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Remote Sensing Science

Remote Sensing Science (Biquarterly) 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 pl... [More] Remote Sensing Science (Biquarterly) 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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Paper Infomation

Hyperspectral Estimation of Total Nitrogen Content in Orchard Soil Based on Successive Projections Algorithm

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Author: Zizhen Li, Xicun Zhu, Zhongyu Tian, Meixuan Li, Yufeng Peng, Xinyang Yu, Xueyuan Bai, Ling Wang, Yuanmao Jiang

Abstract: Soil total nitrogen content is an important factor to affect the growth of fruit trees. The objective of the paper is to propose a successive projections algorithm for hyperspectral estimation of soil total nitrogen content in orchard. A total of 60 soil samples were collected from apple orchard, Guanli Town, Qixia City, Shandong Province. The spectral reflectivity of the samples was measured by a ASD Field-Spce4.0 spectrometer in the laboratory. The total nitrogen content of the samples was measured by a Kjeldahl apparatus. The spectral data were preprocessed by Savitzky-Golay smoothing and first-order derivative transformation. The sensitive bands were screened by successive projections algorithm. Partial least squares regression (PLSR) model, random forest (RF) model and support vector machine regression (SVR) model were established respectively, and the accuracy of the models was tested and compared. The sensitive wavelengths selected by successive projections algorithm were 849, 2100, 1305 and 2427nm, the determination coefficients (R2) of PLSR, SVR and RF models were 0.822, 0.724 and 0.683, and the relative percent deviation (RPD) were 2.329, 1.940 and 1.702, respectively. PLSR model has the highest accuracy and can play a good role in the estimation of total nitrogen content in orchard soil; the accuracy of SVR and RF models is lower than that of PLSR models, but the estimated results reach the usable level.

Keywords: Orchard Soil, Total Nitrogen Content, Successive Projections Algorithm, Hyperspectral

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