基于实测光谱的植被指数对水稻叶面积指数的响应特征分析

doi:10.3964/j.issn.1000-0593(2018)01-0205-07

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Abstract Leaf area index (LAI), the most frequently used parameter for monitoring agricultural ecology, could be utilized to provide scientific basis for crop disease, growth and carbon cycle monitoring as well as yield estimation. Vegetation indices (VIs), which can be employed to indicate LAI, are important data sources for satellite-based LAI production. And the most widely used one is Normalized Difference Vegetation Index (NDVI). Several standard satellite LAI products such as MODIS use NDVI as an input. However, the saturation characteristics of NDVI would introduce errors in the production of LAI. To find a possible optimized VI to derive the LAI of rice, 28 sets of spectral observations and corresponding LAI data were collected in the sample fields of Jiangxi Province. Four commonly used VIs including NDVI, Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) were extracted based on the in suit rice spectra. The response performance of the ground-based VIs to concurrent LAI measurements was assessed in this study. The linear regression results showed that the other three VIs had better adaptability than NDVI (R²=0.38), while EVI had the best performance (R²=0.82). MSAVI (R²=0.744) and SAVI (R²=0.751 1) also showed a better performance than NDVI. To continue, the differences between the results of ground-based model and the lookup table of MODIS LAI backup algorithm were compared. The MODIS LAI backup algorithm was derived from the empirical relationship between NDVI and LAI based on eight coarse biomass types. For biomass 1, it contained cereal and grass, and rice belonged to this category. In this paper, the lookup table of biomass 1 based on MODIS LAI backup algorithm was validated using the in situ LAI and spectral observations. The mean predict error of the algorithm was more than 3.2; and mean relative tolerance was up to 530%. This means large error will be introduced in rice LAI monitoring of this area if we use MODIS LAI backup algorithm. The low accuracy of MODIS backup algorithm may be caused by the coarse biomass classification system. In fact, different vegetation types included in biomass 1 had very significant difference in their canopy characteristics. Mixing them all in one class would result in an unacceptable errors to the LAI inversion for a specified crop type such as rice. The different saturation ranges of NDVI to inverse the LAI were also considered. The NDVI values kept unchanged with the increase of LAI when LAI was greater than 4 in the MODIS backup algorithm. Nevertheless, for the regression based on the rice field measured LAI and spectral observations, the saturation domain of NDVI was reached when LAI was larger than 2. After that, the accuracy comparison of the four ground-based VI models was implemented using root mean square error. The results showed that the mean predict error for NDVI model was 1.019 and only 0.55 for EVI model, which was only 1/6 of MODIS backup algorithm and 1/2 of NDVI model. Compared with the other three VIs, an addition blue band was utilized in the calculation of EVI to attenuate the aerosol impact on red band. This may be one of the possible reasons to explain the better performance of EVI. Therefore, an algorithm based on EVI could be developed as an alternative approach to improve the accuracy of LAI inversion.

Key words：Leaf area index; Vegetation indices; Rice; MODIS; Response

Received: 2017-01-08 Accepted: 2017-05-22

ZTFLH:

TP-7

Corresponding Authors: CHEN Xiao-ling E-mail: xiaoling_chen@whu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)01-0205-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I01/205

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