By Topic

TiSeG: A Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the Quality Assessment Science Data Set

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
RenÉ R. Colditz ; Nat. Comm. for the Knowledge & Use of Biodiversity (CONABIO), Mexico City ; Christopher Conrad ; Thilo Wehrmann ; Michael Schmidt
more authors

Time series generated from remotely sensed data are important for regional to global monitoring, estimating long-term trends, and analysis of variations due to droughts or other extreme events such as El Nintildeo. Temporal vegetation patterns including phenological states, photosynthetic activity, or biomass estimations are an essential input for climate modeling or the analysis of the carbon cycle. However, long-term analysis requires accurate calibration and error estimation, i.e., the quality of the time series determines its usefulness. Although previous attempts of quality assessment have been made with NOAA-AVHRR data, a first rigorous concept of data quality and validation was introduced with the MODIS sensors. This paper presents the time-series generator (TiSeG), which analyzes the pixel-level quality-assurance science data sets of all gridded MODIS land (MODLand) products suitable for time-series generation. According to user-defined settings, the tool visualizes the spatial and temporal data availability by generating two indices, the number of invalid pixels and the maximum gap length. Quality settings can be modified spatially and temporally to account for regional and seasonal variations of data quality. The user compares several quality settings and masks or interpolates the data gaps. This paper describes the functionality of TiSeG and shows an example of enhanced vegetation index time-series generation with numerous settings for Germany. The example indicates the improvements of time series when the quality information is employed with a critical weighting between data quality and the necessary quantity for meaningful interpolation.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:46 ,  Issue: 10 )