Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs)

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	4.8	7.4	2017	20.8 Days	CHF 2600	Submit
Forests forests	2.5	4.6	2010	16.8 Days	CHF 2600	Submit
Geomatics geomatics	2.8	5.1	2021	22.6 Days	CHF 1200	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	33.1 Days	CHF 1900	Submit
Land land	3.2	5.9	2012	17.5 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Water water	3.0	6.0	2009	18.9 Days	CHF 2600	Submit

26 pages, 17314 KB

Open AccessArticle

An AESRGAN Remote Sensing Super-Resolution Model for Accurate Water Extraction

by Hongjie Liu, Wenlong Song, Juan Lv, Yizhu Lu, Long Chen, Yutong Zhao, Shaobo Linghu, Yifan Duan, Pengyu Chen, Tianshi Feng and Rongjie Gui

Remote Sens. 2026, 18(8), 1108; https://doi.org/10.3390/rs18081108 - 8 Apr 2026

Viewed by 357

Abstract

Accurate monitoring of water spatiotemporal dynamics is critical for hydrological process analysis and climate impact assessment. While remote sensing enables effective water monitoring, public satellite imagery is limited by mixed-pixel effects that hinder small river detection, and high-resolution commercial data suffers from low [...] Read more.

Accurate monitoring of water spatiotemporal dynamics is critical for hydrological process analysis and climate impact assessment. While remote sensing enables effective water monitoring, public satellite imagery is limited by mixed-pixel effects that hinder small river detection, and high-resolution commercial data suffers from low temporal frequency and restricted coverage. To address these limitations, this study proposes a deep learning-based super-resolution (SR) framework for multispectral remote sensing imagery. This paper constructs a matched dataset for GF2 and Sentinel-2 imagery and develops an Attention Enhanced Super Resolution Generative Adversarial Network (AESRGAN). By integrating attention mechanisms and a spectral-structural loss design, the network is optimized to adapt to the characteristics of multispectral remote sensing imagery. Experimental results demonstrate that AESRGAN achieves strong reconstruction performance, with a Peak Signal-to-Noise Ratio (PSNR) of 33.83 dB and a Structural Similarity Index Measure (SSIM) of 0.882. Water extraction based on the reconstructed imagery using the U-Net++ model achieved an overall accuracy of 0.97 and a Kappa coefficient of 0.92. In addition, the reconstructed imagery improved the estimation accuracy of river length, width, and area by 0.34%, 3.28%, and 8.51%, respectively. The proposed framework provides an effective solution for multi-source remote sensing data fusion and high-precision surface water monitoring, offering new potential for long-term hydrological observation using medium-resolution satellite imagery. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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20 pages, 29969 KB

Open AccessArticle

A Study on Integration of Topographic Clustering and Physical Constraints for Flood Propagation Simulation

by Xu Zhang, Xiaotao Li, Yingwei Sun, Qiaomei Su, Shifan Yuan, Mei Yang, Qianfang Lou and Bingyuan Chen

Remote Sens. 2026, 18(6), 885; https://doi.org/10.3390/rs18060885 - 13 Mar 2026

Viewed by 277

Abstract

Global climate change is increasing extreme rainfall events, and severe floods are becoming more frequent. Flood storage and detention basins (FSDBs) are an important part of the flood control system in China. They play a key role in regional flood emergency response and [...] Read more.

