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

Abstract

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval.If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model OPEN ACCESSRemote Sens. 2015, 7 4605 parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

“…The changes in RMSE of NDVI and slope NDVI, land cover type, and slope were selected as auxiliary variables in this study. This is because they are widely used in the generation of high-resolution reference LAI map in mountainous areas based on literature research [31,32,38,39,45]. NDVI is calculated as a ratio between the red (R) and near infrared (NIR) reflectance in the form of (NIR − R)/(NIR + R), and it may be the most widely used vegetation indices in the generation of high-resolution reference LAI map.…”

“…Although the reflectance data can be used in high-resolution LAI mapping, vegetation indexes are more often used in validation activities. Commonly used vegetation indexes include normalized difference vegetation index (NDVI) [36], simple ratio (SR) [37], and reduced simple ratio (RSR) [38]. In mountainous areas, slope extracted from digital elevation model (DEM) data is often used to account for the terrain effects [31,39].…”

Comparison of Three Theoretical Methods for Determining Dry and Wet Edges of the LST/FVC Space: Revisit of Method Physics

“…To calculate r c m and r c x from r sm and r sx , maximum possible LAI is required [21]. Since the vegetation under each site is maize, a maximum possible LAI of 5.0 was selected in the Moran method [32]. FVC was calculated from NDVI using a non-linear relationship, i.e., FVC = [(NDVI − NDVI min )/(NDVI max − NDVI min )] 2 where NDVI min and NDVI max were set as 0.2 and 0.86 [19].…”

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

“…This problem may be caused by the use of the PROSAIL model, which is mainly related to leaf biochemical content, but during the non-growth period, few leaves are left. Yin, Li [52] found that the canopy structure representation of the radiative transfer (RT) model has substantial implications for LAI.…”