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| Inversion of Leaf Area Index Based on GF-6 WFV Spectral Vegetation
Index Model |
| WANG Xiao-xuan1, LU Xiao-ping1*, MENG Qing-yan2, 3, LI Guo-qing4, WANG Jun4, ZHANG Lin-lin2, 3, YANG Ze-nan1 |
1. Henan Polytechnic University, Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines, Ministry of Natural Resources, Jiaozuo 454000, China
2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
3. Sanya Institute of Remote Sensing，Sanya 572029，China
4. Henan Institute of Remote Sensing and Geomatics, Zhengzhou 450000, China
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Abstract Agriculture is not only the basic pillar of national economic development but also the basic social development industry. With the progress and development of agricultural remote sensing technology in China, many remote sensing satellites, such as Gaofen-1, 2 and 6, have been launched, providing important technical support for agricultural situation monitoring, crop growth and agricultural industrial structure adjustment in China. Agricultural remote sensing has become an important means of agricultural science and technology innovation and precision agriculture. LAI is an important key index that can be used to measure vegetation canopy’s physiological and biochemical characteristics. LAI can not only be used to evaluate the initial energy exchange on the surface of the vegetation canopy but also provide corresponding quantitative structural data and reflect the spectral energy information of the vegetation canopy. At the same time, the leaf area index is a key input to the production model of the terrestrial ecosystem and land use process in the context of terrestrial climate change. In addition, when it is found that the vegetation canopy is directly or indirectly affected by human activities and climate change, LAI is also a very important measurement standard for terrestrial ecosystems to cope with climate change. There are few kinds of researches on leaf area index inversion of GF-6 WFV remote sensing image, and the traditional spectral vegetation index model has a weak mechanism and stability. This article is based on GF-6 WFV remote sensing image in the Luancheng county as the experimental zone. Through spectral vegetation index and the measured leaf area index structure of 5 kinds of traditional spectral vegetation index model and 15 kinds of red edge of ratooning buds to participate in the spectrum of the vegetation index model inversion leaf area index, evaluating model using R2 and RMSE. At the same time, using the actual leaf area index was not involved in the modeling, model and using the MODIS LAI product authentication model. The experimental results showed that: (1) Correlation analysis showed that, on the whole, 20 spectral vegetation indexes were significantly correlated with LAI, with the correlation coefficient above 0.4, and the spectral index correlation of red-edge participating structures was higher than that of non-red-edge participating structures, among which NDSI had the best correlation. (2) Fitting analysis showed that, on the whole, 20 spectral vegetation indexes had a better fitting effect with LAI, among which NDS13 had the highest fitting accuracy, R2 was 0.803, and RMSE were 0.301 2. R2 and RMSE was 0.803 and 0.301 2, respectively. (3) As seen from the spatial distribution of inversion maps, the inversion results were in line with the actual local situation. (4) The verified model of measured LAI showed that the overall LAI fitting of measured LAI and NDSI3 model inversion is good, with R2 and RMSE of 0.804 and 0.312 5 respectively, indicating that this model can effectively invert the growth status of maize at the milk stage. (5) The verification model of MODIS LAI products indicated that the LAI of MODIS mean it is higher than the LAI of GF-6, mainly due to the serious mixing of MODIS image pixels and the low spatial resolution. In summary, GF-6 WFV has a strong ability to invert LAI, and the spectral vegetation index model with red edges in its image can effectively invert LAI at the milk stage, providing a basis for maize growth potential monitoring.
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Received: 2020-10-20
Accepted: 2021-12-30
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Corresponding Authors:
LU Xiao-ping
E-mail: LXP@hpu.edu.cn
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