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The Use of Remote Sensing in Hydrology

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

Deadline for manuscript submissions: closed (28 February 2017) | Viewed by 103737

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Special Issue Editors

Dr. Frédéric Frappart

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Guest Editor

Interactions Sol Plante Atmosphère, UMR 1391 INRAE/Bordeaux Science Agro 71, Avenue Edouard Bourlaux, 33882 Villenave d'Ornon, France
Interests: remote sensing; water cycle; carbon cycle; wetlands
Special Issues, Collections and Topics in MDPI journals

Dr. Luc Bourrel

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Guest Editor

Géosciences Environnement Toulouse, UMR 5563, Université de Toulouse, CNRS-IRD-OMP-CNES, 31400 Toulouse, France
Interests: hydroclimatology; water cycle; wetlands; extreme events; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remotely sensed data are nowadays commonly used for regional/global monitoring of hydrological variables including soil moisture, rainfall, water levels, flood extent, evapotranspiration or land water storage and the forcing, the calibration or the assimilation into hydrodynamics or hydrological or hydrometeorological models. In the years to come, recent and future satellite sensors, some of them specifically designed for hydrological purposes, will provide systematic observations of hydrological parameters (e.g., surface and sub-surface storages, and fluxes) at high spatial and temporal resolutions. This will offer new applications for the hydrological community. This Special Issue aims to present reviews and recent advances of general interest in the use of remote sensing for hydrology. Manuscripts can be related to any hydrological reservoir (e.g., surface storage, soil moisture, groundwater, …) or flux (e.g., rainfall, evapotranspiration, discharge, …), the integration of satellite data into hydrological models, and improvements for hydrology that can be expected from future satellite missions.

Dr. Frédéric Frappart
Dr. Luc Bourrel
Guest Editors

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Keywords

Remotely sensed observations (SAR, multi-spectral/hyperspectral images, passive microwave, satellite altimetry, gravimetry from space)
surface water (level, extent, discharge)
groundwater
rainfall and evapotranspiration

Published Papers (13 papers)

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23 pages, 4205 KiB

Open AccessArticle

Validation of Satellite Estimates (Tropical Rainfall Measuring Mission, TRMM) for Rainfall Variability over the Pacific Slope and Coast of Ecuador

by Bolívar Erazo, Luc Bourrel, Frédéric Frappart, Oscar Chimborazo, David Labat, Luis Dominguez-Granda, David Matamoros and Raul Mejia

Water 2018, 10(2), 213; https://doi.org/10.3390/w10020213 - 16 Feb 2018

Cited by 35 | Viewed by 8665

Abstract

A dense rain-gauge network within continental Ecuador was used to evaluate the quality of various products of rainfall data over the Pacific slope and coast of Ecuador (EPSC). A cokriging interpolation method is applied to the rain-gauge data yielding a gridded product at 5-km resolution covering the period 1965–2015. This product is compared with the Global Precipitation Climatology Centre (GPCC) dataset, the Climatic Research Unit–University of East Anglia (CRU) dataset, the Tropical Rainfall Measuring Mission (TRMM/TMPA 3B43 Version 7) dataset and the ERA-Interim Reanalysis. The analysis reveals that TRMM data show the most realistic features. The relative bias index (Rbias) indicates that TRMM data is closer to the observations, mainly over lowlands (mean Rbias of 7%) but have more limitations in reproducing the rainfall variability over the Andes (mean Rbias of −28%). The average RMSE and Rbias of 68.7 and −2.8% of TRMM are comparable with the GPCC (69.8 and 5.7%) and CRU (102.3 and −2.3%) products. This study also focuses on the rainfall inter-annual variability over the study region which experiences floods that have caused high economic losses during extreme El Niño events. Finally, our analysis evaluates the ability of TRMM data to reproduce rainfall events during El Niño years over the study area and the large basins of Esmeraldas and Guayas rivers. The results show that TRMM estimates report reasonable levels of heavy rainfall detection (for the extreme 1998 El Niño event) over the EPSC and specifically towards the center-south of the EPSC (Guayas basin) but present underestimations for the moderate El Niño of 2002–2003 event and the weak 2009–2010 event. Generally, the rainfall seasonal features, quantity and long-term climatology patterns are relatively well estimated by TRMM. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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7236 KiB

Open AccessArticle

A Comparative Study of GRACE with Continental Evapotranspiration Estimates in Australian Semi-Arid and Arid Basins: Sensitivity to Climate Variability and Extremes

by Hong Shen, Marc Leblanc, Frédéric Frappart, Lucia Seoane, Damien O’Grady, Albert Olioso and Sarah Tweed

