Journal
REMOTE SENSING OF ENVIRONMENT
Volume 115, Issue 1, Pages 97-106Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2010.08.009
Keywords
Real-time inversion; Leaf Area Index; SARIMA model; Time series analysis; Ensemble Kalman Filter; MODIS
Funding
- Chinese 973 Program [2007CB714407]
- NASA [NNX09AN36G]
- National Natural Science Foundation of China [40871163, 40701102]
- EU
- National Science and Technology Support Project [2008BAC34B03]
- NASA [NNX09AN36G, 110808] Funding Source: Federal RePORTER
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Real-time/near real-time inversion of land surface biogeophysical variables from satellite observations is required to monitor rapid land surface changes, and provide the necessary input for numerical weather forecasting models and decision support systems. This paper develops a new inversion method for the real-time estimation of the Leaf Area Index (LAI) of land surfaces from MODIS time series reflectance data (MOD09A1). It consists of a series of procedures, including time series data smoothing, data quality control and real-time estimation of LAI. After the historical LAI time series is smoothed by a multi-step Savitzky-Golay filter to determine the upper LAI envelope, a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model is used to derive the LAI climatology. Based on the climatology from the SARIMA model to evolve in time, a dynamic model is then constructed and used to provide the short-range forecast of LAI. Predictions from this model are used with Ensemble Kalman Filter (EnKF) techniques to recursively update biophysical variables as new observations arrive. The validation results produced using MODIS surface reflectance data and field-measured LAI data at eight BELMANIP sites show that the real-time inversion method is able to efficiently produce a relatively smooth LAI product. In addition, the accuracy is significantly improved over the MODIS LAI product. (C) 2010 Elsevier Inc. All rights reserved.
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