Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Field Description and Data Collection
2.2. Sentinel-2 Imagery and Pre-Processing
2.3. Establishment and Evaluation of the CNN Prediction Model
3. Results and Discussion
3.1. Influence of Using Sentinel-2 Bands
3.2. Influence of Using NDVI, GVMI, and NDWI
3.3. Influence of Using Combinations
3.4. Comparison of Different CNN Architectures
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- United Nations Department of Economic and Social Affairs (DESA), Population Division. World Population Prospects: The 2015 Revision, Key Findings and Advance Tables; Working Paper No. ESA/P/WP.241; Department of Economic and Social Affairs, Population Division: New York, NY, USA, 2015; pp. 1–66. [Google Scholar]
- FAO. Agriculture, Food, and Water: A Contribution to the World Water Development Report; FAO: Rome, Italy, 2003; p. 64. [Google Scholar]
- Alexandratos, N.; Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision; ESA Working Paper No. 12-03; Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA): Rome, Italy, 2012; p. 154. [Google Scholar]
- FAO. The Future of Food and Agriculture—Trends and Challenges; FAO: Rome, Italy, 2017; p. 296. [Google Scholar]
- Jain, S.K.; Singh, V.P. Water Resources Systems Planning and Management; Elsevier: Amsterdam, The Netherlands, 2003. [Google Scholar]
- Ray, D.K.; West, P.C.; Clark, M.; Gerber, J.S.; Prishchepov, A.V.; Chatterjee, S. Climate Change Has Likely Already Affected Global Food Production. PLoS ONE 2019, 14, e0217148. [Google Scholar] [CrossRef] [PubMed]
- Strzepek, K.; Boehlert, B. Competition for Water for the Food System. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 2927–2940. [Google Scholar] [CrossRef] [PubMed]
- FAO. The State of the World’s Land and Water Resources for Food and Agriculture (SOLAW)—Managing Systems at Risk; Food and Agriculture Organization of the United Nations: Rome, Italy; Earthscan: London, UK, 2011. [Google Scholar]
- Dabach, S.; Lazarovitch, N.; Šimůnek, J.; Shani, U. Numerical Investigation of Irrigation Scheduling Based on Soil Water Status. Irrig. Sci. 2013, 31, 27–36. [Google Scholar] [CrossRef]
- Jimenez, A.-F.; Cardenas, P.-F.; Jimenez, F.; Canales, A.; López, A. A Cyber-Physical Intelligent Agent for Irrigation Scheduling in Horticultural Crops. Comput. Electron. Agric. 2020, 178, 105777. [Google Scholar] [CrossRef]
- Ihuoma, S.O.; Madramootoo, C.A. Recent Advances In Crop Water Stress Detection. Comput. Electron. Agric. 2017, 141, 267–275. [Google Scholar] [CrossRef]
- Romano, N. Soil Moisture at Local Scale: Measurements and Simulations. J. Hydrol. 2014, 516, 6–20. [Google Scholar] [CrossRef]
- Halloran, L.J.S.; Rau, G.C.; Andersen, M.S. Heat as A Tracer to Quantify Processes and Properties in the Vadose Zone: A Review. Earth-Sci. Rev. 2016, 159, 358–373. [Google Scholar] [CrossRef]
- Entekhabi, D.; Njoku, E.G.; O’Neill, P.E.; Kellogg, K.H.; Crow, W.T.; Edelstein, W.N.; Entin, J.K.; Goodman, S.D.; Jackson, T.J.; Johnson, J.; et al. The Soil Moisture Active Passive (SMAP) Mission. Proc. IEEE 2010, 98, 704–716. [Google Scholar] [CrossRef]
- Mohamed, E.S.; Ali, A.; El-Shirbeny, M.; Abutaleb, K.; Shaddad, S.M. Mapping Soil Moisture and their Correlation with Crop Pattern Using Remotely Sensed Data in Arid Region. Egypt. J. Remote Sens. Space Sci. 2020, 23, 347–353. [Google Scholar] [CrossRef]
- Liang, S.; Wang, J. Advanced Remote Sensing: Terrestrial Information Extraction and Applications, 2nd ed.; Academic Press: London, UK, 2020. [Google Scholar]
