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Remote Sensing
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Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II
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Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7376

Special Issue Editors

Prof. Dr. Yongshuo Fu

grade E-Mail Website
Guest Editor
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: vegetation phenology; climate change; ecohydrology
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Zhang

E-Mail Website
Guest Editor
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: drought; extreme climate; eco-hydrology; hydrological simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Senthilnath Jayavelu

E-Mail Website
Guest Editor
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
Interests: deep learning; reinforcement learning; optimizations; multiagent systems; materials informatics; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Shengli Tao

E-Mail Website
Guest Editor
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Interests: ecology; forest; water; lidar; microwave
Special Issues, Collections and Topics in MDPI journals
Dr. Xuesong Zhang

E-Mail Website
Guest Editor
Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
Interests: agriculture; carbon cycle; hydrology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change, especially extreme climate events such as drought and heat waves, have profoundly influenced the terrestrial water cycle and vegetation growth and subsequently also affected fluvial geomorphology patterns and carbon and energy balance, as well as water safety and food security. Understanding the extent of the hydrology and vegetation response to the ongoing climate change and investigating the mechanisms behind these changes will not only help to fight the negative effects of climate change but also to provide effective adaptive measures. Therefore, it is essential to explore the changes in hydrology and vegetation under climate change at a basin and regional scale—even at a global scale. With the development of high-resolution satellites and unmanned aerial vehicles (UAVs), the capacity of remote sensing to monitor the changes of hydrology and vegetation have been significantly improved.

The purpose of this Special Issue is to present new research advances on the applications of remote sensing techniques, such as multi/hyper-spectral light detection and ranging (LiDAR) from satellites and UAVs, for monitoring the changes of hydrology and vegetation under climate change. The contributions focusing on applications in hydrology and vegetation, both algorithmic and methodological. In particular, new approaches and novel contributions, such as the fusion method, knowledge extraction and machine learning and deep learning methods, are preferred. Studies based on multi-spectral and hyper-spectral LiDAR data from UAV platforms will be especially welcome.

This Special Issue of Remote Sensing calls for papers related to new technological advancements in the application of remote sensing techniques in the domains of hydrology and vegetation. The following topics are suggested:

  • Hydrology and vegetation mapping and change detection (multi/hyper-spectral LiDAR);
  • Vegetation response to extreme drought;
  • Water quality monitoring (multi/hyper-spectra, RS);
  • Vegetation health monitoring;
  • Phenotyping estimation and disease detection of forest;
  • Time-series analysis monitoring for agriculture and forest;
  • Machine learning and deep learning;
  • Novel methods for phenotyping from UAV imagery (e.g., leaf nitrogen, leaf area index or biomass);
  • Reconstruction of forest structures using LiDAR;

Fluvial network topology and its climatic dependence.

Prof. Dr. Yongshuo Fu
Dr. Xuan Zhang
Dr. Senthilnath Jayavelu
Dr. Shengli Tao
Dr. Xuesong Zhang
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

  • hydrology and ecohydrology
  • water cycle
  • UAV remote sensing
  • forest ecology
  • phenology extraction
  • yield prediction
  • climate dynamics
  • vegetation dynamic
  • modeling climate change
  • machine learning and deep learning
  • river basin geometry and topology

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Published Papers (4 papers)

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Research

29 pages, 11071 KiB  
Article
Impacts of Climatic Fluctuations and Vegetation Greening on Regional Hydrological Processes: A Case Study in the Xiaoxinganling Mountains–Sanjiang Plain Region, Northeastern China
by Chi Xu, Zhijie Zhang, Zhenghui Fu, Shenqing Xiong, Hao Chen, Wanchang Zhang, Shuhang Wang, Donghui Zhang, Heng Lu and Xia Jiang
Remote Sens. 2024, 16(15), 2709; https://doi.org/10.3390/rs16152709 - 24 Jul 2024
Viewed by 1056
Abstract
The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact [...] Read more.
The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management. Full article
(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II)
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20 pages, 3323 KiB  
Article
Construction of a High-Resolution Waterlogging Disaster Monitoring Framework Based on the APSIM Model: A Case Study of Jingzhou and Bengbu
by Jian Zhang, Bin Pan, Wenxuan Shi, Yu Zhang, Shixiang Gu, Jinming Chen and Quanbin Xia
Remote Sens. 2024, 16(14), 2581; https://doi.org/10.3390/rs16142581 - 14 Jul 2024
Viewed by 903
Abstract
This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to [...] Read more.
This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters. Full article
(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II)
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18 pages, 4539 KiB  
Article
Unraveling the Impacts of River Network Connectivity on Ecological Quality Dynamics at a Basin Scale
by Xia Li, Xiaobiao Mo, Cheng Zhang, Qing Wang, Lili Xu, Ze Ren, Gregory W. McCarty and Baoshan Cui
Remote Sens. 2024, 16(13), 2370; https://doi.org/10.3390/rs16132370 - 28 Jun 2024
Cited by 3 | Viewed by 1295
Abstract
The ecological quality of river basins is significantly influenced by the complex network of river structures and their connectivity. This study measured the temporal and spatial variability of ecological quality, as reflected by remote sensing ecological indices (RSEI), and examined their responses to [...] Read more.
The ecological quality of river basins is significantly influenced by the complex network of river structures and their connectivity. This study measured the temporal and spatial variability of ecological quality, as reflected by remote sensing ecological indices (RSEI), and examined their responses to river network connectivity (RNC). In total, 8 RNC indices, including river structure of river density (Dr), water surface ratio (Wr), edge-node ratio (β), and network connectivity (γ), and node importance indices of betweenness centrality (BC), PageRank (PG_R), out_degree centrality (Out_D), and in_closeness centrality (In_C), were generated at the subbasin scale. Our results highlighted the significance of RNC in influencing both the values and variability of RSEI, and the extent of this influence varied across different time periods. Specifically, three distinct clusters can be extracted from the temporal variability of RSEI, representing wet, near-normal, and dry years. The river structure index of γ significantly influenced the spatial patterns of subbasin RSEIs, particularly in wet years (R2 = 0.554), whereas β displayed a pronounced U-shape correlation with subbasin RSEIs in dry years (R2 = 0.512). Although node importance indices did not correlate directly with subbasin RSEI levels, as the river structure indices did, they significantly positively affected temporal variability of subbasin RSEIs (EI_SD_t). Higher values of PG_R, Out_D, and In_C were associated with increased subbasin RSEI variability. Based on these correlations, we developed RNC-based RSEI and EI_SD_t models with high adjusted coefficients of determination to facilitate the assessment of ecosystem quality. This study provides essential insights into ecosystem dynamics related to river connectivity within a basin and offers valuable guidance for effective watershed management and conservation efforts aimed at enhancing ecological resilience and sustainability. Full article
(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II)
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22 pages, 12213 KiB  
Article
Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets
by Sijal Dangol, Xuesong Zhang, Xin-Zhong Liang, Martha Anderson, Wade Crow, Sangchul Lee, Glenn E. Moglen and Gregory W. McCarty
Remote Sens. 2023, 15(9), 2417; https://doi.org/10.3390/rs15092417 - 5 May 2023
Cited by 10 | Viewed by 3402
Abstract
Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including [...] Read more.
Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling. Full article
(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II)
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