Abstract
Vegetation phenology is closely related to seasonal dynamics of the lower atmosphere, and important elements in global models and vegetation monitoring. Time-series NDVI data based on imagery from AVHRR or MODIS are suitable for phenological monitoring, because these sensors provide data with a high temporal frequency. In order to reduce noises caused by cloud contamination or atmospheric variability, Maximum Value Composite (MVC) is applied to the data. However, composite data have undesirable noises due to remained cloud contamination and inequality of observation intervals. Though MVC technique is applied, these noises disturb phenological monitoring. This paper proposed new noise reduction algorithm which integrates Best Index Slope Extraction (BISE) and Maximum Value Interpolated (MVI) algorithms. Integrated algorithm was applied to timeseries NDVI data consists of NOAA AVHRR composite images. This algorithm worked well in areas dominated by vegetation such as cropland including double cropping area, deciduous broadleaf forest and evergreen needleleaf forest. We confirmed that the algorithm reduced effects of cloud contamination, and equalized each observation interval. Therefore, applying developed algorithm to time-series NDVI data allows us to phenological monitoring more precisely.
