Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Zhijun Zhen,Jean-Philippe Gastellu-Etchegorry,Wenhan Qin,Jian Li,Michael Henke,Peng Lu,Nicolas Lauret,Jordan Guilleux,Eric Chavanon,Shengbo Chen,Lisai Cao,Tiangang Yin

doi:10.3390/s21062115

Abstract

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Highlights

The saturation effects limit the use of Vegetation indices (VIs)
We explored the possibility of using the negative soil adjustment factor to mitigate the saturation effects
Two data sets were used for the validation, including a long time series observation of MODerate resolution Imaging Spectroradiometer (MODIS) Leaf area index (LAI) data in the Apiacás area and a global LAI dataset

Summary

Introduction

As major land cover of the planet, forests have become a key priority in studies of the biodiversity and the carbon cycle of terrestrial ecosystems [1,2]. It is essential to record forest dynamics to understand the terrestrial carbon cycle better and improve forest management practices [3,4]. Vegetation indices (VIs) are often used to estimate LAI from broad spectral bands [7,8]. Their analytical expressions differ significantly, the implementations of these indices can be divided roughly into three categories: (1) Intrinsic VIs such as simple ratio (SR) [9] and normalized difference vegetation index (NDVI) [10]. Their analytical expressions differ significantly, the implementations of these indices can be divided roughly into three categories: (1) Intrinsic VIs such as simple ratio (SR) [9] and normalized difference vegetation index (NDVI) [10]. (2) Soil adjusted

Methods

Results

Discussion

Conclusion