ESA CCI土壤湿度资料在中国东部的综合评估

doi:10.6046/zrzyyg.2023308

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摘要

卫星遥感的土壤湿度产品为研究区域尺度的气候变化、水文效应等提供了便利,然而由于观测标准不统一、仪器更迭等因素,长时间序列土壤湿度数据集在我国的验证和应用犹显不足。该文基于中国气象局农气数据集和国际土壤水分网络(international soil moisture network, ISMN)的土壤湿度资料,首先构建了1981—2013年中国东部地区土壤湿度的月数据集; 并在此基础上对比分析了同时间段欧洲航天局气候变化倡议项目(European apace agency climate change initiative, ESA CCI)的4种微波遥感土壤湿度产品(包括主动、被动、融合和校正的融合产品)在中国东部的表现能力。结果显示,主动产品和被动产品分别低估、高估了中国东部的土壤湿度,其中主动产品的最大偏差分布在华北和西北地区,相对偏差分别达到-30.9%和-29.6%,被动产品在东北和西北地区的相对偏差分别为39.1%和26.5%,融合产品可以很好地改进东北地区和西北地区主动产品低估、被动产品高估的现象,相对偏差分别减少到24.3%和3.7%。对区域平均的月土壤湿度的变化特征而言,主动产品和融合产品在江淮地区的表现最优,最高相关系数达0.66,被动产品和融合产品在东北地区的相关系数达0.44和0.47,华北地区和西北地区较差。通过对遥感产品方差来源进行分析,主动产品在描述土壤湿度的演变特征方面更具优势,被动产品在精度方面表现更优,融合产品在精度方面表现最佳。该文同时研究了CCI集成卫星设备的更迭对产品表现的影响,结果表明,不同时间段的主动产品在东北和西北表现较一致,被动传感器在再现土壤湿度的变化特征方面还有一定的差距,融合产品的整体方差明显优于主动产品和被动产品,但是在相关系数方面,融合产品在东北和西北较主动产品基本相当。校正后的融合产品没有特别明显的改进,这在一定程度上证明了利用CCI融合产品进行长期研究的可行性。研究结果有助于更深刻地理解不同卫星产品数据集的误差结构和特性,为研究者挑选相应数据产品、以及进行长时间序列的研究提供了证据支持。

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	凌肖露
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关键词 ：土壤湿度, 卫星遥感, 设备更替, 长时间序列, ESA CCI, 中国东部, 突变检验和校正, 综合评估

Abstract：

Soil moisture products based on remote sensing are crucial for investigating climatic change and hydrological effects on a regional scale. However, there is a lack of verification and application of long-term soil moisture datasets in China due to factors such as inconsistent observation standards and instrument upgrades. Using the agro-meteorological dataset from the China Meteorological Administration and soil moisture data from the International Soil Moisture Network (ISMN), this study constructed a monthly dataset of soil moisture in eastern China covering the period from 1981 to 2013. Accordingly, this study analyzed and compared the performance of four microwave remote sensing-based soil moisture products developed by the European Space Agency’s Climate Change Initiative (ESA CCI): active, passive, combined, and combined adjusted products. The results indicate that active and passive products underestimated and overestimated soil moisture in eastern China, respectively. The maximum deviations from active products were found in the northern and northwestern regions, with relative deviations of -30.9% and -29.6%, respectively. In contrast, the passive products showed relative deviations of 39.1% and 26.5%, respectively for soil moisture in northeastern and northwestern regions. The combined products mitigated the underestimation of the active products and the overestimation of the passive product in these regions, reducing the relative deviations to 24.3% and 3.7%, respectively. Regarding the variation characteristics of regional monthly average soil moisture, both the active and combined products performed best for soil moisture in the Yangtze-Huaihe (YH) region, with the highest correlation coefficient of 0.66. The passive and combined products yielded correlation coefficients of 0.44 and 0.47, respectively for soil moisture in the northeastern region and performed poorly for soil moisture in the northern and northwestern regions. The analysis of the variance sources of the remote sensing-based products indicates that the active products enjoyed more advantages in describing the evolutionary characteristics of soil moisture, the passive products demonstrated greater accuracy, and the combined products yielded the highest accuracy overall. Additionally, this study investigated the impacts of changes in the integrated satellite equipment of CCI on product performance. The results indicate that the active products exhibited consistent performance for soil moisture in the northeastern and northwestern regions in different periods. However, passive sensors still exhibited gaps in reproducing the variations in soil moisture. The combined products exhibited better overall variance than both active and passive products. However, these products yielded comparable correlation coefficients with the active products for soil moisture in the northeastern and northwestern regions. The combined products presented no notable improvement after correction, proving that it is feasible to conduct long-term research using the combined products of CCI. The results of this study contribute to a deeper understanding of the error structures and characteristics of various satellite product datasets, providing evidence for researchers to select appropriate data products and conduct research on long time series.

