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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 56-62     DOI: 10.6046/zrzyyg.2021216
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Construction of new vegetation water index based on PROSPECT-VISIR model
WANG Jie1(), WANG Guanghui1,2(), LIU Yu1, QI Jianwei1, ZHANG Tao1
1. Land Satellite Remote Sensing Application Center, MNR, Beijing 100048, China
2. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
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Abstract  

In this paper, the leaf reflectance simulation data of visible to mid-infrared spectral range under the condition of different leaf parameters were obtained using the PROSPECT-VISIR leaf model. The spectral characteristic bands of vegetation leaves were analyzed to find the range of bands within which leaf reflectance is sensitive to changes in water content. On the basis of several common vegetation water indexes derived from visible and near-infrared bands, four new vegetation water index models were proposed by addition of the reflectance of the mid-infrared band, namely mid-infrared normalized difference infrared index (NDIIM), mid-infrared normalized difference water index (NDWIM), mid-infrared normalized multi-band drought index (NMDIM) and mid-infrared normalized difference vegetation index (NDVIM). Based on the leaf reflectance simulation data, the sensitivity of four new vegetation water indexes and that of common water index to leaf water content were compared, and the quantitative relationship model between, on one hand, new vegetation water index and, on the other hand, leaf water content and dry matter content, was established. The fitting degree of NMDIM was 0.972, showing the best performance. Finally, a leaf water content estimation model was developed based on two new vegetation water indexes NMDIM and NDIIM so that accurate estimation of leaf water content can be achieved even when the dry matter content is unknown (the root mean square error of the model was 0.002 1g/cm2).
Keywords leaf radiative transfer model      vegetation water index      mid-infrared reflectance      leaf water content     
ZTFLH:  TP79  
Corresponding Authors: WANG Guanghui     E-mail: wangjie0039@163.com;wanggh@lasac.cn
Issue Date: 20 June 2022
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Jie WANG
Guanghui WANG
Yu LIU
Jianwei QI
Tao ZHANG
Cite this article:   
Jie WANG,Guanghui WANG,Yu LIU, et al. Construction of new vegetation water index based on PROSPECT-VISIR model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 56-62.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021216     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/56
Fig.1  Relationship between leaf reflectance and 3 leaf parameters
Fig.2  Relationship of leaf reflectance with water content and dry matter content
传统植
被指数		 公式① 新型植
被指数		 公式
NDII NDII = $\frac{\rho_{820}\ \ -\ \rho_{1600}}{\rho_{820}\ \ +\ \rho_{1600}}$ NDIIM NDIIM= $\frac{\rho_{1600}\ \ -\ \rho_{4200}}{\rho_{1600}\ \ +\ \rho_{4200}}$
NDWI NDWI= $\frac{\rho_{860}\ \ -\ \rho_{1240}}{\rho_{860}\ \ +\ \rho_{1240}}$ NDWIM NDWIM= $\frac{\rho_{1240}\ \ -\ \rho_{4200}}{\rho_{1240}\ \ +\ \rho_{4200}}$
NMDI NMDI= $\frac{\rho_{860}\ \ -\ (\rho_{1640}\ \ -\ \rho_{2130}\ \ )}{\rho_{860}\ \ +\ (\rho_{1640}\ \ +\ \rho_{2130}\ \ )}$ NMDIM NMDIM= $\frac{\rho_{860}\ \ -\ (\rho_{4200}\ \ -\ \rho_{2130}\ \ )}{\rho_{860}\ \ +\ (\rho_{4200}\ \ +\ \rho_{2130}\ \ )}$
NDVI NDVI= $\frac{\rho_{895}\ \ -\ \rho_{675}}{\rho_{895}\ \ +\ \rho_{675}}$ NDVIM NDVIM= $\frac{\rho_{895}\ \ -\ \rho_{4200}}{\rho_{895}\ \ +\ \rho_{4200}}$
Tab.1  Four new water index of vegetation
Fig.3  Relationship between new water index, traditional water index and leaf water content
关系式 R2 RMSE
NDIIM = 0.792 7 + 0.017 1×lnCw 0.912 2.885e-4
NDWIM = 0.887 2 +0.011 8×lnCw 0.963 1.263e-4
NMDIM = 0.896 8 +0.031 2×lnCw 0.997 9.268e-5
NDVIM= 0.876 2 +0.028 7×lnCw 0.994 1.179e-4
Tab.2  Relationship between new vegetation water index and leaf water content
Fig.4  Relationship between new water index, traditional water index and leaf dry matter content
关系式 R2 RMSE
NDIIM= 0.762 2 - 2.840Cm 0.982 1.386e-4
NDWIM = 0.791 3 -0.998Cm 0.905 1.182e-4
NMDIM = 0.861 3 -1.981Cm 0.991 6.697e-5
NDVIM = 0.800 1 -0.931Cm 0.901 1.188e-4
Tab.3  Relationship between new vegetation water index and leaf dry matter content
新型植
被指数		 a0 a1 a2 R2 RMSE
NDIIM 0.703 -0.015 0 -2.987 0.932 0.001 7
NDWIM 0.789 -0.000 7 -1.115 0.811 0.003 9
NMDIM 0.840 -0.005 0 -2.002 0.972 0.001 3
NDVIM 0.812 -0.002 6 -1.043 0.837 0.002 7
Tab.4  Coefficients of four vegetation water index
Fig.5  Comparison between the retrieved value and actual value of water content
Fig.6  Comparison between the retrieved value and actual value of water content
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