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
刁万英,刘 刚*,胡克林 . 基于高光谱特征与人工神经网络模型对土壤含水量估算 [J]. 光谱学与光谱分析, 2017, 37(03): 841-846.
DIAO Wan-ying, LIU Gang*, HU Ke-lin . Estimation of Soil Water Content Based on Hyperspectral Features and the ANN Model . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 841-846.
[1] Krzeminska D M, Steele-Dunne Susan C, Bogaard T A, et al. Hydrological Processes, 2012, 26(14): 2143. [2] Lunt I A, Hubbard S S, Rubin Y. Hydrogeology Journal, 2005, 307(1-4): 254. [3] Heathman G C, Cosh Michael H, Han E J, et al. Geoderma, 2012, 170: 195. [4] Rahimzadeh-Bajgiran P, Berg A A, Champagne C, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 83: 94. [5] Liu W D, Baret F, Gu X F, et al. Remote Sensing of Environment, 2002, 81(2-3): 238. [6] Whiting M L. Proc SPIE, 2009, 7454: 74540D. [7] Rijal S, Zhang X D, Jia X H. Soil Science Society of America Journal, 2013, 77(4): 1133. [8] Zhang T T, Li L, Zheng B J. Journal of Applied Remote Sensing, 2013, 7:073587. [9] LI Mei-ting, WU Hong-qi, JIANG Ping-an, et al(李美婷, 武红旗, 蒋平安, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(8): 2117. [10] Prakash R, Singh D, Pathak N P. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1): 196. [11] Dematte J A M, Nanni M R, Dasilva A P, et al. International Journal of Remote Sensing, 2010, 31(2): 403. [12] Li X C, Zhang Y J, Bao Y S, et al. Remote Sensing, 2014, 6(7): 6221. [13] Jin X L, Du J, Liu H J, et al. Agricultural and Forest Meteorology, 2016, 218-219: 250. [14] Bowers S A, Hanks R J. Soil Science, 1965, 100(2): 130. [15] Liu W D, Baret F, Gu X F, et al. International Journal of Remote Sensing, 2003, 24(10): 2069. [16] Bottcher K, Glasser C, Mooney S J. Remote Sensing Letters, 2012, 3(7): 557. [17] Mather P M. Computer Processing of Remotely-Sensed Images: An Introduction, Wiley, Chichester, U K, 2000.