Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation
In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/16/3990 |
| _version_ | 1797583348434993152 |
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| author | Dimo Dimov Patrick Noack |
| author_facet | Dimo Dimov Patrick Noack |
| author_sort | Dimo Dimov |
| collection | DOAJ |
| description | In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R<sup>2</sup> coefficient of determination of 0.92. |
| first_indexed | 2024-03-10T23:36:40Z |
| format | Article |
| id | doaj.art-71931b2a498741849ea8976a20b07f2a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:36:40Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-71931b2a498741849ea8976a20b07f2a2023-11-19T02:53:00ZengMDPI AGRemote Sensing2072-42922023-08-011516399010.3390/rs15163990Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield EstimationDimo Dimov0Patrick Noack1Geocledian GmbH, 84028 Landshut, GermanyCompetence Center for Digital Agriculture, University of Applied Sciences Weihenstephan-Triesdorf, 85354 Freising, GermanyIn this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R<sup>2</sup> coefficient of determination of 0.92.https://www.mdpi.com/2072-4292/15/16/3990photogrammetryyield estimationcanopy surface modelsprecision farmingPleiades |
| spellingShingle | Dimo Dimov Patrick Noack Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation Remote Sensing photogrammetry yield estimation canopy surface models precision farming Pleiades |
| title | Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation |
| title_full | Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation |
| title_fullStr | Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation |
| title_full_unstemmed | Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation |
| title_short | Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation |
| title_sort | exploring the potential of multi temporal crop canopy models and vegetation indices from pleiades imagery for yield estimation |
| topic | photogrammetry yield estimation canopy surface models precision farming Pleiades |
| url | https://www.mdpi.com/2072-4292/15/16/3990 |
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