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遥感技术与应用  2022, Vol. 37 Issue (6): 1361-1372    DOI: 10.11873/j.issn.1004-0323.2022.6.1361
土壤水分专栏     
L波段微波辐射计机载与卫星观测的对比研究
杨娜(),徐少博(),劳从坤,张恒杰,汤燕杰
河南理工大学 测绘与国土信息工程学院,河南 焦作 454000
Comparisons between Airborne and Space Borne L-band Microwave Radiometer Observations
Na Yang(),Shaobo Xu(),Congkun Lao,Hengjie Zhang,Yanjie Tang
School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China
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摘要:

“闪电河流域水循环和能量平衡遥感综合试验”以滦河上游的闪电河流域为试验区,开展了机载L波段微波辐射计观测。将其获取的机载亮温数据与SMOS、SMAP卫星亮温(L1C)进行对比研究。首先根据观测时间、角度和极化方式制定了机、星数据的选用方案,进而针对二者尺度差异设计了3种空间匹配策略,以数值差(卫星—机载)、相关系数(R)和无偏均方根误差(ubRMSE)作为量化指标,对机载和卫星观测亮温进行差异分析,结果显示:机载亮温与卫星亮温之差随角度变化的特征趋势与理论相符,其可靠性得到了初步验证;3种空间匹配策略下的总平均亮温差显著不同,证实了空间尺度差异与匹配策略对多源数据的验证及对比的量化影响;机载亮温与SMAP亮温的总体差小于与SMOS,反映了探测方式、传感器硬件设计以及空间组织方式的异同。

关键词: 微波遥感亮度温度L波段机载微波辐射计SMOSSMAP    
Abstract:

Flights onboard L-band radiometer are carried out in the Shandian River Basin in the “Soil Moisture Experiment in the Luan River (SMELR)”, airborne brightness temperature (TB) observations are acquired. In this paper, studies on the comparison of airborne TB and that of SMOS and SMAP are performed. The data of airborne and satellites are selected according to their observation periods, incidence angles and polarization mode, and three spatial matching schemes are proposed according to the difference of their scales. By taking the direct numerical bias (satellites minus airborne), correlation coefficient (R) and unbiased square root error (ubRMSE) as indicators, the quantitative difference between airborne TB and that of satellites are explored, results show that: the variation of the TB difference between airborne and satellite with the incidence angle are consistent with the theory, and the reliability of the airborne TB observations are initially verified; the total average TB difference varies greatly under difference spatial matching schemes, and the quantitative influence of spatial matching methods in the validation and comparison of multi-source and multi-scale data are confirmed; the TB difference between airborne and SMAP is smaller than that of SMOS, which reflects the similarities and differences in sensor design, observation mechanism and spatial mapping methods.

Key words: Microwave remote sensing    Brightness temperature    L-band    Airborne microwave radiometer    SMOS    SMAP
收稿日期: 2020-10-14 出版日期: 2023-02-15
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: 徐少博     E-mail: yangna@hpu.edu.cn;xushaobo_HPU@outlook.com
作者简介: 杨娜(1980-),女,黑龙江大庆人,讲师,硕士生导师,主要从事微波遥感土壤水分反演与应用研究。E?mail:yangna@hpu.edu.cn
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引用本文:

杨娜,徐少博,劳从坤,张恒杰,汤燕杰. L波段微波辐射计机载与卫星观测的对比研究[J]. 遥感技术与应用, 2022, 37(6): 1361-1372.

Na Yang,Shaobo Xu,Congkun Lao,Hengjie Zhang,Yanjie Tang. Comparisons between Airborne and Space Borne L-band Microwave Radiometer Observations. Remote Sensing Technology and Application, 2022, 37(6): 1361-1372.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.6.1361        http://www.rsta.ac.cn/CN/Y2022/V37/I6/1361

