Please wait a minute...
img

官方微信

遥感技术与应用  2021, Vol. 36 Issue (5): 1009-1021    DOI: 10.11873/j.issn.1004-0323.2021.5.1009
土壤水分专栏     
SMAP土壤水分产品在淮河流域的适用性评估
王皓1(),郝莹2(),袁松1,陈光舟2,靳莉莉2
1.安徽省气象台,安徽 合肥 230000
2.淮河流域气象中心,安徽 合肥 230000
Applicability Assessment of SMAP Soil Moisture Products in the Huaihe River Basin
Hao Wang1(),Ying Hao2(),Song Yuan1,Guangzhou Chen2,Lili Jin2
1.Anhui Provincial Meteorological Observatory,Hefei 230000,China
2.Huaihe River Basin Meteorological Center,Hefei 230000,China
 全文: PDF(9052 KB)   HTML
摘要:

选取淮河流域为研究区域,利用2016年6月至2019年5月流域内的313个土壤水分观测站0~10 cm土壤体积含水量数据,使用多种指标分析SMAP卫星(Soil Moisture Active Passive)9 km分辨率土壤水分产品(L2_SM_P_E)精度的空间和时间(年、月、日尺度)特征,并讨论植被、土壤、地形等对精度影响。结果表明:①整体来看,L2_SM_P_E在淮河流域达不到0.04 m3/m3的预期精度,存在湿区高估、干区低估的现象,但可以较好地反映流域土壤水分的空间分布特征,也能较为准确地指示高湿区和低湿区。②L2_SM_P_E的精度存在明显的区域差异和季节差异。冬季精度明显优于其他季节,流域大部分地区的无偏均方根误差(ubRMSE)均接近预期精度,且在流域北部的部分地区、伏牛山区和大别山区达到了预期精度。在春秋季,流域北部和大别山区的精度较高。夏季L2_SM_P_E的可用性较差。③L2_SM_P_E和降水有较好的一致性,对降水的响应比土壤水分观测值敏感。在降水过程中和降水结束后,L2_SM_P_E的误差以随机误差为主;当土壤相对干燥,则以系统性负偏差为主。④L2_SM_P_E的精度与采样点的土壤类型关系并不密切,山地地区的精度要优于其他地区。

关键词: SMAP土壤水分淮河流域评估    
Abstract:

The Huaihe River Basin is selected as the research area. Based on the daily average data of 313 soil moisture observation stations in the Huaihe River basin from June 2016 to May 2019 , the soil moisture products (L2_SM_P_E) of SMAP (Soil Moisture Active Passive) with 9 km resolution were assessed by using a variety of indicators. In addition, the influence of vegetation, soil, topography on accuracy were discussed. The results show that: (1) Generally, L2_SM_P_E cannot reach the expected accuracy of 0.04 m3/m3 in Huaihe River Basin, which has the characteristic of overestimating in wet area and underestimating in dry area, but it can better reflect the spatial distribution characteristics of soil moisture in the basin, and also can indicate the high wet area and low wet area. (2) There are obvious regional and seasonal differences in L2_SM_P_E accuracy. The accuracy in winter is obviously better than that in other seasons, the unbiased root mean square error (ubRMSE) in most areas of the basin is close to the expected accuracy.In some northern parts of the basin and Funiu Mountains and Dabie Mountains,it has reached the expected accuracy. In spring and autumn, the accuracy of the northern part of the basin and Dabie Mountains is higher. In summer, the availability of L2_SM_P_E is poor. (3) L2_SM_P_E have good consistency with precipitation, and its response to precipitation is more sensitive than the observed value of soil moisture. During and after precipitation, the error of L2_SM_P_E is mainly random error; when the soil is relatively dry, it is mainly negative systematic error. (4)The accuracy of L2_SM_P_E is not closely related to the soil type at the sampling point. The accuracy of mountain areas is better than other areas.

