基于Argo剖面浮标观测的海洋溶解氧空间-质量迭代插值方法

doi:10.12082/dqxxkx.2023.220720

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (10): 2000-2011.doi: 10.12082/dqxxkx.2023.220720

• 地球信息科学理论与方法 • 上一篇下一篇

基于Argo剖面浮标观测的海洋溶解氧空间-质量迭代插值方法

岳林峰¹^,²^,³(), 薛存金¹^,²^,^*(), 王振国¹^,²^,³, 牛超然¹^,²^,³

1.中国科学院空天信息创新研究院数字地球重点实验室，北京 100094
2.可持续发展大数据国际研究中心，北京 100094
3.中国科学院大学，北京 100049

收稿日期:2022-09-23 修回日期:2023-02-02 出版日期:2023-10-25 发布日期:2023-09-22
通讯作者: * 薛存金（1979—），男，山东菏泽人，博士，研究员，主要研究方向为海洋地理信息系统、海洋时空分析方法。 E-mail: xuecj@radi.ac.cn
作者简介:岳林峰（1998—），男，河南安阳人，硕士生，研究方向为海洋时空数据挖掘分析。E-mail: yuelinfeng20@mails.ucas.ac.cn
基金资助:
中国科学院A类战略先导专项(XDA19060103);可持续发展大数据国际研究中心创新研究计划项目(CBAS2022IRP05)

An Iterative Space-quality Interpolation Method for Marine Dissolved Oxygen Data Observed by Argo Floats

YUE Linfeng¹^,²^,³(), XUE Cunjin¹^,²^,^*(), WANG Zhenguo¹^,²^,³, NIU Chaoran¹^,²^,³

1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-09-23 Revised:2023-02-02 Online:2023-10-25 Published:2023-09-22
Contact: * XUE Cunjin, E-mail: xuecj@radi.ac.cn
Supported by:
Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19060103);Innovative Research Program of International Research Center of Big Data for Sustainable Development Goals(CBAS2022IRP05)

摘要/Abstract

摘要：

溶解氧是探究海洋内部各种生物地球化学循环过程以及评估海洋健康的重要参数。目前，基于Argo剖面浮标的观测方式已经成为获取海洋溶解氧信息的主要来源之一，但是这种方式获取到的海洋溶解氧资料在空间上是离散分布的。如何利用空间插值方法将离散的Argo溶解氧剖面观测数据构建成连续的空间网格数据是当前研究关注的热点，本文提出了一种面向Argo溶解氧剖面观测数据的空间-质量特征的迭代插值方法（ASOSQIM）。针对观测样点空间上分布离散、不规则的特点，ASOSQIM首先对溶解氧观测数据进行空间初始化来构建初始背景场，然后对Argo溶解氧剖面观测样点进行空间异质性分区，采用空间-质量定权的方式赋予不同质量标示数据相应的权重系数来解决参与插值的数据量及其精度问题，最后利用迭代插值的思想并集成溶解氧空间分区特征和质量标识，实现了基于Argo剖面浮标观测的溶解氧浓度空间插值。利用交叉验证法和WOA18资料对ASOSQIM进行方法验证和对比分析，结果表明：① ASOSQIM的插值精度相比于其他空间插值方法具有大幅度的提升，与2022年同期Argo浮标溶解氧观测结果进行对比，验证了该方法的优越性和可行性； ② 利用ASOSQIM得到的溶解氧浓度插值结果与WOA18溶解氧资料具有高度一致性，溶解氧浓度绝对误差在20 μmol/kg范围内的区域占比达到85%以上，表明利用该方法得到的插值结果具有可靠性。

关键词: 海洋溶解氧, 空间异质性, 空间-质量定权, 迭代插值, 空间初始化, 质量标识, Argo, WOA18

Abstract:

