RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA
The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/271/2023/isprs-annals-X-4-W1-2022-271-2023.pdf |
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| author | S. Hamzeh M. Hajeb S. K. Alavipanah J. Verrelst |
| author_facet | S. Hamzeh M. Hajeb S. K. Alavipanah J. Verrelst |
| author_sort | S. Hamzeh |
| collection | DOAJ |
| description | The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m<sup>2</sup>/m<sup>2</sup> for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI. |
| first_indexed | 2024-04-10T22:55:51Z |
| format | Article |
| id | doaj.art-769b07bc8f634189b2813303461d129d |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-10T22:55:51Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-769b07bc8f634189b2813303461d129d2023-01-14T11:12:38ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-01-01X-4-W1-202227127710.5194/isprs-annals-X-4-W1-2022-271-2023RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATAS. Hamzeh0M. Hajeb1S. K. Alavipanah2J. Verrelst3Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IranDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IranDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IranImage Processing Laboratory (IPL), Parc Científic, Universitat de València, València, SpainThe PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m<sup>2</sup>/m<sup>2</sup> for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/271/2023/isprs-annals-X-4-W1-2022-271-2023.pdf |
| spellingShingle | S. Hamzeh M. Hajeb S. K. Alavipanah J. Verrelst RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA |
| title_full | RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA |
| title_fullStr | RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA |
| title_full_unstemmed | RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA |
| title_short | RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA |
| title_sort | retrieval of sugarcane leaf area index from prisma hyperspectral data |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/271/2023/isprs-annals-X-4-W1-2022-271-2023.pdf |
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