野外地物光谱仪和水体光谱仪测量水体光谱差异研究

doi:10.3964/j.issn.1000-0593(2025)03-0753-08

Abstract
References
Related Citation (15)

Download: PDF (4782 KB)
Export: BibTeX | EndNote (RIS)

Abstract In-site measured water spectral data is the basis of remote sensing inversion. Field object spectrometers are usually used to measure water spectral data. However, the usability of field object spectrometers for water measurements is difficult to evaluate. This article uses the data from the water measurement experiments of the field object spectrometer and the water spectrometer to conduct a difference analysis in terms of noise immunity, measurement results, external environment affecting the two types of instruments, and water quality parameter inversion. Before comparing the differences between the two types of spectrometers for water spectrum measurement, a comparison of the anti-noise capabilities of the two instruments found that the water spectrometer has a stronger anti-noise ability, so the data of the water spectrometer is used as a reference standard for analysis. First of all, regarding the difference in solar irradiance, the correlation coefficient of the solar irradiance measured by the two types of instruments varies in the range of 0.5~0.75, and there is a correlation between the solar irradiance of the two instruments; Secondly, by comparing the changes in skylight radiance at the five wavelengths of 361, 411, 461, 511, and 561 nm, we found that the range of changes in solar radiance obtained by the water spectrometer is smaller. The data acquisition is more stable than the field object spectrometer. By comparing the changes in the total radiance of the sea surface at different times, it is found that there are differences in the radiance obtained by the water spectrometer at different times, and the two change curves are different. The signal-to-noise ratio of the field object spectrometer decreases when the sunlight is weak, resulting in drastic fluctuations in the observed values of each band. The calculated reflectivity difference is obvious before 10 am and after 4 pm. Finally, the comparison shows that the external environment under different wind speed conditions impacts the air-water interface reflectance during the processing of the two instruments. The data needs to be corrected according to the value of the air-water interface reflectance corresponding to the wind speed so that the remote sensing reflectance obtained can be more precise. After the spectrometer obtains the water reflection spectrum curve, it is usually used for quantitative inversion of water quality parameters. To compare and analyze the impact of the observation results of the two spectrometers on the inversion accuracy of water quality parameters, this paper uses the water reflection spectrum curves observed by the two instruments to estimate the suspended matter concentration. The inversion is compared with the experimental test results of water samples collected at the corresponding locations. It is found that the two results are quite different. The accuracy of the water spectrometer is higher than that of the field object spectrometer. In addition, the two instruments could not be mixed, making the result error even greater. Through the processing and analysis of data obtained by two spectrometers and the inversion of water quality parameters, this article provides suggestions for water measurement using field object spectrometers to obtain more accurate and reliable water spectrum curves.

Key words：Spectrometer; Water spectrum measurement; Remote sensing reflectance; Difference analysis

Received: 2023-12-12 Accepted: 2024-06-21

ZTFLH:

P715.4

Corresponding Authors: CUI Jian-yong E-mail: cui_jianyong@upc.edu.cn

Cite this article:

CAO Xiao-yi,CUI Jian-yong,DONG Wen, et al. Research on Water Spectral Differences Based on Field Object Spectrometer and Water Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 753-760.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)03-0753-08 OR https://www.gpxygpfx.com/EN/Y2025/V45/I03/753

[1] WU Tai-xia, YAN Lei, XIANG Yun, et al(吴太夏, 晏磊, 相云, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010，30(2): 448.
[2] LI Tong-ji, CHEN Qing-lian, ZHU Jian-hua(李铜基, 陈清莲, 朱建华). Marine Science Bulletin(海洋通报), 2002, 21(6): 9.
[3] Mobley C D. Applied Optics, 1999, 38(36): 7442.
[4] Toole D A, Siegel D A, Menzies D W, et al. Applied Optics, 2000, 39(3): 456.
[5] Doxaran D，Froidefond J M，Castaing P. International Journal of Remote Sensing, 2002, 23(23)：5079.
[6] YANG Meng-yi, JIANG Xue-zhong, HE Qing(杨孟毅, 蒋雪中, 何青). Infrared(红外), 2017, 38(12): 6.
[7] YANG Meng-yi, ZENG Jian, ZHANG Bo-hu, et al(杨孟毅, 曾剑, 张伯虎, 等). Remote Sensing Information(遥感信息), 2018, 33(5): 21.
[8] Mueller J L, Fargion G S. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 3, Volume 1. NASA/TM-2002-210004/Rev 3-Vol 1. 2002.
[9] LIANG Wen-dong, ZHOU Yao-qi, SUN Qi, et al(梁文栋, 周瑶琪, 孙棋, 等). Marine Geology Frontiers(海洋地质前沿), 2015, 31(12): 27.
[10] TANG Jun-wu, TIAN Guo-liang, WANG Xiao-yong, et al(唐军武, 田国良, 汪小勇, 等). National Remote Sensing Bulletin(遥感学报), 2004，8(1): 37.
[11] LIU Si-feng, CAI Hua, YANG Ying-jie, et al(刘思峰, 蔡华, 杨英杰, 等). Systems Engineering-Theory & Practice(系统工程理论与实践), 2013, 33(8): 2041.
[12] YAN Guang-jian, JIANG Hai-lan, YAN Kai, et al(阎广建, 姜海兰, 闫凯, 等). National Remote Sensing Bulletin(遥感学报), 2021, 25(1): 83.
[13] Gordon H R, Brown O B, Evans R H, et al. Journal of Geophysical Research，1988，93(D9)：10909.
[14] Ruddick K G, De Cauwer V, Park Y J, et al. Limnology and Oceanography, 2006, 51(2)：1167.
[15] PAN Ying-yang, GUO Qiao-zhen, FU Ying(潘应阳, 国巧真, 付盈). Environmental Monitoring in China(中国环境监测), 2018, 34(3): 148.
[16] HAN Xiu-zhen, WU Zhao-yang, ZHENG Wei, et al(韩秀珍, 吴朝阳, 郑伟，等). Journal of Applied Meteorological Science(应用气象学报), 2010, 21(6): 724.
[17] Hou Y K, Zhang A B, Lv R L, et al. Environmental Science and Pollution Research International, 2022, 29(42): 63640.