Dissolved oxygen is an important parameter for exploring various biogeochemical cycles in the ocean interior and assessing ocean health. At present, the observation method based on Argo floats has become one of the main sources to obtain the information of Marine dissolved oxygen, but the data obtained by this method are distributed discretely in space. How to use spatial interpolation method to construct discrete dissolved oxygen profiles of Argo floats into continuous spatial grid data is the focus of current research. Therefore, an iterative interpolation method (ASOSQIM) for spatial-quality characteristics of dissolved oxygen observation data using Argo floats is proposed in this paper. It is of great significance to integrate discrete Marine dissolved oxygen data to obtain spatially distributed continuous grid data for exploring and revealing the variation law of Marine dissolved oxygen concentration and providing reliable data support and information services for related fields. In view of the discrete and irregular spatial distribution of the observed sample points, ASOSQIM carries out a series of processing on the dissolved oxygen observation data using Argo floats, such as monthly statistics, quality recontrol and standardized interpolation. Based on the processed dissolved oxygen observation data, the spatial initialization was carried out, and the dissolved oxygen concentration on the regular grid after initialization was taken as the initial value of spatial interpolation calculation. Then, the dissolved oxygen observation data using Argo floats were divided into different spatial heterogeneity zones, and the corresponding weight coefficients were assigned to data of different quality flags by using the space-quality weighting method to solve the problem of the amount of data involved in interpolation and its accuracy. Finally, the idea of iterative interpolation was used to integrate the spatial partitioning characteristics and quality flags of dissolved oxygen. The spatial interpolation of dissolved oxygen concentration based on observation data of Argo floats is realized. The cross-validation method and WOA18 data were used to verify and compare ASOSQIM. The results show that: (1) the interpolation accuracy of ASOSQIM is significantly improved compared with other spatial interpolation methods. The advantages and feasibility of this method are verified by comparing with the dissolved oxygen observation results of Argo floats in 2022. (2) The dissolved oxygen concentration interpolation results obtained by ASOSQIM are highly consistent with the dissolved oxygen data of WOA18, and the absolute error of dissolved oxygen concentration accounts for more than 85% in the range of 20μmol/kg, indicating that the spatial interpolation results of dissolved oxygen concentration obtained by this method are reliable.

Key words: marine dissolved oxygen, spatial heterogeneity, space-quality weighting, iterative interpolation, space initialization, quality flags, Argo, WOA18

岳林峰, 薛存金, 王振国, 牛超然. 基于Argo剖面浮标观测的海洋溶解氧空间-质量迭代插值方法[J]. 地球信息科学学报, 2023, 25(10): 2000-2011.DOI:10.12082/dqxxkx.2023.220720

YUE Linfeng, XUE Cunjin, WANG Zhenguo, NIU Chaoran. An Iterative Space-quality Interpolation Method for Marine Dissolved Oxygen Data Observed by Argo Floats[J]. Journal of Geo-information Science, 2023, 25(10): 2000-2011.DOI:10.12082/dqxxkx.2023.220720

图/表 7

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	MAE/(μmol/kg)	RMSE/(μmol/kg)	MARE/%
ASOSQIM_1	2.90	5.94	1.06
ASOIM_1	3.26	6.18	1.06
IDW_1	4.21	7.06	1.56
KRG_1	4.12	6.85	1.53
GPI_1	8.20	12.64	3.18
Cressman_1	4.05	8.23	1.66
ASOSQIM_7	3.47	8.40	1.44
ASOIM_7	4.08	8.76	1.52
IDW_7	4.94	9.43	2.01
KRG_7	4.83	9.24	1.97
GPI_7	8.69	15.74	3.68
Cressman_7	5.20	10.17	1.88

	MAE/(μmol/kg)	RMSE/(μmol/kg)	MAPE/%
ASOSQIM_1	4.38	7.67	1.46
ASOIM_1	5.83	10.64	2.20
IDW_1	7.27	11.42	2.72
KRG_1	7.12	11.06	2.67
GPI_1	11.87	15.15	4.07
Cressman_1	8.22	13.45	3.62
ASOSQIM_7	4.39	12.23	2.65
ASOIM_7	6.04	13.84	3.48
IDW_7	8.04	16.09	3.97
KRG_7	7.98	16.00	3.91
GPI_7	13.11	24.62	6.90
Cressman_7	9.28	15.06	4.39

编号	浮标ID	周期	观测值/（μmol/kg）	插值结果/（μmol/kg）	绝对误差/（μmol/kg）	相对误差/%
1	1902381	1	210.831	211.825	0.994	0.47
2	5906035	99	240.255	241.454	1.199	0.50
3	5904844	183	215.156	216.732	1.576	0.73
4	5905103	144	325.733	327.549	1.816	0.56
5	5905138	187	298.229	293.810	4.419	1.48
6	5905986	174	195.506	197.730	2.224	1.14
7	5905109	159	223.558	221.725	1.833	0.82
8	5906208	72	251.583	256.715	5.132	2.04
9	5906218	67	252.084	254.358	2.274	0.90
10	5906221	70	313.106	318.269	5.163	1.65
11	5905974	181	200.025	195.742	4.283	2.14
12	5906035	98	246.273	249.353	3.080	1.25
13	5906303	51	194.703	195.370	0.667	0.34
14	5906310	39	229.277	234.260	4.983	2.17
15	5906343	30	201.162	209.158	7.996	3.97
16	6902981	52	294.120	295.728	1.608	0.55
17	5906475	5	203.489	201.456	2.033	1.00
18	5906503	3	199.267	202.761	3.494	1.75
19	5906624	113	320.351	326.284	5.933	1.85
20	7900566	75	288.801	295.325	6.524	2.26

基于Argo剖面浮标观测的海洋溶解氧空间-质量迭代插值方法

An Iterative Space-quality Interpolation Method for Marine Dissolved Oxygen Data Observed by Argo Floats

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