Research on L-MEB Brightness Temperature Simulation based on Key Auxiliary Parameter Data

doi:10.11873/j.issn.1004-0323.2022.6.1385

Remote Sensing Technology and Application

2022, Vol. 37

Issue (6): 1385-1391 DOI: 10.11873/j.issn.1004-0323.2022.6.1385

Research on L-MEB Brightness Temperature Simulation based on Key Auxiliary Parameter Data

Gaoyan Cao(

),Na Yang(

)

School of Surveying and Land Information Engineering，Henan Polytechnic University，Jiaozuo 454003，China

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Abstract

Soil moisture plays a very important role in the energy exchange and water cycle between land and atmosphere. At present，microwave remote sensing satellites represented by SMOS are the main way to obtain global soil moisture information， and the brightness temperature simulation is a crucial link in the SMOS satellite retrieval algorithm. Based on the L-MEB model， this paper investigates the influence of key auxiliary parameters on brightness temperature simulation and the feasibility of using rich and reliable measured data to simulate brightness temperature using ISMN measured data and SoilGrids soil texture data. The results show that soil moisture and soil temperature are transient in time and have a stochastic effect on the brightness temperature simulation， while sand and clay content are stable in time and belong to the slowly varying background parameters， which have a systematic effect on the brightness temperature simulation. The correlation coefficients between the simulated H- and V- polarized brightness temperatures and SMOS simulated brightness temperatures in this paper reached 0.59 and 0.65， respectively， which proved that it is feasible and effective to use ISMN measured data and SoilGrids data as auxiliary data for brightness temperature simulation.

Key words: Brightness temperature L-MEB Model Soil Moisture SMOS ISMN SoilGrids

Received: 21 October 2021 Published: 15 February 2023

ZTFLH:

S152.7

Corresponding Authors: Na Yang E-mail: 1129294840@qq.com;yangna800522@foxmail.com

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Cite this article:

Gaoyan Cao,Na Yang. Research on L-MEB Brightness Temperature Simulation based on Key Auxiliary Parameter Data. Remote Sensing Technology and Application, 2022, 37(6): 1385-1391.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.6.1385 OR http://www.rsta.ac.cn/EN/Y2022/V37/I6/1385

Fig.1 Distribution of ISMN Qualified Sites

Table 1 Parameter values

Fig.2 Relationship between simulated brightness temperature and soil moisture and soil temperature （5 cm）

Fig. 3 Relationship between simulated brightness temperature and soil temperature （5， 50 cm）

Fig. 4 The relationship between simulated brightness temperature and soil temperature， air temperature

Fig.5 Relationship between simulated brightness temperature and clay and sand content

Table 2 Comparison of brightness temperature

Fig.6 Relationship between simulated brightness temperature，SMOS L2 brightness temperature and soil moisture difference

1	Zhao Tianjie. The new development and future prospects of the L-band of passive microwave inversion of soil moisture［J］. Progress in Geography，2018，37（2）：198-213.
1	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.
2	Liu J， Chai L， Dong J， 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: . doi: 10.1016/j.rse.2020.112225
3	Lao Congkun， Yang Na， Xu Shaobo，et al. Research on the influence of retrieval strategy on SMOS soil moisture retrieval algorithm［J］. Remote Sensing Technology and Application， 2020，35（1）：65-73.
3	劳从坤，杨娜，徐少博，等. 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.
4	Dharssi I， Bovis K J， Macpherson B， et al. Operational assimilation of ASCAT surface soil wetness at the Met Office［J］. Hydrology and Earth System Sciences，2011，15（8）：2729-2746.
5	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.
6	Sun Jingxia， Zhang Dongyou， Hou Yuchu.Collaborative inversion of forest surface soil moisture based on multi-source remote sensing data［J］. Remote Sensing Technology and Application， 2021，36（3）： 564-570.
6	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.
7	Yang Tao， Gong Huili， Li Xiaojuan， et al.Research prog ress in remote sensing monitoring of soil moisture［J］Acta Ec-ologica Sinica， 2010，30（22）： 6264-6277.
7	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）：6264-6277.
8	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： . doi: 10.1016/j.rse.2014.04.006
9	Njoku E G， Li L. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz［J］. IEEE Transactions on Geoscience and Remote Sensing， 1999， 37（1）： 79-93.
10	Jackson T J. III. Measuring surface soil moisture using passive microwave remote sensing［J］. Hydrological Processes， 1993， 7（2）： 139-152.
11	O'Neill P， Bindlish R， Chan S， et al. Algorithm Theoretical Basis Document. Level 2 & 3 Soil Moisture （Passive） Data Products[C]//SMAP Science Documents, 2018.
12	Owe M， de Jeu R， Holmes T. Multisensor historical climatology of satellite-derived global land surface moisture［J］. Journal of Geophysical Research， 2008， 113（F1）. DOI： . doi: 10.1029/2007JF000769
13	Owe M， de Jeu R， Walker J. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index［J］.IEEE Transactions on Geoscience and Remote Sensing， 2001， 39（8）： 1643-1654.
14	Zhao T， Shi J， Entekhabi D， et al. Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm［J］. Remote Sensing of Environment， 2021， 257： 112321. DOI： . doi: 10.1016/j.rse.2021.112321
15	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）： 11229-11237.
16	Wigneron J P， Kerr Y， Waldteufel P， et al. L-band Microwave Emission of the Biosphere （L-MEB） Model： Description and calibration against experimental data sets over crop fields［J］.Remote Sensing of Environment，2007，107（4）：639-655.
17	Kerr Y H， Waldteufel P， Wigneron J， et al. The SMOS mission： New tool for monitoring key elements ofthe global aater cycle［J］. Proceedings of the IEEE， 2010， 98（5）： 666-687.
18	Kerr Y HWaldteufel P P， Wigneron J P， Ferrazzoli P， et al. The SMOS soil moisture retrieval algorithm［J］. IEEE Transactions on Geoscience and Remote Sensing， 2012， 50（5）： 1384-1403.
19	Mahmoodi A， Buchan I.Algorithm theoretical basis document （ATBD） for the SMOS level 2 soil moisture processor development continuation project［C］∥ Eur. Space Agency Array Syst. Comput. Inc.， Toronto， ON， Canada， Tech. Rep. SO-TN-ARR-L2PP-0037， 2011.
20	Panciera R， Walker J P， Kalma J D， et al. Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm［J］. Remote Sensing of Environment， 2009， 113（2）： 435-444.
21	Dorigo W A， Wagner W， Hohensinn R， et al. The international soil moisture network： A data hosting facility for global in situ soil moisture measurements［J］. Hydrology and Earth System Sciences， 2011， 15（5）： 1675-1698.
22	Dorigo W A， Xaver A， Vreugdenhil M， et al. Global automated quality control of in situ soil moisture data from the International Soil Moisture Network［J］. Vadose zone journal， 2013， 12（3）： 1-21.
23	Hengl T， Mendes De Jesus J， Heuvelink G B M， et al. SoilGrids250m： Global gridded soil information based on machine learning［J］. PLOS ONE， 2017， 12（2）： e169748.DOI: . doi: 10.1371/journal.pone.0169748
24	Talone M， Portabella M， Martinez J， et al. About the optimal grid for SMOS Level 1C and Level 2 products［J］. IEEE Geoscience and Remote Sensing Letters，2015，12（8）：1630-1634.

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