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

DOI: http://dx.doi.org/10.15439/2024F4108

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 475–484 (2024)

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Abstract. Hyperspectral images from newly launched (ASI-PRISMA and DLR-EnMAP) and future satellite (ESA-CHIME) are an opportunity, thanks to the high spectral resolution and full range continuity, to improve the retrieval of information about the crop parameters and status. The high dimensionality of hyperspectral data and the non-linear relationship between the crop biophysical parameters and their spectral signature make quantitative estimation of crop characteristics challenging, to address these problems we tested different configurations of neural networks (fully connected and convolutional). We tested the different architectures on two training dataset, one consists in ground data collected in three experiments, in different locations and seasons, the second one (hybrid) is composed by synthetic data generated using a radiative transfer model (PROSAIL-PRO). Preliminary results for LAI, CCC and CNC retrieval are encouraging in particular when ground data are exploited demonstrating of the potentiality of NN to fully exploit the information density of the hyperspectral data.

References

1. T. B. Hank et al., “Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges,” Surv. Geophys., vol. 40, no. 3, pp. 515–551, May 2019, http://dx.doi.org/10.1007/s10712-018-9492-0.
2. FAO, Ed., The state of food and agriculture - Climate change, agriculture and food security. in The state of food and agriculture, no. 2016. Rome: FAO, 2016.
3. M. Wójtowicz, A. Wójtowicz, and J. Piekarczyk, “Application of remote sensing methods in agriculture,” 2016.
4. P. J. Zarco-Tejada, N. Hubbard, and P. Loudjani, “Precision agriculture: an opportunity for EU farmers: potential support with the CAP 2014-2020,” 2014.
5. L. Lassaletta et al., “Nitrogen use in the global food system: past trends and future trajectories of agronomic performance, pollution, trade, and dietary demand,” Environ. Res. Lett., vol. 11, no. 9, p. 095007, Sep. 2016, http://dx.doi.org/10.1088/1748-9326/11/9/095007.
6. S. M. Ogle, K. Butterbach-Bahl, L. Cardenas, U. Skiba, and C. Scheer, “From research to policy: optimizing the design of a national monitoring system to mitigate soil nitrous oxide emissions,” Curr. Opin. Environ. Sustain., vol. 47, pp. 28–36, Dec. 2020, http://dx.doi.org/10.1016/j.cosust.2020.06.003.
7. K. Berger et al., “Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions,” Remote Sens. Environ., vol. 242, p. 111758, Jun. 2020, http://dx.doi.org/10.1016/j.rse.2020.111758.
8. P. J. Curran, “Remote sensing of foliar chemistry,” Remote Sens. Environ., vol. 30, no. 3, pp. 271–278, Dec. 1989, http://dx.doi.org/10.1016/0034-4257(89)90069-2.
9. Y. Fu et al., “An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives,” Eur. J. Agron., vol. 124, p. 126241, Mar. 2021, http://dx.doi.org/10.1016/j.eja.2021.126241.
10. G. Castellano, P. D. Marinis, and G. Vessio, “Applying Knowledge Distillation to Improve Weed Mapping With Drones,” presented at the 18th Conference on Computer Science and Intelligence Systems, Sep. 2023, pp. 393–400. http://dx.doi.org/10.15439/2023F960.
11. J. Verrelst et al., “Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods,” Surv. Geophys., vol. 40, no. 3, pp. 589–629, May 2019, http://dx.doi.org/10.1007/s10712-018-9478-y.
12. I. Gallo, M. Boschetti, A. U. Rehman, and G. Candiani, “Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images,” Remote Sens., vol. 15, no. 19, p. 4765, Sep. 2023, http://dx.doi.org/10.3390/rs15194765.
13. M. Weiss, F. Jacob, and G. Duveiller, “Remote sensing for agricultural applications: A meta-review,” Remote Sens. Environ., vol. 236, p. 111402, Jan. 2020, http://dx.doi.org/10.1016/j.rse.2019.111402.
14. K. Berger et al., “Retrieval of aboveground crop nitrogen content with a hybrid machine learning method,” Int. J. Appl. Earth Obs. Geoinformation, vol. 92, p. 102174, Oct. 2020, http://dx.doi.org/10.1016/j.jag.2020.102174.
15. G. Candiani et al., “Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission,” Remote Sens., vol. 14, no. 8, p. 1792, Apr. 2022, http://dx.doi.org/10.3390/rs14081792.
16. G. Tagliabue et al., “Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery,” ISPRS J. Photogramm. Remote Sens., vol. 187, pp. 362–377, May 2022, http://dx.doi.org/10.1016/j.isprsjprs.2022.03.014.
17. R. Heidarian Dehkordi et al., “Towards an Improved High-Throughput Phenotyping Approach: Utilizing MLRA and Dimensionality Reduction Techniques for Transferring Hyperspectral Proximal-Based Model to Airborne Images,” Remote Sens., vol. 16, no. 3, p. 492, Jan. 2024, http://dx.doi.org/10.3390/rs16030492.
18. K. Berger, Z. Wang, M. Danner, M. Wocher, W. Mauser, and T. Hank, “Simulation of Spaceborne Hyperspectral Remote Sensing to Assist Crop Nitrogen Content Monitoring in Agricultural Crops,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia: IEEE, Jul. 2018, pp. 3801–3804. http://dx.doi.org/10.1109/IGARSS.2018.8518537.
19. J.-B. Féret, “PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents,” Remote Sens. Environ., 2021.
20. W. Verhoef, “Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling: The SAIL Model,” 1984.
21. J.-B. Féret and F. de Boissieu, prosail: PROSAIL leaf and canopy radiative transfer model and inversion routines. 2023. [Online]. Available: https://gitlab.com/jbferet/prosail
22. M. Weiss, S. Jay, and F. Baret, “S2ToolBox Level 2 products: LAI, FAPAR, FCOVER - Version 1.1.,” 2016.