Abstract
Considering that large quantities of soil hyperspectral data include the redundancy and overlap of spectral information, the technology of selecting feature wavelength can effectively solve these problems, and improve the accuracy and stability of the soil organic matter (SOM) content retrieval model. Traditional methods of wavelength selection mainly attempt to establish the empirical relationship between reflectance and SOM contents, and the performance is directly related to the quality and representativeness of the “training data”. This study first distinguished the sensitive wavelength interval of SOM through the sensitivity analysis (SA) of the SOM to soil reflectance in radiative transfer model. Then sensitive wavelength points of SOM were ascertained using the successive projection algorithm (SPA): 468, 476, 496, 599, 775 and 900nm. Results show that SOM content can be estimated with high accuracy (root-mean-square error of prediction (RMSEP) $\text{R}^{2})>82.9$ %) by adopting the selected six wavelengths. Especially at 599nm, the accuracy of SOM content estimation is the highest (RMSEP: 0.176%, R2: 90.4%). Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SA-SPA with the SOM radiative transfer model improves the generalizability and accuracy of the result. The research provides theoretical basis and technical support for the remote sensing retrieval of SOM, the development of rapid spectral instruments, and the bands setting of sensor instrument.
Highlights
Soil organic matter (SOM) is an important part of soil, whose content is generally regarded as a criterion to assess soil fertility and an important indicator of soil degradation [1], [2]
The unknown parameter a1 and a2 were acquired by the least-squares algorithm combining the calibration set, wavelength-by-wavelength, in the range of 450-2500 nm
Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SOM radiative transfer model improves the generalizability and accuracy of the result
Summary
Soil organic matter (SOM) is an important part of soil, whose content is generally regarded as a criterion to assess soil fertility and an important indicator of soil degradation [1], [2]. And accurately grasping the spatial change of SOM content is of great significance for precision agriculture. Hyperspectral remote sensing technology owing to its characteristics of high spectral resolution, multiple bands and strong. Continuity will gradually replace the traditional monitoring methods based on chemical analysis [3]–[7]. It can obtain subtle spectral information of ground objects and provides a powerful tool for quantitative prediction of SOM content. For practical applications, the spectral information overlaps severely. Selecting the feature wavelengths of SOM is the key to improving the predictive capability of model [8]–[10]
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