Abstract

High-quality leaf area index (LAI) products retrieved from satellite observations are urgently needed for crop growth monitoring and yield estimation, land-surface process simulation and global change studies. In recent years, sequential assimilation methods have been increasingly used to retrieve LAI from time series remote-sensing data. However, the inherent characteristics of these sequential assimilation methods result in temporal discontinuities in the retrieved LAI profiles. In this study, a sequential assimilation method with incremental analysis update (IAU) was developed to jointly update model states and parameters and to retrieve temporally continuous LAI profiles from time series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. Based on the existing multi-year Global Land Surface Satellite (GLASS) LAI product, a dynamic model was constructed to evolve LAI anomalies over time. The sequential assimilation method with an IAU technique takes advantage of the Kalman filter (KF) technique to update model parameters, uses the ensemble Kalman filter (EnKF) technique to update LAI anomalies recursively from time series MODIS reflectance data and then calculates the temporally continuous LAI values by combining the LAI climatology data. The method was tested over eight Committee on Earth Observing Satellites-Benchmark Land Multisite Analysis and Intercomparison of Products (CEOS-BELMANIP) sites with different vegetation types. The results indicate that the sequential method with IAU can precisely reconstruct the seasonal variation patterns of LAI and that the LAI profiles derived from the sequential method with IAU are smooth and continuous.

Highlights

  • Leaf area index (LAI), which is defined as one-half of the total green leaf area per unit of horizontal ground surface area [1], is an important biophysical parameter and is related to ecological processes, such as photosynthesis, respiration and transpiration

  • A new sequential assimilation method with incremental analysis update (IAU) was developed in this study to retrieve LAI from time series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data

  • Global Land Surface Satellite (GLASS) LAI data, and the LAI anomaly was obtained by subtracting the LAI climatology from the GLASS LAI value for each point in time

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Summary

Introduction

Leaf area index (LAI), which is defined as one-half of the total green leaf area per unit of horizontal ground surface area [1], is an important biophysical parameter and is related to ecological processes, such as photosynthesis, respiration and transpiration. It is widely used in climate and ecology models, including ecological system function models, crop growth models and net primary productivity models. Currently available empirical and physical methods generally use only satellite observations acquired at a specific time to retrieve LAI, which may cause temporally discontinuous and inaccurate LAI estimation. The retrieved LAI profiles exhibit many time series fluctuations, and some LAI values are missing, especially during vegetation growing seasons, because of persistent cloud cover

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