Please wait a minute...
img

官方微信

遥感技术与应用  2020, Vol. 35 Issue (1): 65-73    DOI: 10.11873/j.issn.1004-0323.2020.1.0065
土壤水分专栏     
反演策略对SMOS土壤水分反演算法的影响研究
劳从坤(),杨娜(),徐少博,汤燕杰,张恒杰
河南理工大学 测绘与国土信息工程学院,河南 焦作 454000
Study on Retrieval Strategy of SMOS Soil Moisture Retrieval Algorithm
Congkun Lao(),Na Yang(),Shaobo Xu,Yanjie Tang,Hengjie Zhang
School of Surveying and Land Information Engineering,Henan Polytechnic University, Jiaozuo 454000, China
 全文: PDF(2589 KB)   HTML
摘要:

为降低SMOS土壤水分反演算法的复杂度、提高土壤水分反演精度,对SMOS土壤水分反演策略进行调整:将多参数反演改为单参数反演以简化观测与模拟亮温的代价函数,以固定步长(0.001 m3/m3)代替不定步长从而避免复杂的矩阵运算,将围绕土壤水分先验值的少量局部搜索调整为全土壤水分区间(0~0.05 m3/m3)的密集全局搜索。利用美国USCRN 44个站点实测土壤水分分别与SMOS官方反演的土壤水分和SMOS调整算法反演的土壤水分进行对比分析。结果表明:与SMOS相比,算法调整后土壤水分的平均绝对偏差MAD、均方根误差RMSE和无偏均方根误差ubRMSE分别降低了0.012、0.018和0.020 m3/m3。

关键词: 土壤水分SMOS反演算法亮温模拟    
Abstract:

In order to reduce the complexity of SMOS official soil moisture retrieval algorithm and improve the accuracy of soil moisture retrievals, a new retrieval strategy on SMOS soil moisture retrieval algorithm was developed. In the new retrieval strategy on SMOS soil moisture retrieval algorithm, the fixed step size (0.001 m3/m3) was used to replace the flexible step size obtained by the SMOS matrix operation. The multi-parameter was changed to a single-parameter in the cost function. The data from 44 USCRN sites in the United States were compared with the soil moisture retrieved from SMOS official algorithm as well as the adjustment of SMOS algorithm. The results show that compared with the SMOS official algorithm, the average absolute deviation, root mean square error,and unbiased root mean square error of the adjustment of SMOS algorithm are reduced by 0.012 m3/m3, 0.018 m3/m3,and 0.020 m3/m3,respectively.

Key words: Soil moisture    SMOS    Inversion algorithm    Brightness temperature simulation
收稿日期: 2019-12-09 出版日期: 2020-04-01
ZTFLH:  TP701  
基金资助: 国家自然科学基金青年基金项目(41501363)
通讯作者: 杨娜     E-mail: lck920911@foxmail.com;yangna800522@foxmail.com
作者简介: 劳从坤(1992-),男,河南淮阳人,硕士研究生,主要从事微波遥感土壤水分反演研究。E?mail:lck920911@foxmail.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  
劳从坤
杨娜
徐少博
汤燕杰
张恒杰

引用本文:

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.

Congkun Lao,Na Yang,Shaobo Xu,Yanjie Tang,Hengjie Zhang. Study on Retrieval Strategy of SMOS Soil Moisture Retrieval Algorithm. Remote Sensing Technology and Application, 2020, 35(1): 65-73.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0065        http://www.rsta.ac.cn/CN/Y2020/V35/I1/65