Global climate change is increasing extreme rainfall events, and severe floods are becoming more frequent. Flood storage and detention basins (FSDBs) are an important part of the flood control system in China. They play a key role in regional flood emergency response and regulation. Therefore, accurate simulation of flood evolution after the activation of FSDBs is urgently needed. This study proposes a high-accuracy flood evolution simulation method that combines terrain clustering and physical propagation constraints. We first build a 2 m resolution digital elevation model (DEM) using GF-7 stereo imagery and laser altimetry data. We then introduce an improved superpixel segmentation algorithm (TSLIC). This method reduces the number of computational units while preserving key micro-topographic features. It groups high-resolution grids into terrain units with similar elevation characteristics and continuous spatial structure. Based on these terrain units, we develop a flood evolution model called RS-CFPM. The model combines flow velocity estimated from the Manning equation with flood propagation speed derived from radar remote sensing. It uses a water balance framework and includes a propagation time delay constraint. This design helps overcome the limitation of traditional static inundation methods that ignore flood travel time. We apply the proposed method to simulate the flood inundation process during the “23·7” extreme basin-scale flood event in the Haihe River Basin. Comparison with multi-temporal radar observations shows that the errors of simulated water level and inundation extent in the Dongdian FSDB are both within 10%. The computational efficiency is also improved by more than 60% compared with traditional methods. This study provides a new approach for rapid and accurate simulation of flood inundation processes in FSDBs under emergency conditions. The method can support flood emergency operation and decision-making. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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30 pages, 7158 KB

Open AccessArticle

Extracting Duckweed/Algal Bloom-Type Black–Odorous Waters from Remote Sensing Images Based on SwinTf-Unet Model

by Jingtao Sun, Chenyang Li and Lijun Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(2), 67; https://doi.org/10.3390/ijgi15020067 - 3 Feb 2026

Viewed by 615

Abstract

Duckweed/algal bloom-type black–odorous waters (DAWs) exhibit composite optical properties of vegetation and pollution, posing intractable remote sensing identification challenges in complex environments. Current methods suffer from three critical limitations: a misclassification rate exceeding 25% due to spectral confusion with artificial green covers, an [...] Read more.

Duckweed/algal bloom-type black–odorous waters (DAWs) exhibit composite optical properties of vegetation and pollution, posing intractable remote sensing identification challenges in complex environments. Current methods suffer from three critical limitations: a misclassification rate exceeding 25% due to spectral confusion with artificial green covers, an 18.7% false-negative rate for small patches (stemming from the imbalance between CNNs and Transformers), and insufficient feature dimensionality to characterize the dual properties of DAWs. To address these gaps, this study proposes a novel method that integrates the ASGICTVS feature set with a customized SwinTf-Unet model. The ASGICTVS feature set combines vegetation-sensitive metrics, optical water quality indicators, and visual features. The SwinTf-Unet model utilizes an optimized 4 × 4 window, an embedded feature fusion module, and an adaptive shifted window stride to balance global context capture and local detail reconstruction. Experiments on 21,104 GF-2 satellite samples demonstrate that the method achieves 87.50% precision, 88.41% recall, an 85.32% F1-score, and an 83.46% Intersection over Union (IoU), outperforming DeepLabV3+ by 14.56 percentage points in the IoU. With an inference time of 0.87 s per 512 × 512-pixel image and a stable performance across cross-regional datasets (IoU: 82.1–85.3%), it exhibits strong efficiency and generalization. This study resolves DAW spectral confusion, enables high-precision segmentation, and establishes a standardized feature threshold system, providing reliable technical support for large-scale automated DAW monitoring and regional water environment management. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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21 pages, 11525 KB

Open AccessArticle

Fusion of BeiDou and MODIS Precipitable Water Vapor Using the Random Forest Algorithm: A Case Study of Multi-Source Data Synergy in Hunan Province, China

by Minghan Sun, Zhiguo Pang, Jingxuan Lu, Wei Jiang, Xiangdong Qin and Zhuoyue Zhou

Remote Sens. 2026, 18(1), 104; https://doi.org/10.3390/rs18010104 - 27 Dec 2025

Viewed by 585

Abstract

The accurate monitoring of water vapor is essential for understanding the hydrological cycle and improving weather forecasting. Although the Moderate-resolution Imaging Spectroradiometer (MODIS) provides spatially continuous precipitable water vapor (PWV), validation in Hunan Province reveals a systematic underestimation, with correlations to radiosonde (RS-PWV) [...] Read more.

