Extended Data-Based Mechanistic Method for Improving Leaf Area Index Time Series Estimation with Satellite Data

Abstract

Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. This study proposes an extended data-based mechanistic method (EDBM) for estimating LAI time series from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data-based mechanistic model is universalized to supply the LAI background information, and then the vegetation canopy radiative-transfer model (PROSAIL) is coupled to calculate reflectances with the same observation geometry as MODIS reflectance data. The ensemble Kalman filter (ENKF) is introduced to improve LAI estimation based on the difference between simulated and observed reflectances. Field measurements from seven Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites and reference maps from the Imagine-S project La Albufera, Spain site were used to validate the model. The results demonstrate that when compared with field measurements, the LAI time-series estimates obtained using this approach were superior to those obtained with the MODIS 500 m resolution LAI product. The root mean square errors (RMSE) of the MODIS LAI product and of the LAI estimated with the proposed method were 1.26 and 0.5, respectively. When compared with reference LAI maps, the results indicate that the estimated LAI is spatially and temporally consistent with LAI reference maps. The average differences between EDBM and the LAI reference map on the selected four days was 0.32.

Assessment of plant species diversity based on hyperspectral indices at a fine scale

“…Except for plant diversity estimation, the methodology used in the present study can help in recovering estimates made through remote sensing data for other ecological applications 41 , 42 . Ecological condition evaluations, such as riparian condition 43 , vegetation eco-restoration 44 and forest cover mapping 45 , all of which are variables derived from satellite or airborne imageries at a grain of 30 m or coarser, might be improved by hyperspectral data and stepwise linear regression on narrower sensitive wavebands.…”

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

“…Finally, the PROSPECT model can be simplified to include only the plate transmission coefficient θ and the Remote Sens. 2018, 10, 437 6 of 24 absorption coefficient k, which are given in Equations (21) and (22), where k is related to the absorption K i of biochemistry component i, its weight C i and the absorption of albino leaf k e before 500 nm.…”

“…Therefore, direct multiangle observations from satellite sensors are desperately needed to drive model inversion procedures for estimation of various biophysical/structural variables of the land surface. For example, the bi-directional reflectance product of MODIS (i.e., MOD09A1) has typically been used for LAI and albedo estimation through the ACRM [21] as well as the SAIL CR model, which were reported to yield high-accuracy retrievals based on such direct satellite multiangle observations [22][23][24].…”

Quantitative Remote Sensing of Land Surface Variables: Progress and Perspective

“…Zhou et al [11] presented a Data-Based Mechanistic (DBM) model to estimate LAI profile from MODIS time series data. The DBM model was trained with historical data of LAI and surface reflectance.…”