Overview of the radiometric and biophysical performance of the MODIS vegetation indices

Huete, Alfredo; Didan, Kamel; Miura, Tomoaki; Rodríguez, E.; Gao, Xiang; Ferreira, Laerte Guimarães

doi:10.1016/s0034-4257(02)00096-2

Cited by 6,840 publications

(4,096 citation statements)

References 37 publications

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“…Major factors influencing empirical estimates of f PAR using SVIs are vegetation spectral properties, pixel heterogeneity, background reflectance, solar zenith and view zenith angle, vegetation shadow fractions, atmospheric scattering and bidirectional reflectance effects (Li and Strahler, 1985;Myneni and Williams, 1994). Some of these factors can be mitigated using more advanced indexing techniques such as the enhanced vegetation index (EVI) (Huete et al, 2002;, which seek to enhance the vegetation signal with improved sensitivity in high biomass regions thereby de-coupling canopy background signals and reducing atmospheric influences, however, broad scaled direct application of vegetation indices remains challenging (e.g. Running and Nemani, 1988).…”

Section: Empirical Determination Of Absorbed Parmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

246

171

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Section: Empirical Determination Of Absorbed Parmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

246

171

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“…The EVI is directly related to the photosynthetic production of plants and indirectly related to the green biomass (Huete et al, 2002). As a result, the EVI remains sensitive to increases in canopy density beyond the level where the NDVI becomes saturated (Huete et al, 2002). Xiao et al (2004) also found that the EVI had a stronger linear relationship with the gross primary production than did the NDVI.…”

Section: Selection Of the Environmental Variablesmentioning

confidence: 98%

“…The EVI is less sensitive to soil and atmospheric effects than the normalized difference vegetation index (NDVI) because it incorporates the blue spectral band. The EVI is directly related to the photosynthetic production of plants and indirectly related to the green biomass (Huete et al, 2002). As a result, the EVI remains sensitive to increases in canopy density beyond the level where the NDVI becomes saturated (Huete et al, 2002).…”

Section: Selection Of the Environmental Variablesmentioning

confidence: 99%

Regional representativeness assessment and improvement of eddy flux observations in China

Zhang

Gao

et al. 2015

Science of The Total Environment

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• The network of flux towers performed well in representing the environment of more than half of China.• The towers in wetlands and barelands were poorly represented.• Representativeness increased with towers added in forests, grasslands, wetlands and barelands.• Substantial gains in representation were achieved by adding new towers on the Tibet Plateau. Both the amounts of data describing the site-scale carbon flux at a high temporal and spatial resolution collected in China and the number of eddy covariance flux towers have been increasing during the last decade. To correctly upscale these fluxes to the regional and global level, the representativeness of the current network of flux towers must be known. The present study quantifies the representativeness of the flux network for the regional carbon exchange. This analysis combined the total solar radiation, air temperature, vapor pressure and the enhanced vegetation index to indicate the environmental characteristics of each 1-km pixel cell and flux tower. Next, the Euclidean distance from each pixel to the tower was calculated to determine the representativeness of the existing flux towers. To improve the regional representativeness, additional tower locations were pinpointed by identifying and clustering the underrepresented areas. The existing network of flux towers performed well in representing the environmental conditions of the middle and the northeastern portions of China. The wellrepresented areas covered 60.9% of the total areas. The towers in croplands and grasslands represented the vegetation types well, but the wetlands and barelands were poorly represented. The representativeness of the flux network increased with the addition of nine towers located in forests, grasslands, wetlands and barelands. The representativeness of 27.5% of the land areas improved. In addition, the well-represented areas were enlarged by 15.2%. Substantial gains in representation were achieved by adding new towers on the Tibet Plateau. The representativeness of the northwest and southwest was improved less significantly, suggesting that more towers are required to capture certain ecosystem behaviors. a b s t r a c t a r t i c l e i n f o

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“…This method was modified for generating MODIS vegetation indices by incorporating the smallest view angle constraint to minimize sensor view variations associated with the anisotropic surface reflectance properties, i.e., Bidirectional Reflectance Distribution Function (BRDF) and helps reduce spatial and temporal discontinuities in the composited product [59].…”

Section: Etm+ Ndvi Composingmentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Remote Sensing

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Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced

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Overview of the radiometric and biophysical performance of the MODIS vegetation indices

Cited by 6,840 publications

References 37 publications

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Regional representativeness assessment and improvement of eddy flux observations in China

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

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