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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。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.
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Key words:
- Soil moisture product /
- In-situ soil moisture /
- Hebei Plain /
- Farmland /
- North China Plain
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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
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 productsFY3B 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 productERA-Land — 0.1°×0.1° 0~7 1 h 陆表模型产品
Land surface model productsGLDAS GLEAM_V3.5b 0.25°×0.25° 0~10 3 h GLEAM NOAH_V2.1 0.25°×0.25° 0~10 3 h 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
ProductRMSE
(m3·m−3)Bias
(m3·m−3)ubRMSE
(m3·m−3)r P 样本个数
Number of samples望都
WangduFY3B 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
BazhouFY3B 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
WeixianFY3B 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
LuanchengFY3B 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. 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 -
[1] SENEVIRATNE S I, CORTI T, DAVIN E L, et al. Investigating soil moisture-climate interactions in a changing climate: a review[J]. Earth-Science Reviews, 2010, 99(3/4): 125−161 [2] KOSTER R D, MAHANAMA S P P, LIVNEH B, et al. Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow[J]. Nature Geoscience, 2010, 3(9): 613−616 doi: 10.1038/ngeo944 [3] KORNELSEN K C, COULIBALY P. Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications[J]. Journal of Hydrology, 2013, 476: 460−489 doi: 10.1016/j.jhydrol.2012.10.044 [4] 杨涛, 宫辉力, 李小娟, 等. 土壤水分遥感监测研究进展[J]. 生态学报, 2010, 30(22): 6264−6277YANG T, GONG H L, LI X J, et al. Progress of soil moisture monitoring by remote sensing[J]. Acta Ecologica Sinica, 2010, 30(22): 6264−6277 [5] LONG D, BAI L L, YAN L, et al. Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution[J]. Remote Sensing of Environment, 2019, 233: 111364 doi: 10.1016/j.rse.2019.111364 [6] 陈泓羽, 吴静, 李纯斌, 等. 卫星土壤水分产品在青藏高原地区的适用性评价[J]. 生态学报, 2020, 40(24): 9195−9207CHEN H Y, WU J, LI C B, et al. Applicability evaluation of satellite soil moisture products in Qinghai-Tibet Plateau[J]. Acta Ecologica Sinica, 2020, 40(24): 9195−9207 [7] 潘宁, 王帅, 刘焱序, 等. 土壤水分遥感反演研究进展[J]. 生态学报, 2019, 39(13): 4615−4626PAN N, WANG S, LIU Y X, et al. Advances in soil moisture retrieval from remote sensing[J]. Acta Ecologica Sinica, 2019, 39(13): 4615−4626 [8] LIU J, CHAI L N, DONG J Z, et al. Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method[J]. Remote Sensing of Environment, 2021, 255: 112225 doi: 10.1016/j.rse.2020.112225 [9] AL-YAARI A, WIGNERON J P, DUCHARNE A, et al. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to land data assimilation system estimates[J]. Remote Sensing of Environment, 2014, 149: 181−195 doi: 10.1016/j.rse.2014.04.006 [10] KIM H, WIGNERON J P, KUMAR S, et al. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions[J]. Remote Sensing of Environment, 2020, 251: 112052 doi: 10.1016/j.rse.2020.112052 [11] CUI C Y, XU J, ZENG J Y, et al. Soil moisture mapping from satellites: an intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales[J]. Remote Sensing, 2017, 10(2): 33 doi: 10.3390/rs10010033 [12] ZENG J Y, LI Z, CHEN Q, et al. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in situ observations[J]. Remote Sensing of Environment, 2015, 163: 91−110 doi: 10.1016/j.rse.2015.03.008 [13] FU H Y, ZHOU T T, SUN C L. Evaluation and analysis of AMSR2 and FY3B soil moisture products by an in situ network in cropland on pixel scale in the northeast of China[J]. Remote Sensing, 2019, 11(7): 868 doi: 10.3390/rs11070868 [14] WANG G Q, ZHANG X J, YINGLAN A, et al. A spatio-temporal cross comparison framework for the accuracies of remotely sensed soil moisture products in a climate-sensitive grassland region[J]. Journal of Hydrology, 2021, 597: 126089 doi: 10.1016/j.jhydrol.2021.126089 [15] 沈彦俊, 刘昌明. 华北平原典型井灌区农田水循环过程研究回顾[J]. 中国生态农业学报, 2011, 19(5): 1004−1010SHEN Y J, LIU C M. Agro-ecosystems water cycles of the typical irrigated farmland in the North China Plain[J]. Chinese Journal of Eco-Agriculture, 2011, 19(5): 1004−1010 [16] 刘中培, 王富强, 于福荣. 