Key words: SMAP    Soil moisture    Huaihe River Basin    Comparative assessment
收稿日期: 2020-06-28 出版日期: 2021-12-07
ZTFLH:  S152.7  
基金资助: 中国气象局预报员专项(CMAYBY2018-032)
通讯作者: 郝莹     E-mail: dibazhang@qq.com;DG1328006@smail.nju.edu.cn
作者简介: 王皓(1987-),男,安徽滁州人,工程师,主要从事GIS应用研究。E?mail:dibazhang@qq.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
王皓
郝莹
袁松
陈光舟
靳莉莉

引用本文:

王皓,郝莹,袁松,陈光舟,靳莉莉. SMAP土壤水分产品在淮河流域的适用性评估[J]. 遥感技术与应用, 2021, 36(5): 1009-1021.

Hao Wang,Ying Hao,Song Yuan,Guangzhou Chen,Lili Jin. Applicability Assessment of SMAP Soil Moisture Products in the Huaihe River Basin. Remote Sensing Technology and Application, 2021, 36(5): 1009-1021.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.5.1009        http://www.rsta.ac.cn/CN/Y2021/V36/I5/1009

图1  淮河流域地形图 审图号:GS(2020)303
图2  SMAP数据与观测站点在淮河流域的分布 审图号:GS(2020)303
图3  地面观测数据与SMAP数据的日平均土壤水分空间分布 审图号:GS(2020)303
图4  评估指标的空间分布 审图号:GS(2020)303
季节样本数地面观测值SMAP数值

相关系数

R

偏差

Bias

均方根误差

RMSE

无偏均方根误差

ubRMSE

64 6230.2360.1770.383-0.0590.1300.115
61 6490.2530.2380.397-0.0160.1330.132
59 6310.2580.1910.421-0.0670.1260.107
48 0030.2660.1460.447-0.1200.1590.104
总体233 9060.2520.1900.384-0.0620.1360.121
表1  评估指标的季节分布特征
图5  无偏均方根误差的季节空间分布 审图号:GS(2020)303
图6  评估指标的月分布特征
图7  无偏均方根误差的逐月空间分布 审图号:GS(2020)303
图8  地面观测数据与SMAP数据逐日评估结果
分类