The accurate monitoring of water vapor is essential for understanding the hydrological cycle and improving weather forecasting. Although the Moderate-resolution Imaging Spectroradiometer (MODIS) provides spatially continuous precipitable water vapor (PWV), validation in Hunan Province reveals a systematic underestimation, with correlations to radiosonde (RS-PWV) around 0.40 and average RMSE and MAE reaching 23.80 and 18.04 mm. To address this issue, high-accuracy PWV derived from the BeiDou Navigation Satellite System (BDS-PWV), which show high consistency with RS-PWV, were incorporated. A random forest daily-scale water vapor fusion model was developed based on the differential characteristics of dry and wet season residuals. By employing day of year (DOY), latitude, longitude, and elevation as auxiliary factors, the model establishes a seasonal fusion framework that dynamically transitions between dry and wet seasons. Validation shows that the fusion PWV aligns closely with RS-PWV, reducing average RMSE and MAE to 4.71 and 3.81 mm, corresponding to improvements of 80.21% and 78.88% over MODIS, with accuracy increases exceeding 75% at all stations. The fusion model effectively mitigates MODIS’s underestimation and weather sensitivity, producing high-accuracy, spatially continuous daily PWV fields and offering strong potential for improving precipitation and weather forecasting in complex regions such as Hunan Province. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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22 pages, 3875 KB

Open AccessArticle

A Remote Sensing-Driven Dynamic Risk Assessment Model for Cyclical Glacial Lake Outbursts: A Case Study of Merzbacher Lake

by Tianshi Feng, Wenlong Song, Xingdong Li, Yizhu Lu, Kaizheng Xiang, Shaobo Linghu, Hongjie Liu and Long Chen

Remote Sens. 2026, 18(1), 47; https://doi.org/10.3390/rs18010047 - 24 Dec 2025

Viewed by 664

Abstract

The increasing threat of Glacial Lake Outburst Floods (GLOFs), intensified by climate change, underscores the urgency for developing advanced early warning systems. The near-annual, cyclical outbursts of Lake Merzbacher in the Tien Shan mountains present a severe downstream threat, yet its remote location [...] Read more.

The increasing threat of Glacial Lake Outburst Floods (GLOFs), intensified by climate change, underscores the urgency for developing advanced early warning systems. The near-annual, cyclical outbursts of Lake Merzbacher in the Tien Shan mountains present a severe downstream threat, yet its remote location and lack of instrumentation pose a significant challenge to traditional monitoring. To bridge this gap, we develop and validate a dynamic risk assessment framework driven entirely by remote sensing data. Methodologically, the framework introduces an innovative Ice-Water Composite Index (IWCI) to resolve the challenge of lake area extraction under mixed ice-water conditions. This is coupled with a high-fidelity 5 m resolution Digital Elevation Model (DEM) of the lake basin, autonomously generated from GF-7 Dual-Line Camera (DLC) imagery, which enables accurate daily volume retrieval. Through systematic feature engineering, nine key hydro-thermal drivers are quantified from MODIS and other products to train a Random Forest (RF) machine learning model, establishing the non-linear relationship between catchment processes and lake volume. The model demonstrates robust predictive performance on an independent validation set (2023–2024) (R² = 0.80, RMSE = 5.15 × 10⁶ m³), accurately captures the complete lake-filling cycle from initiation to near-peak stage. Furthermore, feature importance analysis quantitatively confirms that Positive Accumulated Temperature (PAT) is the dominant physical mechanism governing the lake’s storage dynamics. This end-to-end framework offers a transferable paradigm for GLOF hazard management, enabling a critical shift from static, regional assessments to dynamic, site-specific early warning in data-scarce alpine regions. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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25 pages, 5120 KB

Open AccessArticle

Application of a Hybrid CNN-LSTM Model for Groundwater Level Forecasting in Arid Regions: A Case Study from the Tailan River Basin

by Shuting Hu, Mingliang Du, Jiayun Yang, Yankun Liu, Ziyun Tuo and Xiaofei Ma

ISPRS Int. J. Geo-Inf. 2026, 15(1), 6; https://doi.org/10.3390/ijgi15010006 - 21 Dec 2025

Cited by 2 | Viewed by 944

Abstract

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction [...] Read more.

