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Remote Sensing Science

Remote Sensing Science is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of remote sensing science and technology. The main focus of the journal is the academic papers and comments of latest improvement in the fields of basic theory, technology development and application of remote sensing science, report of latest research result, aiming at providing a good communication platform to tran... [More] Remote Sensing Science is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of remote sensing science and technology. The main focus of the journal is the academic papers and comments of latest improvement in the fields of basic theory, technology development and application of remote sensing science, report of latest research result, aiming at providing a good communication platform to transfer, share and discuss the theoretical and technical development of remote-sensing theory development for professionals, scholars, researchers and administrative staffs in this field, reflecting the academic front level, promote academic change and seize the theory, practice front line, research level and development direction of remote sensing science.

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ISSN Print:2329-8138

ISSN Online:2329-8146

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Paper Infomation

Inversion of Canopy Nitrogen Content in Apple Orchard Based on GF-1 Satellite Image

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Author: Shujing Cao, Xicun Zhu, Jingling Xiong, Ruiyang Yu, Xueyuan Bai, Yuanmao Jiang, Dongsheng Gao, Guijun Yang

Abstract: The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74–41.978*NPCI-10.78*NDVI. The R2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.

Keywords: GF-1, Nitrogen Content, Inversion, Apple Tree, Canopy

References:

[1] Li Wenqing, Zhang Min, Shu Huairui. The Phyiological Effects of Nitrogen on Fruit Trees[J]. Journal of Shandong Agricultural University (Natural Science), 2002(01): 96-100.

[2] Zhao Chunjiang. Research and Practice of Precision Agriculture[M]. Beijing Science Press, 2009: 17-18.

[3] Huang Rugen, Liu Zhenhua, Hu Yueming, Xiao Beisheng. Retrieval of typical subtropical crop canopy SPAD value in South China using GF-1 remote sensing image[J]. Journal of South China Agricultural University, 2015, 36(4): 105-111.

[4] Li Fenling, Chang Qingrui, Shen Jian, Wang Li. Remote sensing estimation of winter wheat leaf nitrogen content based on GF-1 satellite data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(9): 157-164.

[5] Jia Yuqiu, Li Bing, Cheng Yongzheng, Liu Ting, Guo Yan, Wu Xihong, Wang Laigang. Comparison between GF-1 images and Landsat-8 images in monitoring maize LAI[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(9): 173-179.

[6] Wang Lihui, Du Jun, Huang Jinliang, Yang Ruixia, Huang Wei. Retrieving Leaf Area Index of maize based on GF-1 multispectral Remote Sensing data[J]. Journal of Center China Normal University (Natural Science), 2016, 50(1): 120-127.

[7] Zhu Yunfang, Zhu Li, Li Jiaguo, Chen Yijin, Zhang Yonghong, Hou Haiqian, Ju Xing, Zhang Yazhou. The study of inversion of chlorophyll a in Taihu based on GF-1 WFV image and BP neural network[J]. Acta Scientiae Circumstantiae,2017, 37(1): 130-137

[8] Jordan C F. Derivation of leaf area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4):663-666.

[9] Richardson A J. Distinguishing vegetation from soil background information[J]. Photogram Engineering and Remote sensing, 1977, 43(12):1541-1552.

[10] Rouse Jr J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the Great Plains with ERTS[J]. NASA special publication, 1974, 351: 309.

[11] Gitelson A A, Merzlyak M N. Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll[J]. Journal of Plant Physiology, 1996, 148(s 3–4):494–500.

[12] Liu H Q, Huete A. A Feedback Based Modification of the NDVI to Minimize Canopy Background and Atmospheric Noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2): 457-465.

[13] Gitelson A A, Kaufman Y J, Stark R, Rundquist D. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1): 76-87.

[14] Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3):289-298.

[15] Penuelas J, Gamon J A, Fredeen A L, Merino J, Field C B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves[J]. Remote Sensing of Environment, 1994, 48(2): 135-146.

[16] Filella I, Serrano L, Serra J, Peńuelas J. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 1995, 35: 1400-1405.

[17] Schleicher T D, Bausch W C, Delgado J A, Ayers P D. Evaluation and refinement of the nitrogen reflectance index (NRI) for site-specific fertilizer management[C]. 2001 ASAE Annual International Meeting, St-Joseph, MI, USA. ASAE Paper. 2001 (01-11151).

[18] Huete A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25(3): 295-309.

[19] Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996, 55(2): 95-107.

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