图1  土壤水分和地表粗糙度函数关系[23]
参数名称 参考值
地表反射率 QR=0.0 Np=2.0
土壤粗糙度

CXMVT1/2=0.490 0/0.165 0

CWP1/2/3=0.067 7/-0.000 6/0.004 8

HR_MIN/MAX=0.0/0.5

植被光学厚度与单次散射反照率 低矮植被

τ=0.24 ω =0.0

cl=0.24 a_L=2.33 b_L=0.00

森林植被 τ=0.50 ω =0.08
土壤等效温度 bw0=0.3 w 0 =0.3
表1  参数取值
参数名称 ID 单位
气温 T
降水量 P_CALC mm
太阳辐射 SOLARAD W/m2
表面温度 SUR_TEMP
相对湿度 RH %
土壤水分* SOIL_MOISTURE m3/m3
土壤温度* SOIL_TEMP
表2  USCRN观测参量,小时级
年份 筛选/总量 年份 筛选/总量 年份 筛选/总量
2000 0/2 2006 0/97 2012 77/222
2001 0/8 2007 0/121 2013 73/222
2002 0/25 2008 0/137 2014 71/223
2003 0/45 2009 23/155 2015 77/153
2004 0/72 2010 36/201 2016 69/155
2005 0/82 2011 52/219 2017 63/156
2018 61/157
表3  站点筛选的小时级数据
图2  站点空间分布
FROM-GLC 序号 本文
农田 1 低矮植被
草地 3
灌木 4
森林 2 森林
裸地 9 裸地
湿地 5 其他
6
苔原 7
非渗透表面 8
雪/冰 10
表4  FROM-GLC地表类型及调整
总体平均 SMOS 方案一 方案二
MAD 0.112 0.115 0.100
RMSE 0.133 0.132 0.115
ubRMSE 0.087 0.084 0.067
R 2 0.083/max0.529 0.142/max0.889 0.083/max0.458
表5  土壤水分反演精度对比
图3  3个站点的土壤水分反演结果
站点1* 站点2** 站点3***
SMOS/方案一/二 SMOS/方案一/二 SMOS/方案一/二
MAD 0.075/0.052/0.032 0.102/0.084/0.253 0.154/0.238/0.189
RMSE 0.113/0.060/0.037 0.121/0.106/0.267 0.169/0.249/0.203
ubRMSE 0.108/0.049/0.033 0.082/0.103/0.086 0.072/0.071/0.074
R 2 0.000/0.000/0.006 0.003/0.054/0.004 0.611/0.648/0.565
表6  各站点土壤水分反演结果
1 Lin Libin , Bao Yansong , Zuo Quan ,et al .Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data[J].Remote Sensing Technology and Application,2018,33(4):750-758.林利斌,鲍艳松,左泉, 等 .基于Sentinel-1与FY-3C数据反演植被覆盖地表土壤水分[J].遥感技术与应用,2018,33(4):750-758.
2 Brocca L , Ponziani F , Moramarco T ,et al .Improving Landslide Forecasting Using ASCAT-derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy[J].Remote Sensing,2012,4(5):1232-1244.
3 Brocca L , Melone F , Moramarco T ,et al .Improving Runoff Prediction Through the Assimilation of the ASCAT Soil Moisture Product[J].Hydrology and Earth System Sciences,2010,14(10):1881-1893.
4 Rahmani A , Golian S , Brocca L .Multiyear Monitoring of Soil Moisture over Iran Through Satellite and Reanalysis Soil Moisture Products[J].International Journal of Applied Earth Observation and Geoinformation,2016,48:85-95.
5 Entekhabi D , Rodriguez-Iturbe I , Castelli F .Mutual Interaction of Soil Moisture State and Atmospheric Processes[J].Journal of Hydrology,1996,184(1-2):3-17.
6 Korres W , Reichenau T G , Schneider K .Patterns and Scaling Properties of Surface Soil Moisture in an Agricultural Landscape: An Ecohydrological Modeling Study[J].Journal of Hydrology,2013,498:89-102.
7 Conil S , Douville H , Tyteca S .The Relative Influence of Soil Moisture and SST in Climate Predictability Explored within Ensembles of AMIP Type Experiments[J].Climate Dynamics,2006,28(2-3):125-145.
8 Zhao Tianjie .New Development and Prospect of L-band for Soil Moisture Retrieval from Passive Microwave [J].Advances in Geographical Sciences,2018,37(2):198-213.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.
9 Jackson T J , Bindlish R , Cosh M H ,et al .Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(5):1530-1543.
10 Bitar A A , Leroux D , Kerr Y H .Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(5):1572-1586.
11 Gherboudj I , Magagi R , Goita K ,et al .Validation of SMOS Data over Agricultural and Boreal Forest Areas in Canada[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(5):1623-1635.
12 Dente L , Su Z , Wen J .Validation of SMOS Soil Moisture Products over the Maqu and Twente Regions[J].Sensors,2012,12(8):9965-9986.
13 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.
14 Peng Jian , Niesel J , Loew A ,et al .Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-based Measurements[J].Remote Sensing,2015,7(11):15729-15747.
15 Niclos R , Rivas R , Garcia-Santos V ,et al .SMOS Level-2 Soil Moisture Product Evaluation in Rain-fed Croplands of the Pampean Region of Argentina[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(1):499-512.