Key words： soil moisture satellite remote sensing instrument replacement long time series ESA CCI eastern China break-adjusted COMBINED product comprehensive evaluation

收稿日期: 2023-10-09 出版日期: 2024-12-23

ZTFLH:

TP79

基金资助:国家自然科学基金面上项目“陆地植被特征在东亚次季节-季节气候预测中的同化及应用”(42075114);江苏高校优势学科建设项目“测绘科学与技术学科”(140119001);徐州市重点研发计划——现代农业面上项目“基于农业遥感大数据和陆面模型的作物产量精细化监测”(KC21132)

作者简介: 凌肖露(1986-),女,博士,副教授,主要从事生态遥感和大气环境遥感的研究。Email: lingxl@cumt.edu.cn。

引用本文:

凌肖露, 陈朝荣, 郭维栋, 秦凯, 张锦龙. ESA CCI土壤湿度资料在中国东部的综合评估[J]. 自然资源遥感, 2024, 36(4): 92-106.
LING Xiaolu, CHEN Chaorong, GUO Weidong, QIN Kai, ZHANG Jinlong. Comprehensive evaluation of ESA CCI soil moisture data in eastern China. Remote Sensing for Natural Resources, 2024, 36(4): 92-106.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023308 或 https://www.gtzyyg.com/CN/Y2024/V36/I4/92

Fig.1 土壤湿度观测站点分布、分区及下垫面类型分布(审图号: GS京(2024)2618号)

Fig.2 1992—2013年生长季节(4—9月)平均的土壤湿度的空间分布(审图号: GS京(2024)2618号)

Fig.3 1992—2013年4—9月卫星遥感产品和台站观测的偏差空间分布(审图号: GS京(2024)2618号)

Fig.4-1 1992—2013年4—9月卫星遥感产品和台站观测的μbRMSE的空间分布(审图号: GS京(2024)2618号)

Fig.4-2 1992—2013年4—9月卫星遥感产品和台站观测的μbRMSE的空间分布(审图号: GS京(2024)2618号)

Fig.5 1992—2013年4—9月卫星遥感产品和台站观测的泰勒图分布

Tab.1 不同区域不同产品的统计值

Fig.6 1981—2013年4—9月区域平均的月土壤湿度随时间的演变

Fig.7 1981—2013年4—9月区域平均的月土壤湿度产品的误差分解

产品类型	时间段	主动传感器	被动传感器
主动产品	1992年01月—1997年05月	ERS1/2 (AMI-WS)	N/A
	1997年05月—2003年02月	ERS2 (AMI-WS)	N/A
	2003年02月—2006年12月	ERS1/2 (AMI-WS)	N/A
	2007年01月—2012年11月	Metop-A ASCAT	N/A
	2012年11月—2013年12月	Metop-A ASCAT, Metop-B ASCAT	N/A
被动产品	1992年01月—1997年12月	N/A	SSM/I (a/d)
	1998年01月—2002年06月	N/A	SSM/I (a/d), TMI (a/d)
	2002年07月—2007年09月	N/A	AMSR-E (a/d), TMI (a/d)
	2007年10月—2010年01月	N/A	AMSR-E (a/d), Windsat (a/d), TMI (a/d)
	2010年01月—2011年05月	N/A	AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d)
	2011年06月—2011年09月	N/A	AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d), FY3B (a/d)
	2011年10月—2012年06月	N/A	WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d)
	2012年07月—2013年09月	N/A	SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d)
	2013年10月—2013年12月	N/A	SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d)
融合产品	1992年01月—1997年12月	AMI-WS	SSM/I (a/d)
	1998年01月—2002年06月	AMI-WS	SSM/I (a/d), TMI (a/d)
	2002年07月—2006年12月	AMI-WS	AMSR-E (a/d), TMI (a/d)
	2007年01月—2007年09月	Metop-A ASCAT	AMSR-E (a/d), TMI (a/d)
	2007年10月—2010年01月	Metop-A ASCAT	AMSR-E (a/d), Windsat (a/d), TMI (a/d)
	2010年01月—2011年05月	Metop-A ASCAT	AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d)
	2011年06月—2011年09月	Metop-A ASCAT	AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d), FY3B (a/d)
	2011年10月—2012年06月	Metop-A ASCAT	WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d)
	2012年07月—2012年10月	Metop-A ASCAT	SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d)
	2012年11月—2013年09月	Metop-A ASCAT, Metop-B ASCAT	SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d)
	2013年10月—2013年12月	Metop-A ASCAT, Metop-B ASCAT	SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d)

Tab.2 ESA CCI SM 产品验证时间分段及其包括的传感器罗列

Fig.8 主动产品、被动产品和融合产品月土壤湿度在不同时间段不同研究区域的误差分解

Fig.9 融合产品月土壤湿度校正前后不同区域的误差分解

Fig.10 主动产品(A)、被动产品(P)、融合产品(C)和校正后的融合产品(CA)的月土壤湿度在不同数值范围的相对偏差分布

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