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Remote Sens. 2017, 9(9), 968; https://doi.org/10.3390/rs9090968

Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands

1
School of Information Engineering, China University of Geosciences, Beijing 100083, China
2
School of Geographical Sciences, Northeast Normal University, Changchun 130024, China
*
Author to whom correspondence should be addressed.
Received: 2 August 2017 / Revised: 16 September 2017 / Accepted: 18 September 2017 / Published: 19 September 2017
(This article belongs to the Special Issue Land Surface Phenology)
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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. View Full-Text
Keywords: leaf area index; time series; MODIS; temporal interpolation; spatial-temporal constraints; vegetation phenology; heterogeneous landscape; semi-arid grasslands leaf area index; time series; MODIS; temporal interpolation; spatial-temporal constraints; vegetation phenology; heterogeneous landscape; semi-arid grasslands
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ding, C.; Liu, X.; Huang, F. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands. Remote Sens. 2017, 9, 968.

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