样本数

N

相关系数

R

偏差

Bias

均方根误差

RMSE

无偏均方根误差

ubRMSE

植被类型栽培植被219 9800.387-0.0620.1360.121
阔叶林9 1720.218-0.0790.1360.111
针叶林3 8690.445-0.0530.1340.124
其他植被8850.2050.0040.0960.096
土壤类型半水成土147 2000.370-0.0830.1370.108
人为土29 7200.2020.0540.1430.132
淋溶土24 3230.391-0.0450.1270.119
半淋溶土19 9920.275-0.1030.1390.094
初育土12 6710.493-0.06070.1260.111
地形地貌平原202 6480.386-0.0670.1380.120
台地18 6910.4390.0040.1180.118
丘陵9 7610.307-0.0980.1360.093
小起伏山地1 9370.8180.0100.1000.099
中起伏山地8690.606-0.2130.2240.072
表2  不同下垫面特征分类评估结果
图9  不同下垫面类型的无偏均方根误差月分布
1 Ding Xu, Lai Xin, Fan Guangzhou. Soil moisture characteristics and climate response of different climatic regional in China[J]. Plateau and Mountain Meteorology Research,2016,36(4):28-35.
1 丁旭,赖欣,范广洲.中国不同气候区土壤湿度特征及其气候响应[J].高原山地气象研究,2016,36(4):28-35.
2 Chen Haishan, Zhou Jing. Impact of interannual soil moisture anomaly on simulation of extreme climate events in China. part II: sensitivity experiment analysis[J].Chinese Journal of Atmospheric Sciences,2013,37(1): 1-13.
2 陈海山,周晶.土壤湿度年际变化对中国区域极端气候事件模拟的影响研究Ⅱ.敏感性试验分析[J].大气科学,2013,37(1):1-13.
3 Ma Zhuguo, Fu Congbin, Xie Li, et al. Some problems in the study on the relationship between soil moisture and climatic change[J].Advance in Earth Science,2001,16(4):563-566.
3 马柱国,符淙斌,谢力,等.土壤湿度和气候变化关系研究中的某些问题[J].地球科学进展,2001,16(4):563-568.
4 Zhang Chao, Wang Huixiao. A brief review of advances in soil water research[J]. Agricultural Research in the Arid Areas, 2003,21(4):117-125.
4 张超,王会肖.土壤水分研究进展及简要评述[J].干旱地区农业研究,2003,21(4):117-125.
5 Zhao Tianjie. Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects[J]. Progress in Geography, 2018,37(2):198-213.
5 [赵天杰.被动微波反演土壤水分的L波段新发展及未来展望[J].地理科学进展, 2018,37(2):198-213.
6 Pan Ning, Wang Shuai, Liu Yanxu, et al. Advances in soil moisture retrieval from remote sensing[J]. Acta Ecologica Sinica,2019,39(13):4615-4626.
6 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):4615-4626.
7 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.
doi: 10.1109/TGRS.2012.2184548
8 Kang C S, Kanniah K D, Kerr Y H, et al. Analysis of in-situ soil moisture data and validation of SMOS soil moisture products at selected agricultural sites over a tropical region[J]. International Journal of Remote Sensing, 2016,37(16):3636-3654. DOI:10.1080/01431161.2016.1201229.
doi: 10.1080/01431161.2016.1201229
9 Parinussa R M, Wang G, Holmes T R H, et al. Global surface soil moisture from the microwave radiation imager on board the fengyun-3B satellite[J]. International Journal of Remote Sensing,2014,35(19):7007-7029. DOI:10.1080/01431161.2014.960622.
doi: 10.1080/01431161.2014.960622
10 Bao Yansong, Mao Fei, Min Jinzhong, et al. Retrieval of bare soil moisture from FY-3B/MWRI data[J]. Remote Sensing for Land and Resources,2014,26(4):131-137.
10 鲍艳松,毛飞,闵锦忠,等.基于FY-3B/MWRI数据的裸土区土壤湿度反演[J].国土资源遥感,2014,26(4):131-137.
11 Imaoka K, Kachi M, Kasahara M, et al. Instrument performance and calibration of AMSR-E and AMSR2[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Science,2010,38(8):13-18.
