Seasonality extraction by function fitting to time-series of satellite sensor data

TLDR

The method is general and applicable to various satellite‑derived time‑series datasets. The study introduces a new method for extracting seasonality information from satellite sensor time‑series data. The method fits asymmetric Gaussian models to the time‑series via nonlinear least squares, uses the resulting smooth curves to derive seasonality parameters, and is implemented in the TIMESAT program, tested on AVHRR NDVI data over Africa with cloud‑derived uncertainty estimates.

Abstract

A new method for extracting seasonality information from time-series of satellite sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of satellite-derived time-series data.