Abstract
Continuous satellite-derived leaf area index (LAI) time series are critical for modeling land surface process. In this study, we present an interpolation algorithm to predict the missing data in LAI time series for ecosystems with high within-ecosystem heterogeneity, particularly heterogeneous grasslands. The algorithm is based on spatial-temporal constraints, i.e., the missing data in the LAI time series of a pixel are predicted by the phenological links with other pixels. To address the uncertainties in the construction and selection of reference curves in a heterogeneous landscape, the algorithm constructs a reference dataset for each missing data in the LAI time series from all pixels showing very strong linear phenological links with the target pixel within a region. We also use an iterative process to update the available spatial-temporal constraints. We tested the algorithm with an eight-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the Songnen grasslands, Northeast China in 2010 and 2011. The validation dataset was generated based on high quality time series by artificially adding data gaps. The algorithm achieved high overall interpolation accuracies with high coefficient of determination R2 (>0.9) and low root mean square error (RMSE) (<0.2) in both dry (2010) and wet (2011) years. The algorithm showed advantages in predicting missing data for different seasons and proportions of missing data versus the algorithm that uses regional average LAI curve as a reference. These results suggest that the proposed algorithm could more effectively characterize spatial-temporal constraint information in heterogeneous grasslands for temporal interpolation.
Highlights
IntroductionAs a key biophysical variable that controls terrestrial ecosystem processes (e.g., exchange of energy, water, and carbon), leaf area index (LAI) time series are critical for ecosystem and climate modeling [1,2,3]
As a key biophysical variable that controls terrestrial ecosystem processes, leaf area index (LAI) time series are critical for ecosystem and climate modeling [1,2,3]
The algorithm showed advantages in predicting missing data for different seasons and proportions of missing data versus the algorithm that uses regional average LAI curve as a reference. These results suggest that the proposed algorithm could more effectively characterize spatial-temporal constraint information in heterogeneous grasslands for temporal interpolation
Summary
As a key biophysical variable that controls terrestrial ecosystem processes (e.g., exchange of energy, water, and carbon), leaf area index (LAI) time series are critical for ecosystem and climate modeling [1,2,3]. A number of satellite-derived time series reconstruction methods have been developed, for instance, the Savitzky–Golay filter [7], the Fourier analysis [8,9,10,11], the wavelet transform [12,13,14], and function fitting [15,16] These algorithms have shown their efficiencies for smoothing time series in diverse ecosystems [17,18,19,20,21,22]. The abilities of these algorithms for reconstructing time series are limited by insufficient temporal information
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