|
|
|
|
|
|
Study on Soil Water Retrieval Technology of Yellow River Source Based on SPA Algorithm and Machine Learning |
JIANG Chuan-li1, ZHAO Jian-yun1, 2*, DING Yuan-yuan1, ZHAO Qin-hao1, MA Hong-yan1 |
1. Department of Geological Engineering, Qinghai University, Xining 810016, China
2. Key Laboratory of Cenozoic Resource & Environment in North Margin of the Tibetan Plateau, Xining 810016, China
|
|
|
Abstract Soil moisture determines a region’s ecological carrying capacity and soil physical and chemical properties to a certain extent. It is significant to obtain soil moisture content accurately and quickly for ecological environment monitoring and soil degradation restoration. Hyperspectral remote sensing is widely used in soil parameter inversion, but the research on hyperspectral characteristics and parameter inversion of alpine meadow soil needs further study. Consequently, to develop a hyperspectral inversion model of soil moisture content in alpine meadows applicable to fragile alpine ecosystems, 102 soil samples were collected from Henan County in the Yellow River source area. Multiple linear stepwise regression (MLSR), partial least squares regression (PLSR) and back propagation neural network (BPNN) methods were used to model the soil moisture content with the original spectrum and its mathematically transformed characteristic bands, and the inversion accuracy was verified by the coefficient of determination (R2), root mean square error (RMSE) and the residual ratio of prediction (RPD). The major findings are as follows: (1) In the visible-near infrared band, the spectral reflectance of soil samples has water absorption interval near 710, 780 and 950 nm, and the absorption intensity is different. The reflectance tends to decrease rapidly and increase slowly with increasing soil moisture content. (2) SPA algorithm was used to select the spectrum’s characteristic bands after S-G smoothing, four transformations as independent variables and water content as dependent variables. Then MLSR and PLSR were used to establish the inversion model. The PLSR model corresponding to the first-order differential (FD) and first-order logarithmic differential (FDL) transformations can achieve a rough inversion of soil moisture in alpine meadows, and the PLSR model corresponding to the FD transformation is accurate. (3) In the BPNN inversion models, except for the model corresponding to continue to remove (CR), the R2 of other models is greater than 0.9, and RMSE is between 0.048 and 0.074. In all the models, the BPNN model corresponding to FD, FDL and LG transform is highly accurate, with R2 and RPD greater than 0.8 and 2.5 respectively. The BPNN model corresponding to the LG transform has the highest accuracy, with R2, RMSE and RPD up to 0.967, 0.038 and 5.039, respectively. Therefore, the BPNN model can achieve relatively accurate hyperspectral inversion of soil moisture content of alpine meadow in the source region of the Yellow River, which can provide the technical basis and data support for ecological environment monitoring and soil restoration in this region and even other alpine regions.
|
Received: 2022-03-22
Accepted: 2022-06-02
|
|
Corresponding Authors:
ZHAO Jian-yun
E-mail: zhaojianyun1981@163.com
|
|
[1] HUANG Qian,DING Ming-jun,CHEN Li-wen, et al(黄 倩,丁明军,陈利文, 等). Journal of Soil and Water Conservation(水土保持学报), 2022, 36(1): 189.
[2] Wanders N, Bierkens M F P, Jong S M D, et al. Water Resources Research, 2015, 50(8): 6874.
[3] Guillod B P, Orlowsky B, Miralles D G, et al. Nature Communications, 2015, 6: e6443.
[4] ZHAO Jian-yun,DING Yuan-yuan,DU Mei, et al(赵健赟,丁圆圆,杜 梅, 等). Science Technology and Engineering(科学技术与工程), 2021, 21(24): 10209.
[5] HU Guang-yin,DONG Zhi-bao,LU Jun-feng, et al(胡光印,董治宝,逯军峰, 等). Acta Ecologica Sinica(生态学报), 2011, 31(14): 3872.
[6] LI Xin-xing,LIANG Bu-wen,BAI Xue-bing, et al(李鑫星,梁步稳,白雪冰, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(12): 3705.
[7] Peng J, Loew A, Merlin O, et al. Reviews of Geophysics, 2017, 55(2): 341.