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction methods. To address this, a hybrid CNN-LSTM deep learning model is constructed. This model is designed to extract multivariate coupled features and capture temporal dependencies from multi-variable time series data, while simultaneously simulating the nonlinear and delayed responses of aquifers to groundwater abstraction. Specifically, the convolutional neural network (CNN) component extracts the multivariate coupled features of hydro-meteorological driving factors, and the long short-term memory (LSTM) network component models the temporal dependencies in groundwater level fluctuations. This integrated architecture comprehensively represents the combined effects of natural recharge–discharge processes and anthropogenic pumping on the groundwater system. Utilizing monitoring data from 2021 to 2024, the model was trained and tested using a rolling time-series validation strategy. Its performance was benchmarked against traditional models, including the autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN), and standalone LSTM. The results show that the CNN-LSTM model delivers superior performance across diverse hydrogeological conditions: at the upstream well AJC-7, which is dominated by natural recharge and discharge, the Nash–Sutcliffe efficiency (NSE) coefficient reached 0.922; at the downstream well AJC-21, which is subject to intensive pumping, the model maintained a robust NSE of 0.787, significantly outperforming the benchmark models. Further sensitivity analysis reveals an asymmetric response of the model’s predictions to uncertainties in pumping data, highlighting the role of key hydrogeological processes such as delayed drainage from the vadose zone. This study not only confirms the strong applicability of the hybrid deep learning model for groundwater level prediction in data-scarce arid regions but also provides a novel analytical pathway and mechanistic insight into the nonlinear behavior of aquifer systems under significant human influence. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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28 pages, 6273 KB

Open AccessArticle

Environmental Sensitivity Index Assessment Based on Factors in Oil Spill Impact in Coastal Zone Using Spatial Data and Analytical Hierarchy Process Approach: A Case Study in Myanmar

by Tin Myo Thu, Veeranum Songsom, Thongchai Suteerasak and Kyaw Thinn Latt

ISPRS Int. J. Geo-Inf. 2025, 14(12), 460; https://doi.org/10.3390/ijgi14120460 - 24 Nov 2025

Cited by 1 | Viewed by 1277

Abstract

Oil spills threaten marine ecosystems and hinder progress toward Sustainable Development Goal (SDG) 14 on ocean conservation and sustainable marine resource use. Coastal ecosystems in Myanmar face growing risks from expanding maritime infrastructure, including ports, special economic zones, and offshore projects. This study [...] Read more.

Oil spills threaten marine ecosystems and hinder progress toward Sustainable Development Goal (SDG) 14 on ocean conservation and sustainable marine resource use. Coastal ecosystems in Myanmar face growing risks from expanding maritime infrastructure, including ports, special economic zones, and offshore projects. This study aims to develop a spatial Environmental Sensitivity Index (ESI) map for the Tanintharyi region by integrating biological, socio-economic, and physical factors. Using the Analytical Hierarchy Process (AHP), weighting values were derived from local conservation and livelihood experts to ensure regional relevance. The inclusion of chlorophyll-a as a biological indicator improves the assessment of marine productivity and ecosystem health, linking ESI mapping to ocean acidification. The results showed that 8% of the area was very highly sensitive, 25% was highly sensitive, and 23% was moderately sensitive. The most sensitive zones were concentrated along the southern coastline, particularly in Thayetchaung Township, due to dense mangroves, critical habitats, and resource-dependent fisheries. This study presents the first spatial ESI assessment for Tanintharyi, providing a practical framework for oil spill preparedness and ecosystem protection, with potential for future enhancement through integration with oil spill simulation modeling. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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23 pages, 5538 KB

Open AccessArticle

The TAM-xLSTM Model for Hourly River Flow Forecasting: A Case Study of Qiandongnan, Guizhou Province, China

by Renfeng Liu, Dingdong Wang, Liangyi Wang, Chi Cheng, Xiaoling Xia and Ziheng Yang

Water 2025, 17(17), 2644; https://doi.org/10.3390/w17172644 - 7 Sep 2025

Cited by 1 | Viewed by 1563

Abstract

Accurate river flow forecasting is vital for flood warning and water resource management, yet hourly-scale prediction in small catchments remains underexplored despite its importance for rapid response flood control. To address this gap, this study proposes an enhanced temporal attention module xLSTM (TAM-xLSTM) [...] Read more.

