Special Issue "Applications of Remote Sensing and GIS in Hydrology II"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (18 May 2020).

Special Issue Editors

Dr. Frédéric Frappart
E-Mail Website
Guest Editor
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, UMR 5566, CNES/CNRS/IRD/UPS, Observatoire Midi-Pyrénées, 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: remote sensing; hydrology and ecology
Special Issues and Collections in MDPI journals
Dr. Nicolas Baghdadi
E-Mail Website
Guest Editor
Dr. Pere Quintana
E-Mail Website
Guest Editor
Ebre Observatory, Universitat Ramon Llull- CSIC, Tarragona, Spain
Interests: meteorology; hydrology; climate; land surface; impacts of climate change; hydrological extremes; floods; droughts; water resources; distributed hydrological modelling; meteorological analysis
Dr. Mehrez Zribi
E-Mail Website
Guest Editor
CESBIO (CNRS/UPS/CNES/IRD)18, Avenue Edouard Belin, 31401 Toulouse Cedex 9, France
Interests: microwave remote sensing; airborne instrumentation; land hydrology
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, remote sensing has become increasingly important in Earth system science, and, especially, for the monitoring of the terrestrial water cycle with the launch of a great number of satellites, covering various applications (rainfall, soil moisture, flood extent, surface water level, terrestrial water storage, snow and ice, floods). This has paved the way for an explosion in the use of remote sensing data, especially through the use of geographic information systems (GIS). This Special Issue aims to present reviews and recent advances of general interest in the use of remote sensing and GIS for hydrology. Manuscripts can be related to any use of remote sensing and/or GIS for any hydrological application. They can be focused on the monitoring of water reservoir (e.g., surface storage, soil moisture, soil roughness, groundwater, snow and ice) or flux (e.g., rainfall, evapotranspiration, discharge) at any scale, as well as on the management of water resources. Observations taking into account the spatial and temporal variability are needed to calibrate the models and control their forecasts. Remote sensing now provides access to useful factors in land surface monitoring. The assimilation of satellite measurements and products in the function models of hydrological processes and water management procedures allows an improvement in the understanding of the continental water cycle.

Dr. Frédéric Frappart
Dr. Nicolas Baghdadi
Dr. Pere Quintana
Dr. Mehrez Zribi
Guest Editors

Manuscript Submission Information

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Keywords

  • hydrological cycle
  • satellite
  • remote sensing
  • GIS

Published Papers (11 papers)

