Enhancing Surface Soil Moisture Estimation through Integration of Artificial Neural Networks Machine Learning and Fusion of Meteorological, Sentinel-1A and Sentinel-2A Satellite Data
For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R2) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R2 of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area.
References
[1]
Colliander, A., et al. (2017) Spatial Downscaling of SMAP Soil Moisture Using MODIS Land Surface Temperature and NDVI during SMAPVEX15. IEEE Geoscience and Remote Sensing Letters, 14, 2107-2111. https://doi.org/10.1109/LGRS.2017.2753203
[2]
Al-Bakri, J., Suleiman, A., Abdulla, F. and Ayad, J. (2011) Potential Impact of Climate Change on Rainfed Agriculture of a Semi-Arid Basin in Jordan. Physics and Chemistry of the Earth, Parts A/B/C, 36, 125-134. https://doi.org/10.1016/j.pce.2010.06.001
[3]
Wagner, W., et al. (2013) The ASCAT Soil Moisture Product: A Review of Its Specifications, Validation Results, and Emerging Applications. Meteorologische Zeitschrift, 22, 5-33. https://doi.org/10.1127/0941-2948/2013/0399
[4]
Huang, C., Chen, W., Li, Y., Shen, H. and Li, X. (2016) Assimilating Multi-Source Data into Land Surface Model to Simultaneously Improve Estimations of Soil Moisture, Soil Temperature, and Surface Turbulent Fluxes in Irrigated Fields. Agricultural and Forest Meteorology, 230-231, 142-156. https://doi.org/10.1016/j.agrformet.2016.03.013
[5]
Liu, Y., Yang, Y., Jing, W. and Yue, X. (2018) Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China. Remote Sensing, 10, Article No. 1. https://doi.org/10.3390/rs10010031
[6]
Kang, C.S., et al. (2021) Global Soil Moisture Retrievals from the Chinese FY-3D Microwave Radiation Imager. IEEE Transactions on Geoscience and Remote Sensing, 59, 4018-4032. https://doi.org/10.1109/TGRS.2020.3019408
[7]
Chaudhary, S.K., et al. (2022) Machine Learning Algorithms for Soil Moisture Estimation Using Sentinel-1: Model Development and Implementation. Advances in Space Research, 69, 1799-1812. https://doi.org/10.1016/j.asr.2021.08.022
[8]
Kumar, S.V., Dirmeyer, P.A., Peters-Lidard, C.D., Bindlish, R. and Bolten, J. (2018) Information Theoretic Evaluation of Satellite Soil Moisture Retrievals. Remote Sensing of Environment, 204, 392-400. https://doi.org/10.1016/j.rse.2017.10.016
[9]
Panciera, R., et al. (2008) The NAFE’05/CoSMOS Data Set: Toward SMOS Soil Moisture Retrieval, Downscaling, and Assimilation. IEEE Transactions on Geoscience and Remote Sensing, 46, 736-745. https://doi.org/10.1109/TGRS.2007.915403
[10]
Gaur, N. and Mohanty, B.P. (2013) Evolution of Physical Controls for Soil Moisture in Humid and Subhumid Watersheds. Water Resources Research, 49, 1244-1258. https://doi.org/10.1002/wrcr.20069
[11]
Attema, E.P.W. and Ulaby, F.T. (1978) Vegetation Modeled as a Water Cloud. Radio Science, 13, 357-364. https://doi.org/10.1029/RS013i002p00357
[12]
Dubois, P.C., van Zyl, J. and Engman, T. (1995) Measuring Soil Moisture with Imaging Radars. IEEE Transactions on Geoscience and Remote Sensing, 33, 915-926. https://doi.org/10.1109/36.406677
[13]
Fung, A.K., Li, Z. and Chen, K.S. (1992) Backscattering from a Randomly Rough Dielectric Surface. IEEE Transactions on Geoscience and Remote Sensing, 30, 356-369. https://doi.org/10.1109/36.134085
[14]
Oh, Y., Sarabandi, K. and Ulaby, F.T. (2002) Semi-Empirical Model of the Ensemble-Averaged Differential Mueller Matrix for Microwave Backscattering from Bare Soil Surfaces. IEEE Transactions on Geoscience and Remote Sensing, 40, 1348-1355. https://doi.org/10.1109/TGRS.2002.800232
[15]
Topp, G.C., Davis, J.L. and Annan, A.P. (1980) Electromagnetic Determination of Soil Water Content: Measurements in Coaxial Transmission Lines. Water Resources Research, 16, 574-582. https://doi.org/10.1029/WR016i003p00574
[16]
Baghdadi, N. and Zribi, M. (2006) Evaluation of Radar Backscatter Models IEM, OH and Dubois Using Experimental Observations. International Journal of Remote Sensing, 27, 3831-3852. https://doi.org/10.1080/01431160600658123
[17]
Bindlish, R. and Barros, A.P. (2000) Multifrequency Soil Moisture Inversion from SAR Measurements with the Use of IEM. Remote Sensing of Environment, 71, 67-88. https://doi.org/10.1016/S0034-4257(99)00065-6
[18]
Choker, M., et al. (2017) Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements. Water, 9, Article No. 1. https://doi.org/10.3390/w9010038
[19]
Dave, R., Kumar, G., Pandey, D.Kr., Khan, A. and Bhattacharya, B. (2021) Evaluation of Modified Dubois Model for Estimating Surface Soil Moisture Using Dual Polarization RISAT-1 C-Band SAR Data. Geocarto International, 36, 1459-1469. https://doi.org/10.1080/10106049.2019.1655801
[20]
Kweon, S.-K. and Oh, Y. (2014) Estimation of Soil Moisture and Surface Roughness from Single-Polarized Radar Data for Bare Soil Surface and Comparison with Dual- and Quad-Polarization Cases. IEEE Transactions on Geoscience and Remote Sensing, 52, 4056-4064. https://doi.org/10.1109/TGRS.2013.2279183
[21]
Mirsoleimani, H.R., Sahebi, M.R., Baghdadi, N. and El Hajj, M. (2019) Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. Sensors, 19, Article No. 14. https://doi.org/10.3390/s19143209
[22]
Walker, J.P., Troch, P.A., Mancini, M., Willgoose, G.R. and Kalma, J.D. (1997) Profile Soil Moisture Estimation Using the Modified IEM. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing—A Scientific Vision for Sustainable Development, Vol. 3, 1263-1265.
