Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Tinghui Wu, Jian Yu, Jingxia Lu, Xiuguo Zou and Wentian Zhang
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Tinghui Wu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jian Yu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jingxia Lu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xiuguo Zou: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Wentian Zhang: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney NSW 2007, Australia

Agriculture, 2020, vol. 10, issue 7, 1-14

Abstract: Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R 2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields.

Keywords: hyperspectral imaging; soil moisture content; wavelet transform; CARS-SPA algorithm; inversion model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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