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Remote Sensing and Modeling of Land Surface Water

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 39704

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Special Issue Editors

Dr. Huan Wu

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Guest Editor

School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Interests: remote sensing; modeling; hydrology; meteorology
Special Issues, Collections and Topics in MDPI journals

Dr. Sujay Kumar

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Guest Editor

Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: land surface modeling; hydrology; data assimilation; remote sensing; optimization
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Dr. Maoyi Huang

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Pacific Northwest National Laboratory, Richland, WA 99352, USA
Interests: land–atmosphere interactions; surface water hydrology; ecosystem modeling; regional climate modeling
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Dr. Sagy Cohen

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Guest Editor

Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA
Interests: hydrology; geomorphology; numerical modeling; geospatial analysis; remote sensing
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Dr. Nergui Nanding

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Guest Editor

School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Interests: radar/satellite precipitation, multi-source rainfall merging; extreme hydrological events

Special Issue Information

Dear Colleagues,

Floods and droughts, as well as other water-related hazards (e.g., landslides, mudslides) are highly associated with the dynamics of land surface water (e.g., from lakes/reservoirs, rivers, wetlands, soil layers). Monitoring and forecasting of land surface water in both urban and rural environments with sufficient accuracy and spatiotemporal resolution have been desired by many governmental, humanitarian agencies and users from different sectors (e.g., private and commercial). Remote sensing and process-based modeling are two powerful and rapidly advancing technologies that are able to provide important, timely information of land surface water. However, accurate estimation of land surface water and its changes with either remote sensing-based or process-based modeling approaches still faces grand challenges and subject to large uncertainties. These uncertainties in estimation and prediction of land surface water greatly impede the practical activities, such as disaster preparation and mitigation, decision-making, and water resources management.

Remote sensing-based and process-based modeling methods each have their own strengths and weaknesses. Therefore, effective integration of the two methods and other source information for better understanding of causation and the geophysical process of land surface water generation and movement, and thus an optimized information utilization, is urgently needed.

We solicit high quality, original research contributions from both remote sensing and modeling work that study land surface water across a variety of spatial scales.

Remote sensing of land surface water dynamics;
Physical or statistical modeling of land surface water dynamics;
New methods of integrating estimates of land surface water both from remote sensing and modeling
Data assimilation of remote sensing and in-situ observation to improve modeling of land surface water and related critical datasets
Ensembles and probabilistic hydrometeorological modeling and forecasting of land surface water
Coupled hyper-resolution large-scale hydrological and meteorological modeling
Characterization of uncertainty in retrospective and operational modeled and remotely sensed results
Interdisciplinary and integrated model and application results from areas of remote sensing, hydrology, and meteorology

Dr. Huan Wu
Dr. Sujay V. Kumar
Dr. Maoyi Huang
Dr. Sagy Cohen
Dr. Nergui Nanding
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

Remote Sensing
Hydrological modeling
Data assimilation
Land surface water
Uncertainty

Published Papers (9 papers)

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Research

23 pages, 4052 KiB

Open AccessArticle

Investigating the Error Propagation from Satellite-Based Input Precipitation to Output Water Quality Indicators Simulated by a Hydrologic Model

by Jennifer Solakian, Viviana Maggioni and Adil Godrej

Remote Sens. 2020, 12(22), 3728; https://doi.org/10.3390/rs12223728 - 13 Nov 2020

Cited by 3 | Viewed by 2004

Abstract

This study investigated the propagation of errors in input satellite-based precipitation products (SPPs) on streamflow and water quality indicators simulated by a hydrological model in the Occoquan Watershed, located in the suburban Washington, D.C. area. A dense rain gauge network was used as reference to evaluate three SPPs which are based on different retrieval algorithms. A Hydrologic Simulation Program-FORTRAN (HSPF) hydrology and water quality model was forced with the three SPPs to simulate output of streamflow (Q), total suspended solids (TSS), stream temperature (TW), and dissolved oxygen (DO). Results indicate that the HSPF model may have a dampening effect on the precipitation-to-streamflow error. The bias error propagation of all three SPPs showed a positive dependency on basin scale for streamflow and TSS, but not for TW and DO. On a seasonal basis, bias error propagation varied by product, with larger values generally found in fall and winter. This study demonstrated that the spatiotemporal variability of SPPs, along with their algorithms to estimate precipitation, have an influence on water quality simulations in a hydrologic model. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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20 pages, 4111 KiB

Open AccessArticle

The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

by Cassandra Nickles, Edward Beighley and Dongmei Feng

Remote Sens. 2020, 12(19), 3241; https://doi.org/10.3390/rs12193241 - 06 Oct 2020

Cited by 6 | Viewed by 2896

Abstract

The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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30 pages, 11022 KiB

Open AccessArticle

Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain

by Andrew Ogilvie, Jean-Christophe Poussin, Jean-Claude Bader, Finda Bayo, Ansoumana Bodian, Honoré Dacosta, Djiby Dia, Lamine Diop, Didier Martin and Soussou Sambou