图1  试验区DEM(STRM30)与机载飞行路线(规划)
SMAP IDSMOS ID距离 /kmSMAP IDSMOS ID距离 /km
3168-27140692369.043168-26440697535.64
3169-2712.043168-2655.18
3170-2716.873170-26840702638.08
3168-27040692374.743171-2681.47
3169-2704.743170-26640702645.49
3168-26840692383.273170-2676.71
3168-2697.463171-2666.52
3168-26740692396.203171-2677.57
3170-27040697494.023169-26540702655.72
3171-2705.243170-2651.99
3171-2719.203171-26414.46
3169-26940697506.223171-2659.68
3170-2692.883168-263407026610.40
3171-2699.893169-2636.12
3169-26740697514.163169-2644.57
3169-2686.533170-2639.20
3168-26640697523.833170-2648.25
3169-2663.993171-26315.80
表1  SMOS与SMAP格点中心的距离

机载飞行

次数

日期

2018年9月

机载时间

(LST)

卫星

升降轨

A/D

卫星时间

(UTC)

卫星时间

(LST)

机-星时差

/min

是否选用

是/否

117日12:53—16:33SMOSA16日 21:00:23—3617日 05:00:23—36473
D17日 11:11:06—1917日 19:11:06—19-
SMAPA17日 10:08:20—3317日 18:08:20—33-
D16日 22:05:04—1717日 07:05:04—17348
219日9:03—11:20SMOSA18日 21:22:34—4719日 05:22:34—47221
-----
SMAPA19日 09:44:00—1319日 17:44:00—13-
D18日 22:40:47—5919日 06:40:47—59143
324日10:14—14:09SMOSA23日 21:27:55—28:0824日 05:27:55—28:08287
-----
SMAPA24日 09:31:53—32:0724日 17:31:53—32:07-
D23日 22:52:55—53:0824日 06:52:55—53:08202
426日10:51—14:17SMOSA25日 21:50:04—1726日 05:50:04—17301
D26日 10:20:43—5226日 18:20:43—52-
SMAP-----
-----
表2  机载观测时间与卫星过境时间及数据选用情况
图2  机载飞行区间、SMOS和SMAP格点、地表温度测站的空间分布
亮温H/V(K)日期最小值平均值中值最大值

机载

(五角度)

17155.4—173.9/164.1—166.7217.0—230.2/233.7—247.1218.5—231.0/234.2—247.8245.1—261.0/266.7—283.0
19184.4—205.7/203.7—217.1230.8—245.5/252.5—256.6231.8—245.7/252.9—256.7259.3—291.8/277.5—289.1
24155.2—205.2/163.0—206.6241.6—250.6/255.4—264.9243.0—251.1/255.7—264.9263.4—270.9/282.4—297.8
SMOS17(32.5°)221.0 /242.2239.6 /261.4243.0 /260.7256.1 /284.6
19(五角度)220.7—227.5/245.2—263.7237.6—274.5/284.4—292.7227.4—244.4/277.1—287.1259.7—375.9/315.0—360.4
24(五角度)219.7—240.5/158.8—244.5242.2—252.6/246.3—266.6238.5—254.5/249.3—264.5261.7—283.1/276.5—291.0

SMAP

(40°)

