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Special Issue "Remote Sensing of Water Resources"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2015)

Special Issue Editors

Guest Editor
Dr. Deepak R. Mishra

Department of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USA
Website | E-Mail
Interests: Water quality (inland waters, estuaries, coastal, and open ocean waters); Wetlands health, productivity, and carbon sequestration; Benthic habitat mapping, Cyber-innovated environmental sensing
Guest Editor
Dr. Eurico J. D’Sa

Department of Oceanography and Coastal Sciences, Louisiana State University, 306 Howe-Russell Geosciences Bldg., Baton Rouge, LA 70803, USA
E-Mail
Interests: ocean color remote sensing; bio-optical properties of coastal and ocean waters; physical-biogeochemical interactions; optical properties of colored dissolved organic matter; coastal biogeochemical processes
Guest Editor
Dr. Sachidananda Mishra

Remote Sensing Scientist, Dow Agrosciences, 9330 Zionsville Rd, Indianapolis, IN 46268, USA
E-Mail
Phone: +1-504-289-5153
Interests: environmental remote sensing, remote sensing of coastal and open ocean waters, remote sensing and bio-geochemistry of harmful algal bloom, bio-optical algorithm development, ocean optics and satellite oceanography, climate change

Special Issue Information

Dear Colleagues,

Water resources, the major driving force on our planet, support numerous ecosystems and cultural services from maintaining biodiversity, nutrient cycling, and enhancing primary productivity; to recreation, ecotourism, transport, and other cultural uses. The pressure on water resources has been on the rise and will continue to increase in the coming years because of increased frequency of drought, urbanization, urban population growth, deforestation, increased use of fertilizers and pesticides, and spread of invasive species. Therefore, accurate, inexpensive, and fast monitoring tools using remote sensing technology are needed for timely implementation of conservation and restoration measures in problematic areas. This special issue on “remote sensing of water resources” is specifically designed to highlight some of the remote sensing-driven applied research currently being performed to solve the aforementioned problems in water resources. In addition to the specific topics listed below, manuscripts in the area of novel algorithm development, models for new satellite sensor, and long-term time series and trend analysis of water quality parameters for specific water bodies are also encouraged.

Authors are encouraged to submit articles with respect to the following topics.

List of topics to be covered in this special issue

  1. Nuisance blooms detection and management
  2. Coastal water quality issues (e.g., hypoxia, algal blooms, light limitation, pollutants)
  3. Submerged aquatic vegetation and benthic algae
  4. Effect of land use land cover change on water quality
  5. Nutrient management and water quality
  6. Draught and water quality
  7. Spread of invasive species and water quality
  8. Tools (software, GUI) used for monitoring water resources

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. Deepak R. Mishra
Dr. Eurico D’Sa
Dr. Sachidananda Mishra
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 papers will be 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 monthly 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 1600 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.


Published Papers (16 papers)

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Editorial

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Open AccessEditorial Preface: Remote Sensing of Water Resources
Remote Sens. 2016, 8(2), 115; doi:10.3390/rs8020115
Received: 1 February 2016 / Accepted: 2 February 2016 / Published: 4 February 2016
PDF Full-text (159 KB) | HTML Full-text | XML Full-text
Abstract
The Special Issue (SI) on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles
[...] Read more.
The Special Issue (SI) on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Research

