A review of Canadian remote sensing and hydrology, 1999–2003

Pietroniro, Alain; Leconte, Robert

doi:10.1002/hyp.5771

Cited by 31 publications

(21 citation statements)

References 77 publications

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“…Ulaby et al, 1978;Dobson and Ulaby, 1986;Oh et al, 1992;Fung et al, 1992;Chen et al, 1995;Dubois et al, 1995;Altese et al, 1996;Rakotoarivony et al, 1996;Shi et al, 1997;Wang et al, 1997;Sano et al, 1998;Weimann et al, 1998;Bindlish and Barros, 2000;Quesney et al, 2000;Baghdadi et al, 2002a;Le Hégarat et al, 2002;Pauwels et al, 2002;Satalino et al, 2002;Zribi and Dechambre, 2002;Srivastava et al, 2003;Leconte et al, 2004;Oh, 2004;Baghdadi et al, 2006a;Holah et al, 2005;Pietroniro and Leconte, 2005;Zribi et al, 2005). The results obtained using C-band show, for bare soils, that the radar backscattering coefficient ( °) follows an exponential or logarithmic function with the soil surface roughness, and increases linearly with the volumetric soil moisture for values between approximately 5% and 35-40%.…”

Section: Sensitivity Of the Radar Signal To Sscmentioning

confidence: 99%

Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

Baghdadi

Cerdan

Zribi

et al. 2007

Hydrological Processes

135

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Abstract:Synthetic aperture radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and radar wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction.This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal radar configurations and the precision associated with the estimation of soil surface characteristics.The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35°). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high-incidence data, which is possible with certain current sensors (RADARSAT-1 and ASAR both in band C). When L-band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the radar signal to Soil Surface Characteristics (SSC) increases with wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RADARSAT-2, TerraSAR-X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping.

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Section: Sensitivity Of the Radar Signal To Sscmentioning

confidence: 99%

Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

Baghdadi

Cerdan

Zribi

et al. 2007

Hydrological Processes

135

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“…Another example is the prediction of flood hydrographs and spatial distribution of hydrological characteristics over a river basin by the use of a DEM, soil and landuse data within a GIS framework (de Smedt et al, 2004). 2.1. Delineation of reservoirs by Remote Sensing using Landsat and ENVISAT ASAR imagery Pietroniro and Leconte, 2005 define remote sensing as the "science and art of obtaining information about an object, area, or phenomenon through the analyses of data acquired by a sensor that is not in direct contact with the target of investigation" while Elachi and van Zyl, 2006 simply define it as acquiring information about an object without any physical contact with the object under investigation. Radio waves, through the microwave, submillimeter, far infrared, near infrared, visible, ultraviolet, x-ray and gamma-ray regions of the electromagnetic spectrum are covered in most remote sensing techniques (Elachi and van Zyl, 2006).…”

Section: Gis and Remote Sensing For Water Resources And Hydrologymentioning

confidence: 99%

Delineation of small reservoirs using radar imagery in a semi-arid environment: A case study in the upper east region of Ghana

Annor

Giesen

Liebe

et al. 2009

Physics and Chemistry of the Earth, Parts A/B/C

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The importance of small reservoirs during droughts for the local population in most semi-arid environments cannot be over estimated. Water stored in these reservoirs allow for all-year-round irrigated agriculture for some farmers and ensures that there is little or no domestic and drinking water shortages for the local population during dry periods. In order to manage the water effectively for competing uses, the actual storage of these reservoirs need to be accurately estimated. Recent attempts to delineate these reservoirs using remote sensing with Landsat imagery have been quite successful especially in the Upper East region of Ghana, West Africa. This was done to determine the number; spatial distribution and storage volumes of reservoirs for effective water management and reservoir planning. However, the accuracy of the lateral delineation of these reservoirs needs further studies since it is paramount for its monitoring especially for purposes such as forecasting of crop failure in the dry seasons.This thesis explains how radar images (ENVISAT-ASAR) can be used to provide all year-round monitoring. Radar has the important advantage that it is independent of cloud cover hence can be used in the rainy season. It can also be used to acquire both day and night time images. This study shows how a monthly regional inventory of storage in small reservoirs can be obtained. The study area is the Upper East Region of Ghana, West Africa. In comparing ground data with ENVISAT data, it becomes clear that reeds, which often can be found in the shallow tail-ends of reservoirs, can not be readily distinguished from the surrounding vegetation.

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“…With the growing availability of high resolution terrain and landcover image data that provide important hydrological and water resources information (Pietroniro and Leconte, 2005), GIS modelling approaches are becoming popular for runoff simulation tasks. Perhaps the most important of these data sources is the digital elevation model (DEM).…”

Section: Introductionmentioning

confidence: 99%

Comparing alpine watershed attributes from LiDAR, Photogrammetric, and Contour‐based Digital Elevation Models

Hopkinson

Hayashi

Peddle

2008

Hydrological Processes

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Abstract:As part of an alpine hydrological study in the Canadian Rocky Mountains, three digital elevation model (DEM) data sets were obtained for the purpose of watershed characterization. The data sources were: (1) archived public access BC TRIM (Terrain Resource Information Management) 1 : 20 000 contour vectors; (2) stereo aerial photography DEM with a derived point spacing between 5 m and 20 m; (3) airborne LiDAR (light detection and ranging) with point spacing from 1 m to 4 m. GIS layers of terrain and watershed attributes were created for each of the three DEM data sets at grid cell resolutions of 5 m and 25 m. Watershed attributes investigated were: DEM elevation, area, hypsometry, and stream network topology. In areas of lower relief and forest cover, the TRIM contour DEM contained topological errors at both 5 m and 25 m resolutions due to the poor representation of terrain from widely spaced contours. The photo DEM introduced obvious stream topology errors at 25 m due to the inability of the photo DEM to discern subtle terrain beneath forest canopies. The photo and TRIM DEMs overestimated basin hypsometry relative to the LiDAR watersheds at highest elevations due, in part, to their inability to represent the inside of gullies and steps associated with geological strata. In the case of the photo DEM, selectively digitizing break lines such as cliff edges, while missing shadowed areas, led to the creation of an interpolated surface that was biased towards the outer extremities of the terrain. Conversely, relative to the photo-based datasets, the LiDAR DEM better captured the inside of gullies and steps while under-sampling break line features, leading to a bias in the interpolated surface towards internal terrain extremities. As would be expected, the quality and resolution of the terrain data increased from BC TRIM to photo to LiDAR. If modelling watersheds within the Canadian Rockies at the meso scale and above, BC TRIM (or equivalent) 1 : 20 000 contour vectors would be most appropriate given availability and cost considerations. The benefits of LiDAR are apparent if higher resolution and more accurate watershed attribute information is needed detailing first-order hydrological channel features on steep shadowed mountain slopes or zero-order hill-slope depressions beneath forest canopies. Such landscape features provide preferential storages for winter snowpack in mountainous watersheds, suggesting that in the future LiDAR might be a tool of choice for snowpack resource monitoring in these regions.

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A review of Canadian remote sensing and hydrology, 1999–2003

Cited by 31 publications

References 77 publications

Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

Delineation of small reservoirs using radar imagery in a semi-arid environment: A case study in the upper east region of Ghana

Comparing alpine watershed attributes from LiDAR, Photogrammetric, and Contour‐based Digital Elevation Models

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