Please wait a minute...
Journal of Integrative Agriculture  2012, Vol. 12 Issue (6): 1048-1058    DOI: 10.1016/S1671-2927(00)8629
AGRICULTURAL ECONOMICS AND MANAGEMENT Advanced Online Publication | Current Issue | Archive | Adv Search |
The Monitoring Analysis for the Drought in China by Using an Improved MPI Method
 MAO Ke-biao,  XIA Lang, TANG Hua-jun, HAN Li-juan
1.Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2.Center for Land Resource Research in Northwest China, Shaanxi Normal University, Xi’an 710062, P.R.China
3.A-World Consulting, Hong Kong Logistics Association, Hong Kong, P.R.China
4.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences/Beijing Normal University, Beijing 100101, P.R.China
5.National Meteorological Center, Beijing 100081, P.R.China
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
摘要  MPI (microwave polarization index) method can use different frequencies at vertical polarization to retrieve soil moisture from TMI (tropical microwave imager) data, which is mainly suitable for bare soil. This paper makes an improvement for MPI method which makes it suitable for surface covered by vegetation. The MPI by using single frequency at different polarizations is used to discriminate the bare soil and vegetation which overcomes the difficulty in previous algorithms by using optical remote sensing data, and then the revision is made according to the different land surface types. The validation by using ground measurement data indicates that revision for different land surface types can improve the retrieval accuracy. The average error is about 24.5% by using the ground truth data obtained from ground observation stations, and the retrieval error is about 13.7% after making a revision by using ground measurement data from local observation stations for different surface types. The improved MPI method and precipitation are used to analyze the drought in Southwest China, and the analysis indicates the soil moisture retrieved by improved MPI method can be used to monitor the drought.

Abstract  MPI (microwave polarization index) method can use different frequencies at vertical polarization to retrieve soil moisture from TMI (tropical microwave imager) data, which is mainly suitable for bare soil. This paper makes an improvement for MPI method which makes it suitable for surface covered by vegetation. The MPI by using single frequency at different polarizations is used to discriminate the bare soil and vegetation which overcomes the difficulty in previous algorithms by using optical remote sensing data, and then the revision is made according to the different land surface types. The validation by using ground measurement data indicates that revision for different land surface types can improve the retrieval accuracy. The average error is about 24.5% by using the ground truth data obtained from ground observation stations, and the retrieval error is about 13.7% after making a revision by using ground measurement data from local observation stations for different surface types. The improved MPI method and precipitation are used to analyze the drought in Southwest China, and the analysis indicates the soil moisture retrieved by improved MPI method can be used to monitor the drought.
Keywords:  drought      soil moisture      climate change      microwave remote sensing  
Received: 20 December 2010   Accepted:
Fund: 

This work was supported by the National Basic Research Program of China (2010CB951503), the National Natural Science Foundation of China (40930101), the Open Fund of the State Key Laboratory of Remote Sensing Science, jointly sponsored by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University, China, and the Open Fund of Key Laboratory of Agrometeorological Safeguard and Applied, China Meteorological Administration.

Corresponding Authors:  MA Ying, Tel/Fax: +852-21144988, E-mail: maying_helen@163.com; Correspondence TANG Hua-jun, Tel: +86-10-82109395, E-mail: hjtang@mail.caas.net.cn   

Cite this article: 

MAO Ke-biao, XIA Lang, TANG Hua-jun, HAN Li-juan. 2012. The Monitoring Analysis for the Drought in China by Using an Improved MPI Method. Journal of Integrative Agriculture, 12(6): 1048-1058.

[1]Bindlish R. 2002. Soil moisture retrieval using the C-band polarimetric scanning radiometer during the southern great plains 1999 experiments. IEEE Transactions on Geoscience and Remote Sensing, 40, 2151-2161.

[2]Calvet J C, Wigneron J P, Mougin E, Kerr Y H, Brito L S. 1994. Plant water content and temperature of the Amazon forest from satellite microwave radiometry. IEEE Transactions on Geoscience and Remote Sensing, 32, 397-408.