Accurate river flow forecasting is vital for flood warning and water resource management, yet hourly-scale prediction in small catchments remains underexplored despite its importance for rapid response flood control. To address this gap, this study proposes an enhanced temporal attention module xLSTM (TAM-xLSTM) model that combines temporal feature extraction with timestep-level attention to better capture dynamic variations and dependencies. Case studies in the Qiandongnan region demonstrate that TAM-xLSTM substantially outperforms baseline models during wet season forecasting at Panghai Station, reducing RMSE by 9.6%, MAE by 24.1%, and Theil’s U by 6.6%, while increasing NSE by 4.8%. These results highlight the model’s ability to improve short-term river flow prediction in complex mountainous terrain and its potential to support effective flood warning and water resource management. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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21 pages, 6342 KB

Open AccessArticle

Enhancing Transboundary Water Governance Using African Earth Observation Data Cubes in the Nile River Basin: Insights from the Grand Ethiopian Renaissance Dam and Roseries Dam

by Baradin Adisu Arebu, Esubalew Adem, Fahad Alzahrani, Nassir Alamri and Mohamed Elhag

Water 2025, 17(13), 1956; https://doi.org/10.3390/w17131956 - 30 Jun 2025

Cited by 2 | Viewed by 3978

Abstract

The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations [...] Read more.

The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations from Space (WOfS) platform, to quantify the hydrological impacts of GERD’s three filling phases (2019–2022) on Sudan’s Roseires Dam. Using Sentinel-2 satellite data processed through the Open Data Cube framework, we analyzed water extent changes from 2018 to 2023, capturing pre- and post-filling dynamics. Results show that GERD’s water spread area increased from 80 km² in 2019 to 528 km² in 2022, while Roseires Dam’s water extent decreased by 9 km² over the same period, with a notable 5 km² loss prior to GERD’s operation (2018–2019). These changes, validated against PERSIANN-CDR rainfall data, correlate with GERD’s filling operations, alongside climatic factors like evapotranspiration and reduced rainfall. The study highlights the potential of Earth Observation (EO) technologies to support transparent, data-driven transboundary water governance. Despite the Cooperative Framework Agreement (CFA) ratified by six upstream states in 2024, mistrust persists due to Egypt and Sudan’s non-ratification. We propose enhancing the Nile Basin Initiative’s Decision Support System with EO data and AI-driven models to optimize water allocation and foster cooperative filling strategies. Benefit-sharing mechanisms, such as energy trade from GERD, could mitigate downstream losses, aligning with the CFA’s equitable utilization principles and the UN Watercourses Convention. This research underscores the critical role of EO-driven frameworks in resolving Nile Basin conflicts and achieving Sustainable Development Goal 6 for sustainable water management. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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22 pages, 6539 KB

Open AccessArticle

Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement

by Jun Wang, Matthew C. Garthwaite, Charles Wang and Lee Hellen

Sensors 2025, 25(11), 3566; https://doi.org/10.3390/s25113566 - 5 Jun 2025

Cited by 3 | Viewed by 2050

Abstract

The Global Navigation Satellite System (GNSS), Internet of Things (IoT) and cloud computing technologies enable high-precision positioning with flexible data communication, making real-time/near-real-time monitoring more economical and efficient. In this study, a multi-sensor GNSS-IoT system was developed for measuring precise water surface elevation [...] Read more.