- Babaeian, E.; Sadeghi, M.; Jones, S.B.; Montzka, C.; Vereecken, H.; Tuller, M. Ground, Proximal, and Satellite Remote Sensing of Soil Moisture. Rev. Geophys. 2019, 57, 530–616. [Google Scholar] [CrossRef] [Green Version]
- Verstraeten, W.W.; Veroustraete, F.; Feyen, J. Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation. Sensors 2008, 8, 70–117. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hegazi, E.H.; Yang, L.; Huang, J. A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images. Remote Sens. 2021, 13, 4964. [Google Scholar] [CrossRef]
- Wang, L.; Qu, J.J. Satellite Remote Sensing Applications for Surface Soil Moisture Monitoring: A Review. Front. Earth Sci. China 2009, 3, 237–247. [Google Scholar] [CrossRef]
- Sadeghi, M.; Jones, S.B.; Philpot, W.D. A Linear Physically-Based Model for Remote Sensing of Soil Moisture Using Short Wave Infrared Bands. Remote Sens. Environ. 2015, 164, 66–76. [Google Scholar] [CrossRef]
- Ångström, A. The Albedo of Various Surfaces of Ground. Geogr. Ann. 1925, 7, 323–342. [Google Scholar] [CrossRef]
- Chen, M.; Zhang, Y.; Yao, Y.; Lu, J.; Pu, X.; Hu, T.; Wang, P. Evaluation of the OPTRAM Model to Retrieve Soil Moisture in the Sanjiang Plain of Northeast China. Earth Space Sci. 2020, 7, e2020EA001108. [Google Scholar] [CrossRef]
- Wang, H.; Li, X.; Long, H.; Xu, X.; Bao, Y. Monitoring the Effects of Land Use and Cover Type Changes on Soil Moisture Using Remote-Sensing Data: A Case Study in China’s Yongding River Basin. Catena 2010, 82, 135–145. [Google Scholar] [CrossRef]
- Amani, M.; Parsian, S.; MirMazloumi, S.M.; Aieneh, O. Two New Soil Moisture Indices Based on the NIR-Red triangle Space of Landsat-8 Data. Int. J. Appl. Earth Obs. Geoinf. 2016, 50, 176–186. [Google Scholar] [CrossRef]
- Hezarian, F.; Khalilimoghadam, B.; Zoratipour, A.; Nejad, M.F.; Yusefi, A. Assessment of the Capability of Satellite Images in Determining the Topsoil Moisture Content in the Dust Hotspot of Southeastern Ahvaz in Iran. Eurasian Soil Sci. 2022, 55, 1576–1590. [Google Scholar] [CrossRef]
- Lobell, D.B.; Asner, G.P. Moisture Effects on Soil Reflectance. Soil Sci. Soc. Am. J. 2002, 66, 722–727. [Google Scholar] [CrossRef]
- Yang, X.; Yu, Y.; Li, M. Estimating Soil Moisture Content Using Laboratory Spectral Data. J. For. Res. 2019, 30, 1073–1080. [Google Scholar] [CrossRef]
- Hashim, B.M.; Sultan, M.A.; Attyia, M.N.; Al Maliki, A.A.; Al-Ansari, N. Change Detection and Impact of Climate Changes to Iraqi Southern Marshes Using Landsat 2 MSS, Landsat 8 OLI and Sentinel 2 MSI Data and GIS Applications. Appl. Sci. 2019, 9, 2016. [Google Scholar] [CrossRef] [Green Version]
- Sanchez, N.; Alonso-Arroyo, A.; Martínez-Fernández, J.; Camps, A.; González-Zamora, A.; Pablos, M.; Herrero-Jiménez, C.M.; Gumuzzio, A. Multisensor Experiments Over Vineyard: New Challenges for the Gnss-R Technique. In Proceedings of the ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: 36th International Symposium on Remote Sensing of Environment, Berlin, Germany, 11–15 May 2015. [Google Scholar]
- Ambrosone, M.; Matese, A.; Di Gennaro, S.F.; Gioli, B.; Tudoroiu, M.; Genesio, L.; Miglietta, F.; Baronti, S.; Maienza, A.; Ungaro, F.; et al. Retrieving Soil Moisture in Rainfed and Irrigated Fields Using Sentinel-2 Observations and A Modified OPTRAM Approach. Int. J. Appl. Earth Obs. Geoinf. 2020, 89, 102113. [Google Scholar] [CrossRef]
- Ma, C.; Li, X.; McCabe, M.F. Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data. Remote Sens. 2020, 12, 2303. [Google Scholar] [CrossRef]