[23]
Weiß, T., Ramsauer, T., Löw, A. and Marzahn, P. (2020) Evaluation of Different Radiative Transfer Models for Microwave Backscatter Estimation of Wheat Fields. Remote Sensing, 12, Article No. 18. https://doi.org/10.3390/rs12183037
[24]
Zribi, M. and Dechambre, M. (2003) A New Empirical Model to Retrieve Soil Moisture and Roughness from C-Band Radar Data. Remote Sensing of Environment, 84, 42-52. https://doi.org/10.1016/S0034-4257(02)00069-X
[25]
Singh, A., Gaurav, K., Rai, A.K. and Beg, Z. (2021) Machine Learning to Estimate Surface Roughness from Satellite Images. Remote Sensing, 13, Article No. 19. https://doi.org/10.3390/rs13193794
[26]
Cai, Y., Zheng, W., Zhang, X., Zhangzhong, L. and Xue, X. (2019) Research on Soil Moisture Prediction Model Based on Deep Learning. PLOS ONE, 14, e0214508. https://doi.org/10.1371/journal.pone.0214508
[27]
Suk Lee, C., Sohn, E., Park, J.D. and 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, 43-67. https://doi.org/10.1080/15481603.2018.1489943
[28]
Yuan, Q., Xu, H., Li, T., Shen, H. and Zhang, L. (2020) Estimating Surface Soil Moisture from Satellite Observations Using a Generalized Regression Neural Network Trained on Sparse Ground-Based Measurements in the Continental U.S. Journal of Hydrology, 580, Article ID: 124351. https://doi.org/10.1016/j.jhydrol.2019.124351
[29]
Del Frate, F., Ferrazzoli, P. and Schiavon, G. (2003) Retrieving Soil Moisture and Agricultural Variables by Microwave Radiometry Using Neural Networks. Remote Sensing of Environment, 84, 174-183. https://doi.org/10.1016/S0034-4257(02)00105-0
[30]
Elshorbagy, A. and Parasuraman, K. (2008) On the Relevance of Using Artificial Neural Networks for Estimating Soil Moisture Content. Journal of Hydrology, 362, 1-18. https://doi.org/10.1016/j.jhydrol.2008.08.012
[31]
Santi, E., et al. (2020) Soil Moisture and Forest Biomass Retrieval on a Global Scale by Using CyGNSS Data and Artificial Neural Networks. 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, 26 September-2 October 2020, 5905-5908. https://doi.org/10.1109/IGARSS39084.2020.9323896
[32]
Ahmad, S., Kalra, A. and Stephen, H. (2010) Estimating Soil Moisture Using Remote Sensing Data: A Machine Learning Approach. Advances in Water Resources, 33, 69-80. https://doi.org/10.1016/j.advwatres.2009.10.008
[33]
Santi, E., Paloscia, S., Pettinato, S. and Fontanelli, G. (2016) Application of Artificial Neural Networks for the Soil Moisture Retrieval from Active and Passive Microwave Spaceborne Sensors. International Journal of Applied Earth Observation and Geoinformation, 48, 61-73. https://doi.org/10.1016/j.jag.2015.08.002
Ulaby, F.T., Moore, R.K. and Fung, A.K. (1981) Microwave Remote Sensing: Active and Passive. Volume 1. Microwave Remote Sensing Fundamentals and Radiometry. https://ntrs.nasa.gov/citations/19820039342
[36]
Fathololoumi, S., Vaezi, A.R., Alavipanah, S.K., Ghorbani, A. and Biswas, A. (2020) Comparison of Spectral and Spatial-Based Approaches for Mapping the Local Variation of Soil Moisture in a Semi-Arid Mountainous Area. Science of the Total Environment, 724, Article ID: 138319. https://doi.org/10.1016/j.scitotenv.2020.138319
[37]
Greifeneder, F., et al. (2018) The Added Value of the VH/VV Polarization-Ratio for Global Soil Moisture Estimations from Scatterometer Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 3668-3679. https://doi.org/10.1109/JSTARS.2018.2865185
[38]