The Global Navigation Satellite System (GNSS), Internet of Things (IoT) and cloud computing technologies enable high-precision positioning with flexible data communication, making real-time/near-real-time monitoring more economical and efficient. In this study, a multi-sensor GNSS-IoT system was developed for measuring precise water surface elevation (WSE). The system, which includes ultrasonic and accelerometer sensors, was deployed on a floating platform in Googong reservoir, Australia, over a four-month period in 2024. WSE data derived from the system were compared against independent reference measurements from the reservoir operator, achieving an accuracy of 7 mm for 6 h averaged solutions and 28 mm for epoch-by-epoch solutions. The results demonstrate the system’s potential for remote, autonomous WSE monitoring and its suitability for validating satellite Earth observation data, particularly from the Surface Water and Ocean Topography (SWOT) mission. Despite environmental challenges such as moderate gale conditions, the system maintained robust performance, with over 90% of solutions meeting quality assurance standards. This study highlights the advantages of combining the GNSS with IoT technologies and multiple sensors for cost-effective, long-term WSE monitoring in remote and dynamic environments. Future work will focus on optimizing accuracy and expanding applications to diverse aquatic settings. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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24 pages, 4903 KB

Open AccessArticle

Dynamic Wetland Evolution in the Upper Yellow River Basin: A 30-Year Spatiotemporal Analysis and Future Projections Under Multiple Protection Scenarios

by Zheng Liu, Chunlin Huang, Ting Zhou, Tianwen Feng and Qiang Bie

Land 2025, 14(6), 1219; https://doi.org/10.3390/land14061219 - 5 Jun 2025

Cited by 1 | Viewed by 1224

Abstract

Wetland monitoring is a key means of protecting wetland ecosystems. In order to achieve continuous monitoring of wetlands and predict future patterns, this paper analyzes the spatiotemporal evolution characteristics of wetlands in the upper reaches of the Yellow River from 1990 to 2020, [...] Read more.

Wetland monitoring is a key means of protecting wetland ecosystems. In order to achieve continuous monitoring of wetlands and predict future patterns, this paper analyzes the spatiotemporal evolution characteristics of wetlands in the upper reaches of the Yellow River from 1990 to 2020, and uses the Patch Generation Land Use Simulation (PLUS) model to simulate the spatial distribution of wetlands from 2040 to 2060 under four scenarios: farmland protection (FPS), wetland protection (WPS), comprehensive protection (CPS) and natural development (NDS). The results show that the total area of wetlands in the upper reaches of the Yellow River is on the rise, increasing by 7.12% in 2020 compared with 1990. The changes in various types of wetlands are different: the areas of river and canals increased by 26.39% and 57.97%, respectively, paddy fields increased by 7.95%, lakes remained basically stable, and tidal flats decreased by 5.67%. The simulation results of the future spatial pattern of wetlands show that: under the FPS scenario, farmland and related land use will expand significantly, mainly through the development of beaches, dry land and unused land, while under the WPS scenario, wetlands will be strictly protected, the area of water resource features such as rivers, lakes and reservoirs will increase significantly, and land use changes will be more ecologically oriented. Compared with the CPS and NDS scenarios, the wetland protection and urbanization process in the upper reaches of the Yellow River can be balanced under the FPS and WPS scenarios. This study has important reference value for the protection and sustainable development of wetland ecosystems in the upper reaches of the Yellow River. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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21 pages, 14978 KB

Open AccessArticle

Determining the Spectral Characteristics of Fynbos Wetland Vegetation Species Using Unmanned Aerial Vehicle Data

by Kevin Musungu, Moreblessings Shoko and Julian Smit

Geomatics 2025, 5(2), 17; https://doi.org/10.3390/geomatics5020017 - 29 Apr 2025

Cited by 3 | Viewed by 2656

Abstract

The Cape Floristic Region (CFR) boasts rich biodiversity but faces threats from invasive species and land-use changes. Fynbos wetland vegetation within the CFR is under-mapped despite its crucial role in supporting biodiversity and maintaining hydrological cycles. This study assessed the potential of UAV [...] Read more.

