光谱分辨率对土壤组分建模影响分析

doi:10.3964/j.issn.1000-0593(2021)03-0865-06

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Abstract The laboratory visible-near infrared (VIS-NIR) spectroscopy has been frequently used in quantifying soil components because it is effective, fast and nondestructive etc. The higher spectral resolution is the richer soil information we could obtain. However, hyperspectral data are red undant and should be preprocessed. The study of the effects of different spectral resolutions on the modeling of soil components is relatively inadequate. Taking advantage of the European Land Use/Cover Area Frame Statistical Survey (LUCAS) dataset having 19 036 soil samples, we investigate the effects of different spectral resolutions on modeling soil components: total soil nitrogen (N), organic carbon (OC), calcium carbonate (CaCO₃), and clay. To achieve this, we took the partial least squares regression (PLS) method as the evaluation model and randomly chose 30% samples for independent verification. Firstly, the spectral data which have 4 200 bands with 0.5 nm spectral resolution were resampled to 2，4，8，…，1 024 nm respectively using average reflection value by of uniform interval sampling. The results are as follows: (1) when the spectral resolution was decreased, the inversion accuracy of soil components showed a downward trend; (2) when the spectral resolution was higher than 64 nm, higher model validation accuracies were obtained for estimating the four selected soil components (R²>0.65, RPD>1.7); (3) the accuracy for CaCO₃ and clay components was significantly reduced when the spectral resolution was lower than 128 nm; (4) of the four soil components, CaCO₃ was the most sensitive to spectral resolution. It has higher accuracy (R²>0.86，RPD>2.72) at high spectral resolutions, but the accuracy reduced most rapidly as the spectral resolution decreases. Secondly, based on the spectral response functions for a group of common satellite sensors, the inversion performances of using GF2, S3A, L8, Aster, S3OLCI, and Modis spectral bands are summarized as follows: (1) all sensors achieved higher accuracy for soil N and OC even if GF2 has 4 different bands (R²=0.56; RPD=1.51); (2) a low accuracy was obtained for CaCO₃ and clay; (3) besides the number of spectral bands, the band positions are also important and the sensors (S3A, L8, Aster, and MODIS) having bands in the spectral range 1 100～2 500 nm showed a stronger performance than the sensor (e. g. S3OLCI) without the corresponding bands. The results from this study provide a guiding reference for preprocessing hyperspectral data of soil, selecting suitable satellite data sources and designing new optical sensors for soil Vis-NIR spectroscopy.

Key words：Soil components; Laboratory Vis-NIR spectroscopy (VIS-NIR); Satellite sensor; Spectral resolution; Partial least squares regression model (PLS)

Received: 2020-02-07 Accepted: 2020-06-14

ZTFLH:

O657.3

Corresponding Authors: WANG Qin-jun E-mail: wangqj@radi.ac.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)03-0865-06 OR https://www.gpxygpfx.com/EN/Y2021/V41/I03/865