多源土壤水分产品在河北平原的适用性评价

石嘉丽; 张晓龙; 闵雷雷; 张婧; 王妍; 沈彦军

doi:10.12357/cjea.20210697

多源土壤水分产品在河北平原的适用性评价

doi: 10.12357/cjea.20210697

石嘉丽^{1, 2,},
张晓龙¹,
闵雷雷¹,
张婧³,
王妍^{1, 2, 4},
沈彦军^{1, 2, ,}

1.
中国科学院遗传与发育生物学研究所农业资源研究中心/中国科学院农业水资源重点实验室/河北省节水农业重点实验室　石家庄　050022
2.
中国科学院大学　北京　100049
3.
河北省气候中心　石家庄　050021
4.
河北师范大学地理科学学院/河北省环境演变与生态建设实验室　石家庄　050024

基金项目: 国家自然科学基金项目(42001037)和河北省自然科学基金创新研究群体项目(D2021503001)资助

详细信息

作者简介:
石嘉丽, 主要从事生态水文模拟方向研究。E-mail: shijiali19@mails.ucas.ac.cn

通讯作者:
沈彦军, 主要从事流域生态水文模拟与水环境管理方向研究。E-mail: shenyanjun@sjziam.ac.cn

中图分类号: P426.68
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出版历程
- 收稿日期: 2021-10-19
- 录用日期: 2021-11-27
- 网络出版日期: 2021-12-09
- 刊出日期: 2022-05-18

Adaptability evaluation of soil moisture products in the Hebei Plain

SHI Jiali^{1, 2
,},
ZHANG Xiaolong¹,
MIN Leilei¹,
ZHANG Jing³,
WANG Yan^{1, 2, 4},
SHEN Yanjun^{1, 2
, ,}

1.
Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences / Key Laboratory of Agricultural Water Resources, Chinese Academy of Sciences / Hebei Key Laboratory of Water-saving Agriculture, Shijiazhuang 050022, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Hebei Provincial Climate Center, Shijiazhuang 050021, China
4.
College of Geographical Sciences, Hebei Normal University / Key Laboratory of Environmental Evolution and Ecological Construction of Hebei Province, Shijiazhuang 050024, China

Funds: This research was supported by the National Natural Science Foundation of China (42001037) and the Foundation for Innovative Research Groups of the Natural Science Foundation of Hebei Province (D2021503001).