[3]Chen K S, Wu T D, Tsang L, Li Q, Shi J C, Fung A K. 2003. Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulation. IEEE Transactions on Fig. 7 The scheme map of soil moisture retrieval from AMSR-E. Geoscience and Remote Sensing, 41, 90-101.

[4]Chen K S, Wu T D, Fung A K. 2000. A note on the multiple scattering in an IEM model. IEEE Transactions on Geoscience and Remote Sensing, 38, 249-256.

[5]Choudhury B J, Tucker C J. 1987a. Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer’s data. International Journal of Remote Sensing, 8, 533-538.

[6]Choudhury B J, Tucker C J. 1987b. Monitoring vegetation using Nimbus-7 37 GHz data (some empirical relations). International Journal of Remote Sensing, 8, 1085-1090.

[7]Choudhury B J, Golus R E. 1988. Estimating soil wetness using satellite data. International Journal of Remote Sensing, 9, 1251-1257.

[8]Felde G W. 1998. The effect of soil moisture on the 37 GHz microwave polarization difference index (MPDI). International Journal of Remote Sensing, 19, 1055-1078.

[9]Fung A K. 1994. Microwave scattering and emission models and their applications. Artech House Inc., USA. van Griend A A, Owe M. 1994. Microwave vegetation optical depth and inverse modeling of soil emissivity using Nimbus/SMMR satellite observations. Meteorology and Atmospheric Physics, 54, 225-239.

[10]Jackson T J. 1993. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 7, 139-152.

[11]Jackson T J. 1997. Soil moisture estimation using special satellite microwave/imager satellite data over a grassland region. Water Resources Research, 33, 1485-1484.

[12]Jackson T J, Hsu A Y. 2001. Soil moisture and TRMM microwave imager relationships in the South Great Plians 1999 (SGP99) experiment. IEEE Transactions on Geoscience and Remote Sensing, 39, 1632-1642.

[13]Jackson T J, Schmugge T J. 1991. Vegetation effects on the microwave emission from soils. Remote Sensing of Environment, 36, 203-219.

[14]Jackson T J, Schmugge T, Wang J. 1982. Passive microwave remote sensing of soil moisture under vegetation canopies. Water Resources Research, 18, 1137-1142.

[15]Jackson T J, O’Neill P E. 1990. Attenuation of soil microwave emissision by corn and soybeans at 1.4 and 5 GHz. IEEE Transactions on Geoscience and Remote Sensing, 28, 978-980.

[16]de Jeu R A M, Owe M. 2003. Further validation of a new methodology for surface moisture and vegetation optical depth retrieval. International Journal of Remote Sensing, 24, 1-20.

[17]Kerr Y H, Njoku E G. 1993. On the use of passive microwaves at 37 GHz in remote sensing of vegetation. International Journal of Remote Sensing, 14, 1931-1943.

[18]Mao K B, Qin Z H, Li M C, Zhang L X, Xu B, Jiang L. 2006. An algorithm for surface soil moisture retrieval using the microwave polarization difference index. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). CO, Denver, USA. pp. 3027-3030.

[19]Mao K B, Shi J C, Li Z L, Qin Z H, Li M, Xu B. 2007a. A physicsbased statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data. Science in China (Series D), 7, 1115-1120.

[20]Mao K B, Tang H J, Guo Y, Qiu Y B, Li L Y. 2007b. A neural network technique for retrieving land surface temperature from AMSR-E passive microwave data. In: International Geoscience and Remote Sensing Symposium (IGARSS07). Barcelona, Spain.

[21]Mao K B, Tang H J, Zhang L X, Li M C, Guo Y, Zhao D Z. 2008. A Method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI Data. International Journal of Remote Sensing, 29, 2903-2923.

[22]Meesters A G C, de Jeu R A M, Owe M. 2005. Analytical derivation of derivation of the vegetation optical depth from the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing Letters, 2, 121-123.

[23]Njoku E G, Jackson T J, Lakshmi V, Chan T K. Nghiem S V. 2003. Soil moisture retrieval from AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 41, 215-229.

