and achievable, and the higher the DEM resolution, the better the extraction result. Full article
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9 pages, 7518 KiB  
Article
Estimation of River Discharge Using Unmanned Aerial Vehicle (UAV) Based on Manning Formula for an Ungauged Alpine River in the Eastern Qilian Mountains
by Mingyong Cai, Jixi Gao, Xuanmei Fan, Sihan Liu, Wenming Shen and Chaoyang He
Water 2022, 14(13), 2100; https://doi.org/10.3390/w14132100 - 30 Jun 2022
Cited by 4 | Viewed by 1550
Abstract
River discharge is crucial to water resources development and ecological protection. However, in some arid areas of northwest China, it is still difficult to measure discharge accurately. In this study, unmanned aerial vehicle (UAV) imagery has been used to estimate river discharge at [...] Read more.
River discharge is crucial to water resources development and ecological protection. However, in some arid areas of northwest China, it is still difficult to measure discharge accurately. In this study, unmanned aerial vehicle (UAV) imagery has been used to estimate river discharge at two river sections in the upper reaches of the Shiyang River in the eastern part of the Qilian Mountains based on the Manning formula. The estimated discharges at those two sections are 1.16 m3/s and 3.11 m3/s, respectively. Taking the discharges measured by an acoustic Doppler current profiler (ADCP) as the reference, the relative error of the estimates is below 5%, which is accurate enough for water resources management in mountain basin regions. Multiple high-resolution satellite images were also used to calculate water discharges at the two sections, which were in good agreement with the discharges estimated from UAVs. This study demonstrates the feasibility of using UAVs to estimate river discharge, which is of great significance for future regional-scale water resource assessments. Full article
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13 pages, 3113 KiB  
Article
Application of Cosmic-Ray Neutron Sensor Method to Calculate Field Water Use Efficiency
by Xiuhua Chen, Wenlong Song, Yangjun Shi, Weidong Liu, Yizhu Lu, Zhiguo Pang and Xiao Chen
Water 2022, 14(9), 1518; https://doi.org/10.3390/w14091518 - 09 May 2022
Cited by 6 | Viewed by 2185
Abstract
Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country’s food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance [...] Read more.
Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country’s food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance models to calculate the field water use coefficient, which cannot avoid errors caused by the spatial heterogeneity of SM and insufficient spatial resolution of remote sensing data. Therefore, in this study, the cosmic-ray neutron sensor (CRNS), Time-Domain Reflectometers (TDR) and Automatic Weather Stations (AWS) were used to monitor the meteorological and hydrological data such as SM, atmospheric pressure, and precipitation in the experimental area of Jinghuiqu Irrigation District for three consecutive years. The scale of the CRNS SM lies between the point and the remote sensing. Based on the CRNS SM, the calculation method for canal head and tail water was used to calculate the field water use efficiency to evaluate the level of agricultural irrigation water use in the experimental irrigation area. The results showed that CRNS could accurately detect the change in SM, and four irrigation events were monitored during the winter wheat growth period from October 2018 to June 2019; the calculation result of field water use efficiency in the experimental area was 0.77. According to the field water use efficiency of the same irrigation area from October 2013 to October 2015 in other studies, the field water use efficiency during the growing period of winter wheat in this area increased from 0.503 to 0.770 in 2013–2019, indicating a significant improvement in the field water use level. In general, this study not only solves the problem of low calculation accuracy of field water use efficiency caused by the mismatch of SM monitoring scales but also explores the application potential of CRNS in agricultural irrigation management and water resource allocation. Full article
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17 pages, 2913 KiB  
Article
Numerical Experiments Applying Simple Kriging to Intermittent and Log-Normal Data
by Yonghun Ro and Chulsang Yoo
Water 2022, 14(9), 1364; https://doi.org/10.3390/w14091364 - 22 Apr 2022
Cited by 2 | Viewed by 1451
Abstract
This study evaluates the effect of considering data intermittency and log-normality in applications of simple Kriging. Several sets of synthetic data, both intermittent and log-normal, were prepared for this purpose, and then four different Kriging applications were repeated with these synthetic data under [...] Read more.
This study evaluates the effect of considering data intermittency and log-normality in applications of simple Kriging. Several sets of synthetic data, both intermittent and log-normal, were prepared for this purpose, and then four different Kriging applications were repeated with these synthetic data under different assumptions of data intermittency and log-normality. The effects of these assumptions on the simple Kriging applications were evaluated and compared with each other. As a result, it was found that the derived correlation length of a variogram becomes longer when considering both data intermittency and log-normality, and the sill height becomes smaller when data intermittency is high. The data field generated by simple Kriging was also closer to the original data when considering both data intermittency and log-normality. In the application to rain rate data, the effect of considering data intermittency was confirmed. However, the effect of considering data log-normality was found to be vague. The general assumption of log-normality in relation to the rain rate data seems not to be so valid, at least not for the rain rate data considered in this study. Full article
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Review

Jump to: Editorial, Research

20 pages, 1731 KiB  
Review
A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology
by Zeheng Wu, Yu Huang, Kailin Huang, Kang Yan and Hua Chen
Water 2023, 15(18), 3233; https://doi.org/10.3390/w15183233 - 11 Sep 2023
Cited by 1 | Viewed by 2447
Abstract
As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can [...] Read more.
As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can be seamlessly integrated into unmanned aerial vehicle (UAV) systems, significantly enhancing the spatiotemporal granularity of water level data. However, computer-vision-based water level measurement methods face the core problems of accurately identifying water level lines and elevation calculations, which can lead to measurement errors due to lighting variations and camera position offsets. Although deep learning has received much attention in improving the generation, the effectiveness of the models is limited by the diversity of the datasets. For the radar water level sensor, the hardware structure and signal processing algorithms have to be further improved. In the future, by constructing more comprehensive datasets, developing fast calibration algorithms, and implementing multi-sensor data fusion, it is expected that the robustness, accuracy, and computational efficiency of water level monitoring will be significantly improved, laying a solid foundation for further innovations and developments of hydrological monitoring. Full article
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