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Research

Article
Integration of GIS-Based Multicriteria Decision Analysis and Analytic Hierarchy Process to Assess Flood Hazard on the Al-Shamal Train Pathway in Al-Qurayyat Region, Kingdom of Saudi Arabia
Water 2020, 12(6), 1702; https://doi.org/10.3390/w12061702 - 14 Jun 2020
Cited by 8 | Viewed by 1148
Abstract
Understanding the dynamics of floods in dry environments and predicting an accurate flood hazard map considering multiple standards and conflicting objectives is of great political and planning importance in the Kingdom of Saudi Arabia’s vision for the year 2030, in order to reduce [...] Read more.
Understanding the dynamics of floods in dry environments and predicting an accurate flood hazard map considering multiple standards and conflicting objectives is of great political and planning importance in the Kingdom of Saudi Arabia’s vision for the year 2030, in order to reduce losses in lives, property, and infrastructure. The objectives of this study are (1) to develop a flood vulnerability map identifying flood-prone areas along the Al-Shamal train railway pathway; (2) to forecast the vulnerability of urban areas, agricultural land, and infrastructure to possible future floods hazard; and (3) to introduce strategic solutions and recommendations to mitigate and protect such areas from the negative impacts of floods. In order to achieve these objectives, multicriteria decision analysis based on geographic information systems (GIS-MCDA) is used to build a flood hazard map of the study area. The analytic hierarchy process (AHP) is applied to extract the weights of eight criteria which affect the areas which are prone to flooding hazards, including flow accumulation, distance from the wadi network, slope, rainfall density, drainage density, and rainfall speed. Furthermore, the receiver operating characteristic (ROC Curve) method is used to validate the presented flood hazard model. The results of the study reveal that there are five degrees of flooding hazard along the Al-Shamal train path, ranging from very high to very low. The high and very high hazard zones comprise 19.2 km along the path, which constitutes about 26.45% of the total path length, and are concentrated at the intersections of the Al-Shamal train pathway with the Bayer and Al-Makhrouk wadis. Moderate, low, and very low flood severity areas constitute nearly 53.39 km, representing 73.55% of the total length (72.59 km) of the track. These areas are concentrated at the intersection of the Al-Shamal train track with the Haseidah Al-Gharbiyeh and Hsaidah Umm Al-Nakhleh wadis. Urban and agricultural areas that are vulnerable to high and very high flooding hazards are shown to have areas of 29.23 km2 (22.12%) and 59.87 km2 (46.39%), respectively. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Improved Landsat-Based Water and Snow Indices for Extracting Lake and Snow Cover/Glacier in the Tibetan Plateau
Water 2020, 12(5), 1339; https://doi.org/10.3390/w12051339 - 08 May 2020
Cited by 5 | Viewed by 1186
Abstract
Identifying water and snow cover/glaciers (SCG) accurately is of great importance for monitoring different water resources in the Tibetan Plateau. However, discriminating between water and SCG remains a difficult task because of their similar spectral characteristic according to the physical principles of remote [...] Read more.
Identifying water and snow cover/glaciers (SCG) accurately is of great importance for monitoring different water resources in the Tibetan Plateau. However, discriminating between water and SCG remains a difficult task because of their similar spectral characteristic according to the physical principles of remote sensing. To efficiently distinguish different kinds of water resources automatically, here we proposed two new indices including: (i) the normalized difference water index with no SCG information (NDWIns) to extract lake water and suppress SCG: and (ii) the normalized difference snow index with no water information (NDSInw) to extract SCG and suppress lake water. Both new water and snow indices were tested in the Tibetan Plateau using Landsat series, showing that the overall accuracies of NDWIns and NDSInw were in the range of 94.6–97.0% and 94.9–97.0% in mapping the lake water from SCG and mapping the SCG from lake water, respectively. Further comparisons suggest that these new two indices improved upon the previous normalized difference snow index/modified normalized difference water index (NDSI/MNDWI) in mapping the water body and SCG. While the present study only focuses on the validation over certain areas in Tibetan Plateau, the newly proposed NDWIns and NDSInw have the potential for better monitoring the lake water and snow/glacier areas over other cold regions around the globe. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation
Water 2020, 12(5), 1292; https://doi.org/10.3390/w12051292 - 02 May 2020
Cited by 1 | Viewed by 881
Abstract
Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often [...] Read more.
Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57). Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Long-Term Spatiotemporal Variation and Environmental Driving Forces Analyses of Algal Blooms in Taihu Lake Based on Multi-Source Satellite and Land Observations
Water 2020, 12(4), 1035; https://doi.org/10.3390/w12041035 - 05 Apr 2020
Cited by 2 | Viewed by 1017
Abstract
The algal blooms caused by the eutrophication of lakes is a major environmental problem. In this study, we took China’s Taihu Lake as the research area, using multi-source satellite imagery data to monitor the information of algal blooms from 2008 to 2017. Following [...] Read more.