12 Koike T. Description of the GCOM-W1 AMSR2 soil moisture algorithm[R]. Tokyo: Japan Aerospace Exploration Agency Earth Observation Research Center, 2013:1-119.
13 Entekhabi D, Njoku E G, O'Neill P E, et al. The soil moisture active passive (SMAP) mission[J]. Proceedings of the IEEE,2010,98(5):704-716. DOI:10.1109/IGARSS.2008. 4779267.
doi: 10.1109/IGARSS.2008. 4779267
14 Brown M E, Escobar V, Moran S, et al. NASA’s soil moisture active passive (SMAP) mission and opportunities for applications users[J]. Bulletin of the American Meteorological Society, 2013,94(8):1125-1128. DOI:10.1175/BAMS-D-11-00049.1.
doi: 10.1175/BAMS-D-11-00049.1
15 Entekhabi D, Yueh S, O’Neill P E, et al. SMAP Handbook[S] The National Aeronautics and Space Administration: Wsahington,DC,USA, 2014.
16 Colliander A, Jackson T J, Bindlish R, et al. Validation of SMAP surface soil moisture products with core validation sites[J]. Remote Sensing of Environment, 2017,191:215-231. DOI:10.1016/j.rse.2017.01.021.
doi: 10.1016/j.rse.2017.01.021
17 Sun Y Y, Huang S F, Ma J W, et al. Preliminary evaluation of the SMAP radiometer soil moisture product over China using In situ data[J]. Remote Sensing,2017,9(3):292. DOI:10.3390/rs9030292.
doi: 10.3390/rs9030292
18 Chan S K, Bindlish R, O’Neill P E, et al. Assessment of the SMAP passive soil moisture product[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):1-14. DOI:10.1109/TGRS.2016.2561938.
doi: 10.1109/TGRS.2016.2561938
19 Cui C, Xu J, Zeng J, 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(1):33. DOI:10.3390/rs10010033.
doi: 10.3390/rs10010033
20 Liu P W, Bindlish R, Fang B, et al. Assessing disaggregated SMAP soil moisture products in the united states[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021(99):1-1. DOI:10.1109/JSTARS.2021. 3056001.
doi: 10.1109/JSTARS.2021. 3056001
21 Ma C F, Li X, Wei L, et al. Multi-scale validation of SMAP soil moisture products over cold and arid regions in north-western China using distributed ground observation data[J]. Remote Sensing, 2017,9(4):327.DOI:10.3390/rs9040327.
doi: 10.3390/rs9040327
22 Bai Yu, Meng Zhiguo, Zhao Kai, et al. Pixel-scale soil moisture monitoring network an its preliminary validation of Lband soil moisture products[J]. Remote Sensing Technology and Application,2018,33(1):78-87.
22 白瑜,孟治国,赵恺,等.像元尺度土壤水分监测网络及其对L波段土壤水分产品的初步验证结果[J].遥感技术与应用,2018,33(1):78-87.
23 Zhu Y C, Li X , Pearson S , et al. Evaluation of fengyun-3C soil moisture products using in-situ data from the Chinese automatic soil moisture observation stations: a case study in Henan Province, China[J]. Water,2019,11(2):248. DOI:10.3390/w11020248.
doi: 10.3390/w11020248
24 Liu J, Chai L N, Lu Z, et al. Evaluation of SMAP,SMOS-IC, FY3B, JAXA, and LPRM soil moisture products over the Qinghai-Tibet Plateau and its surrounding areas[J]. Remote Sensing, 2019,11(7):792. DOI:10.3390/rs11070792.
doi: 10.3390/rs11070792