16 Xiang Yiheng , Zhang Mingmin , Zhang Lanhui ,et al .Quality Evaluation of SMOS Remote Sensing Soil Moisture Products on Different Vegetation Types in Qilian Mountains[J].Remote Sensing Technology and Application,2017,32(5):835-843.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.
17 Wigneron J , Chanzy A , Kerr Y H ,et al .Evaluating An Improved Parameterization of the Soil Emission in L-MEB[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(4):1177-1189.
18 Peng B , Zhao T J , Shi J C ,et al .Reappraisal of the Roughness Effect Parameterization Schemes for L-band Radiometry over Bare Soil[J].Remote Sensing of Environment,2017,199:63-77.
19 Jiang T , Zhao K , Zheng X M ,et al .Dynamic bp in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval[J].Chinese Geographical Science,2019,29(2):283-292.
20 Ebrahimi M , Alavipanah S K , Hamzeh S ,et al .Exploiting the Synergy between SMAP and SMOS to Improve Brightness Temperature Simulations and Soil Moisture Retrievals in Arid Regions[J].Journal of Hydrology,2018,557:740-752.
21 Kerr Y H , Al-Yaarib A , Rodriguez-fernandeza N ,et al .Overview of SMOS Performance Intermsof Global Soil Moisture Monitoring Aftersix Years in Operation[J].Remote Sensing of Environment,2016,180:40-63.
22 Wigneron J , Calvet J , de Rosnay P ,et al .L-MEB: A Simple Model at L-band for the Continental Areas-application to the Simulation of A Half-degree Resolution and Global Scale Data Set[C]∥IEE Electromagnetic Waves Series,2006.
23 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.
24 Mo T , Choudhury B J , Schmugge T J ,et al .A Model for Microwave Emission from Vegetation‐covered Fields[J].Journal of Geophysical Research: Oceans,1982,87(C13):1122911237.
25 de Rosnay P , Calvet J , Kerr Y ,et al .SMOSREX: A Long Term Field Campaign Experiment for Soil Moisture and Land Surface Processes Remote Sensing[J].Remote Sensing of Environment,2006,102(3-4):377-389.
26 Grant J P , Saleh-Contell K , Wigneron J P ,et al .Calibration of the L-MEB Model over A Coniferous and a Deciduous Forest[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(3):808-818.
27 Saleh K , Wigneron J P , Waldteufel P ,et al .Estimates of Surface Soil Moisture Under Grass Covers Using L-band Radiometry[J].Remote Sensing of Environment,2007,109(1):42-53.
28 Putuhena W M , Cordery I .Estimation of Interception Capacity of the Forest Floor[J].Journal of Hydrology,1996,180(1):283-299.
29 Saleh K , Wigneron J , de Rosnay P ,et al .Impact of Rain Interception by Vegetation and Mulch on the L-band Emission of Natural Grass[J].Remote Sensing of Environment,2006,101(1):127-139.
30 Kerr Y H , Waldteufel P , Richaume P ,et al .SMOS Level 2 Processor Soil Moisture Algorithm Theoretical basis Document (ATBD)[C]∥SM-ESL (CBSA),2017.
31 Marquardt D W .An Algorithm for Least-squares Estimation of Non Linear Parameters[J].Journal of the Society for Industrial and Applied Mathematics,1963,11:431-441.
[1] 王树果, 马春锋, 赵泽斌, 魏龙. 基于Sentinel-1及Landsat 8数据的黑河中游农田土壤水分估算[J]. 遥感技术与应用, 2020, 35(1): 13-22.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 范悦,邱建秀,董建志,张小虎,王大刚. 基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究[J]. 遥感技术与应用, 2020, 35(1): 85-96.
[8] 王树果,刘伟,梁亮. 基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合[J]. 遥感技术与应用, 2019, 34(6): 1227-1234.
[9] 刘克俭,闫敏,冯琦. 多层土壤观测数据同化的森林碳、水通量模拟[J]. 遥感技术与应用, 2019, 34(5): 950-958.
[10] 宋小霞, 王静, 储小青. 基于多普勒频移的SAR海表流场反演[J]. 遥感技术与应用, 2019, 34(2): 293-302.
[11] 李瑞娟, 李兆富, 郝睿, 张舒昱, 潘剑君. 亚洲区域AMSR2与SMOS土壤水分产品对比研究[J]. 遥感技术与应用, 2019, 34(1): 125-134.
[12] 王广蕊, 李晓峰. 东北地区森林积雪的微波辐射亮温模拟分析[J]. 遥感技术与应用, 2018, 33(6): 1027-1029.
[13] 王恺宁, 王修信, 黄凤荣, 罗涟玲. 喀斯特城市地表温度遥感反演算法比较[J]. 遥感技术与应用, 2018, 33(5): 803-810.
[14] 白瑜,孟治国,赵凯. 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.
[15] 向怡衡,张明敏,张兰慧,贺缠生,王一博,白晓. 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.