Influence of geometrical factors on crop backscattering at C-band

Vecchia, Andrea Della; Ferrazzoli, P.; Guerriero, L.; Blaes, Xavier; Defourny, Pierre; Dente, Laura; Mattia, F.; Satalino, G.; Strozzi, Tazio; Wegmüller, U.

doi:10.1109/tgrs.2005.860489

Cited by 56 publications

(28 citation statements)

References 24 publications

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“…From the figure, it can be seen that the values of the backscattering coefficient change ~6 dB from normal condition (green) to burned condition (red). This increase in the observed σ 0 values with increasing vegetation density is not unexpected, and it was also observed for natural vegetation in [17] and for crops in [18]. Since the images ENL is ~21, this change cannot be attributed to speckle fluctuations, and should be related to changes in the phenological/hydrological condition of the marsh.…”

Section: Estimation Of the Reduction Of Junco Plant Densitysupporting

confidence: 66%

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

et al. 2009

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Abstract:The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented.

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Section: Estimation Of the Reduction Of Junco Plant Densitysupporting

confidence: 66%

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

et al. 2009

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“…As for agricultural areas, we have considered typical characteristics (e.g., leaf and stem dimensions, plant height and density) of crops (Ferrazzoli et al, 2000;Della Vecchia et al, 2006), whereas for forests, biomasses of 25 t ha −1 and 50 t ha −1 have been considered. Note that a more accurate discrimination of vegetation types than a simple distinction between crops and forests would have required the use of detailed maps, generally unavailable in operative applications.…”

Section: The Model-based Fuzzy Thresholdsmentioning

confidence: 99%

An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Pulvirenti

Pierdicca

Chini

et al. 2011

Nat. Hazards Earth Syst. Sci.

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232

140

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Abstract. An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data.

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“…Nonetheless, the general consensus from the literature is that low incidence angles, long wavelengths (L-band) and either HH or HV polarisation are the pre-eminent sensor parameters for soil moisture estimation. To take account of the various sensor configurations and surface parameters, many backscattering models [11,56,57] have been developed over the past 30 years to help determine the relationship between the radar signal and certain biophysical parameters, where numerous studies have been carried out to further the understanding of the effect of surface roughness [58][59][60] and vegetation [60][61][62][63] in soil moisture estimation. These models are generally categorised into three groups; theoretical, empirical and semi-empirical models.…”

Section: Factors Affecting the Microwave Signalmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

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Abstract:The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

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Influence of geometrical factors on crop backscattering at C-band

Cited by 56 publications

References 24 publications

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models

An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

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