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Advances in Remote Sensing of Ecohydrology

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7969

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Prof. Dr. Fei Zhang

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College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
Interests: remote sensing; water environment; landscape pattern; land use/land cover; modelling building; data reconstruction; image fusion
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoping Wang

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College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
Interests: remote sensing; terrestrial water-carbon cycle; environment monitoring and management
Special Issues, Collections and Topics in MDPI journals

Dr. Xingwen Lin

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College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, China
Interests: remote sensing; radiative transfer model; mountain remote sensing; image fusion

Prof. Dr. Hsiang-te Kung

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Department of Earth Sciences, University of Memphis, Memphis, TN 38152, USA
Interests: remote sensing of water resources; remote sensing and ecohydrology; remote sensing and environment

Dr. Gary E. Stinchcomb

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Guest Editor

Department of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA
Interests: soil geomorpholoy; paleopedology; geoarchaeology; hominid-environment interactions

Special Issue Information

Dear Colleagues,

With significant development since the beginning of the 21st century, ecohydrology remote sensing is one of the most active disciplines in the Earth sciences, offering numerous opportunities and advances for watershed ecohydrology and other disciplines of geography. Remote sensing, with the advantages of short period, abundant information, and low cost, plays an important role in ecohydrological modeling. The application of remote sensing technology in ecohydrological models mainly includes the use of remote sensing technology to invert watershed materials related to ecohydrological processes. Remote sensing technology can provide a great deal of information about underlying surface conditions such as soil, vegetation, topography, land use and water bodies in the water system, and can also measure and estimate evapotranspiration, soil water content and water vapor content in clouds that may become rainfall. The application of remote sensing technology in ecohydrological models will greatly promote the development and application of ecohydrological models.

Ecohydrology remote sensing is one of the most active disciplines in the Earth sciences, offering numerous opportunities and advances for watershed ecohydrology and other disciplines of geography. Ecohydrology remote sensing provides many opportunities and advances for basin ecohydrology and other geographical disciplines. Remote Sensing is a world-renowned journal, and this Special Issue will attract many readers.

The Special Issue focuses on precipitation inversion; evapotranspiration estimation; soil water content inversion; environmental erosion (terraces, erosion trenches and siltation dams, etc.) monitoring; vegetation parameters inversion (vegetation index, leaf area index and photosynthetically active radiation, etc.); river and water body extraction and mapping. We are also interested in manuscripts covering high spatial and temporal resolution monitoring, and studies should include enough field observations to calibrate the model and control its prediction accuracy.

Prof. Dr. Fei Zhang
Dr. Xiaoping Wang
Dr. Xingwen Lin
Prof. Dr. Hsiang-te Kung
Dr. Gary E. Stinchcomb
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

ecohydrology remote sensing
soil water content
vegetation parameters
water resources
evapotranspiration
ecological process models
hydrological models

Published Papers (6 papers)

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Research

30 pages, 12509 KiB

Open AccessArticle

Quantifying the Spatiotemporal Changes in Evapotranspiration and Its Components Driven by Vegetation Greening and Climate Change in the Northern Foot of Yinshan Mountain

by Zijun Wang, Yangyang Liu, Zhenqian Wang, Hong Zhang, Xu Chen, Zhongming Wen, Ziqi Lin, Peidong Han and Tingyi Xue

Remote Sens. 2024, 16(2), 357; https://doi.org/10.3390/rs16020357 - 16 Jan 2024

Viewed by 625

Abstract

Evapotranspiration (E), a pivotal phenomenon inherent to hydrological and thermal dynamics, assumes a position of utmost importance within the intricate framework of the water–energy nexus. However, the quantitative study of E on a large scale for the “Grain for Green” projects under the backdrop of climate change is still lacking. Consequently, this study examined the interannual variations and spatial distribution patterns of E, transpiration (Et), and soil evaporation (Eb) in the Northern Foot of Yinshan Mountain (NFYM) between 2000 and 2020 and quantified the contributions of climate change and vegetation greening to the changes in E, Et, and Eb. Results showed that E (2.47 mm/a, p < 0.01), Et (1.30 mm/a, p < 0.01), and Eb (1.06 mm/a, p < 0.01) all exhibited a significant increasing trend during 2000–2020. Notably, vegetation greening emerged as the predominant impetus underpinning the augmentation of both E and Eb, augmenting their rates by 0.49 mm/a and 0.57 mm/a, respectively. In terms of Et, meteorological factors emerged as the primary catalysts, with temperature (Temp) assuming a predominant role by augmenting Et at a rate of 0.35 mm/a. Temp, Precipitation (Pre), and leaf area index (LAI) collectively dominated the proportional distribution of E, accounting for shares of 32.75%, 28.43%, and 25.01%, respectively. Within the spectrum of predominant drivers influencing Et, Temp exerted the most substantial influence, commanding the largest proportion at 33.83%. For Eb, the preeminent determinants were recognized as LAI and Temp, collectively constituting a substantial portion of the study area, accounting for 32.10% and 29.50%, respectively. The LAI exerted a pronounced direct influence on the Et, with no significant effects on E and bare Eb. Wind speed (WS) had a substantial direct impact on both E and Et. Pre exhibited a strong direct influence on E, Et, and Eb. Relative humidity (RH) significantly affected E directly. Temp primarily influenced Eb indirectly through radiation (Rad). Rad exerted a significant direct inhibitory effect on Eb. These findings significantly advanced our mechanistic understanding of how E and its components in the NFYM respond to climate change and vegetation greening, thus providing a robust basis for formulating strategies related to regional ecological conservation and water resources management, as well as supplying theoretical underpinnings for constructing sustainable vegetation restoration strategies involving water resources in the region. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)

