Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Soil Moisture Using Multiple Linear Regression Model and COMS Land Surface Temperature Data

다중선형 회귀모형과 천리안 지면온도를 활용한 토양수분 산정 연구

  • Lee, Yong Gwan (Konkuk university, Department of Civil, Environmental and Plant Engineering) ;
  • Jung, Chung Gil (Konkuk university, Department of Civil, Environmental and Plant Engineering) ;
  • Cho, Young Hyun (Hydrometeorological Cooperation Center, K-water) ;
  • Kim, Seong Joon (Konkuk university, Department of Civil, Environmental and Plant Engineering)
  • Received : 2016.04.07
  • Accepted : 2016.10.11
  • Published : 2017.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

Keywords

References

  1. Ahn, S. R., M. J. Park, G. A. Park, and S. J. Kim, 2009. Assessing Future Climate Change Impact on Hydrologic Components of Gyeongancheon Watershed. Journal of Korea Water Resources Association 142(1): 33-50.
  2. Aubert, D., C. Loumagne, and L. Oudin, 2003. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. Journal of Hydrology 280(1-4): 145-161. https://doi.org/10.1016/S0022-1694(03)00229-4
  3. Baek, J. J. and M. H. Choi, 2012. Availability of Land Surface Temperature from the COMS in the Korea Peninsula. Journal of Korea Water Resources Association 45(8): 755-765 (in Korean). https://doi.org/10.3741/JKWRA.2012.45.8.755
  4. Choi, S.-W., S.-J. Lee, J. Kim, B.-L. Lee, K.-R. Kim, and B.-C. Choi, 2014. a: Agrometeorological observation circumstances of KMA and their improvement plan. Proceedings of Autumn Meeting of KMS 315-316 (in Korean).
  5. Choi, S. W., S. J. Lee, J. Kim, B. L. Lee, K. R. Kim, and B. C. Choi, 2015. Agrometeorological Observation Environment and Periodic Report of Korea Meteorological Administration: Current Status and Suggestions. Korean Journal of agricultural and Forest Meteorology 17(2): 144-155 (in Korean). https://doi.org/10.5532/KJAFM.2015.17.2.144
  6. Crow, W. T., D. G. Miralles, and M. H. Cosh, 2010. A quasi-global evaluation system for satellite-based surface soil moisture retrievals. IEEE Transactions on Geoscience and Remote Sensing 48(6): 2516-2527. https://doi.org/10.1109/TGRS.2010.2040481
  7. Draper, C. S., J. P. Walker, P. J. Steinle, R. A. de Jeu, and T. R. Holmes, 2009. An evaluation of AMSR-E derived soil moisture over Australia. Remote Sensing of Environment 113(4): 703-710. https://doi.org/10.1016/j.rse.2008.11.011
  8. Farrar, T. J., S. E. Nicholson, and A. R. Lare, 1994. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. II. NDVI response to soil oisture. Remote Sensing of Environment 50(2): 121-133. https://doi.org/10.1016/0034-4257(94)90039-6
  9. Holmes, T. R. H., R. A. M. De Jeu, M. Owe, and A. J. Dolman, 2009. Land surface temperature from Ka band (37 GHz) passive microwave observations. Journal of Geophysical Research: Atmospheres 114(D4).
  10. Hong, Ki-OK, Myoung-Seok Suh, and J-H Kang, 2009. Development of a Land Surface Temperature-Retrieval Algorithm from MTSAT-1R Data. Asia-Pacific Journal of Atmospheric Sciences 45(4): 411-421.
  11. Hong, W. Y., M. J. Park, J. Y. Park, R. Ha, G. A. Park, and S. J. Kim, 2009. The Correlation Analysis Between SWAT Predicted Forest Soil Moisture and MODIS NDVI During Spring Season, Journal of The Korean Society of Agricultural Engineers 51(2): 7-14. https://doi.org/10.5389/KSAE.2009.51.2.007
  12. Hutchinson, J. M. S. and T. J. Vought, 2006. Continuous soil moisture mapping using MODIS NDVI and LST products. Papers of the Applied Geography Conferences 29: 140-149.
  13. IPCC, 2007. Climate Change 2007: The Physical Science Basis, IPCC Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  14. Jackson, T. J., R. Hurkmans, A. Hsu, and M. H. Cosh, 2004. Soil moisture algorithm validation using data from the Advanced Microwave Scanning Radiometer (AMSR-E) in Mongolia. Italian Journal of Remote Sensing 30(31): 39-52.
  15. Jackson, T. J., M. H. Cosh, R. Bindlish, P. J. Starks, D. D. Bosch, M. Seyfried, ... and J. Du, 2010. Validation of advanced microwave scanning radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing 48(12): 4256-4272. https://doi.org/10.1109/TGRS.2010.2051035
