News

a.z
Mahbod, M., Safari, S., Rafiee, M. (2022). Spatial downscaling of TRMM satellite precipitation data by NDVI, DEM and surface temperature using regression learner methods. , 14(3), 347-361. doi: 10.22092/ijwmse.2021.351305.1824
Mehdi Mahbod; Saeedeh Safari; Mohammaf Rafie Rafiee. "Spatial downscaling of TRMM satellite precipitation data by NDVI, DEM and surface temperature using regression learner methods". , 14, 3, 2022, 347-361. doi: 10.22092/ijwmse.2021.351305.1824
Mahbod, M., Safari, S., Rafiee, M. (2022). 'Spatial downscaling of TRMM satellite precipitation data by NDVI, DEM and surface temperature using regression learner methods', , 14(3), pp. 347-361. doi: 10.22092/ijwmse.2021.351305.1824
Mahbod, M., Safari, S., Rafiee, M. Spatial downscaling of TRMM satellite precipitation data by NDVI, DEM and surface temperature using regression learner methods. , 2022; 14(3): 347-361. doi: 10.22092/ijwmse.2021.351305.1824

Spatial downscaling of TRMM satellite precipitation data by NDVI, DEM and surface temperature using regression learner methods

مهندسی و مدیریت آبخیز
Article 5, Volume 14, Issue 3, July 2022, Pages 347-361    PDF (1.8 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2021.351305.1824
Authors
Mehdi Mahbod* 1; Saeedeh Safari2; Mohammaf Rafie Rafiee1
1Assistant Professor, Department of Water Sciences & Engineering, College of Agriculture, Jahrom University, Jahrom, Iran
2MSc, Department of Water Sciences & Engineering, College of Agriculture, Jahrom University, Jahrom, Iran
Abstract
Determining precipitation spatial pattern in a catchment is necessary for the calculation of hydrologic quantities such as runoff flow and soil moisture content. Sparse meteorological stations as well as spatial variability of precipitation are major obstacles for accurate spatial estimation of precipitation. The development of remote sensing technology and the possibility of using satellite precipitation products has facilitated attaining spatial precipitation patterns. However, low spatial resolution of satellite precipitation products highlights the need for downscaling methods. Nineteen predictive models were fitted using Regression Learner toolbox in MATLAB software. Annual TRMM precipitation data were downscaled from 2001 to 2017 using Normalized Difference Vegetation Index (NDVI), land surface temperature, land elevation and coordination. Models are divided into five general categories: Linear Regressions, Decision Trees, Support Vector Machines, Ensemble models and Gaussian Process Regression models. Comparing the downscaled TRMM data with gauges data, Boosted Ensemble model had the lowest root mean square error and highest correlation coefficient. On the other hand, two methods of Geographical Distance Adjustment (GDA) and Geographical Ratio Adjustment were compared for calibrating the downscaled precipitation. Smaller errors were obtained using GDA in all models.
Keywords
Fars Province; Geographical distance adjustment; Meteorological station; MODIS; Spatial precipitation pattern
References
  1. Alexakis, D.D. and I.K. Tsanis. 2016. Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data. Environmental Earth Sciences, 75(14): 1-13.
  2. Chen, C., S. Zhao, Z. Duan and Z. Qin. 2015. An improved spatial downscaling procedure for TRMM 3B43 precipitation product using geographically weighted regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(9): 4592-4604.
  3. Duan, Z. and W. Bastiaanssen. 2013. First results from version 7 TRMM 3B43 precipitation product in Combination with a new downscaling-calibration procedure. Remote Sensing of Environment, 131: 1-13.
  4. Funk, C., P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, J. Rowland, B. Remero, G. Husak, J. Michaelsen and A. Verdin. 2014. A quasi-global precipitation time series for drought monitoring. U.S. Geological Survey Data Series, 832(4): 1-12.
  5. Huffman, G.L., D.T. Bolvin, E.J. Nelkin, D.B. Wolff, R.F. Adler, G. Gu, Y. Hong, K.P. Bowman and E.F. Stocker. 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1): 38-55.
