Dependence of attenuation in a vegetation canopy on frequency and plant water content

Vine, David M. Le; Karam, M.A.

doi:10.1109/36.536525

Cited by 76 publications

(27 citation statements)

References 23 publications

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“…The surface roughness is related to the vegetation opacity, and this determines VegWC under the assumption of a uniformly vegetated surface in a unit area. This approach is derived from a linear relationship between the vegetation opacity and vegetation water content (Jackson and Schmugge 1991;Le Vine and Karam 1996). Hence, VegWC can be also defined as total water mass in vegetation per unit of ground area, including leaves as well as stalks and branches.…”

Section: ϫ2mentioning

confidence: 99%

Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

2007

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Vegetation is an important factor in global climatic variability and plays a key role in the complex interactions between the land surface and the atmosphere. This study focuses on the spatial and temporal variability of vegetation and its relationship with land-atmosphere interactions. The authors have analyzed the vegetation water content (VegWC) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), the leaf area index (LAI), the normalized difference vegetation index (NDVI), the land surface temperature (Ts), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Three regions, which have climatically differing characteristics, have been selected: the North America Monsoon System (NAMS) region, the Southern Great Plains (SGP) region, and the Little River Watershed in Tifton, Georgia. Temporal analyses were performed by comparing satellite observations from 2003 and 2004. The introduction of the normalized vegetation water content (NVegWC) derived as the ratio of VegWC and LAI corresponding to the amount of water in individual leaves has been estimated and this yields significant correlation with NDVI and Ts. The analysis of the NVegWC-NDVI relationship in the above listed three regions displays a negative exponential relation, and the Ts-NDVI relationship (TvX relationship) is inversely proportional. The correlation between these variables is higher in arid areas such as the NAMS region, and becomes less correlated in the more humid and more vegetated regions such as the area of eastern Georgia. A land-cover map is used to examine the influence of vegetation types on the vegetation biophysical and surface temperature relationships. The regional distribution of vegetation reflects the relationship between the vegetation biological characteristics of water and the growing environment.

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Section: ϫ2mentioning

confidence: 99%

Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

2007

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“…Shallower incidence angles (near 40 degree) increase the path length through vegetation thus maximizing the crop response and better classification capabilities (Le Toan et al, 1984). Levine and Karam (1996) reported that attenuation through vegetation is proportional to vegetation water content.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Synthetic Aperture Radar data for Crop Cover Classification

Ramana

Srikanth²,

Deepika³

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton.

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“…Recent work has, however, shown that the opacity coefficient depends on both the gravimetric water content of vegetation (Wigneron et al, 1996(Wigneron et al, , 2000Le Vine and Karam, 1996) and the temperature (Wigneron et al, 2000). In addition, the canopy type and structure (Jackson and Schmugge, 1991), the polarisation (van de Griend and Owe, 1996) and wavelength of the radiation (Jackson and Schmugge, 1991) and the look-angle of the sensor (van de Griend and Owe, 1996) may also influence the opacity coefficient.…”

Section: Introductionmentioning

confidence: 99%

Influence of vegetation on SMOS mission retrievals

Lee

Burke

Shuttleworth

et al. 2002

Hydrol. Earth Syst. Sci.

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Using the proposed Soil Moisture and Ocean Salinity (SMOS) mission as a case study, this paper investigates how the presence and nature of vegetation influence the values of geophysical variables retrieved from multi-angle microwave radiometer observations. Synthetic microwave brightness temperatures were generated using a model for the coherent propagation of electromagnetic radiation through a stratified medium applied to account simultaneously for the emission from both the soil and any vegetation canopy present. The synthetic data were calculated at the look-angles proposed for the SMOS mission for three different soil-moisture states (wet, medium wet and dry) and four different vegetation covers (nominally grass, crop, shrub and forest). A retrieval mimicking that proposed for SMOS was then used to retrieve soil moisture, vegetation water content and effective temperature for each set of synthetic observations. For the case of a bare soil with a uniform profile, the simpler Fresnel model proposed for use with SMOS gave identical estimates of brightness temperatures to the coherent model. However, to retrieve accurate geophysical parameters in the presence of vegetation, the opacity coefficient (one of two parameters used to describe the effect of vegetation on emission from the soil surface) used within the SMOS retrieval algorithm needed to be a function of lookangle, soil-moisture status, and vegetation cover. The effect of errors in the initial specification of the vegetation parameters within the coherent model was explored by imposing random errors in the values of these parameters before generating synthetic data and evaluating the errors in the geophysical parameters retrieved. Random errors of 10% result in systematic errors (up to 0.5°K, 3%, and ~0.2 kg m -2 for temperature, soil moisture, and vegetation content, respectively) and random errors (up to ~2°K, ~8%, and ~2 kg m -2 for temperature, soil moisture and vegetation content, respectively) that depend on vegetation cover and soil-moisture status.

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Dependence of attenuation in a vegetation canopy on frequency and plant water content

Cited by 76 publications

References 23 publications

Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

Polarimetric Synthetic Aperture Radar data for Crop Cover Classification

Influence of vegetation on SMOS mission retrievals

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