Advances in Hydrological Remote Sensing

Abstract submission deadline

28 February 2026

Manuscript submission deadline

30 April 2026

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Topic Information

Dear Colleagues,

With the advancement of remote sensing technology, the data for hydrological process simulation and monitoring has become more abundant and diverse. The new technology provides a solid foundation for the study of hydrological processes at different scales such as site, slope, and watershed. Current hydrological research focuses more on large-scale water cycle processes and the coupling relationship between the hydrosphere and other layers. On the other hand, multispectral, high-resolution remote sensing data, and online monitoring data constitute hydrological monitoring big data. These changes pose new challenges to data processing and water cycle research. It also provides new opportunities for the development of hydrological theory and research methods.

This Topic aims to integrate and present the most recent advances that address the challenges in the fields of identification of hydrological processes, hydrological big data analysis and hydrological process simulation. Topics of interest for the publication include, but are not limited to, the following:

• Identification of hydrological processes based on remote sensing
• Dynamic monitoring of water resources
• Remote sensing monitoring of water environment
• Hydrological big data analysis
• Hydrological Process Simulation Based on Big Data

Prof. Dr. Hailong Liu
Dr. Liangliang Jiang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.5	4.6	2010	16.1 Days	CHF 2400	Submit
Geomatics geomatics	-	-	2021	22.1 Days	CHF 1000	Submit
Hydrology hydrology	3.1	4.9	2014	15.3 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Water water	3.0	5.8	2009	17.5 Days	CHF 2600	Submit
Climate climate	3.0	5.5	2013	19.7 Days	CHF 1800	Submit

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Published Papers (4 papers)

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All Atmosphere Geomatics Hydrology Remote Sensing Water Climate

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28 pages, 38630 KiB  

Open AccessArticle

Vegetation Response to Flash Drought Events Considering Resilience in Southwestern China

by Liangliang Jiang, Guangming Wu, Qijin Li and Xiaoran Liu

Water 2025, 17(5), 653; https://doi.org/10.3390/w17050653 - 24 Feb 2025

Viewed by 467

Abstract

Flash drought events occur frequently in Southwestern China, and a notable upward trend is predicted for the future. Attention should be given to how the severity of flash droughts and vegetation vulnerability hinder vegetation from recovering to their original state, leading to losses. [...] Read more.

Flash drought events occur frequently in Southwestern China, and a notable upward trend is predicted for the future. Attention should be given to how the severity of flash droughts and vegetation vulnerability hinder vegetation from recovering to their original state, leading to losses. Vegetation resilience and vulnerability to flash droughts was assessed in dry years by adopting a ‘resistance–resilience’ framework from a new perspective, and we measured the significance of various drought characteristics in affecting vegetation reduction by using the boosted regression tree (BRT) model. The results showed that croplands in the Sichuan Basin displayed low resistance to flash droughts, whereas grasslands and forests in mountainous areas had high resistance. Croplands in the Sichuan Basin demonstrated high vegetation resilience, while Guizhou province showed low vegetation resilience. Most regions experienced high vegetation vulnerability to flash droughts, especially in the Sichuan Basin and Yunnan province. We found that croplands and forests in 2006 exhibited a significant decrease in LAI during flash drought events. Croplands experienced a significant decrease in LAI in regions where the drought duration (DD) exceeded 60 days, and the drought interval (DIV) ranged from 30 to 40 days. Forest regions with a DD exceeding 60 days and a DIV below 20 days experienced a high reduction in LAI. Furthermore, croplands and shrubs could recover once their vulnerability fell below thresholds of 0.34 and 0.30, respectively. The impact of species richness on vegetation resilience can be explored in future research. This study reveals the spatial patterns of vegetation vulnerability and provides information on preventing and managing vegetation deterioration in Southwestern China. Full article

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

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16 pages, 3161 KiB  

Open AccessArticle

Eutrophication Conditions in Two High Mountain Lakes: The Influence of Climate Conditions and Environmental Pollution

by Fátima Goretti García-Miranda, Claudia Muro, Yolanda Alvarado, José Luis Expósito-Castillo and Héctor Víctor Cabadas-Báez

Hydrology 2025, 12(2), 32; https://doi.org/10.3390/hydrology12020032 - 13 Feb 2025

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Abstract

The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their [...] Read more.