[8] LIU Huan-jun,ZHANG Bai,SONG Kai-shan, et al(刘焕军,张 柏,宋开山, 等). Journal of University of Chinese Academy of Sciences(中国科学院大学学报), 2008,(4): 503.
[9] Hummel J W, Sudduth K A, Hollinger S E. Computers and Electronics in Agriculture, 2001, 32(2): 149.
[10] Wang W, Gao M, Wang J. Hyperspectral Parameters and Prediction Model of Soil Moisture in Apple Orchards: IOP Conference Series: Earth and Environmental Science, 2021, 687: 012085.
[11] TIAN Mei-ling,GE Xiang-yu,DING Jian-li, et al(田美玲,葛翔宇,丁建丽, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2020, 57(9): 232.
[12] ZHANG Jun-yong,DING Jian-li,TAN Jiao(张钧泳,丁建丽,谭 娇). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(3): 221.
[13] SHANG Tian-hao,JIA Ping-ping,SUN Yuan, et al(尚天浩, 贾萍萍, 孙 媛, 等). Bulletin of Soil and Water Conservation(水土保持通报), 2020, 40(4): 183.
[14] LIU Huan-jun,WANG Xiang,ZHANG Xiao-kang, et al(刘焕军,王 翔,张小康, 等). Chinese Journal of Soil Science(土壤通报), 2018, 49(1): 38.
[15] Bregman L M. Dokl. Akad. Nauk SSSR, 1965, 162: 487.
[16] NIU Fang-peng,LI Xin-guo,MAMATTURSUN Eziz, et al(牛芳鹏,李新国,麦麦提吐尔逊·艾则孜, 等). Journal of Zhejiang University(Agriculture and Life Sciences)[浙江大学学报(农业与生命科学版)], 2021, 47(5): 673.
[17] CAI Liang-hong,DING Jian-li(蔡亮红,丁建丽). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(16): 144.
[18] TIAN An-hong,FU Cheng-biao,XIONG Hei-gang, et al(田安红,付承彪,熊黑钢, 等). Chinese Journal of Eco-Agriculture(中国生态农业学报), 2020, 28(2): 296.
[19] Li H, Jia S, Le Z. Sensors, 2019, 19(20): 4355.
[20] Žíala D, Zádorová T, Kapička J. Remote Sensing, 2017, 9(1): 28. |
[1] |
SONG Shao-zhong1, FU Shao-yan2, LIU Yuan-yuan2, QI Chun-yan3, LI Jing-peng4, GAO Xun2*. Identification of Rice Origin Using Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1553-1558. |
[2] |
SONG Shao-zhong1, LIU Yuan-yuan2, ZHOU Zi-yang3, TENG Xing3, LI Ji-hong3, LIU Jun-ling1, GAO Xun2*. Identification of Sorghum Breed by Hyperspectral Image Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1392-1397. |
[3] |
LIU Zi-yang1, 2, FENG Shuai1, 2, ZHAO Dong-xue1, 2, LI Jin-peng1, 2, GUAN Qiang1, 2, XU Tong-yu1, 2*. Research on Spectral Feature Extraction and Detection Method of Rice Leaf Blast by UAV Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1457-1463. |
[4] |
ZHOU Zhe-hai,XIONG Tao,ZHAO Shuang,ZHANG Fan,ZHU Gui-xian. Single-Cell Blood Classification Method Based on Fluorescence Optical Tweezers and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1081-1087. |
[5] |
CHEN Pan-pan, REN Yan-min*, ZHAO Chun-jiang, LI Cun-jun, LIU Yu*. Research on the Classification of Yingde Tea Plantations Based on Time Series Sentinel-2 Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1136-1143. |
[6] |
LI Yang1, 2, LI Cui-ling2, 3, WANG Xiu2, 3, FAN Peng-fei2, 3, LI Yu-kang2, ZHAI Chang-yuan1, 2, 3*. Identification of Cucumber Disease and Insect Pest Based on
Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 301-309. |
[7] |
CHEN Jian-hong, REN Jun-yi, YANG Jia, GUO Ya-ya, QIAO Wei-dong. Study on Non-Invasive Blood Glucose Detection Technology Based on Time Frequency Domain Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 318-324. |
[8] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[9] |
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. |
[10] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[11] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
[14] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
[15] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
|
|
|
|