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

Wanying Diao, Gang Liu, Huimin Zhang, Kelin Hu and Xiuliang Jin
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Wanying Diao: National Engineering Laboratory for Improving Quality of Arable Land, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Gang Liu: Department Soil and Water, College Resources and Environment, China Agricultural University, Beijing 100193, China
Huimin Zhang: National Engineering Laboratory for Improving Quality of Arable Land, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Kelin Hu: Department Soil and Water, College Resources and Environment, China Agricultural University, Beijing 100193, China
Xiuliang Jin: Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China

Agriculture, 2021, vol. 11, issue 8, 1-20

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 S g (sum of reflectance in green edge) and A_Depth 780–970 (absorption depth at 780–970 nm). (2) The θ had a significant correlation to the R 900–970 (maximum reflectance at 900–970 nm) and S 900–970 (sum of reflectance at 900–970 nm) for loamy soil. (3) The best spectral feature parameters to estimate θ were R 900–970 and S 900–970 for clay loam soil, respectively. (4) The R 900–970 and S 900–970 showed higher accuracy in estimating θ for sandy loam soil. The R 900–970 and S 900–970 achieved the best estimation accuracy for all four soil textures. Combining spectral feature parameters with ANN produced higher accuracy in estimating θ (R 2 = 0.95 and RMSE = 0.03 m 3 m −3 ) for the four soil textures.

Keywords: bulk density; spectral characteristics; artificial neural networks; soil water content (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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