Remote Sens. 2020, 12(19), 3157; https://doi.org/10.3390/rs12193157 - 26 Sep 2020

Cited by 15 | Viewed by 5135

Abstract

Accurate monitoring of surface water bodies is essential in numerous hydrological and agricultural applications. Combining imagery from multiple sensors can improve long-term monitoring; however, the benefits derived from each sensor and the methods to automate long-term water mapping must be better understood across varying periods and in heterogeneous water environments. All available observations from Landsat 7, Landsat 8, Sentinel-2 and MODIS over 1999–2019 are processed in Google Earth Engines to evaluate and compare the benefits of single and multi-sensor approaches in long-term water monitoring of temporary water bodies, against extensive ground truth data from the Senegal River floodplain. Otsu automatic thresholding is compared with default thresholds and site-specific calibrated thresholds to improve Modified Normalized Difference Water Index (MNDWI) classification accuracy. Otsu thresholding leads to the lowest Root Mean Squared Error (RMSE) and high overall accuracies on selected Sentinel-2 and Landsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. On MODIS imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km

^{2}

) water bodies despite the 500 m spatial resolution. Over 1999–2019, MODIS observations reduce average daily RMSE by 48% compared to the full Landsat 7 and 8 archive and by 51% compared to the published Global Surface Water datasets. Results reveal the need to integrate coarser MODIS observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full Landsat time series before 2013. From 2013, the Landsat 7 and Landsat 8 constellation becomes sufficient, and integrating MODIS observations degrades performance marginally. Combining Landsat and Sentinel-2 yields modest improvements after 2015. These results have important implications to guide the development of multi-sensor products and for applications across large wetlands and floodplains. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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28 pages, 6256 KiB

Open AccessArticle

Recent Changes in Water Discharge in Snow and Glacier Melt-Dominated Rivers in the Tienshan Mountains, Central Asia

by Qifei Zhang, Yaning Chen, Zhi Li, Gonghuan Fang, Yanyun Xiang, Yupeng Li and Huiping Ji

Remote Sens. 2020, 12(17), 2704; https://doi.org/10.3390/rs12172704 - 20 Aug 2020

Cited by 30 | Viewed by 4562

Abstract

Global warming has generally led to changes in river runoffs fed by snow and glacier meltwater in mountain ranges. The runoff of the Aksu River, which originates in the Southern Tienshan Mountains, exhibited a positive trend during 1979–2002, but this trend reversed during 2002–2015. Through a comprehensive analysis, this study aims to estimate potential reasons for changes in the runoff of its two contrasting headwaters: the Toxkan and Kumalak Rivers, based on climatic data, the altitude of the 0 °C isotherm, glacier mass balance (GMB), snow cover area (SCA), snow depth (SD) and the sensitivity model. For the Toxkan River, the decrease in spring runoff mainly resulted from reductions in precipitation, whereas the decrease in summer runoff was mainly caused by early snowmelt in spring and a much-reduced snow meltwater supply in summer. In addition, the obvious glacier area reduction in the catchment (decreased to less than 4%) also contributed to the reduced summer runoff. For the Kumalak River, a sharp decrease rate of 10.21 × 10⁸ m³/decade in runoff was detected due to summertime cooling of both surface and upper air temperatures. Reduced summer temperatures with a positive trend in precipitation not only inhibited glacier melting but also dropped the 0 °C layer altitude, resulting in a significant increase in summertime SCA and SD, a slowing of the glacier negative mass balance, and a lowering of the snow-line altitude. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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20 pages, 5322 KiB

Open AccessArticle

Probabilistic River Water Mapping from Landsat-8 Using the Support Vector Machine Method

by Qihang Liu, Chang Huang, Zhuolin Shi and Shiqiang Zhang

Remote Sens. 2020, 12(9), 1374; https://doi.org/10.3390/rs12091374 - 26 Apr 2020

Cited by 27 | Viewed by 2982

Abstract

River water extent is essential for river hydrological surveys. Traditional methods for river water mapping often result in significant uncertainties. This paper proposes a support vector machine (SVM)-based river water mapping method that can quantify the extraction uncertainties simultaneously. Five specific bands of Landsat-8 Operational Land Imager (OLI) data were selected to construct the feature set. Considering the effect of terrain, a widely used terrain index called height above nearest drainage, calculated from the 1 arc-second Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), was also added into the feature set. With this feature set, a posterior probability SVM model was established to extract river water bodies and quantify the uncertainty with posterior probabilities. Three river sections in Northwestern China were selected as the case study areas, considering their different river characteristics and geographical environment. Then, the reliability and stability of the proposed method were evaluated through comparisons with the traditional Normalized Difference Water Index (NDWI) and modified NDWI (mNDWI) methods and validated with higher-resolution Sentinel-2 images. It was found that resultant probability maps obtained by the proposed SVM method achieved generally high accuracy with a weighted root mean square difference of less than 0.1. Other accuracy indices including the Kappa coefficient and critical success index also suggest that the proposed method outperformed the traditional water index methods in terms of river mapping accuracy and thresholding stability. Finally, the proposed method resulted in the ability to separate water bodies from hill shades more easily, ensuring more reliable river water mapping in mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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22 pages, 4884 KiB