The Cape Floristic Region (CFR) boasts rich biodiversity but faces threats from invasive species and land-use changes. Fynbos wetland vegetation within the CFR is under-mapped despite its crucial role in supporting biodiversity and maintaining hydrological cycles. This study assessed the potential of UAV VIS-NIR data, gathered during Spring and Summer, to identify the spectral characteristics of eleven Fynbos wetland species in a seep wetland. Spectral distances derived from reflectance data revealed distinct spectral clustering of plant species, highlighting which species could be distinguished from each other. UAV data also captured differences in reflectance across spectral bands for both dates. Spectral statistics indicated that certain species could be more accurately classified in Spring than in Summer, and vice versa. These findings underscore the efficacy of UAV multispectral data in analyzing the reflectance patterns of fynbos wetland species. Additionally, the sensitivity of UAV multispectral data to foliar pigment composition across different seasonal stages was confirmed. Lastly, species classification results demonstrated that a random forest classifier is well suited, with relative producer and user accuracies aligning with the derived spectral distances. The results highlight the potential of UAV imagery for monitoring these endemic species and creating opportunities for scalable mapping of Fynbos seep wetlands. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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26 pages, 9389 KB

Open AccessArticle

Unravelling the Characteristics of Microhabitat Alterations in Floodplain Inundated Areas Based on High-Resolution UAV Imagery and Remote Sensing: A Case Study in Jingjiang, Yangtze River

by Yichen Zheng, Dongshuo Lu, Zongrui Yang and Jianbo Chang

Drones 2025, 9(4), 315; https://doi.org/10.3390/drones9040315 - 18 Apr 2025

Cited by 2 | Viewed by 1188

Abstract

The floodplain of a large river plays a crucial role in the river’s ecosystem and serves as an essential microhabitat for river fish to complete their life history events. Over the past four decades, the floodplain represented by the Jingjiang section in the [...] Read more.

The floodplain of a large river plays a crucial role in the river’s ecosystem and serves as an essential microhabitat for river fish to complete their life history events. Over the past four decades, the floodplain represented by the Jingjiang section in the middle reaches of the Yangtze River has experienced a significant reduction in area, complexity, and diversity of fish microhabitats. This study quantitatively analyzed the dynamic changes and geomorphological structure of the floodplain in the Jingjiang reach (JJR) of the Yangtze River using satellite remote sensing images and high-resolution unmanned aerial vehicle (UAV) optical images. We built an enhanced U-Net model incorporating both the CBAM and SE parallel attention mechanisms to classify these images and identify environmental structural units. The accuracy of the enhanced model was 16.39% higher compared to original U-Net model. At the same time, the improved normalized difference water index (mNDWI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) were utilized to extract the flood frequency of the floodplain and analyze the area changes of the floodplain in the JJR. The trend of the flood area in the JJR during the flood season was consistent with the overall trend of flood areas in the flood season, which generally exhibits a downward tendency. In 2022, the floodplain of the JJR underwent substantial anthropogenic disturbances, with 40% of its area comprising anthropogenic environmental units. Compared to historical periods, the impervious surface within the floodplain has increased annually, while ecological units such as riparian forests and trees have gradually diminished or even disappeared, leading to a simplification of structural complexity. These findings provide a critical background and robust data foundation for the protection and restoration of fish habitats and the formulation of strategies for fish population reconstruction in the Yangtze River. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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23 pages, 10852 KB

Open AccessArticle

Precise Drought Threshold Monitoring in Winter Wheat Different Growth Periods Using a Multispectral Unmanned Aerial Vehicle

by Wenlong Song, Hongjie Liu, Yizhu Lu, Juan Lv, Rognjie Gui, Long Chen, Mengyi Li and Xiuhua Chen

Drones 2025, 9(3), 157; https://doi.org/10.3390/drones9030157 - 20 Feb 2025

Cited by 3 | Viewed by 1405

Abstract

Agricultural drought significantly affects crop growth and food production, making accurate drought thresholds essential for effective monitoring and discrimination. This study aims to monitor the threshold ranges for different drought levels of winter wheat during three growth periods using a multispectral Unmanned Aerial [...] Read more.

