Soil Moisture Content Prediction in Loam Soil with RFR Model
DOI:
https://doi.org/10.17108/ActAgrOvar.2024.65.2.43Keywords:
Soil moisture content, RFR, Loam soil, NDVI, NDMI, Feature importanceAbstract
Soil moisture content (SMC) is an important factor in agricultural productivity; it has an impact on crop growth, water use efficiency, and soil health. However, accurately predicting SMC, especially at deeper soil layers, remains challenging due to high variability and limited spatiotemporal data resolution. This study developed and evaluated a Random Forest Regression (RFR) model to predict SMC in loam soil at five different depths (5, 20, 40, 60, and 80 cm) utilizing meteorological data (temperature, humidity, precipitation, wind speed, and solar radiation) and vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Data were collected during the maize vegetation season in 2023 in Mosonmagyaróvár, Hungary. The results showed that the mean SMC ranged from 12.61% to 16.19%. Correlation analysis demonstrated that precipitation and NDMI had the strongest positive correlation with SMC, especially at shallower depths r = 0.78 at 5 cm depth, Solar radiation had a moderate correlation with SMC, especially at the deeper depths. The RFR model performed well at all depths, achieving an R² of 0.86 at 5 cm depth; the model accuracy enhanced at deeper layers, achieving R² values of 0.91 and 0.94 at 60 and 80 cm depths, respectively. The most significant predictors according to the feature importance analysis were precipitation, humidity, and NDMI, with NDMI playing a crucial role in subsurface moisture retention at deeper depths. These findings highlight the potential for machine learning algorithms to optimize irrigation approaches and improve water management in precision agriculture.
References
Acharya, U., Daigh, A. L. M., & Oduor, P. G. (2022). Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images. Remote Sensing, 14(15), 3801. https://doi.org/10.3390/rs14153801
Ågren, A. M., Larson, J., Paul, S. S., Laudon, H., & Lidberg, W. (2021). Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape. Geoderma, 404, 115280. https://doi.org/10.1016/j.geoderma.2021.115280
Ahmed, A. A. M., Deo, R. C., Raj, N., Ghahramani, A., Feng, Q., Yin, Z., & Yang, L. (2021). Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sensing, 13(4), 554. https://doi.org/10.3390/rs13040554
Alahmad, T., Neményi, M., & Nyéki, A. (2023). Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review. Agronomy, 13(10), 2603. https://doi.org/10.3390/agronomy13102603
Bisong, E. (2019). Google Colaboratory. In Building Machine Learning and Deep Learning Models on Google Cloud Platform (pp. 59–64). Apress. https://doi.org/10.1007/978-1-4842-4470-8_7
Caldwell, T. G., Bongiovanni, T., Cosh, M. H., Halley, C., & Young, M. H. (2018). Field and Laboratory Evaluation of the CS655 Soil Water Content Sensor. Vadose Zone Journal, 17(1), 1–16. https://doi.org/10.2136/vzj2017.12.0214
Carranza, C., Nolet, C., Pezij, M., & van der Ploeg, M. (2021). Root zone soil moisture estimation with Random Forest. Journal of Hydrology, 593, 125840. https://doi.org/10.1016/j.jhydrol.2020.125840
de Oliveira, V. A., Rodrigues, A. F., Morais, M. A. V., Terra, M. de C. N. S., Guo, L., & de Mello, C. R. (2021). Spatiotemporal modelling of soil moisture in an A tlantic forest through machine learning algorithms. European Journal of Soil Science, 72(5), 1969–1987. https://doi.org/10.1111/ejss.13123
Du, M., Zhang, J., Elmahdi, A., Wang, Z., Yang, Q., Liu, H., Liu, C., Hu, Y., Gu, N., Bao, Z., Liu, Y., Jin, J., & Wang, G. (2021). Variation Characteristics and Influencing Factors of Soil Moisture Content in the Lime Concretion Black Soil Region in Northern Anhui. Water, 13(16), 2251. https://doi.org/10.3390/w13162251
Fu, R., Xie, L., Liu, T., Zheng, B., Zhang, Y., & Hu, S. (2023). A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland. International Journal of Environmental Research and Public Health, 20(2), 1374. https://doi.org/10.3390/ijerph20021374
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., & Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2
Hegazi, E. H., Yang, L., & Huang, J. (2021). A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images. Remote Sensing, 13(24), 4964. https://doi.org/10.3390/rs13244964
Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
Lee, C. suk, Sohn, E., Park, J. D., & Jang, J.-D. (2019). Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea. GIScience & Remote Sensing, 56(1), 43–67. https://doi.org/10.1080/15481603.2018.1489943
Lee, S.-J., Choi, C., Kim, J., Choi, M., Cho, J., & Lee, Y. (2023). Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images. Remote Sensing, 15(16), 4063. https://doi.org/10.3390/rs15164063
Li, Q., Zhu, Y., Shangguan, W., Wang, X., Li, L., & Yu, F. (2022). An attention-aware LSTM model for soil moisture and soil temperature prediction. Geoderma, 409, 115651. https://doi.org/10.1016/j.geoderma.2021.115651
