Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data
- Download PDF (939554)
- This publication is available only online.
Remote Sensing of Environment. 105: 189-203.
In this paper, we present an improved procedure for collecting no or little atmosphere- and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPARcanopy), leaf (FAPARleaf), and chlorophyll (FAPARchl), respectively, using a coupled leaf-canopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step.
KeywordsBartlett Experimental Forest; MODIS; Snow; Atmosphere contamination; Phenology; PROSPECT; SAIL-2; FAPAR; Markov Chain Monte Carlo (MCMC) method
Zhang, Qingyuan; Xiao, Xiangming; Braswell, Bobby; Linder, Ernst; Ollinger, Scott; Smith, Marie-Louise; Jenkins, Julian P.; Baret, Fred; Richardson, Andrew D.; Moore, Berrien III; Minocha, Rakesh. 2006. Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data. Remote Sensing of Environment. 105: 189-203. https://doi.org/10.1016/j.rse.2006.06.013.