Remote Sensing in Water Resources Management Models, 2nd Volume

Topic Information

Dear Colleagues,

Almost 74% of the Earth's surface is covered with water. However, only 0.02% of all the water on Earth is in streams, lakes, rivers, and reservoirs as freshwater available for direct human consumption. The remaining freshwater is found underground (0.6%), in the atmosphere (0.001%), and in icecaps (2.2%). Freshwater is a scarce resource worldwide due to land use and climate changes. Hence, the need for spatiotemporal data on freshwater, for water resource management is increasing. However, the acquisition of spatial and temporal data on freshwater resources has been a major challenge facing ecological and hydrological researchers and policymakers. With the advent of remote sensing technology in the near past, data collection has fundamentally improved with the introduction of satellite sensors with higher spatial and temporal resolution on space-borne platforms. Most of these datasets are freely available on the Internet. This has been further advanced by the development of open-source remote sensing services, spatially distributed hydrological models, and software for data processing, analysis, and visualization. However, the performance of remote sensing data and hydrological models to capture the effect of ongoing development and management decisions has to be evaluated. Spatiotemporal analysis of freshwater dynamics under land use and climate changes using spatially explicit hydrological models and remote sensing data can provide information for water resource management, the effect of ongoing developments, management decisions, and policy implications. Therefore, water resource modeling is essential for sustainable water resource management. The Topic “Remote Sensing in Water Resources Management Models” invites high-quality papers focused on the design and development of methods, strategies, and new technologies for water resource management and development impact assessment using hydrological models and remote sensing technologies under land use and climate changes. Potential topics include, but are not limited to the following:

• Land use change and water resource management;
• Climate change and water scarcity;
• Remote sensing and water resource management;
• Hydrological modeling and remote sensing;
• Water resource management and sustainable development;
• Population growth and water resource scarcity;
• Spatiotemporal dynamics of water resource management;
• New technologies for water resource management;
• Methods for water resource modeling.

Dr. Jinsong Deng
Prof. Dr. Yang Hong
Prof. Dr. Salah Elsayed
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Environments environments	3.5	5.7	2014	22.8 Days	CHF 1800
Forests forests	2.4	4.4	2010	16.2 Days	CHF 2600
Land land	3.2	4.9	2012	16.9 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Water water	3.0	5.8	2009	17.5 Days	CHF 2600

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Published Papers (4 papers)

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All Environments Forests Land Remote Sensing Water

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19 pages, 6455 KiB  

Open AccessArticle

Assessment of Mango Canopy Water Content Through the Fusion of Multispectral Unmanned Aerial Vehicle (UAV) and Sentinel-2 Remote Sensing Data

by Jinlong Liu, Jing Huang, Mengjuan Wu, Tengda Qin, Haoyi Jia, Shaozheng Hao, Jia Jin, Yuqing Huang and Nathsuda Pumijumnong

Forests 2025, 16(1), 167; https://doi.org/10.3390/f16010167 - 17 Jan 2025

Viewed by 288

Abstract

This study proposes an Additive Wavelet Transform (AWT)-based method to fuse Multispectral UAV (MS UAV, 5 cm resolution) and Sentinel-2 satellite imagery (10–20 m resolution), generating 5 cm resolution fused images with a focus on near-infrared and shortwave infrared bands to enhance the [...] Read more.

This study proposes an Additive Wavelet Transform (AWT)-based method to fuse Multispectral UAV (MS UAV, 5 cm resolution) and Sentinel-2 satellite imagery (10–20 m resolution), generating 5 cm resolution fused images with a focus on near-infrared and shortwave infrared bands to enhance the accuracy of mango canopy water content monitoring. The fused Sentinel-2 and MS UAV data were validated and calibrated using field-collected hyperspectral data to construct vegetation indices, which were then used with five machine learning (ML) models to estimate Fuel Moisture Content (FMC), Equivalent Water Thickness (EWT), and canopy water content (CWC). The results indicate that the addition of fused Sentinel-2 data significantly improved the estimation accuracy of all parameters compared to using MS UAV data alone, with the Genetic Algorithm Backpropagation Neural Network (GABP) model performing best (R² = 0.745, 0.859, and 0.702 for FMC, EWT, and CWC, respectively), achieving R² improvements of 0.066, 0.179, and 0.210. Slope, canopy coverage, and human activities were identified as key factors influencing the spatial variability of FMC, EWT, and CWC, with CWC being the most sensitive to environmental changes, providing a reliable representation of mango canopy water status. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models, 2nd Volume)

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19 pages, 3799 KiB  

Open AccessArticle

Research on Groundwater Drought and Sustainability in Badain Jaran Desert and Surrounding Areas Based on GRACE Satellite

by Xiaojun Liu, Naiang Wang, Yixin Wang, Nan Meng, Yuchen Wang, Bin Qiao, Rongzhu Lu and Dan Yang

Land 2025, 14(1), 173; https://doi.org/10.3390/land14010173 - 15 Jan 2025

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Abstract

Groundwater plays a crucial role in the formation of the Badain Jaran Desert-Sand Dune Lake System, which has been designated a UNESCO World Heritage Site in 2024. However, the region’s wetland ecosystem is significantly impacted by climate change and human activities. This study [...] Read more.

