Towards crop traits estimation from hyperspectral data: evaluation of neural network models trained with real multi-site data or synthetic RTM simulations

• Autorzy: Parigi Lorenzo , Candiani Gabriele , Gallo Ignazio , Toscano Piero , Boschetti Mirco

Identyfikatory

DOI

10.15439/2024F4108

• Konferencja: Federated Conference on Computer Science and Information Systems (19 ; 08-11.09.2024 ; Belgrade, Serbia)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

hyperspectral; neural network; RTM; PROSAIL; synthetic data; wheat

PL

technologia hiperspektralna; sieć neuronowa; RTM; PROSAIL; dane syntetyczne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2024
• Tom: Vol. 39
• Strony: 475--484
• Opis fizyczny: Bibliogr. 22 poz., tab., wykr.

Twórcy

autor

Parigi Lorenzo

• Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133 Milan, Italy
• Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Rome, Italy

• https://orcid.org/0000-0003-4641-7672

autor

Candiani Gabriele

• Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133 Milan, Italy

• https://orcid.org/0000-0003-0575-068X

autor

Gallo Ignazio

• Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy

• https://orcid.org/0000-0002-7076-8328

autor

Toscano Piero

• Institute of BioEconomy, National Research Council, 50145 Florence, Italy

• https://orcid.org/0000-0001-9184-0707

autor

Boschetti Mirco

• Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133 Milan, Italy

• https://orcid.org/0000-0003-2156-4166

Bibliografia

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• 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.
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Uwagi

1. Thematic Sessions: Regular Papers

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).