Jump to: Editorial, Other

Open AccessArticle Long-Term Pattern of Primary Productivity in the East/Japan Sea Based on Ocean Color Data Derived from MODIS-Aqua
Remote Sens. 2016, 8(1), 25; doi:10.3390/rs8010025
Received: 26 August 2015 / Revised: 21 December 2015 / Accepted: 25 December 2015 / Published: 31 December 2015
Cited by 5 | PDF Full-text (5582 KB) | HTML Full-text | XML Full-text
Abstract
The East/Japan Sea (hereafter, the East Sea) is highly dynamic in its physical phenomena and biological characteristics, but it has changed substantially over the last several decades. In this study, a recent decadal trend of primary productivity in the East Sea was analyzed
[...] Read more.
The East/Japan Sea (hereafter, the East Sea) is highly dynamic in its physical phenomena and biological characteristics, but it has changed substantially over the last several decades. In this study, a recent decadal trend of primary productivity in the East Sea was analyzed based on Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived monthly values to detect any long-term change. The daily primary productivities averaged using monthly values from 2003 to 2012 were 719.7 mg·C·m−2·d−1 (S.D. ± 197.5 mg·C·m−2·d−1, n = 120) and 632.3 mg·C·m−2·d−1 (S.D. ± 235.1 mg·C·m−2·d−1, n = 120) for the southern and northern regions of the East Sea, respectively. Based on the daily productivities, the average annual primary production in the East Sea was 246.8 g·C·m−2·y−1, which was substantially higher than that previously reported in deep oceans. However, a decreasing trend (13% per 10 years) in the annual primary production was observed in the East Sea within the study period from 2003 to 2012. The shallower mixed layers caused by increased temperature could be a potential cause for the decline in annual production. However, this decline could also be part of an oscillation pattern that is strongly governed by the Pacific Decadal Oscillation (PDO). A better understanding of primary productivity patterns and their subsequent effects on the marine ecosystem is required for further interdisciplinary studies in the East Sea. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance
Remote Sens. 2015, 7(11), 14731-14756; doi:10.3390/rs71114731
Received: 8 June 2015 / Revised: 19 October 2015 / Accepted: 29 October 2015 / Published: 5 November 2015
Cited by 3 | PDF Full-text (1359 KB) | HTML Full-text | XML Full-text
Abstract
Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting
[...] Read more.
Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake
Remote Sens. 2015, 7(11), 14403-14427; doi:10.3390/rs71114403
Received: 30 June 2015 / Revised: 20 October 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
Cited by 8 | PDF Full-text (1782 KB) | HTML Full-text | XML Full-text
Abstract
The extension and frequency of algal blooms in surface waters can be monitored using remote sensing techniques, yet knowledge of their vertical distribution is fundamental to determine total phytoplankton biomass and understanding temporal variability of surface conditions and the underwater light field. However,
[...] Read more.
The extension and frequency of algal blooms in surface waters can be monitored using remote sensing techniques, yet knowledge of their vertical distribution is fundamental to determine total phytoplankton biomass and understanding temporal variability of surface conditions and the underwater light field. However, different vertical distribution classes of phytoplankton may occur in complex inland lakes. Identification of the vertical profile classes of phytoplankton becomes the key and first step to estimate its vertical profile. The vertical distribution profile of phytoplankton is based on a weighted integral of reflected light from all depths and is difficult to determine by reflectance data alone. In this study, four Chla vertical profile classes (vertically uniform, Gaussian, exponential and hyperbolic) were found to occur in three in situ vertical surveys (28 May, 19–24 July and 10–12 October) in a shallow eutrophic lake, Lake Chaohu. We developed and validated a classification and regression tree (CART) to determine vertical phytoplankton biomass profile classes. This was based on an algal bloom index (Normalized Difference algal Bloom Index, NDBI) applied to both in situ remote sensing reflectance (Rrs) and MODIS Rayleigh-corrected reflectance (Rrc) data in combination with data of local wind speed. The results show the potential of retrieving Chla vertical profiles information from integrated information sources following a decision tree approach. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery
Remote Sens. 2015, 7(10), 14055-14078; doi:10.3390/rs71014055
Received: 19 August 2015 / Revised: 19 September 2015 / Accepted: 10 October 2015 / Published: 26 October 2015
Cited by 2 | PDF Full-text (2232 KB) | HTML Full-text | XML Full-text
Abstract
This study proposed a natural-rule-based-connection (NRBC) method to connect river segments after water body detection from remotely sensed imagery. A complete river network is important for many hydrological applications. While water body detection methods using remote sensing are well-developed, less attention has been
