Prediction of daily evapotranspiration images of rice using machine learning | ||
| تحقیقات آب و خاک ایران | ||
| Volume 53, Issue 12, February 2023, Pages 2793-2807 PDF (1.68 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijswr.2022.350978.669391 | ||
| Authors | ||
| Homa Noghankar1; Mahmoud Raeini2; Mohammad Ali Gholami Sefidkouhi* 3; Majid mobini4 | ||
| 1Department of water engineering, faculty of agricultural engineering, Sari agricultural sciences and natural resources university, sari, Iran | ||
| 2Professor, Department of Water Engineering, Sari Agricultural Sciences and Natural resources university, sari, Iran | ||
| 3Water Engineering, sari agricultural sciences natural resources university, sari, Iran | ||
| 4Department of Electrical and Computer Engineering, Mazandaran Institute of Technology, Babol, Iran | ||
| Abstract | ||
| Short-term prediction of daily plant evapotranspiration (ET) is of great importance in precision agriculture and irrigation management. In this paper, a method for short-term prediction of daily ET maps of rice is presented using satellite images and machine learning algorithms. After merging the bands of Landsat-8 and MODIS images using the STARFM method, daily ET images were produced using the METRIC algorithm and used to predict the ET maps of the following days as input to the relation vector machine (RVM) and long short-term memory (LSTM). Two scenarios were considered for prediction. In the first scenario, model is trained using image of nth day of the growth period as input, and the n+6th day's image as target. Using this configuration, the model can predict ET images at a six-day timestep. In the second scenario, the forecast was made for consecutive days up to six days. The correlation coefficient between the values obtained by RVM and the values calculated by METRIC for the first and second scenario were 0.89 and 0.84, respectively, which indicates the acceptable accuracy of these two scenarios in predicting ET. In the first scenario, R2 values for RVM and LSTM methods were 0.8 and 0.59, respectively, which shows that RVM is more accurate for evapotranspiration prediction compared to LSTM. The values of RMSE for RVM in the first and second scenarios were 0.56 and 0.82, respectively, and the values of MAE were 0.43 and 0.66, respectively, which indicates a lower error in the configuration of the first scenario. | ||
| Keywords | ||
| LSTM METRIC algorithm; Relevance Vector Machine; Satellite image fusion | ||
| References | ||
|
Ahmed AM, Deo RC, Feng Q, Ghahramani A, Raj N, Yin Z, Yang L. (2021). Hybrid deep learning for week-ahead evapotranspiration forecasting. Stoch Environ Res Risk Assess 36, 831–849.
Allen RG, Tasumi M, Morse A, Trezza R, Wright JL, Bastiaanssen W, Kramber W, Lorite I, Robison CW. (2007a). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (metric)—applications. Journal of irrigation and drainage engineering. 133(4):395–406.
Allen RG, Tasumi M, Trezza R. (2007b). Satellite-based energy balance for mapping evap-otranspiration with internalized calibration (metric)—model. Journal of irrigation and drainage engineering. 133(4):380–394.
Althoff D, Bazame HC, Filgueiras R, Dias SHB. (2018). Heuristic methods applied in reference evapotranspiration modeling. Ciˆencia e Agrotecnologia. 42:314–324.
Babaeian E, Paheding S, Siddique N, Devabhaktuni VK, Tuller M. (2021). Forecasting of evapotranspiration from ground and remote sensing observations with deep learning. In: AGU Fall Meeting 2021. AGU.
Bchir, A., M’nassri, S., Dhib, S., Amri, A. E., & Mulla, D. (2021). Estimating and mapping evapotranspiration in olive groves of semi-arid Tunisia using empirical formulas and satellite remote sensing. Arabian Journal of Geosciences, 14(24), 1-9.
Bhandari SK. (2021). Application of machine learning for estimating reference evapotranspiration and crop yield based on climatological data [Master dissertation]. Kansas State University, Manhattan, Kansas
Bhattarai N, Quackenbush LJ, Im J, Shaw SB. (2017). A new optimized algorithm for automating endmember pixel selection in the sebal and metric models. Remote Sensing of Environment. 196:178–192.
Carter C, Liang S. (2019). Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing. International Journal of Applied Earth Observation and Geoinformation. 78:86–92.
Chen, J. M., & Liu, J. (2020). Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sensing of Environment, 237, 111594.
Damavandi, H.G., Shah, R., Stampoulis, D., Wei, Y., Boscovic, D. and Sabo, J. (2019). Accurate prediction of streamflow using long short-term memory network: a case study in the Brazos River Basin in Texas. International Journal of Environmental Science and Development, 10(10), pp.294-300.