Water 2017, 9(9), 614; https://doi.org/10.3390/w9090614 - 05 Sep 2017

Cited by 9 | Viewed by 5900

Abstract

This study examines the dynamics and robustness of large-scale evapotranspiration products in water-limited environments. Four types of ET products are tested against rainfall in two large semi-arid to arid Australian basins from 2003 to 2010: two energy balance ET methods which are forced by optical satellite retrievals from MODIS; a newly developed land surface model (AWRA); and one approach based on observations from the Gravity Recovery and Climate Experiment (GRACE) and rainfall data. The two basins are quasi (Murray-Darling Basin: 1.06 million km²) and completely (Lake Eyre Basin: 1.14 million km²) endorheic. During the study period, two extreme climatic events—the Millennium drought and the strongest La Niña event—were recorded in the basins and are used in our assessment. The two remotely-sensed ET products constrained by the energy balance tended to overestimate ET flux over water-stressed regions. They had low sensitivity to climatic extremes and poor capability to close the water balance. However, these two remotely-sensed and energy balance products demonstrated their superiority in capturing spatial features including over small-scale and complicated landscapes. AWRA and GRACE formulated in the water balance framework were more sensitive to rainfall variability and yielded more realistic ET estimates during climate extremes. GRACE demonstrated its ability to account for seasonal and inter-annual change in water storage for ET evaluation. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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28588 KiB

Open AccessArticle

Characterization of Terrestrial Discharges into Coastal Waters with Thermal Imagery from a Hierarchical Monitoring Program

by Claudia Ferrara, Massimiliano Lega, Giannetta Fusco, Paul Bishop and Theodore Endreny

Water 2017, 9(7), 500; https://doi.org/10.3390/w9070500 - 11 Jul 2017

Cited by 18 | Viewed by 5987

Abstract

Background: The hierarchical use of remotely-sensed imagery from satellites, and then proximally-sensed imagery from helicopter sand drones, can provide a range of spatial and temporal coverage that supports water quality monitoring of complex pollution scenarios. Methods: The study used hierarchical satellite-, helicopter-, and drone-acquired thermal imagery of coastal plumes ranging from 3 to 300 m, near Naples, Italy, and captured temporally- and spatially-overlapping in situ samples to correlate thermal and water quality parameters in each plume and the seawater. Results: In situ sampling determined that between-plume salinity varied by 37%, chlorophyll-a varied by 356%, dissolved oxygen varied by 81%, and turbidity varied by 232%. The radiometric temperature, T_rad, for the plume area of interest had a correlation of 0.81 with salinity, 0.74 with chlorophyll-a, 0.98 with dissolved oxygen, and −0.61 with turbidity. Conclusion: This study established hierarchical use of remote and proximal thermal imagery can provide monitoring of complex coastal areas. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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17390 KiB

Open AccessFeature PaperArticle

European Rice Cropland Mapping with Sentinel-1 Data: The Mediterranean Region Case Study

by Duy Ba Nguyen and Wolfgang Wagner

Water 2017, 9(6), 392; https://doi.org/10.3390/w9060392 - 01 Jun 2017

Cited by 67 | Viewed by 9034

Abstract

Rice farming is one of the most important activities in the agriculture sector, producing staple food for the majority of the world's growing population. Accurate and up-to-date assessment of the spatial distribution of rice cultivated area is a key information requirement of all stakeholders including policy makers, rice farmers and consumers. Timely assessment with high precision is, e.g., crucial for water resource management, market prices control and during humanitarian food crisis. Recently, two Sentinel-1 (S-1) satellites carrying a C-band Synthetic Aperture Radar (SAR) sensor were launched by the European Space Agency (ESA) within the homework of the Copernicus program. The advanced data acquisition capabilities of S-1 provide a unique opportunity to monitor different land cover types at high spatial (20 m) and temporal (twice-weekly to biweekly) resolution. The objective of this research is to evaluate the applicability of an existing phenology-based classification method for continental-scale rice cropland mapping using S-1 backscatter time series. In this study, the S-1 images were collected during the rice growing season of 2015 covering eight selected European test sites situated in six Mediterranean countries. Due to the better rice classification capabilities of SAR cross-polarized measurement as compared to co-polarized data, S-1 cross-polarized (VH) data were used. Phenological parameters derived from the S-1 VH backscatter time series were used as an input to a knowledge-based decision-rule classifier in order to classify the input data into rice and non-rice areas. The classification results were evaluated using multiple regions of interest (ROIs) drawn from high-resolution optical remote sensing (SPOT 5) data and the European CORINE land cover (CLC 2012) product. An overall accuracy of more than 70% for all eight study sites was achieved. The S-1 based classification maps reveal much more details compared to the rice field class contained in the CLC 2012 product. These findings demonstrate the potential and feasibility of using S-1 VH data to develop an operational rice crop monitoring framework at the continental scale. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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Open AccessFeature PaperArticle