  16. Kim, G. S. and H. G. Park, 2010. Soil Moisture Estimation Using CART Algorithm and Ancillary Data. Journal of Korea Water Resources Association 49(3): 263-273. https://doi.org/10.3741/JKWRA.2016.49.3.263
  17. Kim, M. J., J. Kim, and J. M. Yoon, 2014. Estimation of Surface Reflectance by Utilizing Single Visible Reflectance from COMS Meteorological Imager. Korean Journal of Remote Sensing 30(5): 627-639 (in Korean). https://doi.org/10.7780/kjrs.2014.30.5.8
  18. Kwon, H.-J., J. Kim, J.-I. Yoo, J.-Y. Yoon, and J.-W. Hong, 2011. A Study on the suitability for relocating of agrometeorological observation equipment. Korea Meteorological Administration, 59pp (In Korean).
  19. Lee, Y. G., C. G. Jung, J. W. Lee, and S. J. Kim, 2016. Estimation of Daily Land Surface Temperature using COMS in Korea Peninsula. Journal of The Korean Society of Agricultural Engineers 58(4): 37-46. https://doi.org/10.5389/KSAE.2016.58.4.037
  20. Li, L., P. W. Gaiser, B. C. Gao, R. M. Bevilacqua, T. J. Jackson, E. G. Njoku, ... and R. Bindlish, 2010. WindSat global soil moisture retrieval and validation. IEEE Transactions on Geoscience and Remote Sensing 48(5): 2224-2241. https://doi.org/10.1109/TGRS.2009.2037749
  21. Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Binger, R. D. Harmel, and T. L. Veith, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the ASABE 50(3): 885-900. https://doi.org/10.13031/2013.23153
  22. Narasimhan, B., R. J. Srinivasan, J. G. Arnold, M. D. Luzio, 2005. Estimation of long-term soil moisture using a distributed parameter hydrologic model and verification using remotely sensed data. Am Soc Agric Eng. 48(3): 1101-1113. https://doi.org/10.13031/2013.18520
  23. National Institute of Meteorological Sciences, 2007. Development of Meterological Data Processing System of Communication. Ocean and Meteorological Satellite, Korean Meteorological Administration.
  24. Owe, M., R. de Jeu, and T. Holmes, 2008. Multisensor historical climatology of satellite‐derived global land surface moisture. Journal of Geophysical Research: Earth Surface 113(F1).
  25. Parinussa, R. M., T. R. H. Holmes, M. T. Yilmaz, and W. T. Crow, 2011. The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations. Hydrology and Earth System Sciences, 15.10 (2011): 3135-3151. https://doi.org/10.5194/hess-15-3135-2011
  26. Park, J. A. and G. S. Kim, 2011. Estimation of Spatial Distribution of Soil Moisture at Yongdam Dam Watershed Using Artficial Neural Networks. Journal of the Korean Geographical Society 46(3): 319-330.
  27. Park, J. Y., S. R. Ahn, S. J. Hwang, C. H. Jang, G. A. Park, and S. J. Kim, 2014. Evaluation of MODIS NDVI and LST for indicating soil moisture of forest areas based on SWAT modeling. Paddy Water Environ. 12(s1): s77-s88. https://doi.org/10.1007/s10333-014-0425-3
  28. Qiu, Y., B. Fu, J. Wang, and L. Chen, 2003. Spatiotemporal prediction of soil moisture content using multiple-linear regression in a small catchment of the Loess Plateau, China, CATENA 54(1-2): 173-195. https://doi.org/10.1016/S0341-8162(03)00064-X
  29. Wagner, W., V. Naeimi, K. Scipal, R. de Jeu, and J. Martinez-Fernandez, 2007. Soil moisture from operational meteorological satellites. Hydrogeology Journal 15(1): 121-131. https://doi.org/10.1007/s10040-006-0104-6
  30. Wang, H., X. Li, H. Long, X. Xu, and Y. Bao, 2010. Monitoring the effects of land use and cover type changes on soil moieture using remotesensing data: A case study in China's Yongding River basin. CATENA 82(3): 135-145. https://doi.org/10.1016/j.catena.2010.05.008
  31. Zribi, M., N. Baghdadi, N. Holah, and O. Fafin, 2005, New nethodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion. Remote Sensing of Environment 96(3-4): 485-496. https://doi.org/10.1016/j.rse.2005.04.005

Cited by

  1. A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging vol.9, pp.8, 2017, https://doi.org/10.3390/rs9080870