17219.0 /247.1229.3 /255.8227.1 /255.6245.1 /266.8
19227.3 /253.0237.0 /261.0231.6 /258.1253.0 /272.5
24230.9 /254.4239.9 /262.2239.3 /261.9249.1 /270.5
表3  机载和卫星亮温的总体数值分布
图3  2018年9月17日、32.5°机载和卫星亮温
卫星日期/升降轨角度/极化方式
22.5°25°27.5°30°32.5°
SMOS17/A----HV
19/AHVHVHVHVHV
24/AHVHVHVHVHV
SMAP17/D40°HV
19/D
24/D
表4  卫星数据与机载数据的角度和极化方式的匹配
图4  实测地表温度(2018年9月17日—26日)
亮温差/KSMOSSMAP
9月19日9月24日9月19日9月24日
HVHVHVHV
匹配方式角度+-+-+-+-+-+-+-+-
面面未平均22.5°59.18-12.9138.06-3.2015.34-9.3812.06-25.958.43-12.409.83-3.475.71-12.357.16-2.80
25°21.93-10.3533.88-11.508.54-5.3712.54-14.239.16-11.3210.04-4.145.47-11.956.99-3.25
27.5°13.59-6.3140.11-4.5910.08-6.6315.19-5.3210.80-9.9810.69-2.876.48-12.268.38-3.32
30°14.53-10.1541.16-0.9712.07-6.8910.17-6.8310.82-7.3611.13-4.707.07-8.485.64-4.91
32.5°18.00-4.3642.45-3.619.76-7.329.58-6.6512.03-4.1911.17-6.208.21-6.705.81-6.31
总和113.64-44.08195.66-23.8755.79-35.5959.54-58.9851.24-45.2552.86-21.3832.94-51.7433.98-20.59
面面平均22.5°55.84-10.7738.94-5.9513.06-10.228.69-43.506.79-9.526.06-1.77--12.296.95-2.88
25°19.69-9.5736.16-2.119.55-1.3510.71-18.159.41-7.315.77-2.270.49-9.396.04-1.57
27.5°13.88-5.4441.52-0.769.80-8.8813.79-8.294.37-6.157.37-0.207.14-9.487.55-1.25
30°15.09-6.8043.57-11.27-7.929.29-10.976.02-4.406.94-2.094.32-6.162.94-4.28
32.5°20.72-0.7446.04-3.269.95-7.2210.28-7.158.05-1.737.14-1.797.72-4.028.21-4.35
总和125.22-33.32206.23-12.0853.63-35.5952.76-88.0634.64-29.1133.28-8.1219.67-41.3431.69-14.33
面面最邻近22.5°64.92-11.7340.66-4.7113.96-11.0712.15-41.559.91-10.8312.83-2.410.98-14.397.14-0.64
25°21.12-14.4733.23-2.6910.80-3.3711.28-15.477.62-9.8915.47-2.501.12-11.446.29-1.63
27.5°13.74-6.2039.62-2.0211.20-8.1014.07-9.468.23-9.266.81-4.096.47-11.787.83-
30°13.55-11.3541.41-13.10-8.5810.31-9.6213.46-7.735.68-6.208.11-6.744.50-5.30
32.5°22.98-1.4942.31-6.8611.65-6.0413.09-8.707.77-4.076.45-6.707.86-6.074.53-6.68
总和136.31-45.24197.23-16.2860.71-37.1660.9-84.846.99-41.7847.24-21.924.54-50.4230.29-14.25
表5  卫星—机载之间的亮温差异
图5  SMOS、SMAP卫星大面元与机载小面元-未平均方式下的亮温比较
图6  SMOS、SMAP卫星大面元与机载小面元—平均方式下的亮温比较
图7  SMOS、SMAP卫星大面元与机载小面元—最邻近方式下的亮温比较
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. DOI:10.1016/j.earscirev.2010.02.004 .
doi: 10.1016/j.earscirev.2010.02.004
2 Zhao Xingkai, Li Zengyao, Zhu Qingke. Response of soil moisture on climate characteristics based on SPI and SPEI in Loess region of Northern Shaanxi[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016. 47(8): 155-163.赵兴凯, 李增尧, 朱清科. 基于SPI和SPEI陕北黄土区土壤水分对气候特征的响应[J]. 农业机械学报, 2016, 47(8):155-163.
3 Wang L, Xie Z, Jia B,et al. Contributions of climate change and groundwater extraction to soil moisture trends[J]. Earth System Dynamics,2019,10(3):599-599. DOI:10.5194/esd- 10-599-2019 .
doi: 10.5194/esd- 10-599-2019
4 Herceg A, Nolz R, Kalicz P, et al. Predicting impacts of climate change on evapotranspiration and soil moisture for a site with sub-humid climate[J]. Journal of Hydrology and Hydromechanics,2019,67(4):384-392. DOI:10.2478/johh-2019-0017 .
doi: 10.2478/johh-2019-0017
5 Gruber A, Scanlon T, van der Schalie R, et al. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology[J]. Earth System Science Data,2019,11(2):717-739. DOI:10.5194/essd-11-717-2019 .
doi: 10.5194/essd-11-717-2019
6 Leeper R D, Bell J E, Palecki M A.A description and evaluation of US climate reference network standardized soil moisture dataset[J]. Journal of Applied Meteorology and Climatology,2019,58(7):1417-1428. DOI:10.1175/JAMC-D-18-0269.1 .
doi: 10.1175/JAMC-D-18-0269.1
