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Volume 11
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Article

Influences of Soil Bulk Density and Texture on Estimation of Surface Soil Moisture Using Spectral Feature Parameters and an Artificial Neural Network Algorithm

by
Wanying Diao
1,2,
Gang Liu
2,
Huimin Zhang
1,*,
Kelin Hu
2 and
Xiuliang Jin
3
1
National Engineering Laboratory for Improving Quality of Arable Land, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Department Soil and Water, College Resources and Environment, China Agricultural University, Beijing 100193, China
3
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Agriculture 2021, 11(8), 710; https://doi.org/10.3390/agriculture11080710
Submission received: 22 June 2021 / Revised: 14 July 2021 / Accepted: 21 July 2021 / Published: 28 July 2021
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Abstract

Effective monitoring of soil moisture (θ) by non-destructive means is important for crop irrigation management. Soil bulk density (ρ) is a major factor that affects potential application of θ estimation models using remotely-sensed data. However, few researchers have focused on and quantified the effect of ρ on spectral reflectance of soil moisture with different soil textures. Therefore, we quantified influences of soil bulk density and texture on θ, and evaluated the performance from combining spectral feature parameters with the artificial neural network (ANN) algorithm to estimate θ. The conclusions are as follows: (1) for sandy soil, the spectral feature parameters most strongly correlated with θ were Sg (sum of reflectance in green edge) and A_Depth780–970 (absorption depth at 780–970 nm). (2) The θ had a significant correlation to the R900–970 (maximum reflectance at 900–970 nm) and S900–970 (sum of reflectance at 900–970 nm) for loamy soil. (3) The best spectral feature parameters to estimate θ were R900–970 and S900–970 for clay loam soil, respectively. (4) The R900–970 and S900–970 showed higher accuracy in estimating θ for sandy loam soil. The R900–970 and S900–970 achieved the best estimation accuracy for all four soil textures. Combining spectral feature parameters with ANN produced higher accuracy in estimating θ (R2 = 0.95 and RMSE = 0.03 m3 m−3) for the four soil textures.
Keywords: bulk density; spectral characteristics; artificial neural networks; soil water content bulk density; spectral characteristics; artificial neural networks; soil water content

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MDPI and ACS Style

Diao, W.; Liu, G.; Zhang, H.; Hu, K.; Jin, X. Influences of Soil Bulk Density and Texture on Estimation of Surface Soil Moisture Using Spectral Feature Parameters and an Artificial Neural Network Algorithm. Agriculture 2021, 11, 710. https://doi.org/10.3390/agriculture11080710

AMA Style

Diao W, Liu G, Zhang H, Hu K, Jin X. Influences of Soil Bulk Density and Texture on Estimation of Surface Soil Moisture Using Spectral Feature Parameters and an Artificial Neural Network Algorithm. Agriculture. 2021; 11(8):710. https://doi.org/10.3390/agriculture11080710

Chicago/Turabian Style

Diao, Wanying, Gang Liu, Huimin Zhang, Kelin Hu, and Xiuliang Jin. 2021. "Influences of Soil Bulk Density and Texture on Estimation of Surface Soil Moisture Using Spectral Feature Parameters and an Artificial Neural Network Algorithm" Agriculture 11, no. 8: 710. https://doi.org/10.3390/agriculture11080710

APA Style

Diao, W., Liu, G., Zhang, H., Hu, K., & Jin, X. (2021). Influences of Soil Bulk Density and Texture on Estimation of Surface Soil Moisture Using Spectral Feature Parameters and an Artificial Neural Network Algorithm. Agriculture, 11(8), 710. https://doi.org/10.3390/agriculture11080710

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