Comparing accuracy for leaf area index estimation inverting a simple empirical model and a radiative transfer model by using multiangular and hyperspectral data

2005 ◽  
Author(s):  
F. Vuolo ◽  
L. Dini
Keyword(s):  
Radiative Transfer ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Empirical Model ◽  
Hyperspectral Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Index Estimation ◽  
Simple Empirical Model

scholarly journals Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

2015 ◽  
Vol 7 (4) ◽  
pp. 4604-4625 ◽  
Author(s):  
Gaofei Yin ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Weiliang Fan ◽  
Baodong Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Model Selection ◽  
Radiative Transfer ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index

scholarly journals Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2460 ◽  
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Xiuguo Liu ◽  
Lin Du ◽  
Shuo Shi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Gaussian Process Regression ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Look Up Table ◽  
Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

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