The Performance and Potentials of the CryoSat-2 SAR and SARIn Modes for Lake Level Estimation

by Karina Nielsen, Lars Stenseng, Ole Baltazar Andersen and Per Knudsen

Water 2017, 9(6), 374; https://doi.org/10.3390/w9060374 - 25 May 2017

Cited by 25 | Viewed by 4658

Abstract

Over the last few decades, satellite altimetry has proven to be valuable for monitoring lake levels. With the new generation of altimetry missions, CryoSat-2 and Sentinel-3, which operate in Synthetic Aperture Radar (SAR) and SAR Interferometric (SARIn) modes, the footprint size is reduced to approximately 300 m in the along-track direction. Here, the performance of these new modes is investigated in terms of uncertainty of the estimated water level from CryoSat-2 data and the agreement with in situ data. The data quality is compared to conventional low resolution mode (LRM) altimetry products from Envisat, and the performance as a function of the lake area is tested. Based on a sample of 145 lakes with areas ranging from a few to several thousand

{km}^{2}

, the CryoSat-2 results show an overall superior performance. For lakes with an area below 100

{km}^{2}

, the uncertainty of the lake levels is only half of that of the Envisat results. Generally, the CryoSat-2 lake levels also show a better agreement with the in situ data. The lower uncertainty of the CryoSat-2 results entails a more detailed description of water level variations. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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3043 KiB

Open AccessArticle

Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations

by Binh Pham-Duc, Catherine Prigent and Filipe Aires

Water 2017, 9(6), 366; https://doi.org/10.3390/w9060366 - 23 May 2017

Cited by 108 | Viewed by 10337

Abstract

This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of ∼90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation. Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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1446 KiB

Open AccessArticle

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

by Marie Parrens, Ahmad Al Bitar, Frédéric Frappart, Fabrice Papa, Stephane Calmant, Jean-François Crétaux, Jean-Pierre Wigneron and Yann Kerr

Water 2017, 9(5), 350; https://doi.org/10.3390/w9050350 - 17 May 2017

Cited by 34 | Viewed by 7670

Abstract

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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2474 KiB

Open AccessFeature PaperArticle

Evaluation of the Water Cycle in the European COSMO-REA6 Reanalysis Using GRACE

by Anne Springer, Annette Eicker, Anika Bettge, Jürgen Kusche and Andreas Hense

Water 2017, 9(4), 289; https://doi.org/10.3390/w9040289 - 20 Apr 2017

Cited by 12 | Viewed by 8392

Abstract

Precipitation and evapotranspiration, and in particular the precipitation minus evapotranspiration deficit (

P - E

), are climate variables that may be better represented in reanalyses based on numerical weather prediction (NWP) models than in other datasets.

P - E

provides essential information [...] Read more.

Precipitation and evapotranspiration, and in particular the precipitation minus evapotranspiration deficit (

P - E

), are climate variables that may be better represented in reanalyses based on numerical weather prediction (NWP) models than in other datasets.

P - E

provides essential information on the interaction of the atmosphere with the land surface, which is of fundamental importance for understanding climate change in response to anthropogenic impacts. However, the skill of models in closing the atmospheric-terrestrial water budget is limited. Here, total water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) mission are used in combination with discharge data for assessing the closure of the water budget in the recent high-resolution Consortium for Small-Scale Modelling 6-km Reanalysis (COSMO-REA6) while comparing to global reanalyses (Interim ECMWF Reanalysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)) and observation-based datasets (Global Precipitation Climatology Centre (GPCC), Global Land Evaporation Amsterdam Model (GLEAM)). All 26 major European river basins are included in this study and aggregated to 17 catchments. Discharge data are obtained from the Global Runoff Data Centre (GRDC), and insufficiently long time series are extended by calibrating the monthly Génie Rural rainfall-runoff model (GR2M) against the existing discharge observations, subsequently generating consistent model discharge time series for the GRACE period. We find that for most catchments, COSMO-REA6 closes the water budget within the error estimates. In contrast, the global reanalyses underestimate

P - E

with up to 20 mm/month. For all models and catchments, short-term (below the seasonal timescale) variability of atmospheric terrestrial flux agrees well with GRACE and discharge data with correlations of about 0.6. Our large study area allows identifying regional patterns like negative trends of

