基于新型植被指数的冬小麦LAI高光谱反演

doi:10.3864/j.issn.0578-1752.2018.18.005

Abstract

Abstract:

【Objection】The purpose of this study was to analyze the effect of leaf water content on crop canopy spectra and to construct a new spectral index, so as to improve the accuracy of high spectral inversion of crop leaf area index (LAI). 【Method】 Under the support of winter wheat water-fertilizer cross test, the canopy spectral response characteristics of LAI of winter wheat under different recalcitrant cultivars, nitrogen application rates and irrigation amount were analyzed. Through the correlation analysis among the normalized differential red edge index (NDRE), water sensitivity index (WI) and LAI, the paper developed, a new vegetation index, the red-edge resistance water vegetation index (RRWVI) to inverse winter wheat LAI. Several commonly used vegetation indices were used as a reference to analyze the response ability of RRWVI to diagnose the LAI of many key winter wheat varieties. 2/3 of the measured samples were randomly selected to establish a high spectral response model of LAI based on various vegetation indices and 1/3 of the samples not involved in the modeling were used to evaluate the accuracy of the model. 【Result】 The results showed that with the advancement of growth period, the LAI of winter wheat first increased and then decreased, and different water and fertilizer treatments had a greater effect on it. After the flowering stage, the LAI of winter wheat declined significantly, and the LAI of strong gluten wheat (Gaoyou2018) was higher than that of medium-gluten wheat (Jimmy22) during the whole growth period. The spectral reflectance of winter wheat under different nitrogen levels increased with the increase of nitrogen application rate in the near-infrared band (720-1 350 nm), which was completely consistent with the nitrogen fertilizer gradient. The samples with twice-nitrogen treatment had the highest near-infrared reflectance, and the changed in spectral reflectance of winter wheat canopy under different growth stages were generally consistent. There was a high correlation between NDRE and WI in each key growth period, and the correlation between NDRE and LAI was significantly better than that of WI. The correlation between RRWVI and LAI was better than NDRE and WI. Although 8 commonly used vegetation indices are significantly correlated with LAI, RRWVI has the greatest correlation with LAI, and the coefficient of determination R2 of the fitting curve reached 0.86.【Conclusion】 By analyzing the hyperspectral inversion model of winter wheat LAI constructed by all kinds of indices, the newly constructed RRWVI achieved a more reliable inversion effect than frequently-used vegetation indices, such as NDRE and NDVI, indicating that the newly constructed red edge water-resistant vegetation index could effectively improve the accuracy of monitoring winter wheat LAI.

Key words: winter white, hyperspectral, RRWVI, leaf area index, NDRE, NDVI

MeiYan SHU, XiaoHe GU, Lin SUN, JinShan ZHU, GuiJun YANG, YanCang WANG, LiYan ZHANG. High Spectral Inversion of Winter Wheat LAI Based on New Vegetation Index[J].Scientia Agricultura Sinica, 2018, 51(18): 3486-3496.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Table 1

Common vegetation index calculation formula"

植被指数 Vegetable index	名称 Name	公式 Formula	参考文献 References
RRWVI	红边抗水植被指数 Red-edge resistance water vegetable index	$\frac{{{\text{R}}_{\text{970}}}\text{(}{{\text{R}}_{\text{790}}}\text{-}{{\text{R}}_{\text{720}}}\text{)}}{{{\text{R}}_{\text{900}}}\text{(}{{\text{R}}_{\text{790}}}\text{+}{{\text{R}}_{\text{720}}}\text{)}}$
NDVI	归一化植被指数 Normalized difference vegetation index	$\frac{{{\text{R}}_{\text{800}}}\text{-}{{\text{R}}_{\text{670}}}}{{{\text{R}}_{\text{800}}}\text{+}{{\text{R}}_{\text{670}}}}$	[26]
RVI	比值植被指数 Ratio vegetation index	$\frac{{{\text{R}}_{\text{800}}}}{{{\text{R}}_{\text{670}}}}$	[27]
DVI	差值植被指数 Difference vegetation index	${{\text{R}}_{\text{800}}}\text{-}{{\text{R}}_{670}}$	[28]
SAVI	土壤调节植被指数 Soil-adjusted vegetation index	$\frac{\text{1}\text{.5(}{{\text{R}}_{\text{800}}}\text{-}{{\text{R}}_{\text{670}}}\text{)}}{{{\text{R}}_{\text{800}}}\text{+}{{\text{R}}_{\text{670}}}\text{+0}\text{.5}}$	[29]
NDRE	标准化差分红边植被指数 Normalized difference red edge	$\frac{{{\text{R}}_{\text{790}}}\text{-}{{\text{R}}_{\text{720}}}}{{{\text{R}}_{\text{790}}}\text{+}{{\text{R}}_{\text{720}}}}$	[24]
NVI	新植被指数 New vegetable index	$\frac{{{\text{R}}_{\text{777}}}\text{-}{{\text{R}}_{\text{747}}}}{{{\text{R}}_{\text{673}}}}$	[30]
NPCI	归一化色素叶绿素植被指数 Normalized pigment chlorophyll vegetation index	$\frac{{{\text{R}}_{\text{800}}}\text{-}{{\text{R}}_{\text{680}}}}{{{\text{R}}_{\text{800}}}\text{+}{{\text{R}}_{\text{680}}}}$	[31]
WI	水分指数 Water index	$\frac{{{\text{R}}_{\text{900}}}}{{{\text{R}}_{\text{970}}}}$	[25]
PRI	光辐射指数 Photon radiance index	$\frac{{{\text{R}}_{\text{570}}}\text{-}{{\text{R}}_{\text{531}}}}{{{\text{R}}_{\text{570}}}\text{+}{{\text{R}}_{\text{531}}}}$	[32]

Table 1

Fig. 4

Fig. 5

Fig. 6

Table 2

Fig. 7

References 36

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参数 Parameter	模型 Model	R²	RMSE
NDVI	y = 0.1053e^4.3133^x	0.78	1.216
RVI	y = 1.3344e^0.0601^x	0.59	1.754
DVI	y = 0.296e^7.4489^x	0.67	1.429
SAVI	y = 0.1901e^5.3888^x	0.71	1.31
PRI	y = 5.0448e^-21.59^x	0.66	1.433
NVI	y = 1.7431e^0.2685^x	0.49	1.905
NPCI	y = 6.235e^4.2439^x	0.56	1.464
NDRE	y = 0.3073e^5.6259^x	0.73	1.365
RRWVI	y = 0.1614e^8.0405^x	0.83	1.038

High Spectral Inversion of Winter Wheat LAI Based on New Vegetation Index

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