- Nativel, S.; Ayari, E.; Rodriguez-Fernandez, N.; Baghdadi, N.; Madelon, R.; Albergel, C.; Zribi, M. Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation. Remote Sens. 2022, 14, 2434. [Google Scholar] [CrossRef]
- Wang, Q.; Li, J.; Jin, T.; Chang, X.; Zhu, Y.; Li, Y.; Sun, J.; Li, D. Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands. Remote Sens. 2020, 12, 2708. [Google Scholar] [CrossRef]
- Gangat, R.; Van Deventer, H.; Naidoo, L.; Adam, E. Estimating Soil Moisture Using Sentinel-1 and Sentinel-2 Sensors for Dryland and Palustrine Wetland Areas. South Afr. J. Sci. 2020, 116, 1–9. [Google Scholar] [CrossRef]
- Zheng, X.; Feng, Z.; Li, L.; Li, B.; Jiang, T.; Li, X.; Li, X.; Chen, S. Simultaneously Estimating Surface Soil Moisture and Roughness of Bare Soils by Combining Optical and Radar Data. Int. J. Appl. Earth Obs. Geoinf. 2021, 100, 102345. [Google Scholar] [CrossRef]
- Liu, Y.; Qian, J.; Yue, H. Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 7448–7465. [Google Scholar] [CrossRef]
- Jackson, T.J.; Chen, D.; Cosh, M.; Li, F.; Anderson, M.; Walthall, C.; Doriaswamy, P.; Hunt, E.R. Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans. Remote Sens. Environ. 2004, 92, 475–482. [Google Scholar] [CrossRef]
- Yilmaz, M.T.; Hunt Jr, E.R.; Jackson, T.J. Remote Sensing of Vegetation Water Content from Equivalent Water Thickness using Satellite Imagery. Remote Sens. Environ. 2008, 112, 2514–2522. [Google Scholar] [CrossRef]
- Wang, Q.; Jin, T.; Li, J.; Chang, X.; Li, Y.; Zhu, Y. Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel-1 and Sentinel-2. Earth Space Sci. 2022, 9, e2021EA002063. [Google Scholar] [CrossRef]
- Ceccato, P.; Gobron, N.; Flasse, S.; Pinty, B.; Tarantola, S. Designing A Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data: Part 1: Theoretical Approach. Remote Sens. Environ. 2002, 82, 188–197. [Google Scholar] [CrossRef]
- Gao, B.-C. NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Holtgrave, A.-K.; Röder, N.; Ackermann, A.; Erasmi, S.; Kleinschmit, B. Comparing Sentinel-1 and-2 Data and Indices for Agricultural Land Use Monitoring. Remote Sens. 2020, 12, 2919. [Google Scholar] [CrossRef]
- Sun, H.; Liu, H.; Ma, Y.; Xia, Q. Optical Remote Sensing Indexes of Soil Moisture: Evaluation and Improvement Based on Aircraft Experiment Observations. Remote Sens. 2021, 13, 4638. [Google Scholar] [CrossRef]
- Wang, Q.; Li, P.; Pu, Z.; Chen, X. Calibration and Validation of Salt-Resistant Hyperspectral Indices for Estimating Soil Moisture in Arid Land. J. Hydrol. 2011, 408, 276–285. [Google Scholar] [CrossRef]
- Zhan, Z.; Qin, Q.; Ghulan, A.; Wang, D. NIR-Red Spectral Space Based New Method for Soil Moisture Monitoring. Sci. China Ser. D: Earth Sci. 2007, 50, 283–289. [Google Scholar] [CrossRef]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- You, N.; Dong, J. Examining Earliest Identifiable Timing of Crops Using All Available Sentinel 1/2 Imagery and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2020, 161, 109–123. [Google Scholar] [CrossRef]
- Krittanawong, C.; Zhang, H.; Wang, Z.; Aydar, M.; Kitai, T. Artificial Intelligence in Precision Cardiovascular Medicine. J. Am. Coll. Cardiol. 2017, 69, 2657–2664. [Google Scholar] [CrossRef] [PubMed]
- Hardian, R.; Liang, Z.; Zhang, X.; Szekely, G. Artificial Intelligence: The Silver Bullet for Sustainable Materials Development. Green Chem. 2020, 22, 7521–7528. [Google Scholar] [CrossRef]