7 Li Yuanshou, Jia Xiaohong, Qi Yanjun, et al. Sensitivity of soil evapotranspiration to climate change in the permafrost area[J]. Plateau Meteorology, 2019,38(6):1293-1299.
7 李元寿, 贾晓红, 齐艳军,等. 多年冻土区土壤蒸散发对气候变化的敏感性分析[J]. 高原气象, 2019,38(6):1293-1299.
8 Kerr Y H, Al-Yaari A, Rodriguez-Fernandez N, et al. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation[J]. Remote Sensing of Environment,2016,180:40-63. DOI:10.1016/j.rse.2016. 02.042 .
doi: 10.1016/j.rse.2016. 02.042
9 Mecklenburg S, Drusch M, Kaleschke L, et al. ESA’s soil moisture and ocean salinity mission: from science to operational applications[J]. Remote Sensing of Environment, 2016, 180: 3-18. DOI: 10.1016/j.rse.2015.12.025 .
doi: 10.1016/j.rse.2015.12.025
10 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
11 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): 4994-5007. DOI: 10.1109/TGRS.2016.2561938 .
doi: 10.1109/TGRS.2016.2561938
12 Al-Yaari A, Wigneron J-P, Ducharne A, et al. Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)[J]. Remote Sensing of Environment,2014,152:614-626. DOI:10.1016/j.rse.2014.07.013 .
doi: 10.1016/j.rse.2014.07.013
13 Leroux D J, Kerr Y H, Bitar A A, et al. Comparison between SMOS, VUA, ASCAT, and ECMWF soil moisture products over four watersheds in U.S.[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(3): 1562-1571. DOI: 10.1109/TGRS.2013.2252468 .
doi: 10.1109/TGRS.2013.2252468
14 Usowicz B, Marczewski W, Usowicz J B, et al. Comparison of surface soil moisture from SMOS satellite and ground measurements[J]. International Agrophysics,2014, 28(3). DOI: 10.2478/intag-2014-0026 .
doi: 10.2478/intag-2014-0026
15 Jakkila J, Vento T, Rousi T,et al. SMOS soil moisture data validation in the Aurajoki watershed, Finland[J]. Hydrology Research,2014,45(4-5):684-702. DOI:10.2166/nh.2013. 234 .
doi: 10.2166/nh.2013. 234
16 Gonzalez-Zamora A, Sanchez N, Martinez-Fernandez J, et al. Long-term SMOS soil moisture products: a comprehensive evaluation across scales and methods in the Duero Basin (Spain)[J]. Physics and Chemistry of the Earth,2015,83-84:123-136. DOI:10.1016/j.pce.2015.05.009 .
doi: 10.1016/j.pce.2015.05.009
17 Chakravorty A, Chahar B R, Sharma O P, et al. A regional scale performance evaluation of SMOS and ESA-CCI soil moisture products over India with simulated soil moisture from MERRA-Land[J]. Remote Sensing of Environment, 2016, C(186): 514-527. DOI: 10.1016/j.rse.2016.09.011 .
doi: 10.1016/j.rse.2016.09.011
18 Yang Na, Cui Huizhen, Xiang Feng. Validation study on SMOS L2 soil moisture product in agricultural area of China[J]. Journal of Henan Polytechnic University (Natural Science Edition), 2015,34(2):287-291.
18 杨娜,崔慧珍,向峰.SMOS L2土壤水分数据产品在我国农区的验证[J]. 河南理工大学学报(自然科学版),2015,34(2):287-291.
19 Peng J, 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. DOI: 10.3390/rs71115729 .
doi: 10.3390/rs71115729
20 Pierdicca N, Fascetti F, Pulvirenti L, et al. Analysis of ASCAT, SMOS, in-situ and land model soil moisture as a regionalized variable over Europe and North Africa[J]. Remote Sensing of Environment,2015,170:280-289. DOI: 10.1016/j.rse.2015.09.005 .
doi: 10.1016/j.rse.2015.09.005
21 Louvet S, Pellarin T, Al Bitar A, et al. SMOS soil moisture product evaluation over West-Africa from local to regional scale[J]. Remote Sensing of Environment, 2015, 156: 383-394. DOI: 10.1016/j.rse.2014.10.005 .
doi: 10.1016/j.rse.2014.10.005
22 Kedzior M, Zawadzki J. Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland[J]. Geoderma, 2016, 283: 21-31. DOI: 10.1016/j.geoderma.2016.07.023 .
