Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat
Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point...
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/15/2378 |
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| author | Yang Song Jinfei Wang Jiali Shang Chunhua Liao |
| author_facet | Yang Song Jinfei Wang Jiali Shang Chunhua Liao |
| author_sort | Yang Song |
| collection | DOAJ |
| description | Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale. |
| first_indexed | 2024-03-10T18:14:38Z |
| format | Article |
| id | doaj.art-0d5724c068064af7895b5399964795c2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:14:38Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-0d5724c068064af7895b5399964795c22023-11-20T07:49:03ZengMDPI AGRemote Sensing2072-42922020-07-011215237810.3390/rs12152378Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter WheatYang Song0Jinfei Wang1Jiali Shang2Chunhua Liao3Department of Geography, The University of Western Ontario, London, ON N6A 5C2, CanadaDepartment of Geography, The University of Western Ontario, London, ON N6A 5C2, CanadaOttawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, CanadaDepartment of Geography, The University of Western Ontario, London, ON N6A 5C2, CanadaKnowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.https://www.mdpi.com/2072-4292/12/15/2378UAVpoint cloudleaf area indexbiomass estimationyieldSAFY model |
| spellingShingle | Yang Song Jinfei Wang Jiali Shang Chunhua Liao Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat Remote Sensing UAV point cloud leaf area index biomass estimation yield SAFY model |
| title | Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat |
| title_full | Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat |
| title_fullStr | Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat |
| title_full_unstemmed | Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat |
| title_short | Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat |
| title_sort | using uav based sopc derived lai and safy model for biomass and yield estimation of winter wheat |
| topic | UAV point cloud leaf area index biomass estimation yield SAFY model |
| url | https://www.mdpi.com/2072-4292/12/15/2378 |
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