[...] Read more.
This study proposed a natural-rule-based-connection (NRBC) method to connect river segments after water body detection from remotely sensed imagery. A complete river network is important for many hydrological applications. While water body detection methods using remote sensing are well-developed, less attention has been paid to connect discontinuous river segments and form a complete river network. This study designed an automated NRBC method to extract a complete river network by connecting river segments at polygon level. With the assistance of an image pyramid, neighbouring river segments are connected based on four criteria: gap width (Tg), river direction consistency (Tθ), river width consistency (Tw), and minimum river segment length (Tl). The sensitivity of these four criteria were tested, analyzed, and proper criteria values were suggested using image scenes from two diverse river cases. The comparison of NRBC and the alternative morphological method demonstrated NRBC’s advantage of natural rule based selective connection. We refined a river centerline extraction method and show how it outperformed three other existing centerline extraction methods on the test sites. The extracted river polygons and centerlines have a multitude of end uses including rapidly mapping flood extents, monitoring surface water supply, and the provision of validation data for simulation models required for water quantity, quality and aquatic biota assessments. The code for the NRBC is available on GitHub. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Landsat-Based Long-Term Monitoring of Total Suspended Matter Concentration Pattern Change in the Wet Season for Dongting Lake, China
Remote Sens. 2015, 7(10), 13975-13999; doi:10.3390/rs71013975
Received: 20 June 2015 / Revised: 19 October 2015 / Accepted: 20 October 2015 / Published: 23 October 2015
Cited by 11 | PDF Full-text (1618 KB) | HTML Full-text | XML Full-text
Abstract
Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM) pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in
[...] Read more.
Assessing the impacts of environmental change and anthropogenic activities on the historical and current total suspended matter (TSM) pattern in Dongting Lake, China, is a large challenge. We addressed this challenge by using more than three decades of Landsat data. Based on in situ measurements, we developed an algorithm based on the near-infrared (NIR) band to estimate TSM in Dongting Lake. The algorithm was applied to Landsat images to derive TSM distribution maps from 1978 to 2013 in the wet season, revealing significant inter-annual and spatial variability. The relationship of TSM to water level, precipitation, and wind speed was analyzed, and we found that: (1) sand mining areas usually coincide with regions that have high TSM levels in Dongting Lake; (2) water level and seven-day precipitation were both important to TSM variation, but no significant relationship was found between TSM and wind speed or other meteorological data; (3) the increased level of sand mining in response to rapid economic growth has deeply influenced the TSM pattern since 2000 due to the resuspension of sediment; and (4) TSM variation might be associated with policy changes regarding the management of sand mining; it might also be affected by lower water levels caused by the impoundment of the Three Gorges Dam since 2000. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data
Remote Sens. 2015, 7(9), 12478-12502; doi:10.3390/rs70912478
Received: 27 July 2015 / Accepted: 17 September 2015 / Published: 23 September 2015
Cited by 11 | PDF Full-text (2494 KB) | HTML Full-text | XML Full-text
Abstract
Coastal bays, such as Barataria Bay, are important transition zones between the terrigenous and marine environments that are also optically complex due to elevated amounts of particulate and dissolved constituents. Monthly field data collected over a period of 15 months in 2010 and
[...] Read more.
Coastal bays, such as Barataria Bay, are important transition zones between the terrigenous and marine environments that are also optically complex due to elevated amounts of particulate and dissolved constituents. Monthly field data collected over a period of 15 months in 2010 and 2011 in Barataria Bay were used to develop an empirical band ratio algorithm for the Landsat-5 TM that showed a good correlation with the Colored Dissolved Organic Matter (CDOM) absorption coefficient at 355 nm (ag355) (R2 = 0.74). Landsat-derived CDOM maps generally captured the major details of CDOM distribution and seasonal influences, suggesting the potential use of Landsat imagery to monitor biogeochemistry in coastal water environments. An investigation of the seasonal variation in ag355 conducted using Landsat-derived ag355 as well as field data suggested the strong influence of seasonality in the different regions of the bay with the marine