Dingre, S. K., & Gorantiwar, S. D. (2020). Determination of the water requirement and crop coefficient values of sugarcane by field water balance method in semiarid region. Agricultural Water Management, 232, 106042.
Dong, J., Zhu, Y., Jia, X., Han, X., Qiao, J., Bai, C., & Tang, X. (2022). Nation-scale reference evapotranspiration estimation by using deep learning and classical machine learning models in China. Journal of Hydrology, 604, 127207.
Dou X, Yang Y. (2018). Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems. Computers and Electronics in Agriculture. 148:95–106.
Elbeltagi, A., Kumari, N., Dharpure, J. K., Mokhtar, A., Alsafadi, K., Kumar, M., ... & Kuriqi, A. (2021). Prediction of combined terrestrial evapotranspiration index (CTEI) over large river basin based on machine learning approaches. Water, 13(4), 547.
Ferreira LB, da Cunha FF, de Oliveira RA, Fernandes Filho EI. (2019). Estimation of reference evapotranspiration in brazil with limited meteorological data using ann and svm–a new approach. Journal of Hydrology. 572:556–570.
Filgueiras R, Mantovani EC, Dias SHB, Fernandes Filho EI, da Cunha FF, Neale CMU. (2019). New approach to determining the surface temperature without thermal band of satellites. European Journal of Agronomy. 106:12–22.
Gao F, Masek J, Schwaller M, Hall F. (2006). On the blending of the landsat and modis surface reflectance: Predicting daily landsat surface reflectance. IEEE Transactions on Geoscience and Remote sensing. 44(8):2207–2218.
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. (2018). Machine learning in agriculture: A review. Sensors. 18(8):2674.
Luo, J., Dou, X., & Ma, M. (2022). Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China. International Journal of Environmental Research and Public Health, 19(20), 13127.
Newcome, M. E. (2021). The application of unmanned aerial vehicle to precision agriculture: chlorophyll, nitrogen, and evapotranspiration estimation. Advance Journal of Science, Engineering and Technology, 6(5), 9-13.
Numata I, Khand K, Kjaersgaard J, Cochrane MA, Silva SS. (2017). Evaluation of landsat-based metric modeling to provide high-spatial resolution evapotranspiration estimates for amazonian forests. Remote Sensing. 9(1):46.
Patil AP, Deka PC. (2016). An extreme learning machine approach for modeling evapotran-spiration using extrinsic inputs. Computers and Electronics in Agriculture. 121:385–392.
Reyes-Gonz´alez A, Kjaersgaard J, Trooien T, Hay C, Ahiablame L. (2017). Comparative analysis of metric model and atmometer methods for estimating actual evapotranspiration. International Journal of Agronomy.
Roy DK. (2021). Long short-term memory networks to predict one-step ahead reference evapotranspiration in a subtropical climatic zone. Environmental Processes. 8(2):911–941.
Roy DK, Sarkar TK, Kamar SSA, Goswami T, Muktadir MA, Al-Ghobari HM, Alataway A, Dewidar AZ, El-Shafei AA, Mattar MA. (2022). Daily prediction and multi-step forward forecasting of reference evapotranspiration using lstm and bi-lstm models. Agronomy. 12(3):594.
Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat‐Capdevila, A., & Verbist, K. (2018). Satellite remote sensing for water resources management: Potential for supporting sustainable development in data‐poor regions. Water Resources Research, 54(12), 9724-9758.
Talib A, Desai AR, Huang J, Griffis TJ, Reed DE, Chen J. (2021). Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the midwest us. Journal of Hydrology. 600:126579.
Tikhamarine Y, Malik A, Kumar A, Souag-Gamane D, Kisi O. (2019). Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological sciences journal. 64(15):1824–1842.
Tipping ME. (2001). Sparse bayesian learning and the relevance vector machine. Journal of machine learning research. 1(Jun):211–244.
Vapnik VN. (1999). An overview of statistical learning theory. IEEE transactions on neural networks. 10(5):988–999.
Yang R, Singh SK, Tavakkoli M, Amiri N, Karami MA, Rai R. (2022). Continuous video stream pixel sensor: A cnn-lstm based deep learning approach for mode shape prediction. Structural Control and Health Monitoring. 29(3): e2892.
Esmaeili S, Khoshkhoo Y, Babaei Kh, Asadi Oskouei E. (2018). Estimating rice actual evapotranspiration using METRIC algorithm in a part of the north of Iran. J. of Water and Soil Conservation. 24(6): 105-122. | ||
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