基于高光谱特征与人工神经网络模型对土壤含水量估算

doi:10.3964/j.issn.1000-0593(2017)03-0841-06

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Abstract Soil water content (θ) is an important factor for the crop growth and crop production. The objectives of this study were to (i) test various regression models for estimating θ based on spectral feature parameters, and (ii) compare the performance of the proposed models by using artificial neural networks (ANN) and spectral feature parameters. The θ data of sand and loam and concurrent spectral parameters were acquired at the laboratory experiment in 2014. The results showed that: (1) the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm estimate θ had the significant, when sand bulk density was 1.40 g·cm^-3; the maximum reflectance with blue edge and the sum reflectance within 900～970 nm had the best correlation (R²>0.70) when sand bulk density was 1.50 g·cm^-3; while soil bulk density was 1.60 g·cm^-3, the sum reflectance within 780～970 nm and normalized absorption depth in 560～760 nm reached a significant (R²>0.90); when soil bulk density was 1.70 g·cm^-3, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had the best correlation estimate θ (R²>0.88). 2) When the soil type was loam, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had a best correlation estimate θ. The spectral feature parameters the sum reflectance within blue edge (R²=0.26 and RMSE=0.09 m³·m^-3) and 780～970 nm absorption depth (R²=0.32 and RMSE=0.10 m³·m^-3) were best correlated with θ in the sand. The θ model based on maximum reflectance with 900～970 nm (R²=0.92 and RMSE=0.05 m³·m^-3) and the sum reflectance within 900～970 nm had a high correlation (R²=0.92 and RMSE=0.04 m³·m^-3) in the loam. The BP-ANN model presented a better estimation accuracy of θ (R²=0.87 and RMSE=0.05 m³·m^-3) in two soils. Thus, the ANN model has great potential for estimating θ. Thus, the BP-ANN model has great potential for θ estimation.

Key words： Soil water content Spectral feature parameters Artificial neural networks

Received: 2016-03-21 Accepted: 2016-07-29

ZTFLH:

S152.7

Corresponding Authors: LIU Gang E-mail: liug@cau.edu.cn

Cite this article:

DIAO Wan-ying,LIU Gang,HU Ke-lin. Estimation of Soil Water Content Based on Hyperspectral Features and the ANN Model [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 841-846.

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2017)03-0841-06 OR http://www.gpxygpfx.com/EN/Y2017/V37/I03/841