- Müller, A.C.; Guido, S. Introduction to Machine Learning with Python: A Guide for Data Scientists, 1st ed.; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2016. [Google Scholar]
- Alpaydin, E. Introduction to Machine Learning, 3rd ed.; MIT Press: Cambridge, MA, USA, 2014. [Google Scholar]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef]
- Yamashita, R.; Nishio, M.; Do, R.K.G.; Togashi, K. Convolutional Neural Networks: An Overview and Application in Radiology. Insights Into Imaging 2018, 9, 611–629. [Google Scholar] [CrossRef] [Green Version]
- Lee, C.; Panda, P.; Srinivasan, G.; Roy, K. Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning. Front. Neurosci. 2018, 12, 435. [Google Scholar] [CrossRef] [PubMed]
- Zhu, M.; Ye, K.; Xu, C.-Z. Network Anomaly Detection and Identification Based on Deep Learning Methods. In Proceedings of the International Conference on Cloud Computing, Cloud Computing—CLOUD 2018, CLOUD 2018, Lecture Notes in Computer Science, Seattle, WA, USA, 25–30 June 2018; pp. 219–234. [Google Scholar]
- Galib, S.M. Applications of Machine Learning in Nuclear Imaging and Radiation Detection. Ph.D. Thesis, Missouri University of Science and Technology, Rolla, MO, USA, 2019. [Google Scholar]
- Chen, Z.; Gryllias, K.; Li, W. Mechanical Fault Diagnosis Using Convolutional Neural Networks and Extreme Learning Machine. Mech. Syst. Signal Process. 2019, 133, 106272. [Google Scholar] [CrossRef]
- Dorigo, W.A.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Xaver, A.; Gruber, A.; Drusch, M.; Mecklenburg, S.; van Oevelen, P.; et al. International Soil Moisture Network: A Data Hosting Facility for Global In Situ Soil Moisture Measurements. Hydrol. Earth Syst. Sci. 2011, 15, 1675–1698. [Google Scholar] [CrossRef] [Green Version]
- Dorigo, W.A.; Xaver, A.; Vreugdenhil, M.; Gruber, A.; Hegyiova, A.; Sanchis-Dufau, A.D.; Zamojski, D.; Cordes, C.; Wagner, W.; Drusch, M. Global Automated Quality Control of In Situ Soil Moisture Data from the International Soil Moisture Network. Vadose Zone J. 2013, 12, 1–21. [Google Scholar] [CrossRef]
- Smith, A.B.; Walker, J.P.; Western, A.W.; Young, R.I.; Ellett, K.M.; Pipunic, R.C.; Grayson, R.B.; Siriwardena, L.; Chiew, F.H.S.; Richter, H. The Murrumbidgee Soil Moisture Monitoring Network Data Set. Water Resour. Res. 2012, 48, 1–6. [Google Scholar] [CrossRef]
- Young, R.; Walker, J.; Yeoh, N.; Smith, A.; Ellett, K.; Merlin, O.; Western, A. Soil Moisture and Meteorological Observations from the Murrumbidgee Catchment; Department of Civil and Environmental Engineering, The University of Melbourne: Melbourne, VIC, Australia, 2008; p. 54. [Google Scholar]
- Fuchsberger, J.; Kirchengast, G.; Kabas, T. WegenerNet High-Resolution Weather and Climate Data from 2007 to 2020. Earth Syst. Sci. Data 2021, 13, 1307–1334. [Google Scholar] [CrossRef]
- Kabas, T. WegenerNet Klimastationsnetz Region Feldbach: Experimenteller Aufbau und hochauflösende Daten für die Klima-und Umweltforschung. Ph.D. Thesis, University of Graz, Graz, Austria, 2011. [Google Scholar]
- Kirchengast, G.; Kabas, T.; Leuprecht, A.; Bichler, C.; Truhetz, H. WegenerNet: A Pioneering High-Resolution Network for Monitoring Weather and Climate. Bull. Am. Meteorol. Soc. 2014, 95, 227–242. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Louis, J.; Devignot, O.; Pessiot, L. S2 MPC–L2A Product Definition Document; Ref. S2-PDGS-MPC-L2A-PDD-V14.5. 2018. Available online: http://step.esa.int/thirdparties/sen2cor/2.5.5/docs/S2-PDGS-MPC-L2A-PDD-V2.5.5.pdf (accessed on 13 November 2020).
