Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

Xiaoning Zhang,Hu Zhang,Yidong Tong,Siyang Yin,Lei Cui,Changsen Zhao,Jing Guo,Ziti Jiao,Zidong Zhu,Rui Xie,Yadong Dong,Sijie Li

doi:10.3390/rs13234911

Abstract

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.

Highlights

Vegetation monitoring is critical for the development of adaptation strategies to address the challenges caused by climate change and human activities in ecosystems, such as global warming and precision farming [1]
It is foreseeable for the high sensitivity of canopy bi-directional reflectance distribution function (BRDF) to the two essential canopy structure parameters of leaf massleaf per unit area (LAI) and average leaf angle (ALA), which have shown similar results associated to nadir reflectance [45,52]
The sensitivity to Cab may be caused by high chlorophyll absorption in the red band, and the non-negligible BRDF change in the background soil associated with Psoil makes important contributions to the overall canopy BRDF [70]

Summary

Introduction

Satellite remote sensing has provided an effective way to perform quick global investigations [2], where the vegetation structure and biochemical parameters commonly considered in monitoring are derived from physical canopy reflectance models. GEOV2 [4], GLASS [5], GLOMAP [6], MODIS [7,8], PROBA-V [9], and VIIRS [10] products. Reflectances in the red and near-infrared (NIR) bands are commonly adopted due to their sensitivity to vegetation parameters, and observations in the shortwave infrared and blue bands are applied in GEOV2 and PROBA-V, respectively. Neural network regression (e.g., GEOV2, GLASS, and PROBA-V) and look-up tables (e.g., MODIS and VIIRS LAI products) between the directional reflectance and vegetation parameters are usually considered in the estimation. To perform a quick estimation, relationships between the land cover-specific LAI and vegetation indices have been determined to derive the LAI, such as the GLOMAP LAI product [6], as well as the backup algorithm of the MODIS LAI product [8]

Objectives

Methods

Results

Discussion

Conclusion