P - E

in eastern Europe and positive trends in northwestern Europe. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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5544 KiB

Open AccessFeature PaperArticle

Fifteen Years (1993–2007) of Surface Freshwater Storage Variability in the Ganges-Brahmaputra River Basin Using Multi-Satellite Observations

by Edward Salameh, Frédéric Frappart, Fabrice Papa, Andreas Güntner, Vuruputur Venugopal, Augusto Getirana, Catherine Prigent, Filipe Aires, David Labat and Benoît Laignel

Water 2017, 9(4), 245; https://doi.org/10.3390/w9040245 - 31 Mar 2017

Cited by 17 | Viewed by 9151

Abstract

Surface water storage is a key component of the terrestrial hydrological and biogeochemical cycles that also plays a major role in water resources management. In this study, surface water storage (SWS) variations are estimated at monthly time-scale over 15 years (1993–2007) using a hypsographic approach based on the combination of topographic information from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hydrological Modeling and Analysis Platform (HyMAP)-based Global Digital Elevation Models (GDEM) and the Global Inundation Extent Multi-Satellite (GIEMS) product in the Ganges-Brahmaputra basin. The monthly variations of the surface water storage are in good accordance with precipitation from Global Precipitation Climatology Project (GPCP), river discharges at the outlet of the Ganges and the Brahmaputra, and terrestrial water storage (TWS) from the Gravity Recovery And Climate Experiment (GRACE), with correlations higher than 0.85. Surface water storage presents a strong seasonal signal (~496 km³ estimated by GIEMS/ASTER and ~378 km³ by GIEMS/HyMAPs), representing ~51% and ~41% respectively of the total water storage signal and it exhibits a large inter-annual variability with strong negative anomalies during the drought-like conditions of 1994 or strong positive anomalies such as in 1998. This new dataset of SWS is a new, highly valuable source of information for hydrological and climate modeling studies of the Ganges-Brahmaputra river basin. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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Open AccessArticle

Size Distribution, Surface Coverage, Water, Carbon, and Metal Storage of Thermokarst Lakes in the Permafrost Zone of the Western Siberia Lowland

by Yury M. Polishchuk, Alexander N. Bogdanov, Vladimir Yu. Polishchuk, Rinat M. Manasypov, Liudmila S. Shirokova, Sergey N. Kirpotin and Oleg S. Pokrovsky

Water 2017, 9(3), 228; https://doi.org/10.3390/w9030228 - 21 Mar 2017

Cited by 46 | Viewed by 6475

Abstract

Despite the importance of thermokarst (thaw) lakes of the subarctic zone in regulating greenhouse gas exchange with the atmosphere and the flux of metal pollutants and micro-nutrients to the ocean, the inventory of lake distribution and stock of solutes for the permafrost-affected zone are not available. We quantified the abundance of thermokarst lakes in the continuous, discontinuous, and sporadic permafrost zones of the western Siberian Lowland (WSL) using Landsat-8 scenes collected over the summers of 2013 and 2014. In a territory of 105 million ha, the total number of lakes >0.5 ha is 727,700, with a total surface area of 5.97 million ha, yielding an average lake coverage of 5.69% of the territory. Small lakes (0.5–1.0 ha) constitute about one third of the total number of lakes in the permafrost-bearing zone of WSL, yet their surface area does not exceed 2.9% of the total area of lakes in WSL. The latitudinal pattern of lake number and surface coverage follows the local topography and dominant landscape zones. The role of thermokarst lakes in dissolved organic carbon (DOC) and most trace element storage in the territory of WSL is non-negligible compared to that of rivers. The annual lake storage across the WSL of DOC, Cd, Pb, Cr, and Al constitutes 16%, 34%, 37%, 57%, and 73%, respectively, of their annual delivery by WSL rivers to the Arctic Ocean from the same territory. However, given that the concentrations of DOC and metals in the smallest lakes (<0.5 ha) are much higher than those in the medium and large lakes, the contribution of small lakes to the overall carbon and metal budget may be comparable to, or greater than, their contribution to the water storage. As such, observations at high spatial resolution (<0.5 ha) are needed to constrain the reservoirs and the mobility of carbon and metals in aquatic systems. To upscale the DOC and metal storage in lakes of the whole subarctic, the remote sensing should be coupled with hydrochemical measurements in aquatic systems of boreal plains. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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Open AccessArticle

A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes

by Alireza Taravat, Masih Rajaei, Iraj Emadodin, Hamidreza Hasheminejad, Rahman Mousavian and Ehsan Biniyaz