More Information

Corresponding author: E-mail: shenyanjun@sjziam.ac.cn

摘要

摘要: 河北平原地处华北平原中部, 是我国重要的粮食产区, 是世界上冬小麦、夏玉米最高产的地区之一。土壤水分作为作物生长的直接水源和基础条件, 对灌溉决策、干旱预报均有重要意义。虽然多源土壤水分产品已获得了长足发展, 但其在河北平原的适用性还缺乏全面的定量评价。本文利用河北平原望都、霸州、威县、栾城4个站点2018年1月至2019年10月的表层10 cm土壤水分实测数据, 通过相关系数、偏差、均方根误差、无偏均方根误差4个指标, 对比分析了SMOS、SMAP、FY3B、ERA-Land、GLDAS、GLEAM等6种土壤水分产品在河北平原典型农田的具体表现。整体而言, 除夏季FY3B存在高估外, 多源土壤水分产品对河北平原不同站点实际土壤含水量有不同程度的低估, 研究时段内各土壤水分产品平均相关系数排序为GLEAM>FY3B>ERA-Land>GLDAS>SMAP>SMOS, 平均无偏均方根误差排序为GLEAM<GLDAS<SMAP<ERA-Land<SMOS<FY3B。具体表现为: 1)同化多源数据的GLEAM、GLDAS、ERA-Land数据精度较好, 平均相关系数较大而平均无偏均方根误差较低。在土壤含水量高的夏季, 模拟数据更接近实测值。2) FY3B数据缺失较多、波动范围较大且平均无偏均方根误差较大, 但与实测数据相关性较好, 平均相关系数为0.43 m³·m⁻³, 夏季普遍高估土壤含水量, 数据精度较差, 其他季节则低估。3) SMAP整体数据精度高于SMOS, 夏季相关性较高但平均无偏均方根误差较大, 秋季则与之相反, 当实测土壤水介于0.30~0.40 m³∙m⁻³时表现较好。4) SMOS因射频干扰等原因在各站点表现最差, 各站点平均相关系数仅为0.20 m³·m⁻³, 偏差均大于0.10 m³∙m⁻³。
- 土壤水分产品 /
- 土壤水分 /
- 河北平原 /
- 农田 /
- 华北平原
Abstract: The Hebei Plain, located in the central part of the North China Plain, is an important grain production area in China and one of the most productive areas worldwide for winter wheat and summer corn. Soil water is foundation of material transportation and energy transmission; and participates in the carbon-water cycle and energy exchange between the land surface and atmosphere. It is also a direct water source and key element of crop growth, which has an important impact on agricultural production, weather forecasting, and drought prediction. Although multisource soil moisture products have been extensively developed and widely utilized, a comprehensive evaluation of the applicability of these products in the Hebei Plain is lacking. Evaluating the applicability of soil moisture products and using them to understand the soil moisture dynamics of the Hebei Plain are of great significance for agricultural production, moisture monitoring, and irrigation decision-making. To compare and analyze the specific performance of the soil moisture products of SMOS, SMAP, FY3B, ERA-Land, GLDAS, and GLEAM in typical farmland in the Hebei Plain, in-situ soil moisture data of surface soil moisture from Wangdu, Bazhou, Weixian, and Luancheng stations in the Hebei Plain from January 2018 to October 2019 were analyzed by considering correlation coefficients, biases, root mean square errors, and unbiased root mean square errors (ubRMSE). Overall, except the data of FY3B in summer, all soil moisture products underestimated the actual soil water contents of different stations in the Hebei Plain. The average correlation coefficient of each soil moisture product during the study period was ranked as GLEAM > FY3B > ERA-Land > GLDAS > SMAP > SMOS, and the average ubRMSE was ranked as GLEAM < GLDAS < SMAP < ERA-Land < SMOS < FY3B. The specific performance of each soil moisture product showed that 1) based on assimilated multi-source data, the accuracies of GLDAS, GLEAM, and ERA-Land were better than those of SMOS and SMAP, with high correlation coefficients and low ubRMSE. The inversion data of GLDAS, GLEAM, and ERA-Land were relatively close to the in-situ data when the water content was high in summer. 2) Many missing data and large fluctuation ranges were found in the FY3B product, but FY3B had a good relationship with the in-situ data with an average correlation coefficient of 0.43 m³∙m⁻³. The soil water content was generally overestimated in summer and underestimated in the other seasons. The correlation coefficient of FY3B in summer was low, but the opposite was true in autumn. 3) Overall, the data accuracy of SMAP was higher than that of SMOS. The correlation coefficient between SMAP and in-situ data was higher in summer, but the ubRMSE was higher at the same time; however, they had opposite values in autumn. SMAP could capture dynamic changes in soil moisture when the soil moisture content is high. The data accuracy was better when the measured soil water content was between 0.30 m³∙m⁻³ and 0.40 m³∙m⁻³. 4) Owing to radio frequency interference and other reasons, SMOS greatly underestimated the soil moisture content and performed the worst at each station. The average correlation coefficient of each station was only 0.20 m³∙m⁻³, and the biases were all greater than 0.10 m³∙m⁻³.
- Soil moisture product /
- In-situ soil moisture /
- Hebei Plain /
- Farmland /
- North China Plain

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图 1 研究站点及不同土壤水分产品像元分布图

Figure 1. Location of observed stations and soil moisture products pixels

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图 2 研究期间不同土壤水分产品和实测(In-situ)的望都(a, b)、霸州(c, d)、威县(e, f)、栾城(g, h)土壤体积含水量变化

Figure 2. Variations of soil volumetric water contents in Wangdu (a, b), Bazhou (c, d), Weixian (e, f) and Luancheng (g, h) stations from the soil moisture products and in-situ soil moisture (In-situ) during the experiment period

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图 3 望都(a, b)、霸州(c, d)、威县(e, f)、栾城(g, h)实测土壤水分与土壤水分产品散点图

Figure 3. Scatter plots of the in-situ and estimated soil moisture in Wangdu (a, b), Bazhou (c, d), Weixian (e, f), and Luancheng (g, h) stations