end members (lower bay) experiencing generally low but highly variable ag355 and the freshwater end members (upper bay) experiencing high ag355 with low variability. Barataria Bay experienced a significant increase in ag355 during the freshwater release at the Davis Pond Freshwater Diversion (DPFD) following the Deep Water Horizon oil spill in 2010 and following the Mississippi River (MR) flood conditions in 2011, resulting in a weak linkage to salinity in comparison to the other seasons. Tree based statistical analysis showed the influence of high river flow conditions, high- and low-pressure systems that appeared to control ag355 by ~28%, 29% and 43% of the time duration over the study period at the marine end member just outside the bay. An analysis of CDOM variability in 2010 revealed the strong influence of the MR in controlling CDOM abundance in the lower bay during the high flow conditions, while strong winds associated with cold fronts significantly increase CDOM abundance in the upper bay, thus revealing the important role these events play in the CDOM dynamics of the bay. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery
Remote Sens. 2015, 7(9), 12336-12355; doi:10.3390/rs70912336
Received: 4 July 2015 / Revised: 8 September 2015 / Accepted: 14 September 2015 / Published: 22 September 2015
Cited by 6 | PDF Full-text (2058 KB) | HTML Full-text | XML Full-text
Abstract
Accurate information of urban surface water is important for assessing the role it plays in urban ecosystem services under the content of urbanization and climate change. However, high-resolution monitoring of urban water bodies using remote sensing remains a challenge because of the limitation
[...] Read more.
Accurate information of urban surface water is important for assessing the role it plays in urban ecosystem services under the content of urbanization and climate change. However, high-resolution monitoring of urban water bodies using remote sensing remains a challenge because of the limitation of previous water indices and the dark building shadow effect. To address this problem, we proposed an automated urban water extraction method (UWEM) which combines a new water index, together with a building shadow detection method. Firstly, we trained the parameters of UWEM using ZY-3 imagery of Qingdao, China. Then we verified the algorithm using five other sub-scenes (Aksu, Fuzhou, Hanyang, Huangpo and Huainan) ZY-3 imagery. The performance was compared with that of the Normalized Difference Water Index (NDWI). Results indicated that UWEM performed significantly better at the sub-scenes with kappa coefficients improved by 7.87%, 32.35%, 12.64%, 29.72%, 14.29%, respectively, and total omission and commission error reduced by 61.53%, 65.74%, 83.51%, 82.44%, and 74.40%, respectively. Furthermore, UWEM has more stable performances than NDWI’s in a range of thresholds near zero. It reduces the over- and under-estimation issues which often accompany previous water indices when mapping urban surface water under complex environmental conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Seasonal and Inter-Annual Analysis of Chlorophyll-a and Inherent Optical Properties from Satellite Observations in the Inner and Mid-Shelves of the South of Buenos Aires Province (Argentina)
Remote Sens. 2015, 7(9), 11821-11847; doi:10.3390/rs70911821
Received: 5 June 2015 / Revised: 19 August 2015 / Accepted: 25 August 2015 / Published: 15 September 2015
Cited by 4 | PDF Full-text (2119 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The aim of this study is to describe and understand the seasonal and inter-annual physical and biological dynamics of the inner and mid shelves of the Southwestern Buenos Aires Province (Argentina). We used chlorophyll-a (chl-a) concentrations and inherent optical properties
[...] Read more.
The aim of this study is to describe and understand the seasonal and inter-annual physical and biological dynamics of the inner and mid shelves of the Southwestern Buenos Aires Province (Argentina). We used chlorophyll-a (chl-a) concentrations and inherent optical properties (IOPs), derived from ocean color products between 2002 and 2010, as a proxy for the physical and biological parameters of interest. This study focuses on the absorption by phytoplankton, aph(443), particulate backscattering, bbp(443), and absorption due to dissolved and particulate detrital matter, adg(443), and chl-a derived from a multiband quasi-analytical algorithm (QAA). A regionalization based on the coefficient of variation and the Census X-11 method were applied to define regions and to analyze the inter-annual and seasonal variability of the ocean color parameters, with regards to climate variability. The coastal zone presents the highest values of chl-a with two maxima in winter and autumn, while the mid-shelf shows a strong spring chl-a maximum. After 2009, all parameters under study shifted their seasonality and their magnitude changed over the entire area. In the coastal zone, mean values of aph(443) and bbp(443) increased, while in the mid-shelf, chl-a and aph(443) decreased. The observed inter-annual and seasonal behavior of the parameters is tightly related to climate variability of the study area. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data
