Publications by authors named "Huaan Jin"
Article Synopsis
- Researchers have found that the Tibetan Plateau (TP) has been experiencing increased vegetation greening and productivity, but the exact link between these two processes and the impact of long-term changes is still uncertain.
- An assessment of various satellite-derived products for leaf area index (LAI) and gross primary productivity (GPP) identified PML-V2 GPP and MODIS LAI as the most reliable indicators for measuring vegetation changes in the region.
- Over the past two decades, GPP has grown significantly more than LAI, indicating that factors like water availability and vegetation complexity play important roles in productivity, with only a small portion of the TP linking greening directly to productivity increases.
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.
View Article and Find Full Text PDF[Use of ensemble Kalman smoother algorithm for the time-series retrieval of leaf area index from remote sensing data].
Guang Pu Xue Yu Guang Pu Fen Xi
September 2011
Article Synopsis
- The paper presents a new dynamic model for retrieving Leaf Area Index (LAI) using MOD15A2 data combined with a radiative transfer model called MCRM2.
- It introduces the ensemble Kalman smoother (EnKS) method for estimating LAI, and compares its performance against the ensemble Kalman filter (EnKF) and existing MODIS LAI products.
- The study finds that EnKS provides smoother and more continuous LAI profiles, aligning closely with actual climatological data, making it a promising tool for effective LAI retrieval over time.
[Comparison of methods for estimating soybean leaf area index].
Guang Pu Xue Yu Guang Pu Fen Xi
December 2008
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.
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