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 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.
Highlights
Leaf Area Index (LAI) influences vegetation photosynthesis, transpiration and the energy balance of the land surface; it is among the essential inputs of climate, hydrology and ecosystem productivity models [1]
Model for the scattering from arbitrarily inclined leaves (SAIL) test dataset and using the four-scale model for the four-scale synthetic test dataset, the neural network (NN) that contain noise (SAILNN10 and four-scaleNN10) get a lower accuracy compared with the original NNs (SAILNN0 and four-scaleNN0) for the unavailable pure measurements, but they were more robust to the measurement uncertainties
The major aim of this study is to assess the impact of radiative transfer (RT) model selection on LAI retrieval
Summary
Leaf Area Index (LAI) influences vegetation photosynthesis, transpiration and the energy balance of the land surface; it is among the essential inputs of climate, hydrology and ecosystem productivity models [1]. The factors that influence the accuracy of LAI products mainly include radiative transfer (RT) model uncertainty, the accuracy of the inversion method, the quantity and uncertainty of the RS measurements and spatial heterogeneity within the footprint of pixels [10,11]. With the continuing improvement of the quality of reflectance products, the lack of sufficient observations becomes a crucial limiting factor, in equatorial and high-latitude regions, because of high cloud coverage. RT model accuracy is important for further improving the accuracy of LAI products [13]
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