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Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 39

AI-Based Spatiotemporal Crop Monitoring by Cloud Removal in Satellite Images

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DOI: http://dx.doi.org/10.15439/2024F5446

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 485–492 ()

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Abstract. Efficient crop monitoring and crop dynamics fore- casting leveraging diverse satellite and point data are described. Attention-based architecture architecture is adapted for mono- temporal cloud removal which overcomes an issue of crop monitoring. Combining optical (Sentinel-2) and radar (Sentinel- 1) satellite data improves the robustness and accuracy of the model in terms of satellite image reconstruction and normalized difference vegetation index prediction and forecasting. However, available soil-type geographical data and land surface analysis products, do not improve prediction accuracy significantly

References

  1. Emmanuel Abiodun Abioye et al. “Precision irrigation management using machine learning and digital farming solutions”. In: AgriEngineering 4.1 (2022), pp. 70–103. DOI : https://doi.org/10.3390/agriengineering4010006.
  2. Paolo Bertellini et al. “Binary Classification of Agricultural Crops Using Sentinel Satellite Data and Machine Learning Techniques”. In: 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS). IEEE. 2023, pp. 859–864. DOI : 10.15439/2023F1703.
  3. Nicolas R Dalezios et al. “Remotely sensed methodologies for crop water availability and requirements in precision farming of vulnerable agriculture”. In: Water resources management 33 (2019), pp. 1499–1519. http://dx.doi.org/ https://doi.org/10.1007/s11269-018-2161-8.
  4. Jonathan P Dash, Grant D Pearse, and Michael S Watt. “UAV multispectral imagery can complement satellite data for monitoring forest health”. In: Remote Sensing 10.8 (2018), p. 1216. http://dx.doi.org/10.3390/rs10081216.
  5. Patrick Ebel et al. “SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal”. In: IEEE Transactions on Geoscience and Remote Sensing (2022). https://doi.org/10.1109/TGRS.2022.3146246.
  6. Patrick Ebel et al. “UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, pp. 2086–2096. http://dx.doi.org/10.1109/CVPRW59228.2023.00202.
  7. Kenji Enomoto et al. “Filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets”. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017, pp. 48–56. http://dx.doi.org/10.1109/CVPRW.2017.197.
  8. Jichong Han et al. “Prediction of winter wheat yield based on multi-source data and machine learning in China”. In: Remote Sensing 12.2 (2020), p. 236. https://doi.org/10.3390/rs12020236.
  9. Jay Ram Lamichhane et al. “Abiotic and biotic factors affecting crop seed germination and seedling emergence: a conceptual framework”. In: Plant and soil 432 (2018), pp. 1–28. https://doi.org/10.1007/s11104-018-3780-9.
  10. Konstantinos G Liakos et al. “Machine learning in agriculture: A review”. In: Sensors 18.8 (2018), p. 2674. DOI : 10.3390/s18082674.
  11. David B Lobell, Kenneth G Cassman, and Christopher B Field. “Crop yield gaps: their importance, magnitudes, and causes”. In: Annual review of environment and resources 34 (2009), pp. 179–204. https://doi.org/10.1146/annurev.environ.041008.093740.
  12. Andrea Meraner et al. “Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion”. In: ISPRS Journal of Photogrammetry and Remote Sensing 166 (2020), pp. 333–346. https://doi.org/10.1016/j.isprsjprs.2020.05.013.
  13. Michele Meroni et al. “Yield forecasting with machine learning and small data: What gains for grains?” In: Agricultural and Forest Meteorology 308 (2021), p. 108555. https://doi.org/10.1016/j.agrformet.2021.108555.
  14. José M Peña-Barragán et al. “Object-based crop identification using multiple vegetation indices, textural features and crop phenology”. In: Remote Sensing of Environment 115.6 (2011), pp. 1301–1316. http://dx.doi.org/10.1016/j.rse.2011.01.009.
  15. Darius Phiri et al. “Sentinel-2 data for land cover/use mapping: A review”. In: Remote Sensing 12.14 (2020), p. 2291. DOI : https://doi.org/10.3390/rs12142291.
  16. Francis J Pierce and Peter Nowak. “Aspects of precision agriculture”. In: Advances in agronomy 67 (1999), pp. 1–85. DOI : https://doi.org/10.1016/S0065-2113(08) 60513-1.
  17. Yinghui Quan et al. “A novel feature extension method for the forest disaster monitoring using multispectral data”. In: Remote Sensing 12.14 (2020), p. 2261. https://doi.org/10.3390/rs12142261.
  18. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. “U-Net: Convolutional Networks for Biomedical Image Segmentation”. In: (May 2015). http://dx.doi.org/10.48550/ARXIV.1505.04597. https://arxiv.org/abs/ 1505.04597 [cs.CV].
  19. Marc Rußwurm and Marco Körner. “Self-attention for raw optical satellite time series classification”. In: IS-PRS journal of photogrammetry and remote sensing 169 (2020), pp. 421–435. https://doi.org/10.1016/j.isprsjprs.2020.06.006.
  20. Vivien Sainte Fare Garnot and Loic Landrieu. “Lightweight Temporal Self-Attention for Classifying Satellite Images Time Series”. In: arXiv preprint https://arxiv.org/abs/2007.00586 (2020). https://doi.org/10.48550/arXiv.2007.00586.
  21. Mark Sandler et al. “Mobilenetv2: Inverted residuals and linear bottlenecks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 4510–4520. http://dx.doi.org/10.1109/CVPR.2018.00474.
  22. Vishnu Sarukkai et al. “Cloud removal from satellite images using spatiotemporal generative networks”. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020, pp. 1796–1805. http://dx.doi.org/10.1109/WACV45572.2020.9093564.
  23. Mohsen Shahhosseini et al. “Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt”. In: Scientific reports 11.1 (2021), p. 1606. https://doi.org/10.1038/s41598-020-80820-1.
  24. Ramon Torres et al. “GMES Sentinel-1 mission”. In: Remote sensing of environment 120 (2012), pp. 9–24. https://doi.org/10.1016/j.rse.2011.05.028.
  25. Amanda Veloso et al. “Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications”. In: Remote sensing of environment 199 (2017), pp. 415–426. https://doi.org/10.1016/j.rse.2017.07.015.
  26. Jonathan Weekley, Joseph Gabbard, and Jerzy Nowak. “Micro-level management of agricultural inputs: emerging approaches”. In: Agronomy 2.4 (2012), pp. 321–357. DOI : https://doi.org/10.3390/agronomy2040321.
  27. Fang Xu et al. “GLF-CR: SAR-enhanced cloud removal with global–local fusion”. In: ISPRS Journal of Photogrammetry and Remote Sensing 192 (2022), pp. 268–278. http://dx.doi.org/https://doi.org/10.1016/j.isprsjprs.2022.08.002.
  28. Qiangqiang Yuan et al. “Deep learning in environmental remote sensing: Achievements and challenges”. In: Remote Sensing of Environment 241 (2020), p. 111716. https://doi.org/10.1016/j.rse.2020.111716.
  29. Chongyuan Zhang, Afef Marzougui, and Sindhuja Sankaran. “High-resolution satellite imagery applications in crop phenotyping: An overview”. In: Computers and Electronics in Agriculture 175 (2020), p. 105584. https://doi.org/10.1016/j.compag.2020.105584.
  30. Zhe Zhu, Shixiong Wang, and Curtis E Woodcock. “Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images”. In: Remote sensing of Environment 159 (2015), pp. 269–277. https://doi.org/10.1016/j.rse.2014.12.014.