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Liao, R., Yang, P., Wang, Z., Wu, W., & Ren, S. (2018). Development of a Soil Water Movement Model for the Superabsorbent Polymer Application. Soil Science Society of America Journal, 82(2), 436-446. https://doi.org/10.2136/sssaj2017.05.0164
McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proc. of the 9th Python in Science Conf. (SCIPY 2010), 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
Mohseni, F., & Mokhtarzade, M. (2020). A new soil moisture index driven from an adapted long-term temperature-vegetation scatter plot using MODIS data. Journal of Hydrology, 581, 124420. https://doi.org/10.1016/j.jhydrol.2019.124420
Neményi, M., Ambrus, B., Teschner, G., Alahmad, T., Nyéki, A., & Kovács, A. J. (2023). Challenges of ecocentric sustainable development in agriculture with special regard to the internet of things (IoT), an ICT perspective. Progress in Agricultural Engineering Sciences, 19(1), 113–122. https://doi.org/10.1556/446.2023.00099
Ning, J., Yao, Y., Tang, Q., Li, Y., Fisher, J. B., Zhang, X., Jia, K., Xu, J., Shang, K., Yang, J., Yu, R., Liu, L., Zhang, X., Xie, Z., & Fan, J. (2023). Soil moisture at 30 m from multiple satellite datasets fused by random forest. Journal of Hydrology, 625, 130010. https://doi.org/10.1016/j.jhydrol.2023.130010
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
Peng, J., Loew, A., Merlin, O., & Verhoest, N. E. C. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55(2), 341–366. https://doi.org/10.1002/2016RG000543
Python Software Foundation. (2024). Python Language Reference, version 3.10. https://www.python.org
Saha, A., Patil, M., Goyal, V. C., & Rathore, D. S. (2018). Assessment and Impact of Soil Moisture Index in Agricultural Drought Estimation Using Remote Sensing and GIS Techniques. Proceedings 7(1), 2. https://doi.org/10.3390/ECWS-3-05802
Senanayake, I. P., Yeo, I.-Y., Walker, J. P., & Willgoose, G. R. (2021). Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning. Science of The Total Environment, 776, 145924. https://doi.org/10.1016/j.scitotenv.2021.145924
Sentinel Hub. (2024). https://www.sentinel-hub.com
Shokati, H., Mashal, M., Noroozi, A., Abkar, A. A., Mirzaei, S., Mohammadi-Doqozloo, Z., Taghizadeh-Mehrjardi, R., Khosravani, P., Nabiollahi, K., & Scholten, T. (2024). Random Forest-Based Soil Moisture Estimation Using Sentinel-2, Landsat-8/9, and UAV-Based Hyperspectral Data. Remote Sensing, 16(11), 1962. https://doi.org/10.3390/rs16111962
Shukla, A., Panchal, H., Mishra, M., Patel, P. R., Srivastava, H. S., Patel, P., & Shukla, A. K. (2014). Soil moisture estimation using gravimetric technique and FDR probe technique: a comparative analysis. Am. Int. J. Res. Form. Appl. Nat. Sci, 8, 89-92.
Singh, T., Verma, A., Singh, M., Patel, N. D., Dheri, G. S., Singh, K., & Singh, A. (2024). A human machine interface (HMI) assisted portable device for measuring soil efflux using low-cost sensors: design, development and field evaluation. Clean Technologies and Environmental Policy. https://doi.org/10.1007/s10098-024-02909-9
Strashok, O., Ziemiańska, M., & Strashok, V. (2022). Evaluation and Correlation of Normalized Vegetation Index and Moisture Index in Kyiv (2017–2021). Journal of Ecological Engineering, 23(9), 212–218. https://doi.org/10.12911/22998993/151884
Szám, D., Hetesi, Zs., Takács, A. P., Keve, G., Balling, P., & Fekete, Á. (2024). Study of drought periods in the Tokaj-Hegyalja wine region. Progress in Agricultural Engineering Sciences. https://doi.org/10.1556/446.2024.00116
Teshome, F. T., Bayabil, H. K., Hoogenboom, G., Schaffer, B., Singh, A., & Ampatzidis, Y. (2023). Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping. Computers and Electronics in Agriculture, 212, 108064. https://doi.org/10.1016/j.compag.2023.108064
Verhoest, N. E. C., Lievens, H., Wagner, W., Álvarez-Mozos, J., Moran, M. S., & Mattia, F. (2008). On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar. Sensors, 8(7), 4213–4248. https://doi.org/10.3390/s8074213
Wang, X., Xie, H., Guan, H., & Zhou, X. (2007). Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology, 340(1-2), 12-24. https://doi.org/10.1016/j.jhydrol.2007.03.022
Willmott, C. J., Ackleson, S. G., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., O’Donnell, J., & Rowe, C. M. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research: Oceans, 90(C5), 8995–9005. https://doi.org/10.1029/JC090iC05p08995
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557.
Zhang, D., & Zhou, G. (2016). Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review. Sensors, 16(8), 1308. https://doi.org/10.3390/s16081308
Zheng, W., Zhangzhong, L., Zhang, X., Wang, C., Zhang, S., Sun, S., & Niu, H. (2019). A Review on the Soil Moisture Prediction Model and Its Application in the Information System. In Li, D., & Zhao, C. (Eds.), Computer and Computing Technologies in Agriculture XI (pp. 352–364). Springer, Cham. https://doi.org/10.1007/978-3-030-06137-1_32
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