| 光谱学与光谱分析 |
|
|
|
|
|
| Estimation of Soil Water Content Based on Hyperspectral Features and the ANN Model |
| DIAO Wan-ying, LIU Gang*, HU Ke-lin |
| Department of Soil and Water, China Agricultural University, Beijing 100193, China |
|
|
|
|
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.
|
|
Received: 2016-03-21
Accepted: 2016-07-29
|
|
|
|
Corresponding Authors:
LIU Gang
E-mail: liug@cau.edu.cn
|
|
[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. |
| [1] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [2] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [3] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
| [4] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
| [5] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
| [6] |
BAO Pei-jin1, CHEN Quan-li1, 3*, ZHAO An-di1, REN Yue-nan2. Identification of the Origin of Bluish White Nephrite Based on
Laser-Induced Breakdown Spectroscopy and Artificial
Neural Network Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 25-30. |
| [7] |
WANG Dong-sheng1, WANG Hai-long1, 2, ZHANG Fang1, 3*, HAN Lin-fang1, 3, LI Yun1. Near-Infrared Spectral Characteristics of Sandstone and Inversion of Water Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3368-3372. |
| [8] |
DUAN Hong-wei1, 2, GUO Mei3, ZHU Rong-guang3, NIU Qi-jian1, 2. LIBS Quantitative Analysis of Calorific Value of Straw Charcoal Based on XY Bivariate Feature Extraction Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3435-3440. |
| [9] |
WANG Jin-jie1, 2, 3, 4, 5, DING Jian-li1, 4, 5*, GE Xiang-yu1, 4, 5, ZHANG Zhe1, 4, 5, HAN Li-jing1, 4, 5. Application of Fractional Order Differential Technology in the Estimation of Soil Moisture Content Using UAV-Based Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3559-3567. |
| [10] |
ZHAO Zhi-lei1, 2, 3, 4，WANG Xue-mei1, 2, 3，LIU Dong-dong1, 2, 3，WANG Yan-wei1, 2, 3，GU Yu-hong5，TENG Jia-xin1，NIU Xiao-ying1, 2, 3, 4*. Quantitative Analysis of Soluble Solids and Titratable Acidity Content in Angeleno Plum by Near-Infrared Spectroscopy With BP-ANN and PLS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2836-2842. |
| [11] |
ZHU Li-wei, YAN Jin-xin, HUANG Juan, SHI Tao-xiong, CAI Fang, LI Hong-you, CHEN Qing-fu*, CHEN Qi-jiao*. Rapid Determination of Amino Acids in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy and Artificial Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 49-55. |
| [12] |
ZHAO Wen-ya1, 2, MIN Hong2, LIU Shu2*, AN Ya-rui1*, YU Jin3. Application Progress of Artificial Neural Network in Laser-Induced Breakdown Spectral Data Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 1998-2004. |
| [13] |
LIU Yang1, 2, 3, 4, FENG Hai-kuan1, 3, 4*, HUANG Jue2, YANG Fu-qin5, WU Zhi-chao1, 3, 4, SUN Qian1, 2, 3, 4, YANG Gui-jun1, 3, 4. Estimation of Potato Above-Ground Biomass Based on Hyperspectral Characteristic Parameters of UAV and Plant Height[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 903-911. |
| [14] |
YUAN Jing1, 2, WANG Xin1, 2, YAN Chang-xiang1*. A Semi-Empirical Model for Reflectance Spectral of Black Soil with Different Moisture Contents[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3514-3518. |
| [15] |
XIE Huan1, CHEN Zheng-guang1*, ZHANG Qing-hua2. Rapid Discrimination of Japonica Rice Seeds Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3267-3272. |
|
|
|
|