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图 4 各站点季节尺度6种土壤水分产品的精度表现

Figure 4. Soil moisture measurement accuracies of 6 soil moisture products at seasonal scale at each station

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图 5 各站点6种土壤水分产品不同数值范围精度表现

Figure 5. Accuracy of six soil moisture products in different numerical range at each site

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表 1 本研究所用土壤水分产品信息

Table 1. Soil moisture products information used in the study

产品类型 Data type	产品名称 Dataset name	版本 Version	空间分辨率 Spatial resolution	深度 Depth (cm)	时间分辨率 Temporal resolution
微波遥感产品 Microwave remote sensing products	FY3B	FY3B_P	25 km×25 km	0~5	1 d (升轨时间13:30, 降轨1:30) (Ascending time 13:30, decending time 1:30)
	SMOS	SMOS_L3_P	36 km×36 km	0~5	1 d (升轨时间6:00, 降轨时间18:00) (Ascending time 6:00, decending time 18:00)
	SMAP	SMAP_L3_P	36 km×36 km	0~5	1 d (升轨时间18:00, 降轨时间6:00) (Ascending time 18:00, decending time 6:00)
再分析产品 Reanalysis product	ERA-Land	—	0.1°×0.1°	0~7	1 h
陆表模型产品 Land surface model products	GLDAS	GLEAM_V3.5b	0.25°×0.25°	0~10	3 h
陆表模型产品 Land surface model products	GLEAM	NOAH_V2.1	0.25°×0.25°	0~10	3 h

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表 2 各站点各种土壤水分产品的精度检验

Table 2. Error metrics of soil moisture of different soil moisture products in different stations

站点 Station	产品 Product	RMSE (m³·m⁻³)	Bias (m³·m⁻³)	ubRMSE (m³·m⁻³)	r	P	样本个数 Number of samples
望都 Wangdu	FY3B	0.094	−0.012	0.093	0.773	0.001	181
	SMOS	0.190	0.164	0.095	0.341	0.001	303
	SMAP	0.123	0.109	0.057	0.479	0.001	242
	ERA-Land	0.085	0.048	0.071	0.348	0.001	655
	GLDAS	0.120	0.108	0.051	0.530	0.001	655
	GLEAM	0.060	0.032	0.051	0.546	0.001	655
霸州 Bazhou	FY3B	0.168	0.145	0.083	0.402	0.001	186
	SMOS	0.221	0.208	0.076	0.240	0.001	299
	SMAP	0.166	0.156	0.057	0.388	0.001	239
	ERA-Land	0.134	0.120	0.059	0.414	0.001	622
	GLDAS	0.129	0.120	0.048	0.527	0.001	622
	GLEAM	0.094	0.080	0.050	0.446	0.001	622
威县 Weixian	FY3B	0.126	0.007	0.126	0.314	0.001	201
	SMOS	0.144	0.116	0.084	0.282	0.001	311
	SMAP	0.115	0.100	0.057	0.345	0.001	287
	ERA-Land	0.071	0.047	0.053	0.535	0.001	645
	GLDAS	0.059	0.034	0.049	0.338	0.001	645
	GLEAM	0.051	0.028	0.042	0.455	0.001	645
栾城 Luancheng	FY3B	0.127	0.041	0.120	0.248	0.001	214
	SMOS	0.213	0.193	0.092	−0.049	—	255
	SMAP	0.165	0.152	0.063	0.204	0.002	262
	ERA-Land	0.114	0.090	0.070	0.229	0.001	633
	GLDAS	0.087	0.061	0.062	0.113	0.01	633
	GLEAM	0.071	0.049	0.052	0.291	0.001	633
加粗字母表示该站点所有产品中RMSE、Bias、ubRMSE最小值及r最大值。Bold font indicates the minimum value of RMSE, Bias, ubRMSE and maximum value of r for each product on this site.

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表 3 不同站点土壤水分产品像元内土地利用类型占比

Table 3. Proportion of land use types in each pixels of soil moisture products in different stations

站点 Station	耕地 Cropland	建设用地 Construction land	其他 Others
望都 Wangdu	74.76	23.38	1.87
霸州 Bazhou	77.95	18.42	3.63
威县 Weixian	80.33	17.56	2.11
栾城 Luancheng	73.57	21.51	4.92

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