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16 pages, 5560 KiB

Open AccessArticle

Suitability Assessment of Cage Fish Farming Location in Reservoirs through Neural Networks-Based Remote Sensing Analysis

by Mahdi Sedighkia and Bithin Datta

Remote Sens. 2024, 16(2), 236; https://doi.org/10.3390/rs16020236 - 07 Jan 2024

Viewed by 1322

Abstract

The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were utilized including 1—optimal artificial neural network (ONN), 2—adaptive neuro fuzzy inference system in which a hybrid algorithm was used for the training process (ANFIS) and 3—coupled evolutionary algorithm-adaptive neuro fuzzy inference system in which particle swarm optimization was utilized in the training process (EA-ANFIS). Three critical water quality parameters for cage fish farming were selected consisting of water temperature, dissolved oxygen (DO) and total dissolved solids (TDS). Moreover, two measurement indices, the Nash–Sutcliffe model efficiency coefficient (NSE) and root mean square error (RMSE), were utilized to assess the predictive skills of the data driven models. Based on the results in the case study, EA-ANFIS is the best method to simulate water temperature and DO in the reservoir by the remote sensing technique. Furthermore, the ANFIS-based model is the best method to simulate TDS. According to the results in the case study, utilizing the spectral images might not be reliable to simulate DO concentration in the reservoirs. However, the images are robust to simulate water temperature as well as TDS concentration. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)

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20 pages, 38343 KiB

Open AccessArticle

Spatiotemporal Heterogeneity of Water Conservation Function and Its Driving Factors in the Upper Yangtze River Basin

by Chengjian Liu, Lei Zou, Jun Xia, Xinchi Chen, Lingfeng Zuo and Jiarui Yu

Remote Sens. 2023, 15(21), 5246; https://doi.org/10.3390/rs15215246 - 05 Nov 2023

Cited by 2 | Viewed by 1000

Abstract

The water conservation function (WCF), as one of the most critical ecosystem services, has an important impact on the ecological sustainability of a region. Accurately characterizing the spatiotemporal heterogeneity of WCF and further exploring its driving factors are of great significance for river basin management. Here, the WCF of the upper Yangtze River basin (UYRB) from 1991 to 2020 was calculated using the water yield module in the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model. Also, we innovatively applied emerging hot spot analysis (EHSA), which could describe the location and pattern of historical changes more accurately, to investigate the spatiotemporal heterogeneity and evolution of WCF. Based on the Geographical Detector Model (GDM), the main driving factors of WCF and their interactions were revealed. The results showed the following: (1) the WCF in the UYRB experienced a temporal increase at a growth rate of 1.48 mm/a, while remarkable differences were observed across the change rates of sub-watersheds. (2) The spatial variation of the WCF showed a gradual increase from northwest to southeast. Interestingly, the Jinshajing River upstream (JSJU) source area with a low WCF showed an increasing trend (with diminishing cold spots). On the contrary, the downstream regions of the JSJU watershed (with intensifying cold spots) underwent a weakening WCF. (3) Among all driving factors, precipitation (q = 0.701) exhibited the most remarkable prominent impact on the spatial heterogeneity of the WCF. Additionally, the interaction of factors exhibited more explanatory power than each factor alone, such as precipitation and saturated soil hydraulic conductivity (q = 0.840). This research study is beneficial to water resource management and provides a theoretical basis for ecological restoration. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)

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25 pages, 6247 KiB

Open AccessArticle

Comparison of Machine Learning Models to Predict Lake Area in an Arid Area

by Di Wang, Zailin Huo, Ping Miao and Xiaoqiang Tian

Remote Sens. 2023, 15(17), 4153; https://doi.org/10.3390/rs15174153 - 24 Aug 2023