- Main-Knorn, M.; Pflug, B.; Louis, J.; Debaecker, V.; Müller-Wilm, U.; Gascon, F. Sen2Cor for Sentinel-2. In Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland, 4 October 2017; p. 1042704. [Google Scholar]
- Khan, S.; Rahmani, H.; Shah, S.A.A.; Bennamoun, M. A Guide to Convolutional Neural Networks for Computer Vision. In Synthesis Lectures on Computer Vision; Morgan & Claypool: San Rafael, CA, USA, 2018; Volume 8, pp. 1–207. [Google Scholar]
- Sewak, M.; Karim, M.R.; Pujari, P. Practical Convolutional Neural Networks: Implement Advanced Deep Learning Models Using Python; Packt Publishing Ltd.: Birmingham, UK, 2018. [Google Scholar]
- Sadeghi, M.; Babaeian, E.; Tuller, M.; Jones, S.B. The Optical Trapezoid Model: A Novel Approach to Remote Sensing of Soil Moisture Applied to Sentinel-2 and Landsat-8 Observations. Remote Sens. Environ. 2017, 198, 52–68. [Google Scholar] [CrossRef] [Green Version]
- Lin, Y.; Zhu, Z.; Guo, W.; Sun, Y.; Yang, X.; Kovalskyy, V. Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery. Remote Sens. 2020, 12, 1176. [Google Scholar] [CrossRef] [Green Version]
- Pan, H.; Chen, Z.; Ren, J.; Li, H.; Wu, S. Modeling Winter Wheat Leaf Area Index and Canopy Water Content with Three Different Approaches Using Sentinel-2 Multispectral Instrument Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 12, 482–492. [Google Scholar] [CrossRef]
- Pristyanto, Y.; Adi, S.; Sunyoto, A. The effect of feature selection on classification algorithms in credit approval. In Proceedings of the 2019 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 24–25 July 2019; pp. 451–456. [Google Scholar]
- Zhang, L.; Chen, J.; Ma, J.; Liu, H. HN-CNN: A Heterogeneous Network Based on Convolutional Neural Network for m7 G Site Disease Association Prediction. Front. Genet. 2021, 12, 655284. [Google Scholar] [CrossRef] [PubMed]
- Clevers, J.G.; Gitelson, A.A. Remote Estimation of Crop and Grass Chlorophyll and Nitrogen Content using Red-Edge Bands on Sentinel-2 and-3. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 344–351. [Google Scholar] [CrossRef]
- Dash, J.; Curran, P.J. The MERIS Terrestrial Chlorophyll Index. Int. J. Remote Sens. 2004, 25, 5403–5413. [Google Scholar] [CrossRef]
- Delegido, J.; Verrelst, J.; Alonso, L.; Moreno, J. Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content. Sensors 2011, 11, 7063–7081. [Google Scholar] [CrossRef] [Green Version]
- Gitelson, A.A.; Viña, A.; Ciganda, V.; Rundquist, D.C.; Arkebauer, T.J. Remote Estimation of Canopy Chlorophyll Content in Crops. Geophys. Res. Lett. 2005, 32, L08403. [Google Scholar] [CrossRef] [Green Version]
- Xu, N.; Tian, J.; Tian, Q.; Xu, K.; Tang, S. Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index. Remote Sens. 2019, 11, 1192. [Google Scholar] [CrossRef] [Green Version]
- Nevavuori, P.; Narra, N.; Lipping, T. Crop Yield Prediction with Deep Convolutional Neural Networks. Comput. Electron. Agric. 2019, 163, 104859. [Google Scholar] [CrossRef]
- Özgenel, Ç.F.; Sorguç, A.G. Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings. In Proceedings of the International Symposium on Automation and Robotics in Construction, Berlin, Germany, 20–25 July 2018; pp. 1–8. [Google Scholar]