Ullmann, T., Jagdhuber, T., Hoffmeister, D., May, S.M., Baumhauer, R. and Bubenzer, O. (2023) Exploring Sentinel-1 Backscatter Time Series over the Atacama Desert (Chile) for Seasonal Dynamics of Surface Soil Moisture. Remote Sensing of Environment, 285, Article ID: 113413. https://doi.org/10.1016/j.rse.2022.113413
[39]
Baghdadi, N., Zribi, M., Loumagne, C., Ansart, P. and Anguela, T.P. (2008) Analysis of TerraSAR-X Data and Their Sensitivity to Soil Surface Parameters over Bare Agricultural Fields. Remote Sensing of Environment, 112, 4370-4379. https://doi.org/10.1016/j.rse.2008.08.004
[40]
Liu, P.-W., Judge, J., DeRoo, R.D., England, A.W., Bongiovanni, T. and Luke, A. (2016) Dominant Backscattering Mechanisms at L-Band during Dynamic Soil Moisture Conditions for Sandy Soils. Remote Sensing of Environment, 178, 104-112. https://doi.org/10.1016/j.rse.2016.02.062
[41]
Wagner, W., et al. (2022) Widespread Occurrence of Anomalous C-Band Backscatter Signals in Arid Environments Caused by Subsurface Scattering. Remote Sensing of Environment, 276, Article ID: 113025. https://doi.org/10.1016/j.rse.2022.113025
[42]
Holtgrave, A.-K., Förster, M., Greifeneder, F., Notarnicola, C. and Kleinschmit, B. (2018) Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression. PFG, 86, 85-101. https://doi.org/10.1007/s41064-018-0045-4
[43]
Sadeghi, M., Babaeian, E., Tuller, M. and Jones, S.B. (2017) The Optical Trapezoid Model: A Novel Approach to Remote Sensing of Soil Moisture Applied to Sentinel-2 and Landsat-8 Observations. Remote Sensing of Environment, 198, 52-68. https://doi.org/10.1016/j.rse.2017.05.041
[44]
Saran, S., Sterk, G., Nair, R. and Chatterjee, R.S. (2014) Estimation of Near Surface Soil Moisture in a Sloping Terrain of a Himalayan Watershed Using ENVISAT ASAR Multi-Incidence Angle Alternate Polarisation Data. Hydrological Processes, 28, 895-904. https://doi.org/10.1002/hyp.9632
[45]
Ghasemloo, N., Matkan, A.A., Alimohammadi, A., Aghighi, H. and Mirbagheri, B. (2022) Estimating the Agricultural Farm Soil Moisture Using Spectral Indices of Landsat 8, and Sentinel-1, and Artificial Neural Networks. Journal of Geovisualization and Spatial Analysis, 6, Article No. 19. https://doi.org/10.1007/s41651-022-00110-4
[46]
Lin, R., et al. (2022) Improved Surface Soil Moisture Estimation Model in Semi-Arid Regions Using the Vegetation Red-Edge Band Sensitive to Plant Growth. Atmosphere, 13, Article No. 6. https://doi.org/10.3390/atmos13060930
[47]
Shakya, A.K., Ramola, A. and Vidyarthi, A. (2023) Integrated Modelling of Soil Moisture by Evaluating Backscattering Models Dubois, Oh and IoT Sensor Development for Field Moisture Estimation. Modeling Earth Systems and Environment, 9, 3381-3402. https://doi.org/10.1007/s40808-023-01693-7
[48]
Stevanato, L., et al. (2019) A Novel Cosmic-Ray Neutron Sensor for Soil Moisture Estimation over Large Areas. Agriculture, 9, Article No. 202. https://doi.org/10.3390/agriculture9090202
[49]
Petropoulos, G.P., Ireland, G. and Barrett, B. (2015) Surface Soil Moisture Retrievals from Remote Sensing: Current Status, Products & Future Trends. Physics and Chemistry of the Earth, Parts A/B/C, 83-84, 36-56. https://doi.org/10.1016/j.pce.2015.02.009
[50]
West, H., Quinn, N. and Horswell, M. (2019) Remote Sensing for Drought Monitoring & Impact Assessment: Progress, Past Challenges and Future Opportunities. Remote Sensing of Environment, 232, Article ID: 111291. https://doi.org/10.1016/j.rse.2019.111291
[51]
Wang, B., Li, S., Mu, J., Hao, X., Zhu, W. and Hu, J. (2022) Research Advancements in Key Technologies for Space-Based Situational Awareness. Space: Science & Technology, 2022, Article ID: 9802793. https://doi.org/10.34133/2022/9802793
