高光谱成像的土壤剖面水分含量反演及制图

doi:10.3964/j.issn.1000-0593(2019)09-2847-08

Abstract
References
Related Citation (15)

Download: PDF (3817 KB)
Export: BibTeX | EndNote (RIS)

Abstract Traditional methods for acquiring soil moisture can only provide discrete point data, which arenot so appropriate for finely and continuously mapping soil moisture distribution in soil profile. In this paper, the feasibility of predicting and mapping ofsoil moisture content (SMC) in soil profile was studied using near-infrared hyperspectral imaging in the spectral range of 882～1 709 nm. Two soil profiles, located in Dongtai City of Jiangsu Province, were continuously observed in situ for 5 days by the near-infrared hyperspectral imaging system. A total of 280 soil samples were obtained for later SMC measurement by oven-drying method. After a series of preproces on the acquired raw hyperspectral images, including digital number(DN) correction, reflectance correction, mosaicking, geometric correction, image clipping and masking, the average spectral reflectance of each sampling point in the corrected hyperspectral images was extracted for further analysis. Then the extracted spectra (Raw) were preprocessed by LOG₁₀(1/R), Savitzky-Golay (SG), first derivative (FD), second derivative (SD), multiplicative scatter correction (MSC) and standard normal variate (SNV), and partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models were developed and comparedfor a selection of optimum prediction model. Results showed that the soil spectral reflectance gradually decreased with the increase of SMC, and different spectral preprocessing methods had different prediction accuracy. Except for the MSC preprocessing method, the prediction accuracy of the LS-SVM model was higher than the PLSR model with the same spectral preprocessing method. The prediction accuracy of the LS-SVM model with LOG₁₀(1/R) preprocessed spectra was highest with R²_c of 0.96 and RMSE_c of 0.65% for calibration, and R²_p of 0.88, RMSE_p of 1.05% and RPD_p of 2.88 for prediction. The optimum model was then applied to produce high spatial resolution maps of SMC in profiles. The prediction accuracy was high (R²: 0.85～0.95, RMSE: 0.94%～1.02%) by comparing the extracted SMC values from prediction maps with the measured values, and both SMC had the samedistribution tendency in profiles, demonstrating that the SMC prediction mapscould well displaynot only the SMC distribution in profiles in the millimeter scale, but also the changes of SMC at different locations in the profile between different days. Thus, the near-infrared hyperspectral imaging technology combined with optimized prediction model could provide a new approach to quantitatively predict and map high spatial resolution images of SMC in soil profiles in situ, which could help to rapidlyand effectively monitorsoil moisture in profiles in the field.

Key words：Profile; Soil moisture content; Hyperspectral imaging; Partial least squares regression; Least squares support vector machine; Mapping

Received: 2018-07-06 Accepted: 2018-12-09

ZTFLH:

TP79

Corresponding Authors: PAN Xian-zhang E-mail: panxz@issas.ac.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)09-2847-08 OR https://www.gpxygpfx.com/EN/Y2019/V39/I09/2847

[1] LIU Ya, PAN Xian-zhang, WANG Chang-kun, et al(刘娅，潘贤章，王昌昆，等）. Soils(土壤）, 2016, 48(2): 381.
[2] Yin Z, Lei T W, Yan Q H, et al. Computers and Electronics in Agriculture, 2013, 99: 101.
[3] Bowers S A, Hanks R J. Soil Science, 1965, 100(2): 130.
[4] Stoner E R, Baumgardner M F. Soil Science Society of America Journal, 1981, 45: 1161.
[5] Hummel J W, Sudduth K A, Hollinger S E. Computers and Electronics in Agriculture, 2001, 32(2): 149.
[6] Ben-Dor E, Heller D, Chudnovsky A. Soil Science Society of America Journal, 2008, 72(4): 1113.
[7] ViscarraRossel R A, Lobsey C R, Sharman C, et al. Environmental Science & Technology, 2017, 51(10): 5630.
[8] LIU Xian, FEI Liang-jun, WANG Bo, et al(刘显，费良军，王博，等). Journal of Irrigation and Drainage(灌溉排水学报), 2016, 35(9): 20.
[9] WANG Xing-peng, DUAN Ai-wang, LI Shuang(王兴鹏，段爱旺，李双). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(6): 100.
[10] Steffens M, Buddenbaum H. Geoderma, 2013, 195: 122.
[11] Burud I, Moni C, Flo A, et al. Soil and Tillage Research, 2016, 155: 523.
[12] LI Shuo, WANG Shan-qin, SHI Zhou(李硕，汪善勤，史舟). Acta Pedologica Sinica(土壤学报)，2015, 52(5): 1014.
[13] YAN Fu-li, WANG Shi-xin, ZHOU Yi, et al(阎福礼，王世新，周艺，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2006, 25(6): 460.
[14] Shi T Z, Chen Y Y, Liu Y L, et al. Journal of Hazardous Materials, 2014, 265: 166.
[15] Wold S, Sjostrom M, Eriksson L. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109.
[16] Suykens J A K, Van Gestel T, De Brabanter J, et al. Least Square Vector Machines. Singapore: World Scientific, 2002.
[17] Wang C K, Pan X Z. Soil Science Society of American Journal, 2016, 80(5): 1393.
[18] Lobell D B, Asner G P. Soil Science Society of American Journal, 2002, 66: 722.
[19] Tian J, Philpot W D. Remote Sensing of Environment, 2015, 169: 280.
[20] ZHANG Jun-hua, JIA Ke-li(张俊华，贾科利). Chinese Journal of Applied Ecology(应用生态学报), 2015, 3: 884.
[21] YAO Yan-min, WEI Na, TANG Peng-qin, et al(姚艳敏，魏娜，唐鹏钦，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(8): 95.
[22] ZHANG Jun-hua, JIA Ke-li(张俊华，贾科利). Chinese Journal of Applied Ecology(应用生态学报), 2015, 26(3): 884.
[23] Jin X L, Du J, Liu H J, et al. Agricultural and Forest Meteorology, 2016, 218-219: 250.
[24] Jin X L, Song K S, Du J, et al. Agricultural and Forest Meteorology, 2017, 244-245: 57.
[25] LI Chen, ZHANG Guo-wei, ZHOU Zhi-guo, et al(李晨，张国伟，周治国，等). Chinese Journal of Applied Ecology(应用生态学报), 2016, 27(2): 525.