Remote Sens. 2015, 7(9), 11731-11752; doi:10.3390/rs70911731
Received: 6 August 2015 / Revised: 30 August 2015 / Accepted: 8 September 2015 / Published: 14 September 2015
Cited by 5 | PDF Full-text (5342 KB) | HTML Full-text | XML Full-text
Abstract
Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching
[...] Read more.
Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m3, respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia
Remote Sens. 2015, 7(9), 11639-11663; doi:10.3390/rs70911639
Received: 7 April 2015 / Revised: 24 August 2015 / Accepted: 7 September 2015 / Published: 11 September 2015
Cited by 4 | PDF Full-text (835 KB) | HTML Full-text | XML Full-text
Abstract
Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study
[...] Read more.
Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42), the Global Satellite Mapping of Precipitation (GSMaP_MVK+), and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN) at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain) and Kiremt (long rain) seasons, the TRMM did better than the others by far. However, during Bega (dry) season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle River Detection in Remotely Sensed Imagery Using Gabor Filtering and Path Opening
Remote Sens. 2015, 7(7), 8779-8802; doi:10.3390/rs70708779
Received: 12 May 2015 / Revised: 16 June 2015 / Accepted: 6 July 2015 / Published: 13 July 2015
Cited by 7 | PDF Full-text (6107 KB) | HTML Full-text | XML Full-text
Abstract
Detecting rivers from remotely sensed imagery is an initial yet important step in space-based river studies. This paper proposes an automatic approach to enhance and detect complete river networks. The main contribution of this work is the characterization of rivers according to their
[...] Read more.
Detecting rivers from remotely sensed imagery is an initial yet important step in space-based river studies. This paper proposes an automatic approach to enhance and detect complete river networks. The main contribution of this work is the characterization of rivers according to their Gaussian-like cross-sections and longitudinal continuity. A Gabor filter was first employed to enhance river cross-sections. Rivers are better discerned from the image background after filtering but they can be easily corrupted owing to significant gray variations along river courses. Path opening, a flexible morphological operator, was then used to lengthen the river channel continuity and suppress noise. Rivers were consistently discerned from the image background after these two-step processes. Finally, a global threshold was automatically determined and applied to create binary river masks. River networks of the Yukon Basin and the Greenland Ice Sheet were successfully detected in two Landsat 8 OLI panchromatic images using the proposed method, yielding a high accuracy (~97.79%), a high true positive rate (~94.33%), and a low false positive rate (~1.76%). Furthermore, experimental tests validated the high capability of the proposed method to preserve river network continuity. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle A Semi-Analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China
Remote Sens. 2015, 7(5), 5373-5397; doi:10.3390/rs70505373
Received: 9 October 2014 / Revised: 14 April 2015 / Accepted: 20 April 2015 / Published: 28 April 2015
Cited by 9 | PDF Full-text (16994 KB) | HTML Full-text | XML Full-text
Abstract
Suspended sediment concentration (SSC) is one of the most critical parameters in ocean ecological environment evaluation and it can be determined using ocean color remote sensing (RS). The purpose of this study is to develop a model that provides a reliable and sensitive
[...] Read more.
Suspended sediment concentration (SSC) is one of the most critical parameters in ocean ecological environment evaluation and it can be determined using ocean color remote sensing (RS). The purpose of this study is to develop a model that provides a reliable and sensitive evaluation of SSC retrieval using RS data. Data were acquired for and gathered from the Gulf of Bohai where SSC levels are relatively low with an average value below 30 mg·L−1. The study indicates that the most sensitive band to SSC levels in the study area is the NIR band of Landsat5 TM images. A quadratic polynomial semi-analytical model appears to be the best retrieval model based on the relationship between the inherent optical properties (IOPs) and apparent optical properties (AOPs) of water as described by the quasi-analytical algorithm (QAA). The model has a higher precision and effectiveness for SSC retrieval than data-driven statistical models, especially when SSC level is relatively high. The average relative error and the root mean square error (RMSE) are 12.32% and 4.53 mg·L−1, respectively, while the correlation coefficient between observed and estimated SSC by the model is 0.95. Using the proposed retrieval model and TM data, SSC levels of the entire study region in the Gulf of Bohai were estimated. These estimates can serve as the baseline for efficient monitoring of the ocean environment in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS
