Abstract
Soil moisture is one of the critical factors in hydrological cycles and agricultural production. Prediction of Soil Moisture content is essential for the rational use and management of water resources. This paper uses measured soil moisture satellite images captured by the SMAP satellite to train the models. Current day time series forecasting approaches are only applicable to tabular data. In this paper, we suggest a pipeline/framework that uses Dimensionality Reduction and Time Series Forecasting techniques for forecasting satellite image time series. Using this pipeline/framework, we can forecast undiscovered soil moisture from past satellite observations. To examine our final pipeline performance, we will conduct experiments for different models to forecast future satellite images from historical satellite images. Finally we can use these predictions to provide strategies for a drought resistant irrigation system.
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References
Smap - soil moisture active passive – nasa. https://smap.jpl.nasa.gov/mission/description/
Smap (soil moisture active/passive) mission. https://directory.eoportal.org/web/eoportal/satellite-missions/s/smap
Meteorological oceanographic satellite data archival centre, space applications centre. Isro (2017). https://mosdac.gov.in/soil-moisture-0
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisc. Rev.: Comput. Stat. 2(4), 433–459 (2010)
Box, G.E., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. stat. Assoc. 65(332), 1509–1526 (1970)
Burba, D.: An overview of time series forecasting models (2019). https://towardsdatascience.com/an-overview-of-time-series-forecasting-models-a2fa7a358fcb
Cai, Y., Zheng, W., Zhang, X., Zhangzhong, L., Xue, X.: Research on soil moisture prediction model based on deep learning. PLoS One 14(4), e0214508 (2019)
Chang, Y.Y., Sun, F.Y., Wu, Y.H., Lin, S.D.: A memory-network based solution for multivariate time-series forecasting. arXiv preprint. arXiv:1809.02105 (2018)
Cho, K., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint. arXiv:1406.1078 (2014)
Du, S., Li, T., Yang, Y., Horng, S.J.: Multivariate time series forecasting via attention-based encoder-decoder framework. Neurocomputing 388(C), 269–279 (2020)
Gill, M.K., Asefa, T., Kemblowski, M.W., McKee, M.: Soil moisture prediction using support vector machines 1. JAWRA J. Am. Water Resour. Assoc. 42(4), 1033–1046 (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Horé, A., Ziou, D.: Image quality metrics: psnr versus ssim. In: Proceedings of the 2010 IEEE 20th International Conference on Pattern Recognition (2010)
Jagadeesh: Multivariate multi-step time series forecasting using stacked lstm sequence to sequence autoencoder in tensorflow 2.0 / keras (2020). https://rb.gy/fdlazo
Pai, P.F., Lin, K.P., Lin, C.S., Chang, P.T.: Time series forecasting by a seasonal support vector regression model. Expert Syst. Appl. 37(6), 4261–4265 (2010)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (2014)
Wang, Y., Yao, H., Zhao, S.: Auto-encoder based dimensionality reduction. Neurocomputing 184, 232–242 (2016)
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Arya, K.V., Jagadeesh, S. (2022). Time Series Forecasting of Soil Moisture Using Satellite Images. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_33
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DOI: https://doi.org/10.1007/978-3-031-07005-1_33
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