  6. Huffman, G.L., R.F. Adler, D.T. Bolvin and G. Gu. 2009. Improving the global precipitation record: GPCP version 2.1. Geophysical Research Letters, 36(17): 1-5.
  7. Huffman, G.L., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf and U. Schneider. 1997. The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bulletin of the American Meteorological Society, 78(1): 5-20.
  8. Immerzeel, W., M. Rutten and P. Droogers. 2009. Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula. Remote Sensing of Environment, 113(2): 362-370.
  9. Jia, S., W. Zhu, A. Lu and T. Yan. 2011. A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sensing of Environment, 115(12): 3069-3079.
  10. Jing, W., Y. Yang, X. Yue and X. Zhao. 2016. A comparison of different regression algorithms for downscaling monthly satellite-based precipitation over North China. Remote Sensing, 8(10): 835-859.
  11. Juang, K., D. Lee and C. Hsiao. 1998. Kriging with cumulative distribution function of order statistics for delineation of heavy-metal contaminated soils. Soil Science, 163: 797-804.
  12. Katsanos, D., A. Retalis and S. Michaelides. 2016. Validation of a high resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmospheric Research, 169: 459-464.
  13. Kazemzadeh, M. and J. Akbari. 2020. Spatial resolution analysis of TRMM satellite images to estimate meteorological drought index, case study: Iran. Watershed Engineering and Management, 11(4): 903-916 (in Persian).
  14. Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N. Takahashi, S. Seto, M. Hirose, Y.N. Takayabu, T. Ushio and K. Nakagawa. 2007. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: production and validation. IEEE Transactions on Geoscience and Remote Sensing, 45(7): 2259-2275.
  15. Kummerow, C., W. Barnes, T. Kozu, J. Shiue and J. Simpson. 1998. The Tropical Rainfall Measuring Mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15(3): 809-817.
  16. Li, S., Z. Zhao, X. Miaomiao and Y. Wang. 2010. Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environmental Modelling and Software, 25(12): 1789-1800.
  17. Madadi, Gh., S. Hamzeh and A. Noroozi. 2017. Assessment of TRMM satellite imagery in temporal and spatial drought monitoring, case study: West Frontier Basin. Watershed Engineering and Management, 8(4): 362-376 (in Persian).
  18. Mahbod, M., F. Veronesi and A. Shirvani. 2019. An evaluative study of TRMM precipitation estimates over multi-day scales in a semi-arid region, Iran. International Journal of Remote Sensing, 40(11): 4143-4174.
  19. Miri, M., M. Rahimi and A. Noroozi. 2020. Evaluation and comparison of GPM and TRMM daily precipitation with observed precipitation across Iran. Watershed Engineering and Management, 11(4): 972-983 (in Persian).
  20. Nichol, J. and S. Abbas. 2015. Integration of remote sensing datasets for local scale assessment and prediction of drought science of the total. Environment, 505: 503-507.
  21. Shi, Y., L. Song, Z. Xia, Y. Hin, R. Myneni, S. Choi, L. Wang, X. Ni, C. Lao and F. Yang. 2015. Mapping annual precipitation across mainland China in the period 2001-2010 from TRMM 3B43 product using spatial downscaling approach. Remote Sensing, 7: 5849-5878.
  22. Statistics and Machine Learning Toolbox-MATLAB, 2016. URL https://www.mathworks.com/ products/statistics.html (accessed 3.14.20).
  23. Teimory, M., Kh. Asadollah and M. Bakhtiarikia. 2019. Evaluation of remotely sensed precipitation data in runoff simulation for Kalam Basin, Hormozgan using SWAT. Watershed Engineering and Management, 11(3): 562-574 (in Persian).
  24. Zanjani, B., H. Seyed Kaboli and M. Rashidian. 2019. Downscaling TRMM satellite-based precipitation data using non-stationary relationships between precipitation and land surface characteristics. Journal of RS and GIS for Natural Resources, 10(2): 85-101 (in Persian).
  25. Zhang, T., B. Li, Y. Yuan, X. Gao, Q. Sun, L. Xu and Y. Jiang. 2018. Spatial downscaling of TRMM precipitation data considering the impacts of macro-geographical factors and local elevation in the Three-River Headwaters Region. Remote Sensing of Environment, 215: 109-119.
Statistics
Article View: 692
PDF Download: 786


counter
Journal management system. designed by sinaweb