[24]Owe M, Chang A, Golus R E. 1988. Estimating surface soil moisture from satellite microwave measurements and a satellite-derived vegetation index. Remote Sensing of Environment, 24, 131-345.

[25]Owe M, Griend A V, Chang A T C. 1992. Surface moisture and satellite microwave observations in semiarid southern Africa. Water Resource Research, 28, 829-839.

[26]Owe M, Richard D E J, Walker J. 2001. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing, 39, 1643-1654.

[27]Paloscia S, Pampaloni P. 1984. Microwave remote sensing of plant water stress. Remote Sensing of Environment, 16, 249-255.

[28]Paloscia S, Pampaloni P. 1985. Experiment relationships between microwave emission and vegetation features. International Journal of Remote Sensing, 6, 315-323.

[29]Paloscia S, Pampaloni P. 1988. Microwave polarization index for monitoring vegetation growth. IEEE Transactions on Geoscience and Remote Sensing, 26, 617-621.

[30]Paloscia S, Pampaloni P. 1992. Microwave vegetation indexes for detecting biomass and water conditions of agriculture crops. Remote Sensing of Environment, 1, 15-26.

[31]Shi J C, Jiang L M, Zhang L X, Chen K S, Wigneron J P, Chanzy A. 2005. A parameterized multi frequency polarization surface emission model. IEEE Transactions on Geoscience and Remote Sensing, 43, 2831-2841.

[32]Ulaby F T, Razani M, Dobson M C. 1983. Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 21, 51-61.

[33]Wang J R. 1985. Effect of vegetation on soil moisture sensing observed from orbiting microwave radiometers. Remote Sensing of Environment, 17, 141-151.

[34]Wang J R, Schmugge T J. 1980. An empirical model for the complex dielectric permittivity of soil as a function of water content. IEEE Transactions on Geoscience and Remote Sensing, 39, 288-295.

[35]Wigneron J P, Parde M, Waldteufel P, Chanzy A, Kerr Y, Schmidl S, Skou N. 2004. Characterizing the dependence of vegeation model parameters on crop structure, incidence angle, and polarization at L-band. IEEE Transactions on Geoscience and Remote Sensing, 42, 416-425.