Water 2016, 8(11), 478; https://doi.org/10.3390/w8110478 - 25 Oct 2016

Cited by 28 | Viewed by 6524

Abstract

Lake Urmia, the second largest saline Lake on earth and a highly endangered ecosystem, is on the brink of a serious environmental disaster similar to the catastrophic death of the Aral Sea. Progressive drying has been observed during the last decade, causing dramatic changes to Lake Urmia’s surface and its regional water supplies. The present study aims to improve monitoring of spatiotemporal changes of Lake Urmia in the period 1975–2015 using the multi-temporal satellite altimetry and Landsat (5-TM, 7-ETM+ and 8-OLI) images. In order to demonstrate the impacts of climate change and human pressure on the variations in surface extent and water level, Lake Sevan and Van Lake with different characteristics were studied along with the Urmia Lake. Normalized Difference Water Index-Principal Components Index (NDWI-PCs), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and MultiLayer Perceptron Neural Networks (MLP NNs) classifier were investigated for the extraction of surface water from Landsat data. The presented results revealed that MLP NNs has a better performance in the cases where the other models generate poor accuracy. The results show that the area of Lake Sevan and Van Lake have increased while the area of Lake Urmia has decreased by ~65.23% in the past decades, far more than previously reported (~25% to 50%). Urmia Lake’s shoreline has been receding severely between 2010 and 2015 with no sign of recovery, which has been partly blamed on prolonged droughts, aggressive regional water resources development plans, intensive agricultural activities, and anthropogenic changes to the system. The results also indicated that (among the proposed factors) changes in inflows due to overuse of surface water resources and constructing dams (mostly during 1995–2005) are the main reasons for Urmia Lake’s shoreline receding. The model presented in this manuscript can be used by managers as a decision support system to find the effects of building new dams or other infrastructures. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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Review

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Open AccessReview

CryoSat-2 Altimetry Applications over Rivers and Lakes

by Liguang Jiang, Raphael Schneider, Ole B. Andersen and Peter Bauer-Gottwein

Water 2017, 9(3), 211; https://doi.org/10.3390/w9030211 - 13 Mar 2017

Cited by 53 | Viewed by 10665

Abstract

Monitoring the variation of rivers and lakes is of great importance. Satellite radar altimetry is a promising technology to do this on a regional to global scale. Satellite radar altimetry data has been used successfully to observe water levels in lakes and (large) rivers, and has also been combined with hydrologic/hydrodynamic models. Except CryoSat-2, all radar altimetry missions have been operated in conventional low resolution mode with a short repeat orbit (35 days or less). CryoSat-2, carrying a Synthetic Aperture Radar (SAR) altimeter, has a 369-day repeat and a drifting ground track pattern and provides new opportunities for hydrologic research. The narrow inter-track distance (7.5 km at the equator) makes it possible to monitor many lakes and rivers and SAR mode provides a finer along-track resolution, higher return power and speckle reduction through multi-looks. However, CryoSat-2 challenges conventional ways of dealing with satellite inland water altimetry data because virtual station time series cannot be directly derived for rivers. We review the CryoSat-2 mission characteristics, data products, and its use and perspectives for inland water applications. We discuss all the important steps in the workflow for hydrologic analysis with CryoSat-2, and conclude with a discussion of promising future research directions. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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Open AccessFeature PaperReview

Mapping Palaeohydrography in Deserts: Contribution from Space-Borne Imaging Radar

by Philippe Paillou

Water 2017, 9(3), 194; https://doi.org/10.3390/w9030194 - 08 Mar 2017

Cited by 13 | Viewed by 7030

Abstract

Space-borne Synthetic Aperture Radar (SAR) has the capability to image subsurface features down to several meters in arid regions. A first demonstration of this capability was performed in the Egyptian desert during the early eighties, thanks to the first Shuttle Imaging Radar mission. Global coverage provided by recent SARs, such as the Japanese ALOS/PALSAR sensor, allowed the mapping of vast ancient hydrographic systems in Northern Africa. We present a summary of palaeohydrography results obtained using PALSAR data over large deserts such as the Sahara and the Gobi. An ancient river system was discovered in eastern Lybia, connecting in the past the Kufrah oasis to the Mediterranean Sea, and the terminal part of the Tamanrasett river was mapped in western Mauritania, ending with a large submarine canyon. In southern Mongolia, PALSAR images combined with topography analysis allowed the mapping of the ancient Ulaan Nuur lake. We finally show the potentials of future low frequency SAR sensors by comparing L-band (1.25 GHz) and P-band (435 MHz) airborne SAR acquisitions over a desert site in southern Tunisia. Full article

(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)

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