Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models.

Ecosystem models have been widely used for obtaining gross primary productivity (GPP) estimations at multiple scales. Leaf area index (LAI) is a critical variable in these models for describing the vegetation canopy structure and predicting vegetation-atmosphere interactions. However, the uncertainties in LAI datasets and the effects of their representation on simulated GPP remain unclear, especially over complex terrain. Here, five most popular datasets, namely the Long-term Global Mapping (GLOBMAP) LAI, Global LAnd Surface Satellite (GLASS) LAI, Geoland2 version 1 (GEOV1) LAI, Global Inventory Monitoring and Modeling System (GIMMS) LAI, and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI, were selected to examine the influences of LAI representation on GPP estimations at 95 eddy covariance (EC) sites. The GPP estimations from the Boreal Ecosystem Productivity Simulator (BEPS) model and the Eddy Covariance Light Use Efficiency (EC-LUE) model were evaluated against EC GPP to assess the performances of LAI datasets. Results showed that MODIS LAI had stronger linear correlations with GLASS and GEOV1 than GIMMS and GLOMAP at the study sites. The GPP estimations from GLASS LAI had a better agreement with EC GPP than those from other four LAI datasets at forest sites, while the GPP estimations from GEOVI LAI matched best with EC GPP at grass sites. Additionally, the GPP estimations from GLASS and GEOVI LAI presented better performances than the other three LAI datasets at crop sites. Besides, the results also showed that complex terrain had larger discrepancies of LAI and GPP estimations, and flat terrain presented better performances of LAI datasets in GPP estimations. Moreover, the simulated GPP from BEPS was more sensitive to LAI than those from EC - LUE, suggesting that LAI datasets can also lead to different uncertainties in GPP estimations from different model structures. Our study highlights that the satellite-derived LAI datasets can cause uncertainties in GPP estimations through ecosystem models.

[Use of ensemble Kalman smoother algorithm for the time-series retrieval of leaf area index from remote sensing data].

In the present paper, the empirical LAI dynamic model was constructed using the MOD15A2 data set, and the canopy radiative transfer model MCRM2 was coupled with the LAI dynamic model through LAI. The scheme was proposed to retrieve LAI by assimilating MOD09A1 data set into the coupled model. The ensemble Kalman smoother (EnKS) method was first introduced. In order to preferably assess the feasibility of EnKS, the LAI retrieval results of EnKS were compared with the ensemble Kalman filter (EnKF) solutions and MODIS LAI product. The results indicated that the EnKS method achieved ideal results. The retrieved LAI temporal profiles by the EnKS method were smoother and more continuous than the EnKF solutions and the MODIS LAI product, which were in good agreement with the realistic LAI climatology. The developed inversion method in this paper can be applied to retrieve LAI time--continuous profiles effectively.

[Comparison of methods for estimating soybean leaf area index].

Leaf area index (LAI) is an important biophysical parameter, and is the critical variable in many ecology models, productivity models and carbon circulation study. Based on the field experiment data, an evaluation of soybean LAI retrieval methods was conducted using NDVI (normalized difference vegetation index) and RVI (ratio vegetation index), principle component analysis (PCA) and neural network (NN) methods, and the estimate effects of three methods were compared. The results showed that the three methods have an ideal effect on the LAI estimation. R2 of validated model of vegetation indices, PCA, NN were 0.753 (NDVI), 0.758 (RVI), 0.883, 0.899. PCA and NN methods were better with higher precision, and PCA method was the best, as its RMSE (0.202) was slower than the two vegetation indices (RMSEs of NDVI and RVI were 0.594 and 0.616) and NN (RMSE was 0.413) method. While the LAI was small, vegetation indices were obvious for removing the noise from soil and atmospheric effect and obtained the good evaluation result. PCA showed better effect for all LAI. LAI affected the estimating result of NN method moderately. As for the NN method, modeled LAI value and measured LAI regression formula slope was the nearest to 1 with R2 of 0.949, which showed a great potential for LAI estimating. As a whole, PCA and NN methods were the prior selection for LAI estimation, which should be attributed to the application of hyperspectral information of many bands.