- Emami, H.; Dong, M.; Nejad-Davarani, S.P.; Glide-Hurst, C.K. Generating Synthetic CTs from Magnetic Resonance Images using Generative Adversarial Networks. Med. Phys. 2018, 45, 3627–3636. [Google Scholar] [CrossRef] [PubMed]
- Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), Computational and Biological Learning Society, San Diego, CA, USA, 7–9 May 2015; pp. 1–14. [Google Scholar]
- Lu, Y.; Huo, Y.; Yang, Z.; Niu, Y.; Zhao, M.; Bosiakov, S.; Li, L. Influence of the Parameters of the Convolutional Neural Network Model in Predicting the Effective Compressive Modulus of Porous Structure. Front. Bioeng. Biotechnol. 2022, 10, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Sameen, M.I.; Pradhan, B.; Lee, S. Application of Convolutional Neural Networks Featuring Bayesian Optimization for Landslide Susceptibility Assessment. Catena 2020, 186, 104249. [Google Scholar] [CrossRef]
- Ahmed, W.S.; Karim, A.a.A. The Impact of Filter Size and Number of Filters on Classification Accuracy in CNN. In Proceedings of the 2020 International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq, 16–18 April 2020; pp. 88–93. [Google Scholar]
Band | Description | Center Wavelength (nm) | Bandwidth (nm) | Spatial Resolution (m) |
---|---|---|---|---|
B1 | Aerosols | 443 | 20 | 60 |
B2 | Blue | 490 | 65 | 10 |
B3 | Green | 560 | 35 | 10 |
B4 | Red | 665 | 30 | 10 |
B5 | Red Edge 1 | 705 | 15 | 20 |
B6 | Red Edge 2 | 740 | 15 | 20 |
B7 | Red Edge 3 | 783 | 20 | 20 |
B8 | NIR | 842 | 115 | 10 |
B8a | Red Edge 4 | 865 | 20 | 20 |
B9 | Water Vapor | 945 | 20 | 60 |
B10 | SWIR-Cirrus | 1380 | 30 | 60 |
B11 | SWIR 1 | 1610 | 90 | 20 |
B12 | SWIR 2 | 2190 | 180 | 20 |
Input Data | Architecture | R2 | MAE | RMSE |
---|---|---|---|---|
Fourth Combination Green, Red, NIR, SWIR 1, and SWIR 2 bands | CNN architecture (1) | 0.5999 | 0.0335 | 0.0491 |
CNN architecture (2) | 0.6541 | 0.0291 | 0.0456 | |
CNN architecture (3) | 0.6847 | 0.0282 | 0.0436 | |
Fifth Combination Red Edge 3, NIR, Red Edge 4, and SWIR 1 bands | CNN architecture (1) | 0.6123 | 0.0332 | 0.0483 |
CNN architecture (2) | 0.67 | 0.0289 | 0.0446 | |
CNN architecture (3) | 0.7015 | 0.0287 | 0.0424 | |
Sixth Combination Red, Red Edge 1, Red Edge 2, Red Edge 3, NIR, and Red Edge 4 bands | CNN architecture (1) | 0.6128 | 0.033 | 0.0483 |
CNN architecture (2) | 0.6706 | 0.0289 | 0.0445 | |
CNN architecture (3) | 0.7094 | 0.0277 | 0.0418 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hegazi, E.H.; Samak, A.A.; Yang, L.; Huang, R.; Huang, J. Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN). Agronomy 2023, 13, 656. https://doi.org/10.3390/agronomy13030656
Hegazi EH, Samak AA, Yang L, Huang R, Huang J. Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN). Agronomy. 2023; 13(3):656. https://doi.org/10.3390/agronomy13030656
Chicago/Turabian StyleHegazi, Ehab H., Abdellateif A. Samak, Lingbo Yang, Ran Huang, and Jingfeng Huang. 2023. "Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN)" Agronomy 13, no. 3: 656. https://doi.org/10.3390/agronomy13030656