|
|
|
|
|
| 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 |
|
|
|
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.
|
|
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
E-mail: maying_helen@163.com
|
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. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
