Area-averaged vegetative cover fraction estimated from satellite data

Wittich, Klaus-Peter; Hansing, O.

doi:10.1007/bf01245391

Cited by 134 publications

(73 citation statements)

References 11 publications

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“…Sea surface temperature was derived from NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) SST imagery. Vegetation abundance was specified as a function of the Normalized Difference Vegetation Index (NDVI) contained in imagery from the VEGETATION instrument onboard the SPOT satellite platform, following relations established by Wittich and Hansing [1995] and Gutman and Ignatov [1998].…”

Section: Description Of the Simulationsmentioning

confidence: 99%

Modeling the energy balance in Marseille: Sensitivity to roughness length parameterizations and thermal admittance

Demuzere¹,

Ridder²,

Lipzig³

2008

J. Geophys. Res.

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[1] During the ESCOMPTE campaign (Experience sur Site pour COntraindre les Modeles de Pollution atmospherique et de Transport d'Emissions), a 4-day intensive observation period was selected to evaluate the Advanced Regional Prediction System (ARPS), a nonhydrostatic meteorological mesoscale model that was optimized with a parameterization for thermal roughness length to better represent urban surfaces. The evaluation shows that the ARPS model is able to correctly reproduce temperature, wind speed, and direction for one urban and two rural measurements stations. Furthermore, simulated heat fluxes show good agreement compared to the observations, although simulated sensible heat fluxes were initially too low for the urban stations. In order to improve the latter, different roughness length parameterization schemes were tested, combined with various thermal admittance values. This sensitivity study showed that the Zilitinkevich scheme combined with and intermediate value of thermal admittance performs best.

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Section: Description Of the Simulationsmentioning

confidence: 99%

Modeling the energy balance in Marseille: Sensitivity to roughness length parameterizations and thermal admittance

Demuzere¹,

Ridder²,

Lipzig³

2008

J. Geophys. Res.

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“…SMA with fixed or variable endmembers has been used for the estimation of FVC in various environments including arid/semi-arid regions [21][22][23][24] and at scales from regional to global [25][26][27][28]. For grassland, linear SMA models with two endmembers (vegetation and non-vegetation) or three endmembers (live grass, senesced grass and soil) are effective in estimating endmember fractions due to their simplicity and interpretability [2][3][4]13,29].…”

Section: Introductionmentioning

confidence: 99%

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

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Abstract:Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VI soil and VI veg , with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.

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“…NDVI max is set as 0.86 for all sites here. Because the scattering coefficients for the leaves and the soil, the leaf-area index, the leaf angle distribution, and the angle of incident radiation affect the NDVI, the vegetation cover problem becomes more complex [13]. The errors in EF estimation caused by the uncertainty of f c will be discussed in Section 4.2.…”

Section: Fractional Vegetation Cover F Cmentioning

confidence: 99%

“…Therefore, it is advantageous to develop ET models that completely depend on surface parameters/variables retrieved from remote sensing. Surface parameters/variables that can be extracted from remotely sensed data mainly include surface temperature, surface emissivity, albedo, vegetation indices, leaf area index, soil moisture, net radiation, solar radiation, and air temperature [12][13][14][15][16][17][18][19][20][21][22]. Some simple methods can estimate surface energy fluxes by directly using these remotely sensed surface parameters/variables (e.g., empirical equations and the triangle feature space [23][24][25][26][27][28]).…”

Section: Introductionmentioning

confidence: 99%

Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation

Tang

et al. 2014

Remote Sensing

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Abstract:In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day-night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with surface net radiation, this study simplified the daily EF parameterization scheme using incoming solar radiation as an input. Daily EF estimates from the simplified scheme were nearly equivalent to the results from the original scheme. In situ measurements from six Ameriflux sites with different land covers were used to validate the new simplified EF parameterization scheme. Results showed that daily EF estimates for clear skies were consistent with the in situ EF corrected OPEN ACCESSRemote Sens. 2014, 6 4370 by the residual energy method, showing a coefficient of determination of 0.586 and a root mean square error of 0.152. Similar results were also obtained for partly clear sky conditions. The non-closure of the measured energy and heat fluxes and the uncertainty in determining fractional vegetation cover were likely to cause discrepancies in estimated daily EF and measured counterparts. The daily EF estimates of different land covers indicate that the constant coefficients in the simplified EF parameterization scheme are not strongly site-specific.

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Area-averaged vegetative cover fraction estimated from satellite data

Cited by 134 publications

References 11 publications

Modeling the energy balance in Marseille: Sensitivity to roughness length parameterizations and thermal admittance

Modeling the energy balance in Marseille: Sensitivity to roughness length parameterizations and thermal admittance

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation

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