Viewed by 1041

Abstract

Machine learning (ML)-based models are popular for complex physical system simulation and prediction. Lake is the important indicator in arid and semi-arid areas, and to achieve the proper management of the water resources in a lake basin, it is crucial to estimate and predict the lake dynamics, based on hydro-meteorological variations and anthropogenic disturbances. This task is particularly challenging in arid and semi-arid regions, where water scarcity poses a significant threat to human life. In this study, a typical arid area of China was selected as the study area, and the performances of eight widely used ML models (i.e., Bayesian Ridge (BR), K-Nearest Neighbor (KNN), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Random Forest (RF), Adaptive Boosting (AB), Bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XGB)) were evaluated in predicting lake area. Monthly lake area was determined by meteorological (precipitation, air temperature, Standardised Precipitation Evapotranspiration Index (SPEI)) and anthropogenic factors (ET_c, NDVI, LUCC). Lake area determined by Landsat satellite image classification for 2000–2020 was analysed side-by-side with the Standardised Precipitation Evapotranspiration Index (SPEI) on 9 and 12-month time scales. With the evaluation of six input variables and eight ML algorithms, it was found that the RF models performed best when using the SPEI-9 index, with R² = 0.88, RMSE = 1.37, LCCC = 0.95, and PRD = 1331.4 for the test samples. Furthermore, the performance of the ML model constructed with the 9-month time scale SPEI (SPEI-9) as an input variable (ML_SPEI-9) depended on seasonal variations, with the average relative errors of up to 0.62 in spring and a minimum of 0.12 in summer. Overall, this study provides valuable insights into the effectiveness of different ML models for predicting lake area by demonstrating that the right inputs can lead to a remarkable increase in performance of up to 13.89%. These findings have important implications for future research on lake area prediction in arid zones and demonstrate the power of ML models in advancing scientific understanding of complex natural systems. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)

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21 pages, 7207 KiB

Open AccessArticle

Methodology for Mapping the Ecological Security Pattern and Ecological Network in the Arid Region of Xinjiang, China

by Yishan Wang, Fei Zhang, Xingyou Li, Verner Carl Johnson, Mou Leong Tan, Hsiang-Te Kung, Jingchao Shi, Jupar Bahtebay and Xin He

Remote Sens. 2023, 15(11), 2836; https://doi.org/10.3390/rs15112836 - 30 May 2023

Cited by 2 | Viewed by 1720

Abstract

Xinjiang is an important arid region in the northwest of China and plays an important role in the field of ecological security protection in China. Because of its aridity, the identification of critical areas for ecological protection and the optimization of ecological space structure in Xinjiang are of great significance for promoting the harmonious development of the oasis economy, enhancing the ecological environment, and improving human well-being. This study applied an ecological security evaluation from the three dimensions of habitat quality, ecosystem service value, and soil-water conservation to identify the basic situation of the ecological security pattern. The core “source” area of ecological protection was extracted using the morphological spatial pattern analysis (MSPA) method, while the ecological corridor and important ecological nodes were identified using the minimum cumulative resistance model (MCR). The “point-line-plane” three-dimensional ecological network structure was then constructed, providing a case for the development of the ecological security and construction in the oasis. The results showed that in the arid regions of Xinjiang, the ecological land is extremely fragmented and is mainly distributed in the mountains and waters distant from human activities. Overall, there is a substantial geographical disparity with a low level of ecological security, particularly in the ecological marginal areas. The ecological network framework of Xinjiang is characterized by an uneven distribution of “sources”, broken corridor structure, and a low degree of networking. Therefore, this study proposed an ecological space layout system consisting of “7 ecological subsystems, 51 source areas, 87 ecological corridors, and 33 ecological nodes” by combining the regional physical and geographical characteristics with the overall development plan. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)

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18 pages, 6128 KiB

Open AccessArticle

Surface Soil Moisture Retrieval of China Using Multi-Source Data and Ensemble Learning

by Zhangjian Yang, Qisheng He, Shuqi Miao, Feng Wei and Mingxiao Yu

Remote Sens. 2023, 15(11), 2786; https://doi.org/10.3390/rs15112786 - 26 May 2023

Cited by 4 | Viewed by 1444

Abstract

Large-scale surface soil moisture (SSM) distribution is very necessary for agricultural drought monitoring, water resource management, and climate change research. However, the current large-scale SSM products have relatively coarse spatial resolution, which limits their application. In this study, we estimate the 1 km daily SSM in China based on ensemble learning using a multi-source data set including in situ soil moisture measurements from 2980 meteorological stations, MODIS Surface Reflectance products, SMAP (Soil Moisture Active Passive) soil moisture products, ERA5-Land dataset, SRTM DEM and soil texture. Among them, in situ measurements are used as independent variables, and other data are used as dependent variables. In order to improve the spatio-temporal completeness of SSM, the missing value in SMAP soil moisture products were reconstructed using the Discrete Cosine Transformation-penalized Partial Least Square (DCT-PLS) method to provide spatially complete background field information for soil moisture retrieval. The results show that the reconstructed soil moisture value has high quality, and the DCT-PLS method can fully utilize the three-dimensional spatiotemporal information to fill the data gaps. Subsequently, the performance of four ensemble learning models of random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) for soil moisture retrieval was evaluated. The LightGBM outperformed the other three machine learning models, with a correlation coefficient (R²) of 0.88, a bias of 0.0004 m³/m³, and an unbiased root mean square error (ubRMSE) of 0.0366 m³/m³. The high correlation between the in situ soil moisture and the predicted values at each meteorological station further indicate that LightGBM can well capture the temporal variation of soil moisture. Finally, the model was used to map the 1 km daily SSM in China on the first day of each month from May to October 2018. This study can provide some reference and help for future long-term daily 1 km surface soil moisture mapping in China. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)

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