Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework
UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/4/1119 |
| _version_ | 1797606757224153088 |
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| author | Fengxun Zheng Xiaofei Wang Jiangtao Ji Hao Ma Hongwei Cui Yi Shi Shaoshuai Zhao |
| author_facet | Fengxun Zheng Xiaofei Wang Jiangtao Ji Hao Ma Hongwei Cui Yi Shi Shaoshuai Zhao |
| author_sort | Fengxun Zheng |
| collection | DOAJ |
| description | UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing. |
| first_indexed | 2024-03-11T05:19:35Z |
| format | Article |
| id | doaj.art-18bbb52f23fe449da2c1dc6c27d10752 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T05:19:35Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-18bbb52f23fe449da2c1dc6c27d107522023-11-17T17:57:52ZengMDPI AGAgronomy2073-43952023-04-01134111910.3390/agronomy13041119Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion FrameworkFengxun Zheng0Xiaofei Wang1Jiangtao Ji2Hao Ma3Hongwei Cui4Yi Shi5Shaoshuai Zhao6College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaHenan Modern Agricultural Big Data Industry Technology Research Institute Co., Ltd., Zhengzhou 450046, ChinaUAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing.https://www.mdpi.com/2073-4395/13/4/1119crop population phenotypeoptimal estimation inversionunmanned aerial vehicle (UAV)hyperspectralmultispectral |
| spellingShingle | Fengxun Zheng Xiaofei Wang Jiangtao Ji Hao Ma Hongwei Cui Yi Shi Shaoshuai Zhao Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework Agronomy crop population phenotype optimal estimation inversion unmanned aerial vehicle (UAV) hyperspectral multispectral |
| title | Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework |
| title_full | Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework |
| title_fullStr | Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework |
| title_full_unstemmed | Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework |
| title_short | Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework |
| title_sort | synchronous retrieval of lai and cab from uav remote sensing development of optimal estimation inversion framework |
| topic | crop population phenotype optimal estimation inversion unmanned aerial vehicle (UAV) hyperspectral multispectral |
| url | https://www.mdpi.com/2073-4395/13/4/1119 |
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