A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products

Abstract

Abstract. Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.

Global Variability of Simulated and Observed Vegetation Growing Season

“…For example, Chen et al (2017) show that the use of a novel atmospheric correction algorithm (multi-angle implementation of atmospheric correction) could lead to a quality improvement of LAI data. Kandasamy et al (2013) display the abilities and limits of eight methods used in filling gaps in satellite data and state that the performance of these different methods depends on their implementation. In situ data, such as the PhenoCam network (Richardson et al, 2018), may be more reliable for model validation, yet global coverage of these data is missing.…”

Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data

“…Preprocessing of the aggregate green MODIS LAI was implemented to fill gaps and reduce noise in the time series relating to variable atmospheric effects (aerosols and cloud contamination), sensor defects, variable solar geometry, and satellite view angle, changing illumination and differing performance of the main and backup MODIS LAI algorithms (Chen et al, 2004;Kandasamy et al, 2013;Yuan et al, 2011). We used the MODIS LAI quality flags to select noncloudy pixels from main and backup algorithm.…”

Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency

“…The FPAR data were spatially resampled using the MODIS 1 km resolution global land cover product [Friedl et al, 2010] to ensure that all 3 × 3 km windows represented the same land cover type as the local tower footprint. In order to capture the tower footprint, the 3 × 3 km FPAR data were spatially averaged for each 8 day time step [Rahman, 2005], and temporal data gaps were filled using the long-term MODIS FPAR 8 day climatology [Kandasamy et al, 2013]. In order to produce daily FPAR data consistent with daily flux tower GPP values, the continuous 8 day FPAR record was interpolated to a daily time step using smoothing splines [Wahba, 1975].…”