Effect of Land Surface Temperature of MODIS Sensor in Estimating Daily Reference Evapotranspiration in Two Different Climates

Talebi, Hamed; Samadianfard, Saeed; Valizadeh Kamran‬, Khalil

doi:10.22034/ewe.2022.366189.1815

Document Type : Research Paper

Authors

¹ PhD Scholar, Department of Water Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

² Assoc. Professor, Department of Water Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

³ Professor, Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran

https://doi.org/10.22034/ewe.2022.366189.1815

Abstract

Estimating reference evapotranspiration (ET₀) is a fundamental requirement of agricultural water management. However, the lack of necessary meteorological data makes it difficult to estimate ET₀ using the FAO-Penman-Monteith equation wider areas. Therefore, this research examines the estimation of daily reference evapotranspiration using MODIS Land Surface Temperature (LST) from satellite imagery in two climates of Tabriz and Rasht. ET₀ has been estimated using two random forests (RF) and random forests optimized with genetic (GA-RF) algorithms. The parameters used in both stations include the combination of daily land surface temperature (LST_day), nightly land surface temperature (LST_night) and average land surface temperature at night, and day (LST_mean). The obtained results indicated that LST_mean has an excellent ability to estimate ET₀ in both stations. In Tabriz station with a semi-arid climate, GA-RF-7 model with RMSE=0.516 and in Rasht station with a very humid climate, the GA-RF-5 model with RMSE=0.868, have the best performance among the studied models. Moreover, the evaluations revealed that the temperature of the earth's surface at night is as important as the temperature of the earth's surface during the day, and by combining these two parameters, satisfactory results may be obtained.

Keywords

Main Subjects

water resource management

References

Achite, M., Samadianfard, S., Elshaboury, N. and Sharafi, M. (2022). Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization. Environ. Develop. Sustain., 1-19. DOI: 10.1007/s10668-022-02523-z

Alipour, A., Yarahmadi, J. and Mahdavi, M. (2014). Comparative study of M5 model tree and artificial neural network in estimating reference evapotranspiration using MODIS products. J. Climat., 2014. DOI: 10.1155/2014/839205

Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrig. Drain., paper 56. Fao, Rome, 300(9), D05109.

Breiman, L. (2001). Random forests. Machine Learn., 45(1), 5-32. DOI: 10.1023/A:1010933404324

Douna, V., Barraza, V., Grings, F., Huete, A., Restrepo-Coupe, N. and Beringer, J. (2021). Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach. J. Arid Environ., 191, 104513. DOI: 10.1016/j.jaridenv.2021.104513

Falamarzi, Y., Palizdan, N., Huang, Y. F. and Lee, T. S. (2014). Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agri. Water Manage., 140, 26-36. DOI: 10.1016/j.agwat.2014.03.014

Feng, Y., Cui, N., Zhao, L., Hu, X. and Gong, D. (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. J. Hydrol., 536, 376-383. DOI: 10.1016/j.jhydrol.2016.02.053

Hashemi, S., Samadianfard, S. and Sadraddini, A. A. (2022). Evaluation of random forest-genetic algorithm hybrid model in estimating daily solar radiation. Environ. Water Eng., 8(3), 636-653. DOI: 10.22034/JEWE.2022.312038.1654

Hobbins, M. T. (2016). The variability of ASCE standardized reference evapotranspiration: A rigorous, CONUS-wide decomposition and attribution. Transactions of the ASABE, 59(2), 561-576. DOI: 10.13031/trans.59.10975

Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.

Khotbehsara, E. M., Daemei, A. B., & Malekjahan, F. A. (2019). Simulation study of the eco green roof in order to reduce heat transfer in four different climatic zones. Result. Eng., 2, 100010. DOI: 10.1016/j.rineng.2019.100010

Khoshkhoo, Y. and Nikmehr, S. (2021). Application of land surface temperature extracted from satellite images for zoning reference evapotranspiration. Environ. Water Eng., 7(4), 708-722. DOI: 10.22034/jewe.2021.293156.1591

Maeda, E. E., Wiberg, D. A. and Pellikka, P. K. (2011). Estimating reference evapotranspiration using remote sensing and empirical models in a region with limited ground data availability in Kenya. Appl. Geogra., 31(1), 251-258. DOI: 10.1016/j.apgeog.2010.05.011

Moore, R. and Hansen, M. (2011). Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis. AGU Fall Meeting Abstracts. https://ui.adsabs.harvard.edu/abs/2011AGUFMIN43C..02M/abstract

Pagano, T. S. and Durham, R. M. (1993). Moderate resolution imaging spectroradiometer (MODIS). Sensor Systems for the Early Earth Observing System Platforms. DOI: 10.1117/12.152835

Tyralis, H., Papacharalampous, G. and Langousis, A. (2019). A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water, 11(5), 910. DOI: 10.3390/w11050910

Valipour, M. (2015). Temperature analysis of reference evapotranspiration models. Meteorol. Appl., 22(3), 385-394. DOI: 10.1002/met.1465

Wang, S., Lian, J., Peng, Y., Hu, B. and Chen, H. (2019). Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China. Agri. Water Manage., 221, 220-230. DOI: 10.1016/j.agwat.2019.03.027

Wang, W., Liang, S. and Meyers, T. (2008). Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sens. Environ., 112(3), 623-635. DOI: 10.1016/j.rse.2007.05.024

Ye, X., Dong, L.-A. and Ma, D. (2018). Loan evaluation in P2P lending based on random forest optimized by genetic algorithm with profit score. Electron. Commerce Res. Appl., 32, 23-36. DOI: 10.1016/j.elerap.2018.10.004

Yu, W., Nan, Z., Wang, Z., Chen, H., Wu, T. and Zhao, L. (2015). An effective interpolation method for MODIS land surface temperature on the Qinghai–Tibet Plateau. IEEE J. Select. Topic. Appl. Earth Observ. Remote Sens., 8(9), 4539-4550. DOI: 10.1109/JSTARS.2015.2464094.

Zhang, Z., Gong, Y. and Wang, Z. (2018). Accessible remote sensing data based reference evapotranspiration estimation modelling. Agri.Water Manage., 210, 59-69. DOI: 10.1016/j.agwat.2018.07.039.

Environment and Water Engineering

Effect of Land Surface Temperature of MODIS Sensor in Estimating Daily Reference Evapotranspiration in Two Different Climates

References

References

Volume 9, Issue 3
September 2023
Pages 367-383

Effect of Land Surface Temperature of MODIS Sensor in Estimating Daily Reference Evapotranspiration in Two Different Climates

References

References

Volume 9, Issue 3September 2023Pages 367-383

Volume 9, Issue 3
September 2023
Pages 367-383