An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20 year NOAA AVHRR data over Canada

Abstract

Land surface phenology (LSP) is directly related to seasonal variations in the condition of vegetated land surfaces and hence is an important indicator for studying the effect of climate change on ecosystems. Satellite data is widely used to monitor LSP over regional to global extents. Currently, there are many algorithms for generating satellite based LSP products. The Committee of Earth Observing System identifies performance assessments of these products as a pressing need. Performance assessment includes both validation in comparison to independent reference data and evaluation by comparison against estimates of noise free phenology indicators. This study proposes a novel methodology for evaluation of satellite based LSP algorithms and applies this methodology to evaluate three well-known LSP algorithms, Savitzky–Golay based filter (SGF), asymmetric Gaussian fitting (AGF) and logistic function fitting (logistic), using 20years of daily Normalized Difference Vegetation Index composites for Canada and Northern USA. The three LSP methods were evaluated in terms of accuracy for start of season, maximum of season and end of season. Signal to noise ratio (SNR) and the temporal gap frequency (gaps) of the NDVI time series are shown to be proportional to the confidence level for clear sky labeling. The SGF and logistic methods were found to be more sensitive to the SNR than AGF. However, AGF require a minimum proportion of temporal samples for reconstruction. These results highlight the importance of evaluating the trade-off between gap frequency and SNR impacts on LSP methods and indicate that the performance of these methods will depend on both land surface condition and the clear sky identification approach adopted. The developed evaluation approach serves as a basis for assessing the uncertainty in satellite LSP products due to radiometric noise and missing data.