doi: 10.1016/j.geoderma.2016.07.023
23 Al Bitar A, Mialon A, Kerr Y H, et al. The global SMOS Level 3 daily soil moisture and brightness temperature maps[J]. Earth System Science Data, 2017, 9(1): 293-315. DOI: 10.5194/essd-9-293-2017 .
doi: 10.5194/essd-9-293-2017
24 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.
24 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.
25 Rodriguez-Fernandez N J, Sabater J M, Richaume P, et al. SMOS near-real-time soil moisture product: processor overview and first validation results[J]. Hydrology and Earth System Sciences, 2017, 21(10): 5201-5216. DOI: 10.5194/hess-21-5201-2017 .
doi: 10.5194/hess-21-5201-2017
26 Chen F, Crow W T, Bindlish R, et al. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation[J]. Remote Sensing of Environment, 2018, 214: 1-13. DOI: 10.1016/j.rse.2018.05.008 .
doi: 10.1016/j.rse.2018.05.008
27 Zhang L, He C, Zhang M, et al. Evaluation of the SMOS and SMAP soil moisture products under different vegetation types against two sparse in situ networks over arid mountainous watersheds, Northwest China[J]. Science China Earth Sciences, 2019, 62(4): 703-718. DOI: 10.1007/s11430-018-9308-9 .
doi: 10.1007/s11430-018-9308-9
28 Ma H, Zeng J, Chen N, et al. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: a comprehensive assessment using global ground-based observations[J]. Remote Sensing of Environment, 2019, 231: 111215. DOI: 10.1016/j.rse.2019.111215 .
doi: 10.1016/j.rse.2019.111215
29 Alyaari A, Wigneron J P, Dorigo W, et al. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements[J]. Remote Sensing of Environment, 2019, 224: 289-303. DOI: 10.1016/j.rse.2019.02.008 .
doi: 10.1016/j.rse.2019.02.008
30 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
31 Wigneron J-P, Jackson T J, O’Neill P, et al. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms[J]. Remote Sensing of Environment,2017,192:238-262. DOI:10.1016/j.rse.2017. 01.024 .
doi: 10.1016/j.rse.2017. 01.024
32 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.
32 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.
33 Rudiger C, Walker J P, Kerr Y H. On the airborne spatial coverage requirement for microwave satellite validation[J]. IEEE Geoscience and Remote Sensing Letters,2011,8(4): 824-828. DOI: 10.1109/LGRS.2011.2116766 .
doi: 10.1109/LGRS.2011.2116766
34 Ye N, Walker J P, Bindlish R, et al. Evaluation of SMAP downscaled brightness temperature using SMAPEx-4/5 airborne observations[J]. Remote Sensing of Environment, 2019, 221: 363-372. DOI: 10.1016/j.rse.2018.11.033 .
doi: 10.1016/j.rse.2018.11.033
35 Albergel C, Zakharova E, Calvet J-C, et al. A first assessment of the SMOS data in Southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign[J]. Remote Sensing of Environment, 2011, 115(10): 2718-2728. DOI: 10.1016/j.rse.2011.06.012 .
doi: 10.1016/j.rse.2011.06.012
36 Bircher S, Balling J E, Skou N, et al. Validation of SMOS brightness temperatures during the HOBE airborne campaign, Western Denmark[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1468-1482. DOI: 10.1109/TGRS.2011.2170177 .
doi: 10.1109/TGRS.2011.2170177
37 Schlenz F, Dall’Amico J T, Loew A, et al. Uncertainty assessment of the SMOS validation in the upper danube catchment[J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(5):1517-1529. DOI:10.1109/TGRS.2011. 2171694 .
doi: 10.1109/TGRS.2011. 2171694
