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...2002; Friedl et al., 2002; Roy et al., 2002), (b) derive biophysical parameters for other models (Sellers et al., 1994; Moody and Johnson, 2001; Lu et al., 2003), and (c) monitor vegetation dynamics (=-=Sakamoto et al., 2005-=-; Beck et al., 2006). Observing the change of LAI in time and space plays a significant part in understanding and modeling the Xiaoliang Lu is with the Department of Earth & Atmospheric Sciences, Purd...

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...ps generating two EVI2 time series: 1) without wavelet transform; and 2) with wavelet transform. The first filtering procedure applied rules that were designed following the methodologies proposed by =-=[10, 11]-=- in which data were eliminated from the original time series if the reflectance in the blue band is greater than 10% or if the sensor view zenith angle is greater than 32.5◦. These threshold procedure...

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...cycle of a crop is relevant for various activities, such as irrigation scheduling, fertilizer management, evaluating crop productivity, and analyzing seasonal ecosystem carbon dioxide (CO2) exchanges =-=[1]-=-. Especially, in an increasingly food insecure world, a comprehensive understanding of global croplands is a critical need [2,3], and crop phenology is a very important element in it. Detailed informa...

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...imited spatial extents and to limited ecosystems. At large spatial scales, remotely sensed data such as these from the Advanced Very High Resolution Radiometer (AVHRR) and the MODerate resolution Imaging Spectroradiometer (MODIS) sensors are commonly used [15]. MODIS vegetation products such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) have been proved to be effective for quantifying vegetation greenness, canopy carbon and water fluxes [16], [17] and monitoring the phenological changes of vegetation across large spatial scales and over long time periods [18]– [20]. Figure I shows some of the phenological characteristics in an annual NDVI time series. Particularly, the vegetation products generated from MODIS onboard Terra and Aqua offer an unprecedented opportunity for researchers to develop long-term records of vegetation phenology at spatial scales as small as 250 m [21]. Recent efforts in phenology studies have been focused on developing new techniques or improving methods for estimating specific dates of vegetation transitions with improved satellite data e.g., MODIS-NDVI, EVI [15], [21], [22]. Most of these methods analyze the vegetation tim...

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..., logistic curve fitting [4], [8], [17]–[19], forward and backward lag periods of smoothed VI data (delayed moving average) [1], [20], [21], or metrics such as maximum, minimum, and inflection points =-=[5]-=-, [22]–[24]. Several studies estimate key phenological stages using a combination of time-series VI data and environmental information such as humidity and temperature [6], [25], [26]. The smoothed MO...

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... et al., 2012). 190 C. Jin et al. / Remote Sensing of Environment 135 (2013) 189–201Vegetation phenology is a fundamental determinant affecting the ecosystem processes of carbon, water, and energy exchange (Larcher, 2003). It determines the timing and duration of a photosynthetically active canopy and influences the magnitude of carbon and water fluxes throughout the plant growing season (Jolly & Running, 2004). The vegetation indices calculated from the reflectance of spectral bands have been proved to effectively monitor the vegetation phenology (Bradley et al., 2007; Moody & Johnson, 2001; Sakamoto et al., 2005; Xiao, 2006; Zhang et al., 2006). Earlier studies of phenology have focused on vegetation indices derived from visible and near infrared bands, for example, the Normalized Difference Vegetation Index (NDVI), which is calculated as a normalized ratio between near infrared and red spectral bands (Tucker, 1979), and the Enhanced Vegetation Index (EVI), which is calculated from blue, red, and near infrared bands (Huete et al., 2002). Both NDVI and EVI have been shown to effectively track the seasonality and spatial patterns of savanna phenology (Archibald & Scholes, 2007; Chidumayo, 2001; Higgins...

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...hed NDVI profile which is convenient for calculating derivatives (WIT & SU, 2005) The calculations of derivates are very important for the estimation the start of growing season and senescence dates (=-=SAKAMOTO et al., 2005-=-). The version of HANTS used was implemented in IDL by (WIT, 2005) and is under the GNU General Public License. The data set of temporal NDVI images, which contained a series of 23 samples per year wa...