[36]Wu T D, Chen K S, Shi J, Fung A K. 2001. A transition model for the reflection coefficient in surface scattering. IEEE Transactions on Geoscience and Remote Sensing, 39, 2040-2050.
[1] Dili Lai, Md. Nurul Huda, Yawen Xiao, Tanzim Jahan, Wei Li, Yuqi He, Kaixuan Zhang, Jianping Cheng, Jingjun Ruan, Meiliang Zhou. Evolutionary and expression analysis of sugar transporters from Tartary buckwheat revealed the potential function of FtERD23 in drought stress[J]. >Journal of Integrative Agriculture, 2025, 24(9): 3334-3350.
[2] Qing Li, Zhuangzhuang Sun, Zihan Jing, Xiao Wang, Chuan Zhong, Wenliang Wan, Maguje Masa Malko, Linfeng Xu, Zhaofeng Li, Qin Zhou, Jian Cai, Yingxin Zhong, Mei Huang, Dong Jiang. Time-course transcriptomic information reveals the mechanisms of improved drought tolerance by drought priming in wheat[J]. >Journal of Integrative Agriculture, 2025, 24(8): 2902-2919.
[3] Liulong Li, Zhiqiang Mao, Pei Wang, Jian Cai, Qin Zhou, Yingxin Zhong, Dong Jiang, Xiao Wang. Drought priming enhances wheat grain starch and protein quality under drought stress during grain filling[J]. >Journal of Integrative Agriculture, 2025, 24(8): 2888-2901.
[4] Xuehao Zhang, Qiuling Zheng, Yongjiang Hao, Yingying Zhang, Weijie Gu, Zhihao Deng, Penghui Zhou, Yulin Fang, Keqin Chen, Kekun Zhang. Physiology and transcriptome profiling reveal the drought tolerance of five grape varieties under high temperatures[J]. >Journal of Integrative Agriculture, 2025, 24(8): 3055-3072.
[5] Yang Chen, Xuyu Feng, Xiao Zhao, Xinmei Hao, Ling Tong, Sufen Wang, Risheng Ding, Shaozhong Kang. Biochar application enhances soil quality by improving soil physical structure under particular water and salt conditions in arid region of Northwest China[J]. >Journal of Integrative Agriculture, 2025, 24(8): 3242-3263.
[6] Baohua Liu, Ganqiong Li, Yongen Zhang, Ling Zhang, Dianjun Lu, Peng Yan, Shanchao Yue, Gerrit Hoogenboom, Qingfeng Meng, Xinping Chen. Optimizing management strategies to enhance wheat productivity in the North China Plain under climate change[J]. >Journal of Integrative Agriculture, 2025, 24(8): 2989-3003.
[7] Xiaoli Zhang, Daolin Ye, Xueling Wen, Xinling Liu, Lijin Lin, Xiulan Lü, Jin Wang, Qunxian Deng, Hui Xia, Dong Liang. Genome-wide analysis of RAD23 gene family and a functional characterization of AcRAD23D1 in drought resistance in Actinidia[J]. >Journal of Integrative Agriculture, 2025, 24(5): 1831-1843.
[8] Shakoor Abdul, Zaib Gul, Ming Xu. Tracing the contribution of cattle farms to methane emissions through bibliometric analyses[J]. >Journal of Integrative Agriculture, 2025, 24(4): 1220-1233.
[9] Yuxin Wang, Huan Zhang, Shaopei Gao, Hong Zhai, Shaozhen He, Ning Zhao, Qingchang Liu. The ABA-inducible gene IbTSJT1 positively regulates drought tolerance in transgenic sweetpotato[J]. >Journal of Integrative Agriculture, 2025, 24(4): 1390-1402.
[10] Gang Fu, Guangyu Zhang, Huakun Zhou. Effects of long-term experimental warming on phyllosphere epiphytic bacterial and fungal communities of four alpine plants[J]. >Journal of Integrative Agriculture, 2025, 24(3): 799-814.
[11] Yu Li, Shikui Dong, Qingzhu Gao, Yong Zhang, Hasbagan Ganjurjav, Guozheng Hu, Xuexia Wang, Yulong Yan, Fengcai He, Fangyan Cheng. Large herbivores increase the proportion of palatable species rather than unpalatable species in the plant community[J]. >Journal of Integrative Agriculture, 2025, 24(3): 859-870.
[12] Ruowei Li, Jian Sun, Guodong Han, Zixuan Qi, Yunhui Li, Junhe Chen, Wen He, Mengqi Zhang, Chaowei Han, Jieji Duo. Ecological risks linked with ecosystem services in the upper reach of the Yellow River under global changes[J]. >Journal of Integrative Agriculture, 2025, 24(3): 966-983.
[13] Lulu Yu, Muhammad Ahsan Asghar, Antonios Petridis, Fei Xu. Unlocking Dendrobium officinale’s drought resistance: Insights from transcriptomic analysis and enhanced drought tolerance in tomato[J]. >Journal of Integrative Agriculture, 2025, 24(11): 4282-4293.
[14] Kun Xiao, Ying Sun, Wei Wu, Xuewen Zhou, Zhicheng Zhang, Qiuyao Lai, Chen Huang, Zhenhua Xiong, Qinchuan Xin. Quantifying the contribution of triple compound extreme events to global yield loss of major staple crops from 1982 to 2016[J]. >Journal of Integrative Agriculture, 2025, 24(10): 4078-4099.
[15] Jiayue He, Yanhua Chen, Yanrong Hao, Dili Lai, Tanzim Jahan, Yaliang Shi, Hao Lin, Yuqi He, Md. Nurul Huda, Jianping Cheng, Kaixuan Zhang, Jinbo Li, Jingjun Ruan, Meiliang Zhou. Combining GWAS and RNA-seq approaches identifies the FtADH1 gene for drought resistance in Tartary buckwheat[J]. >Journal of Integrative Agriculture, 2025, 24(10): 3739-3756.
No Suggested Reading articles found!