The algal blooms caused by the eutrophication of lakes is a major environmental problem. In this study, we took China’s Taihu Lake as the research area, using multi-source satellite imagery data to monitor the information of algal blooms from 2008 to 2017. Following the analyses of the temporal and spatial variation trends of the blooms, water quality and meteorological data from land observation stations were employed to investigate the main environmental driving forces of the algal bloom outbreaks. The results show that, over the decade, the blooms with medium and higher hazard degrees mainly occurred in summer and autumn, and especially in autumn. From 2008 to 2016, the algal blooms outbreak degree was relatively stable, but, in 2017, it was severe, and the Northwest Lake area and the northern bays had heavier blooms than the other lake areas. From the analyses of the environmental driving forces, the variation trend of total nitrogen (TN) and total phosphorus (TP) concentrations in Taihu Lake from 2008 to 2017 was moderate, and the minimum concentrations of TN and TP both exceeded the threshold for algal bloom outbreaks. It was also found that the algal bloom area had notable correlations with the sunshine duration, wind speed and direction, precipitation, and air pressure. The research results of this paper will provide a theoretical basis for the scientific prediction of the occurrence of algal blooms in Taihu Lake. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Statistical Evaluation of the Latest GPM-Era IMERG and GSMaP Satellite Precipitation Products in the Yellow River Source Region
Water 2020, 12(4), 1006; https://doi.org/10.3390/w12041006 - 01 Apr 2020
Cited by 10 | Viewed by 1187
Abstract
As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of [...] Read more.
As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Automatic Extraction of Supraglacial Lakes in Southwest Greenland during the 2014–2018 Melt Seasons Based on Convolutional Neural Network
Water 2020, 12(3), 891; https://doi.org/10.3390/w12030891 - 22 Mar 2020
Cited by 3 | Viewed by 1387
Abstract
The mass loss of the Greenland Ice Sheet (GrIS) has implications for global sea level rise, and surface meltwater is an important factor that affects the mass balance. Supraglacial lakes (SGLs), which are representative and identifiable hydrologic features of surface meltwater on GrIS, [...] Read more.
The mass loss of the Greenland Ice Sheet (GrIS) has implications for global sea level rise, and surface meltwater is an important factor that affects the mass balance. Supraglacial lakes (SGLs), which are representative and identifiable hydrologic features of surface meltwater on GrIS, are a means of assessing surface ablation temporally and spatially. In this study, we have developed a robust method to automatically extract SGLs by testing the widely distributed SGLs area—in southwest Greenland (68°00′ N–70°00′ N, 48°00′ W–51°30′ W), and documented their dynamics from 2014 to 2018 using Landsat 8 OLI images. This method identifies water using Convolutional Neural Networks (CNN) and then extracts SGLs with morphological and geometrical algorithms. CNN combines spectral and spatial features and shows better water identification results than the widely used adaptive thresholding method (Otsu), and two machine learning methods (Random Forests (RF) and Support Vector Machine (SVM)). Our results show that the total SGLs area varied between 158 and 393 km2 during 2014 to 2018; the area increased from 2014 to 2015, then decreased and reached the lowest point (158.73 km2) in 2018, when the most limited surface melting was observed. SGLs were most active during the melt season in 2015 with a quantity of 700 and a total area of 393.36 km2. The largest individual lake developed in 2016, with an area of 9.30 km2. As for the elevation, SGLs were most active in the area, with the elevation ranging from 1000 to 1500 m above sea level, and SGLs in 2016 were distributed at higher elevations than in other years. Our work proposes a method to extract SGLs accurately and efficiently. More importantly, this study is expected to provide data support to other studies monitoring the surface hydrological system and mass balance of the GrIS. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy
Water 2020, 12(3), 866; https://doi.org/10.3390/w12030866 - 20 Mar 2020
Cited by 10 | Viewed by 1462
Abstract
The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging [...] Read more.
The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model
Water 2019, 11(10), 2138; https://doi.org/10.3390/w11102138 - 15 Oct 2019
Cited by 1 | Viewed by 1128
Abstract
An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble [...] Read more.
An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models). Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran
Water 2019, 11(8), 1624; https://doi.org/10.3390/w11081624 - 06 Aug 2019
Cited by 8 | Viewed by 1899
Abstract
Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an [...] Read more.
Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4–9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models
Water 2019, 11(8), 1596; https://doi.org/10.3390/w11081596 - 31 Jul 2019
Cited by 20 | Viewed by 1920
Abstract
In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. [...] Read more.
In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m3/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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Article
Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models
Water 2019, 11(7), 1362; https://doi.org/10.3390/w11071362 - 30 Jun 2019
Cited by 9 | Viewed by 1273
Abstract
Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period [...] Read more.
Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
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