Groundwater plays a crucial role in the formation of the Badain Jaran Desert-Sand Dune Lake System, which has been designated a UNESCO World Heritage Site in 2024. However, the region’s wetland ecosystem is significantly impacted by climate change and human activities. This study utilizes GRACE satellite data and in situ observation data to establish a groundwater storage anomaly (GWSA) time series for the Badain Jaran Desert and its surrounding areas (BJDCA) from 2003 to 2022. The analysis reveals the spatiotemporal patterns of groundwater drought and sustainability, as well as the underlying factors affecting regional groundwater sustainability. The results indicate that 99.2% of the study area exhibited a significant decline in GWSA (α ≤ 0.01), with the annual mean GRACE Groundwater Drought Index (GGDI) dropping from 1.44 to −1.54, reflecting a worsening groundwater drought. In 2022, the GGDI in the southeastern part of the BJDCA reached as low as −3.04, highlighting severe groundwater stress. Furthermore, the Sustainability Index (SI) of the study area declined markedly from 1.00 to 0.01, underscoring the critical impact of human activities on groundwater resources in the BJDCA. These findings provide valuable insights for formulating more effective groundwater resource management policies and promoting sustainable development in arid regions. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models, 2nd Volume)

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20 pages, 18176 KiB  

Open AccessArticle

Estimation of Water Demand for Riparian Forest Vegetation Based on Sentinel-2 Data: A Case Study of the Kokyar River Basin

by Xianhe Liu, Yilinuer Alifujiang, Abdugheni Abliz, Halidan Asaiduli, Panqing Ye and Buasi Nurahmat

Forests 2024, 15(10), 1749; https://doi.org/10.3390/f15101749 - 4 Oct 2024

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Abstract

In recent years, due to the shortage of water resources and the fragile ecological environment in arid areas, the relationship between vegetation and water resources has been relatively close. The unreasonable allocation of water resources and the excessive demand for ecological water use [...] Read more.

In recent years, due to the shortage of water resources and the fragile ecological environment in arid areas, the relationship between vegetation and water resources has been relatively close. The unreasonable allocation of water resources and the excessive demand for ecological water use have led to ecological and environmental problems such as river interruption, land desertification, and the extensive withering of vegetation in arid areas; therefore, rapid, accurate estimation of the vegetation ecological water demand has become a hot research topic in related fields. In this study, we classified the land use types in the lower reaches of the Kokyar River Basin based on Sentinel-2A data and calculated the water requirements of each type of vegetation using a combination of the area quota method and improved Penman–Monteith (PM) based on different vegetation coverage levels. The results revealed that in 2020, the water demand of planted woodlands within 0–2 km of the watershed will be the highest, and the water demand of naturally growing arboreal woodlands will be the lowest, and the water demand of the surrounding desert riparian vegetation forests will be very small in relation to the ecological base flow and will not affect the downstream water use for agriculture, industry, and domestic use for the time being. The ecological water demand of the vegetation in the study area can be accurately estimated using Sentinel-2A data, and the research results provide technical support and a theoretical basis for rapid estimation of the ecological water demand of vegetation in typical riparian forests in arid areas and for the allocation of water resources. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models, 2nd Volume)

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16 pages, 4510 KiB  

Open AccessArticle

A Comparison of Multiple DEMs and Satellite Altimetric Data in Lake Volume Monitoring

by Cui Yuan, Fangpei Zhang and Caixia Liu

Remote Sens. 2024, 16(6), 974; https://doi.org/10.3390/rs16060974 - 10 Mar 2024

Cited by 1 | Viewed by 1484

Abstract

Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric [...] Read more.

Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric data combined with the lake area, rarely has a comparison been made between the two methods. Therefore, we conducted a comparison between the two methods in Texas for 14 lakes with abundant validation data. First, we constructed the lake hypsometric curve by five commonly applied DEMs (SRTM, ASTER, ALOS, GMTED2010, and NED) or satellite altimetric products combined with the gauge lake area. Second, the lake volume was estimated by combining the hypsometric curve with the gauge lake area time series. Finally, the estimation error has been quantitatively calculated. The results show that the relative lake volume estimation error (rVSD) of the altimetric data (4%) is only 10–18% of that of the DEMs (22–41%), and the DEM with the highest resolution (NED) has the least rVSD with an average of 22%. Therefore, for large-scale lake monitoring, we suggest the application of satellite altimetric data with the lake area to estimate the lake volume of large lakes, and the application of high-resolution DEM with the lake area to calculate the lake volume of small lakes that are gapped by satellite altimetric data. Full article

(This article belongs to the Topic Remote Sensing in Water Resources Management Models, 2nd Volume)

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