Open AccessArticle

Satellite-Based Evapotranspiration in Hydrological Model Calibration

by Lulu Jiang, Huan Wu, Jing Tao, John S. Kimball, Lorenzo Alfieri and Xiuwan Chen

Remote Sens. 2020, 12(3), 428; https://doi.org/10.3390/rs12030428 - 29 Jan 2020

Cited by 39 | Viewed by 4758

Abstract

Hydrological models are usually calibrated against observed streamflow (Q_obs), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ET_RS) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ET_RS in the hydrological calibration of a widely used land surface model coupled with a source–sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Q_obs and ET_RS-based model calibrations, respectively, through comprehensive sensitivity tests. The ET_RS-based model calibration results in a mean Kling–Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Q_obs, the ET_RS calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ET_RS calibration runs. This study confirms that with reasonable precipitation input, the ET_RS-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ET_RS for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ET_RS observations continue to improve. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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24 pages, 22469 KiB

Open AccessArticle

Impact of Climate Variabilities and Human Activities on Surface Water Extents in Reservoirs of Yongding River Basin, China, from 1985 to 2016 Based on Landsat Observations and Time Series Analysis

by Mingli Wang, Longjiang Du, Yinghai Ke, Maoyi Huang, Jing Zhang, Yong Zhao, Xiaojuan Li and Huili Gong

Remote Sens. 2019, 11(5), 560; https://doi.org/10.3390/rs11050560 - 07 Mar 2019

Cited by 38 | Viewed by 4112

Abstract

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has witnessed increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology presented in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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21 pages, 6692 KiB

Open AccessArticle

Long-Term Dynamic of Poyang Lake Surface Water: A Mapping Work Based on the Google Earth Engine Cloud Platform

by Yingbing Wang, Jun Ma, Xiangming Xiao, Xinxin Wang, Shengqi Dai and Bin Zhao

Remote Sens. 2019, 11(3), 313; https://doi.org/10.3390/rs11030313 - 05 Feb 2019

Cited by 79 | Viewed by 7444

Abstract

In recent years, the shrinkage of Poyang Lake, the largest freshwater lake in China, has raised concerns for society. The regulation of the Three Gorges Dam (TGD) has been argued to be a cause of the depletion of the lake by previous studies. However, over the past few decades, the lake’s surface water dynamic has remained poorly characterized, especially before the regulation of the TGD (2003). By calculating the inundation frequency with an index- and pixel-based water detection algorithm on Google Earth Engine (GEE), this study explored the spatial–temporal variation of the lake during 1988–2016 and compared the differences in Poyang Lake’s water body between the pre- and post-TGD periods. The year-long water body area of the lake has shown a significant decreasing trend over the past 29 years and has shifted to a smaller regime since 2006. The inundation frequency of the lake has also generally decreased since 2003, particularly at the central part of the lake, and the effects of this trend have been most severe in the spring and autumn seasons. The lake’s area has shown significant correlation with the precipitation of the Poyang Lake Basin on an inner-annual scale. The drivers of and relevant factors relating to the inter-annual variation of the lake’s surface water should be further investigated in the future. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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20 pages, 13752 KiB

Open AccessArticle

Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations

by Wensong Zhang, Hang Pan, Chunqiao Song, Linghong Ke, Jida Wang, Ronghua Ma, Xinyuan Deng, Kai Liu, Jingying Zhu and Qianhan Wu

Remote Sens. 2019, 11(1), 25; https://doi.org/10.3390/rs11010025 - 24 Dec 2018

Cited by 11 | Viewed by 4653

Abstract

The number of reservoirs is rapidly increasing owing to the growth of the world’s economy and related energy and water needs. Yet, for the vast majority of reservoirs around the world, their locations and related information, especially for newly dammed reservoirs, are not readily available due to financial, political, or legal considerations. This study proposes an automated method of identifying newly dammed reservoirs from time series of MODIS-derived NDWI (normalized difference water index) images. Its main idea lies in the detection of abrupt changes in the NDWI time series that are associated with land-to-water conversion due to the reservoir impoundment. The proposed method is tested in the upper reach of the Yellow River that is severely regulated by constructed reservoirs. Our results show that five newly dammed reservoirs were identified in the test area during 2000–2018. Validated against high-resolution Google Earth imagery, our method is effective to determine both locations of the emerging medium-size reservoirs and the timing of their initial water impoundments. Such information then allows for a refined calculation of the reservoir inundation extents and storage capacities through the combination of higher-resolution Landsat imagery and SRTM DEM. The comparison of our estimated reservoir areas and capacities against documented information further indicates that the integration of multi-mission remote sensing data may provide useful information for understanding reservoir operations and impacts on river discharges. Our method also demonstrates a potential for regional or global inventory of emerging reservoirs, which is crucial to assessing human impacts on river systems and the global water cycle. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)

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