Soil-adjusted power comparison and application conditions of vegetation indices based on PROSAIL model

XU Wenjing; WANG Shaojun

doi:10.11834/jrs.20143265

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Soil-adjusted power comparison and application conditions of vegetation indices based on PROSAIL model
Vol. 18, Issue 4, Pages: 826-842(2014)
Published： 2014 ，
DOI： 10.11834/jrs.20143265

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[1]徐雯靓,王少军.PROSAIL模型模拟下的植被指数土壤调节能力比较与适用环境分析[J].遥感学报,2014,18(04):826-842.

XU Wenjing, WANG Shaojun. Soil-adjusted power comparison and application conditions of vegetation indices based on PROSAIL model. [J]. Journal of Remote Sensing 18(4):826-842(2014)
[1]徐雯靓,王少军.PROSAIL模型模拟下的植被指数土壤调节能力比较与适用环境分析[J].遥感学报,2014,18(04):826-842. DOI： 10.11834/jrs.20143265.

XU Wenjing, WANG Shaojun. Soil-adjusted power comparison and application conditions of vegetation indices based on PROSAIL model. [J]. Journal of Remote Sensing 18(4):826-842(2014) DOI： 10.11834/jrs.20143265.

摘要

为了消除土壤背景信息对植被指数的影响

近几十年发展了土壤调节植被指数系列（SAVI family）。在不同环境条件下

不同指数抗土壤影响的能力不同。在总结了以消除土壤影响为目的的植被指数建立过程的基础上

利用PROSAIL辐射传输模型模拟的两组数据集

比较分析了NDVI、SAVI、TSAVI、MSAVI、OSAVI和GESAVI在不同叶面积指数（LAI）对应不同土壤背景的情况下抗土壤干扰、表达植被信息的能力

指出了不同植被指数应用的最适环境条件。结合植被指数—信噪比图

将这6种植被指数分成3类:在中低LAI值下

若植被覆盖度均匀

OSAVI和TSAVI有较强的消除土壤影响、表达植被信息的能力;当区域LAI分布不均、植被类型混杂时

MSAVI在表达植被信息时具有较好的稳定性。根据每类植被指数的特征

利用MODIS-VI和MODIS-LAI产品初步验证了上述结论的有效性。

Abstract

Soil-adjusted Vegetation Indexes（ SAVIs） have been developed to eliminate the effect of soil background on Vegetation Indices（ VIs）. The soil-adjusted power of VIs varies with environment conditions. After summarizing the development of SAVI family

we compared and evaluated NDVI

SAVI

TSAVI

MSAVI

OSAVI

and GESAVI in terms of their resistance to soil variations over a series of Leaf Area Indices（ LAI）. Two datasets in use were simulated through PROSAIL model. The optimal application conditions of these VIs were noted. Based on the signal-to-noise ratio

these six VIs are classified into three categories. OSAVI and TSAVI can adjust soil noise betterly when applied to uniformly distributed canopy with intermediate-low LAI. MSAVI is more stable in the expression of vegetation information when LAI or vegetation species is heterogeneous. Analysis results based on M ODIS-VI and MODIS-LAI products over corresponding canopy preliminarily validates our conclusion.