石家庄平原区浅层地下水位变化研究[J]. 南水北调与水利科技, 2012, 10(5): 124−127LIU Z P, WANG F Q, YU F R. Variation of shallow groundwater level in Shijiazhuang Plain[J]. South-to-North Water Diversion and Water Science & Technology, 2012, 10(5): 124−127 [17] 陈宗培. 河北平原小麦-玉米不同灌溉制度下产量和水分生产力潜力及差距研究[D]. 保定: 河北农业大学, 2020CHEN Z P. Potential and gap of yield and water productivity of wheat-maize under different irrigation systems in Hebei Plain[D]. Baoding: Hebei Agricultural University, 2020 [18] 杨纲. 微波遥感土壤水分产品真实性检验方法研究[D]. 泰安: 山东农业大学, 2020YANG G. The research of validation of microwave remote sensing soil moisture products[D]. Tai’an: Shandong Agricultural University, 2020 [19] JACKSON T J, COSH M H, BINDLISH R, et al. Validation of advanced microwave scanning radiometer soil moisture products[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4256−4272 doi: 10.1109/TGRS.2010.2051035 [20] SU Z, WEN J, DENTE L, et al. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products[J]. Hydrology and Earth System Sciences, 2011, 15(7): 2303−2316 doi: 10.5194/hess-15-2303-2011 [21] KERR Y H, WALDTEUFEL P, RICHAUME P, et al. The SMOS soil moisture retrieval algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1384−1403 doi: 10.1109/TGRS.2012.2184548 [22] 陈勇强. SMOS和SMAP数据可靠性研究[D]. 焦作: 河南理工大学, 2019CHEN Y Q. The reliability study of SMOS and SMAP data[D]. Jiaozuo: Henan Polytechnic University, 2019 [23] 张明敏. 高寒山区土壤水分数据集验证及降尺度研究[D]. 兰州: 兰州大学, 2020ZHANG M M. Evaluation and downscaling of soil moisture datasets in the alpine mountain ranges[D]. Lanzhou: Lanzhou University, 2020 [24] MARTENS B, MIRALLES D G, LIEVENS H, et al. Gleam V3: satellite-based land evaporation and root-zone soil moisture[J]. Geoscientific Model Development, 2017, 10(5): 1903−1925 doi: 10.5194/gmd-10-1903-2017 [25] ZENG J Y, CHEN K S, CUI C Y, et al. A physically based soil moisture index from passive microwave brightness temperatures for soil moisture variation monitoring[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2782−2795 doi: 10.1109/TGRS.2019.2955542 [26] XU L, CHEN N C, ZHANG X, et al. In-situ and triple-collocation based evaluations of eight global root zone soil moisture products[J]. Remote Sensing of Environment, 2021, 254: 112248 doi: 10.1016/j.rse.2020.112248 [27] ALBERGEL C, DORIGO W, BALSAMO G, et al. Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses[J]. Remote Sensing of Environment, 2013, 138: 77−89 doi: 10.1016/j.rse.2013.07.009 [28] ZENG J Y, CHEN K S, BI H Y, et al. A comprehensive analysis of rough soil surface scattering and emission predicted by AIEM with comparison to numerical simulations and experimental measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1696−1708 doi: 10.1109/TGRS.2016.2629759 [29] XU J, CAI H J, SADDIQUE Q, et al. Evaluation and optimization of border irrigation in different irrigation seasons based on temporal variation of infiltration and roughness[J]. Agricultural Water Management, 2019, 214: 64−77 doi: 10.1016/j.agwat.2019.01.003 [30] OLIVA R, DAGANZO E, KERR Y H, et al. SMOS radio frequency interference scenario: status and actions taken to improve the RFI environment in the 1400–1427 MHz passive band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1427−1439 doi: 10.1109/TGRS.2012.2182775 [31] SOLDO Y, KHAZAAL A, SLOMINSKA E, et al. Monitoring of RFI localizations for the SMOS mission: seasonal variations and systematic errors[C]//2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS. Melbourne, VIC, Australia. IEEE, 2013: 1912–1915 [32] 庄媛, 师春香, 沈润平, 等. 中国区域多种微波遥感土壤湿度产品质量评估[J]. 气象科学, 2015, 35(3): 289−296 doi: 10.3969/2014jms.0054ZHUANG Y, SHI C X, SHEN R P, et al. Quality evaluation of multi-microwave remote sensing soil moisture products over China[J]. Journal of the Meteorological Sciences, 2015, 35(3): 289−296 doi: 10.3969/2014jms.0054 [33] WAGNER W, HAHN S, KIDD R, et al. The ascat soil moisture product: a review of its specifications, validation results, and emerging applications[J]. Meteorologische Zeitschrift, 2013, 22(1): 5−33 doi: 10.1127/0941-2948/2013/0399 [34] KOSTER R D, GUO Z C, YANG R Q, et al. On the nature of soil moisture in land surface models[J]. Journal of Climate, 2009, 22(16): 4322−4335 doi: 10.1175/2009JCLI2832.1 -