Remote Sens. 2015, 7(2), 1441-1460; doi:10.3390/rs70201441
Received: 6 December 2014 / Revised: 25 December 2014 / Accepted: 23 January 2015 / Published: 29 January 2015
Cited by 2 | PDF Full-text (12458 KB) | HTML Full-text | XML Full-text
Abstract
During the spring of 2011 an unprecedented “Super” algal bloom formed in the Indian River Lagoon (IRL), with Chlorophyll a (Chl a) concentrations over eight times the historical mean in some areas and lasted for seven months across the IRL. The European
[...] Read more.
During the spring of 2011 an unprecedented “Super” algal bloom formed in the Indian River Lagoon (IRL), with Chlorophyll a (Chl a) concentrations over eight times the historical mean in some areas and lasted for seven months across the IRL. The European Space Agency’s MEdium Resolution Imaging Spectrometer (MERIS) platform provided multispectral data at 665 and 708 nm, which was used to quantify the phytoplankton Chl a by fluorescence while minimizing the effects of other water column constituents. The three objectives were to: (1) calibrate and validate two Chl a algorithms using all available MERIS data of the IRL from 2002 to 2012; (2) determine the accuracy of the algorithms estimation of Chl a before, during, and after the 2011 super bloom; and (3) map the 2011 algal bloom using the Chl a algorithm that was proven to be effective in other similar estuaries. The chosen algorithm, Normalized Difference Chlorophyll Index (NDCI), was positively correlated with the in-situ measurements, with an R2 value of 0.798. While there was a significant (62.9 ± 25%) underestimation of Chl a using MERIS NDCI, the underestimation appears to be consistent across the data and mostly in the estimations of lower concentrations, suggesting that a qualitative or ratio analysis is still valid. Analysis of the application of the NDCI processed MERIS data provided additional insights that the in-situ measurements were unable to record. The time series MERIS Chl a maps along with in-situ water quality monitoring data depicted that the 2011 IRL bloom started after a heavy rainfall in March 2011 and peaked in October 2011 after a decrease in temperature. The bloom collapse also coincided with heavy rainfall and rapidly decreasing temperatures and salinity through October to November 2011. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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Open AccessArticle Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery
Remote Sens. 2015, 7(1), 1112-1134; doi:10.3390/rs70101112
Received: 2 October 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
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Abstract
We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the
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We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)

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Open AccessTechnical Note Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data
Remote Sens. 2015, 7(10), 13564-13585; doi:10.3390/rs71013564
Received: 28 August 2015 / Revised: 1 October 2015 / Accepted: 13 October 2015 / Published: 19 October 2015
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Abstract
This paper describes the use of machine learning methods to build a decision support system for predicting the distribution of coastal ocean algal blooms based on remote sensing data in Monterey Bay. This system can help scientists obtain prior information in a large
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This paper describes the use of machine learning methods to build a decision support system for predicting the distribution of coastal ocean algal blooms based on remote sensing data in Monterey Bay. This system can help scientists obtain prior information in a large ocean region and formulate strategies for deploying robots in the coastal ocean for more detailed in situ exploration. The difficulty is that there are insufficient in situ data to create a direct statistical machine learning model with satellite data inputs. To solve this problem, we built a Random Forest model using MODIS and MERIS satellite data and applied a threshold filter to balance the training inputs and labels. To build this model, several features of remote sensing satellites were tested to obtain the most suitable features for the system. After building the model, we compared our random forest model with previous trials based on a Support Vector Machine (SVM) using satellite data from 221 days, and our approach performed significantly better. Finally, we used the latest in situ data from a September 2014 field experiment to validate our model. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
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