高分六号遥感影像植被特征及其在冬小麦苗期LAI反演中的应用

doi:10.3724/SP.J.1006.2021.11002

Abstract

Abstract:

As Chinese first high-resolution satellite for precision agricultural observations, the GF-6 remote sensing satellite operates in a network with GF-1. In addition to having the same waveband as the GF-1 WFV sensor, red-edge band is added to the Chinese satellite firstly, which can effectively reflect the unique spectral characteristics of the crop. In order to evaluate the monitoring capabilities of the GF-6 satellite imagery for crops, the seedling stage of winter wheat was selected as the research object. Combined with the ground synchronous observation canopy spectrum and the LAI measured data, we analyzed the quantity of bands, the band spectrum and the features of the added red edge vegetation band of the GF-6 satellite image. Furthermore, we constructed vegetation indices by extracting reflectance data from GF-6 remote sensing images and made comparison between the inversion accuracy of the model established by the combination of different wavebands of GF-6 WFV sensor with the help of artificial neural network. Finally, the application ability of GF-6 WFV red edge band in inversing LAI of winter wheat at seeding stage was explored. The results showed that the GF-6 remote sensing image reflected the characteristics of vegetation more realistically. When inverting the winter wheat LAI, the two red-edge bands and the red-edge vegetation index data of the GF-6 WFV sensor were added, which greatly improved the accuracy of the winter wheat LAI inversion model at seedling stage, with the increased R² of 12.48% and the decreased RMSE of 14.75%.

Key words: GF-6, red edge parameter, LAI inversion, winter wheat, BP neural network

ZHANG Yu-Xun, QI Tuo-Ye, SUN Yuan, QU Xiang-Ning, CAO Yuan, WU Meng-Yao, LIU Chun-Hong, WANG Lei. Vegetation characteristics of GF-6 remote sensing image and application on LAI retrieval of winter wheat at seedling stage[J].Acta Agronomica Sinica, 2021, 47(12): 2532-2540.

Figures/Tables 8

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植被指数 Vegetation index	名称 Name	公式 Equation	参考文献 Reference
NDVI	归一化植被指数 Normalized Difference Vegetation Index	NDVI=(NIR-R)/(NIR+R)	[30]
NDVI₇₁₀	红边归一化植被指数710 Red Edge Normalized Vegetation Index 710	NDVI₇₁₀=(RE Ⅰ-R)/(NIR+R)	[30]
NDVI₇₅₀	红边归一化植被指数750 Red Edge Normalized Vegetation Index 750	NDVI₇₅₀=(REⅡ-R)/(NIR+R)	[30]
ARVI	大气阻抗植被指数 Atmosphere Resistant Vegetation Index	ARVI=(NIR-(2R-BLUE))/(NIR+(2R-BLUE))	[31]
SAVI	土壤调节植被指数 Soil-Adjusted Vegetation Index	SAVI=(NIR-R)/(NIR+R+L)×(1+L) (L=0.5)	[32]
CIgreen	叶绿素指数 Green chlorophyll index	CIgreen=NIR/GREEN-1	[32]

传感器 Sensor	空间分辨率 Spatial resolution (m)	辐射分辨率 Radiometric resolution (bit)	蓝 Blue (nm)	绿 Green (nm)	红 Red (nm)	近红外 NIR (nm)	红边I RE I (nm)	红边II RE II (nm)
GF-6 WFV	16	12	0.45-0.52	0.52-0.59	0.63-0.69	0.77-0.89	0.69-0.73	0.30-0.77
GF-1 WFV	16	10	0.45-0.52	0.52-0.59	0.63-0.69	0.77-0.89	—	—
Landsat8-OLI	30	12	0.45-0.51	0.53-0.59	0.64-0.67	0.85-0.88	—	—

处理 Treatment	精度Accuracy (R²)
处理 Treatment	训练样本 Training samples	测试样本 Testing samples
组合1 Combination 1	0.7235	0.6250
组合2 Combination 2	0.7802	0.6862
组合3 Combination 3	0.7272	0.6338
组合4 Combination 4	0.7944	0.7126

Vegetation characteristics of GF-6 remote sensing image and application on LAI retrieval of winter wheat at seedling stage

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