2014
DOI: 10.3390/rs6065650
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A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

Abstract: Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and d… Show more

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Cited by 21 publications
(17 citation statements)
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“…RCE " | LAI exa´L AI corr | LAI exa (13) 8, the ACE and the RCE generally increased with increasing σNDVI, which means that the correction effect is relatively insignificant when σNDVI is large. The maximum ACE and RCE corresponded to the low and medium LAI values at four different resolutions (Table 1).…”
Section: Analysis Of Correction Errormentioning
confidence: 91%
See 1 more Smart Citation
“…RCE " | LAI exa´L AI corr | LAI exa (13) 8, the ACE and the RCE generally increased with increasing σNDVI, which means that the correction effect is relatively insignificant when σNDVI is large. The maximum ACE and RCE corresponded to the low and medium LAI values at four different resolutions (Table 1).…”
Section: Analysis Of Correction Errormentioning
confidence: 91%
“…Therefore, it is necessary to comprehensively explore the scaling effect correction. Leaf area index (LAI) is an important land surface parameter in running both land surface and global atmospheric circulation models [10][11][12][13]. The scaling effect of LAI estimated from remote sensing data is deeply studied in the aspect of phenomenon description, cause analysis, and establishment of the scale transformation relationships.…”
Section: Introductionmentioning
confidence: 99%
“…The most-notable progress regarding RS-based phenological studies was based on the rich earth observation records by the successive satellite series such as Landsat 1-7 and their derivations such as the Moderate Resolution Imaging Spectroradiometer (MODIS) products (Ganguly et al, 2010), which facilitate implementing regional and even global phenological studies (Cong et al, 2012;Zhang et al, 2003;Stöckli and Vidale, 2004;Soudani et al, 2008;Ganguly et al, 2010;Papeş et al, 2013;Meier et al, 2015). In addition, proposing phenological indicators such as leaf area index (LAI) (Che et al, 2014) and plant species distribution (Bishop et al, 2013) appropriate for different forest compositions and developing more powerful phenologycharacterizing methods such as phenological estimators (Moussus et al, 2010) have also been promoted for advancing phenologyrelevant studies.…”
Section: Introductionmentioning
confidence: 99%
“…In remote sensing information analysis and applications, scale transformation is closely related to many issues, such as the data fusion, 1 classification of ground objects, 2,3 retrieval of parameter, 4,5 and validation of quantitative remote sensing products. [8][9][10][11] The scale effect of LAI estimation using remote sensing imagery has been extensively studied with a focus on describing the phenomenon, analyzing the causes, and establishing the scale transformation relationships. As one of the important surface parameters that can be estimated from remote sensing imagery, the leaf area index (LAI) plays an important role in both land surface and global atmospheric circulation models.…”
Section: Introductionmentioning
confidence: 99%