38 Magagi R, Berg A A, Goita K, et al. Canadian experiment for soil moisture in 2010 (CanEx-SM10): Overview and preliminary results[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(1):347-363. DOI:10.1109/TGRS. 2012.2198920 .
doi: 10.1109/TGRS. 2012.2198920
39 Li Dazhi, Jin Rui, Che Tao, et. al. Soil moisture retrieval from airborne PLMR and MODIS products in the Zhangye oasis of middle stream of Heihe River Basin, China[J]. Advances in Earth Science, 2014,29(2):295-305.
39 李大治, 晋锐, 车涛, 等. 联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究[J]. 地球科学进展,2014,29(2): 295-305.
40 Das N N, Entekhabi D, Njoku E G, et al. Tests of the SMAP combined radar and radiometer algorithm using airborne field campaign observations and simulated cata[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(4):2018-2028. DOI:10.1109/TGRS.2013. 2257605 .
doi: 10.1109/TGRS.2013. 2257605
41 Montzka C, Jagdhuber T, Horn R, et al. Investigation of SMAP fusion algorithms with airborne active and passive L-band microwave remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 3878-3889. DOI: 10.1109/TGRS.2016.2529659 .
doi: 10.1109/TGRS.2016.2529659
42 Colliander A, Jackson T, Mcnairn H, et al. Comparison of airborne Passive and Active L-band System (PALS) brightness temperature measurements to SMOS observations during the SMAP validation experiment 2012 (SMAPVEX12)[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 801-805. DOI: 10.1109/LGRS.2014.2362889 .
doi: 10.1109/LGRS.2014.2362889
43 Colliander A, Cosh M H, Misra S,et al.Comparison of high-re-solution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16)[J]. Remote Sensing of Environment, 2019, 227: 137-150. DOI: 10.1016/j.rse.2019.04.004 .
doi: 10.1016/j.rse.2019.04.004
44 Zhao T, Shi J, Lv L, et al. Soil moisture experiment in the Luan River supporting new satellite mission opportunities[J]. Remote Sensing of Environment, 2020, 240, 111680. DOI: 10.1016/j.rse.2020.111680 .
doi: 10.1016/j.rse.2020.111680
45 McMullan K D, Brown M A, Martin-Neira M, et al. SMOS: The payload[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(3): 594-605. DOI: 10.1109/TGRS.2007.914809 .
doi: 10.1109/TGRS.2007.914809
46 Kerr Y H, Waldteufel P, Wigneron J, et al. The SMOS mission: New tool for monitoring key elements of the global water cycle[J]. Proceedings of the IEEE, 2010, 98(5): 666-687. DOI: 10.1109/JPROC.2010.2043032 .
doi: 10.1109/JPROC.2010.2043032
47 Zhao T, Shi J, Bindlish R, et al. Refinement of SMOS multiangular brightness temperature toward soil moisture retrieval and its analysis over reference targets[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(2):589-603. DOI:10.1109/JSTARS. 2014.2336664 .
doi: 10.1109/JSTARS. 2014.2336664
48 Entekhabi D, Njoku E, O’Neill P, et al. The soil moisture Active/Passive Mission (SMAP)[J]. Proceedings of the IEEE,2010,98(5):704-716. DOI:10.1109/JPROC.2010.2043918 .
doi: 10.1109/JPROC.2010.2043918
49 Mohammed P N, Aksoy M, Piepmeier J R, et al. SMAP L-band microwave radiometer: RFI mitigation prelaunch analysis and first year on-orbit observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6035-6047. DOI: 10.1109/TGRS.2016.2580459 .
doi: 10.1109/TGRS.2016.2580459
50 Zhao T, Hu L, Shi J, et al. Soil moisture retrievals using L-band radiometry from variable angular ground-based and airborne observations[J]. Remote Sensing of Environment, 2020, 248: 111958. DOI: 10.1016/j.rse.2020.111958 .
doi: 10.1016/j.rse.2020.111958
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