Vegetation effects on the microwave emission of soils

Jackson, T.; Schmugge, Thomas J.

doi:10.1016/0034-4257(91)90057-d

Cited by 877 publications

(504 citation statements)

References 14 publications

Supporting

Mentioning

494

Contrasting

Unclassified

Order By: Relevance

“…There is a semi-empirical relation between the optical depth at microwave wavelengths and vegetation water content (VWC), which varies systematically with both wavelength and canopy structure [Jackson and Schmugge, 1991]. The microwave surface emission above a canopy is an integration of the microwave radiation from the whole canopy vertical profile weighted by its transmission.…”

Section: Microwave Emissivity Difference Vegetation Index (Edvi)mentioning

confidence: 99%

“…Thus, a new parameter based on the microwave land surface emissivity difference between two wavelengths is introduced to indicate VWC and other vegetation properties of the canopy with a minimal influence of the soil emission underneath vegetation canopy. Analogous to NDVI and the frequency index of Macelloni et al, When a vegetated surface layer is very thick (e.g., a forest), microwave emissivity from the vegetated layer depends largely on properties of the canopy [Jackson et al, 1984, Jackson and Schmugge, 1991, Paloscia, 19953 Wigneron et al 2003]. Radiation at the frequencies of SSM/I scantily penetrates through the forest.…”

Section: Microwave Emissivity Difference Vegetation Index (Edvi)mentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

Min

Lin

2006

Remote Sensing of Environment

View full text Add to dashboard Cite

show abstract

Section: Microwave Emissivity Difference Vegetation Index (Edvi)mentioning

confidence: 99%

Section: Microwave Emissivity Difference Vegetation Index (Edvi)mentioning

confidence: 99%

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

Min

Lin

2006

Remote Sensing of Environment

View full text Add to dashboard Cite

show abstract

“…At L band any scattering effects are assumed to be negligible [Jackson and Schmugge, 1991]. In the simplified approach taken here the optical depth (q-) is the only parameter used to account for the effects of the vegetation.…”

Section: Water Has a High Dielectric Constant (--•80) Whereas That Omentioning

confidence: 99%

“…where/3 is an empirical constant defined for a specific vegetation cover type and frequency and 0veg is the depth-integrated water content of the canopy [Jackson and Schmugge, 1991]. The main inputs required to determine the radiometric brightness temperature detected by the radiometer are summarized in Figure 1.…”

Section: Water Has a High Dielectric Constant (--•80) Whereas That Omentioning

confidence: 99%

Calibrating a soil water and energy budget model with remotely sensed data to obtain quantitative information about the soil

Burke¹,

Gurney²,

Simmonds³

et al. 1997

Water Resources Research

View full text Add to dashboard Cite

Abstract. A soil water energy and transpiration model (SWEAT) coupled with a microwave emission model (MICRO-SWEAT) was used to predict the microwave brightness temperature of both bare and corn plots during a drying cycle. The predicted microwave brightness temperatures compared favorably to measurements made with an L band (21 cm, 1.4 GHz) passive microwave radiometer. In addition, SWEAT successfully modeled time series of soil water content and soil temperature. The modeled brightness temperature for the bare soil was most sensitive to the parameters describing the soil water retention and conductivity characteristics. These were predicted by varying each parameter in turn until there was a minimum between the measured and modeled brightness temperature. The predicted parameters were in agreement with the measured values to within the experimental error. The microwave brightness temperatures estimated for the corn soil were sensitive to the vegetation parameters as well as to the soil hydraulic properties. This paper analyses microwave emission data collected during a 10-day drying period for a bare and a corn plot. MICRO-SWEAT is validated and used to estimate the soil hydraulic properties. 1689

show abstract

“…The soil vegetation parameters A and B and the bare soil parameters C and D are also estimated separately using backscatter data (Jackson andSchmugge 1991, Bindlish andBarros 2001). For instance, the parameters C and D are estimated, using backscatter data of the bare soil, by nonlinear regression.…”

Section: Introductionmentioning

confidence: 99%

Estimation of water cloud model vegetation parameters using a genetic algorithm

Kumar

Prasad

Arora

2012

Hydrological Sciences Journal

View full text Add to dashboard Cite

The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT.Key words vegetation parameter; backscatter coefficient; inverse problem; genetic algorithm; data errors; bias; root mean squared error; sequential unconstrained minimization technique Estimation des paramètres de végétation dans un modèle de nuage utilisant un algorithme génétique Résumé Le modèle de nuage est utilisé pour rendre compte de l'effet de la teneur en eau de la végétation sur les données de rétrodiffusion radar. De manière générale, le modèle comprend deux paramètres qui caractérisent le terrain végétalisé, A et B, et deux paramètres de sol nu, C et D. Dans la prèsente étude, les paramètres A et B ont été estimés en utilisant une technique d'optimisation par algorithme génétique (AG), et comparés aux estimations obtenues par la technique de minimisation séquentielle sans contraintes (MSSC) à partir des données mesurées de rétrodiffusion. L'estimation des paramètres a été formulée comme un problème d'optimisation par moindres carrés, en minimisant les écarts entre les coefficients de rétrodiffusion extraits de l'image ASAR d'ENVISAT et ceux prévus par le modèle de nuage. Le biais induit par trois fonctions objectif différentes a été analysé statistiquement par génération de données de rétrodiffusion synthétiques. Il a été observé que, lorsque les données de coefficient de rétrodiffusion ne contiennent aucune erreur, les fonctions objectif n'induisent pas de biais dans l'estim...

show abstract

Vegetation effects on the microwave emission of soils

Cited by 877 publications

References 14 publications

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

Calibrating a soil water and energy budget model with remotely sensed data to obtain quantitative information about the soil

Estimation of water cloud model vegetation parameters using a genetic algorithm

Contact Info

Product

Resources

About