Special Issue "Catchment Water Resources Management: Advances in Remote Sensing Based Techniques"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management and Governance".

Deadline for manuscript submissions: closed (29 July 2019).

Special Issue Editor

Prof. Dr. Aris Psilovikos
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Guest Editor
Sustainable Water Resources Management, Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Odos Fytokou, N. Ionia Magnisias 38446, Greece
Interests: water resources simulation, optimization and management; water quality monitoring, simulation and management; temporal and spatial analysis of water quality and quantity parameters; water balance in catchment areas; erosion, floods and sedimentation in catchment areas; artificial neural networks; ANN; Geographic Information System; GIS; Remote Sensing; RS
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Special Issue Information

Dear Colleagues

Water resources management is a multidisciplinary issue that prevailed from the cooperation of a wide range of scientists, such as engineers, earth scientists, agronomists, environmentalists, biologists, and economists. The target is the optimal distribution of limited water resources and the preservation of acceptable levels of water quality, in such a way that all of the users in domestic, agricultural, industrial, and ecological fields’ needs are satisfied with the least controversy and conflicts.

Remote sensing, is a very powerful tool that has been combined with water resources management methods, providing the scientific community with useful satellite data, algorithms, and integrated models. In this way, remote sensing supports the terrestrial and in-situ methods that are concerned with the following issues:

a) Water quantity management: evapotranspiration models; water balance models; land cover and land use; artificial neural networks, focused mainly on the agricultural use of water, but also on the domestic and industrial use as well. Remote sensing techniques can lead to the efficiency of water resources management plans.

b) Water quality management: water temperature; dissolved oxygen; chlorophyll-a; and eutrophication indexes, focused mainly on aquatic ecosystems. The comparison and correlation between terrestrial and satellite data can support empirical formulas for water quality estimation and forecasting, leading to the development of a long-term monitoring protocol for aquatic ecosystems.

Prof. Dr. Aris Psilovikos
Guest Editor

Manuscript Submission Information

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Keywords

  • water resources management
  • evapotranspiration
  • water balance
  • remote sensing
  • water quality management
  • aquatic ecosystems monitoring

Published Papers (4 papers)

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Research

Open AccessArticle
Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing
Water 2019, 11(11), 2226; https://doi.org/10.3390/w11112226 - 25 Oct 2019
Abstract
In this study, a remote sensing-based method of mapping and predicting fish spatial distribution in inland waters is developed. A combination of Earth Observation data, in-situ measurements, and hydroacoustics is used to relate fish biomass distribution and water-quality parameters along the longitudinal transect [...] Read more.
In this study, a remote sensing-based method of mapping and predicting fish spatial distribution in inland waters is developed. A combination of Earth Observation data, in-situ measurements, and hydroacoustics is used to relate fish biomass distribution and water-quality parameters along the longitudinal transect of the Římov Reservoir (Czech Republic) using statistical and machine learning techniques. Parameter variations and biomass distribution are estimated and validated, and apparent trends are explored and discussed, together with potential limitations and weaknesses. Water-quality parameters exhibit longitudinal gradients along the reservoir, while calculations reveal a distinct fish assemblage pattern observed as a patchy overall biomass distribution. Although the proposed methodology has a great potential for sustainable water management, careful planning is needed to ensure the simultaneous acquisition of remote sensing and in-situ data to maximize calibration accuracy. Full article
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Open AccessFeature PaperArticle
Hybrid Methodology for the Estimation of Crop Coefficients Based on Satellite Imagery and Ground-Based Measurements
Water 2019, 11(7), 1364; https://doi.org/10.3390/w11071364 - 30 Jun 2019
Abstract
The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration [...] Read more.
The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration (METRIC) model was used to derive crop coefficient values during the growing season of 2012. The proposed methodology was developed using medium resolution Landsat 7 ETM+ images and meteorological data from a local weather station. Cotton, sugar beets, and corn fields were utilized. During the same period, spectral signatures were obtained for each crop using the field spectroradiometer GER1500 (Spectra Vista Corporation, NY, U.S.A.). Relative spectral responses (RSR) were used for the filtering of the specific reflectance values giving the opportunity to match the spectral measurements with Landsat ETM+ bands. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were then computed, and empirical relationships were derived using linear regression analysis. NDVI, SAVI, and EVI2 were tested separately for each crop. The resulting equations explained those relationships with a very high R2 value (>0.86). These relationships have been validated against independent data. Validation using a new image file after the experimental period gives promising results, since the modeled image file is similar in appearance to the initial one, especially when a crop mask is applied. The CROPWAT model supports those results when using the new crop coefficients to estimate the related crop water requirements. The main benefit of the new approach is that the derived relationships are better adjusted to the crops. The described approach is also less time-consuming because there is no need for atmospheric correction when working with ground spectral measurements. Full article
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Open AccessArticle
Seasonal Effect on Spatial and Temporal Consistency of the New GPM-Based IMERG-v5 and GSMaP-v7 Satellite Precipitation Estimates in Brazil’s Central Plateau Region
Water 2019, 11(4), 668; https://doi.org/10.3390/w11040668 - 31 Mar 2019
Cited by 6
Abstract
This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM [...] Read more.
This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product. Full article
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Open AccessArticle
Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia
Water 2019, 11(3), 556; https://doi.org/10.3390/w11030556 - 17 Mar 2019
Cited by 4
Abstract
Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a [...] Read more.
Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments. Full article
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