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 dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results OPEN ACCESSRemote Sens. 2014, 6 5651 of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively.

Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory

“…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).…”

“…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.…”

Reflecting conifer phenology using mobile terrestrial LiDAR: A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia

“…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.…”

Spatial scaling transformation modeling based on fractal theory for the leaf area index retrieved from remote sensing imagery

“…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.…”