25 Xie Q X, Menenti M, Jia L. Improving the AMSR-E/NASA soil moisture data product using in-situ measurements from the Tibetan platea[J]. Remote Sensing, 2019,11(23):2748. DOI:10.3390/rs11232748.
doi: 10.3390/rs11232748
26 Chen Hongyu,Wu Jing,Li Chunbin,et al.Applicability evaluation of satellite soil moisture products in Qinghai⁃Tibet Plateau[J]. Acta Ecologica Sinica,2020,40(24):9195-9207.
26 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):9195-9207.
27 Wang Yazheng, Yang Yuanjian, Liu Chao, et al. Analysis on the applicability of Fengyun-3 satellite microwave remote sensing soil moisture products in Shandong[J]. Chinese Journal of Agrometeorology, 2021,42(4):318-329.
27 王雅正,杨元建,刘超,等.风云三号卫星微波遥感土壤水分产品在山东地区的适用性分析[J].中国农业气象,2021,42(4):318-329.
28 Xie Qiuxia, Jia Li, Chen Qiting, et al. Evaluation of microwave remote sensing soil moisture products in farming-pastoral area of Shandian River basin[J]. National Remote Sensing Bulletin, 2021,25(4):974-989.
28 谢秋霞,贾立,陈琪婷,等.闪电河流域农牧交错带微波遥感土壤水分产品评价[J].遥感学报, 2021,25(4):974-989.
29 Philip G M, Watson D F. A Precise method for determining contoured surfaces[J]. Australian Petroleum Exploration Association Journal,1982,22(1):205-212. DOI:10.1071/aj81016.
doi: 10.1071/aj81016
30 Watson D F, Philip G M. A refinement of inverse distance weighted interpolation[J]. Geoprocessing, 1985,2:315-327. DOI:10.1016/S0735-1097(97)00186-1.
doi: 10.1016/S0735-1097(97)00186-1
31 Xiang Yiheng, Zhang Mingmin, Zhang Lanhui, et al.Validation of SMOS soil moisture products on different vegetation types in Qilian Mountain[J].Remote Sensing Technology and Application,2017,32(5):835-843.
31 向怡衡,张明敏,张兰慧,等.祁连山区不同植被类型上的SMOS遥感土壤水分产品质量评估[J].遥感技术与应用,2017,32(5):835-843.
[1] 杜妍开,龚丽霞,李强,詹森,张景发. 基于多纹理特征融合的震后SAR图像倒塌建筑物信息提取[J]. 遥感技术与应用, 2021, 36(4): 865-872.
[2] 廖鸿燕,周小成,黄洪宇. 基于无人机遥感技术的台风灾害倒伏绿化树木检测[J]. 遥感技术与应用, 2021, 36(3): 533-543.
[3] 孙景霞,张冬有,侯宇初. 基于多源遥感数据协同反演森林地表土壤水分研究[J]. 遥感技术与应用, 2021, 36(3): 564-570.
[4] 王舒,蒋玲梅,王健. 基于多频被动微波遥感的土壤水分反演—以黑河上游为例[J]. 遥感技术与应用, 2020, 35(6): 1414-1425.
[5] 蒋玲梅,崔慧珍,王功雪,杨建卫,王健,潘方博,苏旭,方西瑶. 积雪、土壤冻融与土壤水分遥感监测研究进展[J]. 遥感技术与应用, 2020, 35(6): 1237-1262.
[6] 王一帆,徐涵秋. 基于客观阈值与随机森林Gini指标的水体遥感指数对比[J]. 遥感技术与应用, 2020, 35(5): 1089-1098.
[7] 董磊磊,王维真,吴月茹. 盐渍土介电特性及模型改进研究[J]. 遥感技术与应用, 2020, 35(4): 786-796.
[8] 王艺晴,韩震,周玮辰,吴义生. SMAP卫星海表面亮温仿真及海表面盐度遥感反演[J]. 遥感技术与应用, 2020, 35(2): 365-371.
[9] 李雷,郑兴明,赵凯,李晓峰,王广蕊. 基于CCI土壤水分产品的干旱指数精度评价及其对东北地区粮食产量的影响[J]. 遥感技术与应用, 2020, 35(1): 111-119.
[10] 罗家顺,邱建秀,赵天杰,王大刚. 基于Sentinel-1数据的黑河中游土壤水分反演[J]. 遥感技术与应用, 2020, 35(1): 23-32.
[11] 陈家利,郑东海,庞国锦,李新. 基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水[J]. 遥感技术与应用, 2020, 35(1): 48-57.
[12] 陆峥,韩孟磊,卢麾,彭雪婷,蒙莎莎,刘进,杨晓帆. 基于AMSR2多频亮温的黑河流域中上游土壤水分估算研究[J]. 遥感技术与应用, 2020, 35(1): 33-47.
[13] 胡路,赵天杰,施建成,李尚楠,樊东,王平凯,耿德源,肖青,崔倩,陈德清. 基于地基微波辐射观测的土壤水分反演算法评估[J]. 遥感技术与应用, 2020, 35(1): 74-84.
[14] 陈勇强,杨娜,胡新,佟明远. SMOS与SMAP过境时段表层土壤水分的稳定性研究[J]. 遥感技术与应用, 2020, 35(1): 58-64.
[15] 劳从坤,杨娜,徐少博,汤燕杰,张恒杰. 反演策略对SMOS土壤水分反演算法的影响研究[J]. 遥感技术与应用, 2020, 35(1): 65-73.