The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their shape and an increase in algae blooms, coupled with the degradation of the basin, which has alerted government entities to the need to address the lakes’ problems. To address the environmental status of El Sol and La Luna, a trophic study was conducted during the period of 2021–2023, including an analysis of the influence of climatic variables, lake water quality, and eutrophication conditions. The trophic state was established based on the eutrophication index. The Pearson correlations defined the eutrophication interrelation between the distinct factors influencing the lakes’ status. El Sol registered higher eutrophication conditions than La Luna. El Sol was identified as seasonal eutrophic and La Luna as transitioning from oligotrophic to mesotrophic, showing high levels of chlorophyll, total phosphorus, and total nitrogen and low water transparency. The principal factors altering the eutrophic conditions were water pollution and climatic variables (precipitation and ambient temperature). Eutrophication was the prime factor impacting perimeter loss at El Sol, whereas at La Luna, it was due to a decline in precipitation. Full article

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

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20 pages, 10145 KiB  

Open AccessArticle

Monitoring and Disaster Assessment of Glacier Lake Outburst in High Mountains Asian Using Multi-Satellites and HEC-RAS: A Case of Kyagar in 2018

by Long Jiang, Zhiqiang Lin, Zhenbo Zhou, Hongxin Luo, Jiafeng Zheng, Dongsheng Su and Minhong Song

Remote Sens. 2024, 16(23), 4447; https://doi.org/10.3390/rs16234447 - 27 Nov 2024

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Abstract

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations [...] Read more.

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations in these remote regions. To explore reproducing the evolution of GLOFs with sparse observations in situ, this study focuses on the outburst event and corresponding GLOFs in August 2018 caused by the Kyagar Glacier lake, a typical glacier lake of the HMA in the Karakoram, which is known for its frequent outburst events, using a combination of multi-satellite remote sensing data (Sentinel-1 and Sentinel-2) and the HEC-RAS hydrodynamic model. The water depth of the glacier lake and downstream was extracted from satellite data adapted by the Floodwater Depth Elevation Tool (FwDET) as a baseline to compare them with simulations. The elevation-water volume curve was obtained by extrapolation and was applied to calculate the water surface elevation (WSE). The inundation of the downstream of the lake outburst was obtained through flood modeling by incorporating a load elevation-water volume curve and the Digital Elevation Model (DEM) into the hydrodynamic model HEC-RAS. The results showed that the Kyagar glacial lake outburst was rapid and destructive, accompanied by strong currents at the end of each downstream storage ladder. A series of meteorological evaluation indicators showed that HEC-RAS reproduced the medium and low streamflow rates well. This study demonstrated the value of integrating remote sensing and hydrodynamic modeling into GLOF assessments in data-scarce regions, providing insights for disaster risk management and mitigation. Full article

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

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17 pages, 5156 KiB  

Open AccessArticle

Identifying Alpine Lakes with Shoreline Features

by Zhimin Hu, Min Feng, Yijie Sui, Dezhao Yan, Kuo Zhang, Jinhao Xu, Rui Liu and Earina Sthapit

Water 2024, 16(22), 3287; https://doi.org/10.3390/w16223287 - 15 Nov 2024

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Abstract

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these [...] Read more.

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these areas pose significant challenges to accurate detection. This paper proposes a method that leverages the high precision of deep learning for small lake and lake boundary extraction combined with deep learning to eliminate noise and errors in the identification results. Using Sentinel-2 data, we accurately identified and delineated alpine lakes in the eastern Himalayas. A total of 2123 lakes were detected, with an average lake area of 0.035 km². Notably, 76% of these lakes had areas smaller than 0.01 km². The slope data is crucial for the lake classification model in eliminating shadow noise. The accuracy of the proposed lake classification model reached 97.7%. In the identification of small alpine lakes, the recognition rate of this method was 96.4%, significantly surpassing that of traditional deep learning approaches. Additionally, this method effectively eliminated most shadow noise present in water body detection results obtained through machine learning techniques. Full article

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

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