2017
DOI: 10.3390/rs9020144
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Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment

Abstract: Vegetation is often represented by the leaf area index (LAI) in many ecological, hydrological and meteorological land surface models. However, the spatio-temporal dynamics of the vegetation are important to represent in these models. While the widely applied methods, such as the Canopy Structure Dynamic Model (CSDM) and the Double Logistic Model (DLM) are solely based on cumulative daily mean temperature data as input, a new spatio-temporal LAI prediction model referred to as the Temperature Precipitation Vege… Show more

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Cited by 6 publications
(6 citation statements)
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“…One simple method is to remove outliers using predefined thresholds, for example, in the best index slope extraction algorithm (Doktor et al, ; L. Y. Sun & Schulz, ) or through an iterative interpolation process (Julien & Sobrino, ; Moreno et al, ). The most common method is to perform temporal smoothing by means of running averages or medians to suppress short‐frequency variations.…”
Section: Remote Sensing Methodsmentioning
confidence: 99%
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“…One simple method is to remove outliers using predefined thresholds, for example, in the best index slope extraction algorithm (Doktor et al, ; L. Y. Sun & Schulz, ) or through an iterative interpolation process (Julien & Sobrino, ; Moreno et al, ). The most common method is to perform temporal smoothing by means of running averages or medians to suppress short‐frequency variations.…”
Section: Remote Sensing Methodsmentioning
confidence: 99%
“…The most frequently used ancillary information is meteorological data, such as the growing degree days and radiation (Barr et al, ; R. Xu et al, ), air temperature (Koetz et al, ; L. Y. Sun & Schulz, ), thermal time (Duveiller et al, ; Lucas et al, ), and precipitation and potential evapotranspiration (ET; Tesemma et al, , ). Indeed, multiple climatic variables can be jointly used to predict LAI (Iio et al, ; Pfeifer et al, ; Savoy & Mackay, ; L. Y.…”
Section: Remote Sensing Methodsmentioning
confidence: 99%
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“…According to the NDVI time series profile of pure vegetation pixels generated from MODIS NDVI time series data acquired in a growing season shown in previous studies [30,31] or the simulated NDVI time-series profile modeled by a double logistic function [44], the NDVI change rate increases with the increase of NDVI at the beginning of the growing stage, then reaches a maximum, and then decreases at the growing stage. The same trend is shown at the senescent stage.…”
Section: Weighting Systemmentioning
confidence: 99%