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Remote Sensing for Terrestrial Hydrologic Variables
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Remote Sensing for Terrestrial Hydrologic Variables

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3835

Special Issue Editors

Dr. Jianbin Su

E-Mail Website
Guest Editor
National Tibetan Plateau Data Center, Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing precipitation; hydrological modeling; error analysis; bias correction; error propagation
Dr. Kun Zhang

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
Interests: remotely sensed evapotranspiration, irrigation, and ecosystem resilience
Dr. Yushan Zhou

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: multi-source remote sensing data processing; glacier change
Dr. Zhiqing Peng

E-Mail
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: Soil moisture retrieval; calibriation and fusion of microwave remote sensing

Special Issue Information

Dear Colleagues,

In the context of global warming and the worsening water resource crisis, the accurate monitoring and modeling of terrestrial hydrologic variables have become increasingly crucial. Terrestrial hydrologic variables such as precipitation, evapotranspiration, soil moisture, and cryosphere elements (e.g., glaciers and snow) are fundamental components of the water cycle. However, traditional methods of measuring these variables often require in situ observations, which can be costly, time-consuming, and limited to specific locations. Remote sensing technologies have revolutionized this field by enabling extensive spatial and temporal coverage, providing data that are critical for hydrologic modeling, water resource management, and disaster monitoring.

This Special Issue seeks to showcase innovative research on the development of new remote sensing techniques, the provision of better products, the improvement in hydrologic models, and the application of these advancements to addressing global challenges in hydrology and water resource management. By assembling such cutting-edge research, this issue aims to foster a deeper understanding of terrestrial hydrologic processes and provide new insights into water cycle dynamics.

Submitted articles may address, but are not limited to, the following topics:

  • Extreme-event monitoring via remote sensing;
  • Remotely sensed evapotranspiration;
  • Soil moisture monitoring and downscaling;
  • Remote sensing in agricultural water management;
  • Glacier mass balance;
  • Glacier dynamics;
  • Soil freezing and thawing;
  • Terrain analysis;
  • Remote sensing product assessment;
  • The calibration and validation of remote sensing data and the derived products;
  • Multi-source data fusion;
  • Algorithm development.

Dr. Jianbin Su
Dr. Kun Zhang
Dr. Yushan Zhou
Dr. Zhiqing Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precipitation
  • soil moisture
  • evapotranspiration
  • snow and glaciers
  • extreme events
  • microwave remote sensing
  • mass balance
  • water budget

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

21 pages, 7222 KiB  
Article
Spatiotemporal Variations and Driving Factors of Water Availability in the Arid and Semiarid Regions of Northern China
by Xiaoyu Han, Yaning Chen, Gonghuan Fang, Zhi Li, Yupeng Li and Yanfeng Di
Remote Sens. 2024, 16(22), 4318; https://doi.org/10.3390/rs16224318 - 19 Nov 2024
Viewed by 212
Abstract
It is anticipated that global warming will modify precipitation and evapotranspiration patterns, consequently affecting water availability. Changes in water availability pose challenges to freshwater supply, food security, and ecosystem sustainability. However, the variations and driving mechanisms of water availability in the arid and [...] Read more.
It is anticipated that global warming will modify precipitation and evapotranspiration patterns, consequently affecting water availability. Changes in water availability pose challenges to freshwater supply, food security, and ecosystem sustainability. However, the variations and driving mechanisms of water availability in the arid and semiarid regions of Northern China remain unclear. This study evaluates the accuracy of three evapotranspiration products and analyzes the changes in water availability in the arid and semiarid regions of Northern China over the past 39 years (1982–2020) along with their driving factors. The results indicate that during this period, precipitation increased at a rate of 7.5 mm/decade, while evapotranspiration rose at a higher rate of 13 mm/decade, resulting in a decline in water availability of 5.5 mm/decade. Spatially, approximately 30.17% of the area exhibited a significant downward trend in water availability, while 65.65% remained relatively stable. Evapotranspiration is the dominant factor leading to the decrease in water availability, with a contribution rate of 63.41%. The increase in evapotranspiration was primarily driven by temperature (32.53% contribution) and the saturation vapor pressure deficit (24.72% contribution). The decline in water availability may further exacerbate drought risks in arid and semiarid regions. The research results can provide a scientific basis for developing water resource management strategies and ecological restoration strategies under environmental change. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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22 pages, 3747 KiB  
Article
Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations
by Chunlin Huang, Ying Zhang and Jinliang Hou
Remote Sens. 2024, 16(21), 3999; https://doi.org/10.3390/rs16213999 - 28 Oct 2024
Viewed by 572
Abstract
In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the [...] Read more.
In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m3/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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19 pages, 7577 KiB  
Article
The Impact of Drought on Vegetation at Basin Scale: A Case Study of the Wei River Basin, China
by Panpan Zhao, Qihui Chai, Bingbo Xie, Hongyang Li, Huicai Yang, Fang Wan and Xudong Huang
Remote Sens. 2024, 16(21), 3997; https://doi.org/10.3390/rs16213997 - 28 Oct 2024
Viewed by 539
Abstract
Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends [...] Read more.
Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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21 pages, 8820 KiB  
Article
Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks
by Meimei Li, Zhongzheng Zhu, Weiwei Ren and Yingzheng Wang
Remote Sens. 2024, 16(19), 3723; https://doi.org/10.3390/rs16193723 - 7 Oct 2024
Viewed by 924
Abstract
Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive [...] Read more.
Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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18 pages, 2212 KiB  
Article
Improvements to a Crucial Budyko-Fu Parameter and Evapotranspiration Estimates via Vegetation Optical Depth over the Yellow River Basin
by Xingyi Wang and Jiaxin Jin
Remote Sens. 2024, 16(15), 2777; https://doi.org/10.3390/rs16152777 - 29 Jul 2024
Viewed by 755
Abstract
Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter ω is used to characterize [...] Read more.
Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter ω is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between ω and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter ω for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter ω established for the three subwatersheds were reasonable (R2 = 0.60, 0.80, and 0.40), and parameter ω was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development. Full article
(This article belongs to the Special Issue Remote Sensing for Terrestrial Hydrologic Variables)
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