Agricultural drought significantly affects crop growth and food production, making accurate drought thresholds essential for effective monitoring and discrimination. This study aims to monitor the threshold ranges for different drought levels of winter wheat during three growth periods using a multispectral Unmanned Aerial Vehicle (UAV). Firstly, based on controlled field experiments, six vegetation indices were used to develop UAV optimal inversion models for the Leaf Area Index (LAI) and Soil–Plant Analysis Development (SPAD) during the jointing–heading period, heading–filling period, and filling–maturity period of winter wheat. The results show that during the three growth periods, the DVI-LAI, NDVI-LAI, and RVI-LAI models, along with the DVI-SPAD, RVI-SPAD, and TCARI-SPAD models, achieved the highest inversion accuracy. Based on the UAV-inversed LAI and SPAD indices, threshold ranges for different drought levels were determined for each period. The accuracy of LAI threshold monitoring during three periods was 92.8%, 93.6%, and 90.5%, respectively, with an overall accuracy of 92.4%. For the SPAD index, the threshold monitoring accuracy during three periods was 93.1%, 93.0%, and 92%, respectively, with an overall accuracy of 92.7%. Finally, combined with yield data, this study explores UAV-based drought disaster monitoring for winter wheat. This research enriches and expands the crop drought monitoring system using a multispectral UAV. The proposed drought threshold ranges can enhance the scientific and precise monitoring of crop drought, which is highly significant for agricultural management. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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22 pages, 4714 KB

Open AccessArticle

Spatiotemporal Evolution and Driving Mechanisms of Ecological Risk in the Yuncheng Salt Lake Wetland, China

by Qicheng He, Zhihao Zhang, Yuan Zhang, Tianyue Sun, Weipeng Wang and Zhifeng Zhang

Water 2025, 17(4), 524; https://doi.org/10.3390/w17040524 - 12 Feb 2025

Cited by 2 | Viewed by 1607

Abstract

As the only large sulfate-type salt lake in the global warm temperate deciduous forest zone, Yuncheng Salt Lake plays a crucial role in maintaining ecosystem stability and establishing a regional ecological barrier due to its unique ecological characteristics. Currently, there is a lack [...] Read more.

As the only large sulfate-type salt lake in the global warm temperate deciduous forest zone, Yuncheng Salt Lake plays a crucial role in maintaining ecosystem stability and establishing a regional ecological barrier due to its unique ecological characteristics. Currently, there is a lack of research on the spatial and temporal differentiation of ecological risks in inland lakes, particularly salt lake wetland ecosystems, under current and future scenarios. Moreover, studies using optimal parameter-based geographical detectors to identify the influencing factors of landscape ecological risks—while avoiding subjective bias—remain limited. This study utilizes land use/land cover data of Yuncheng Salt Lake from 1990 to 2022 to construct a landscape ecological risk assessment model. By employing spatial autocorrelation analysis, the optimal geographical detector, and the Patch-level Land Use Simulation (PLUS) model, the study explores the dynamic evolution of ecological risks in Yuncheng Salt Lake wetlands under different current and future scenarios. Furthermore, it analyzes the influence of various natural and socio-economic factors on ecological risk, aiming to provide valuable insights for targeted ecological risk warning and management measures in inland salt lake regions. The results indicate that: (1) Between 1990 and 2022, the area of built-up land in Yuncheng Salt Lake wetlands increased significantly, primarily due to the continuous decline in farmland area, while the water area initially decreased and then increased. (2) The landscape ecological risk index declined over the study period, indicating an improvement in the ecological risk status of Yuncheng Salt Lake wetlands in recent years, with the overall ecosystem security trending positively. (3) Topographical conditions are the primary factors influencing landscape ecological risk in Yuncheng Salt Lake wetlands, followed by mean annual temperature and population density. The synergistic effect of elevation with annual precipitation and NDVI (Normalized Difference Vegetation Index) exhibits the strongest explanatory power for the landscape ecological risk in the region. (4) Under different future scenarios, the proportion of high ecological risk areas in Yuncheng Salt Lake wetlands is projected to decrease to varying extents, with the ecological protection scenario contributing more effectively to the sustainable development of the salt lake wetland ecosystem. Full article

(This article belongs to the Topic Advances in Earth Observation Technologies to Support Water-Related Sustainable Development Goals (SDGs))

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