Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images
Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, b...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/14/3/378 |
| _version_ | 1827307505814863872 |
|---|---|
| author | Yan Guo Jia He Huifang Zhang Zhou Shi Panpan Wei Yuhang Jing Xiuzhong Yang Yan Zhang Laigang Wang Guoqing Zheng |
| author_facet | Yan Guo Jia He Huifang Zhang Zhou Shi Panpan Wei Yuhang Jing Xiuzhong Yang Yan Zhang Laigang Wang Guoqing Zheng |
| author_sort | Yan Guo |
| collection | DOAJ |
| description | Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (H<sub>dsm</sub>) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R<sup>2</sup>, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the H<sub>dsm</sub>, are 0.58, 4528.23 kg/hm<sup>2</sup>, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm<sup>2</sup>) is slightly smaller than the measured mean AGB (16,960.23 kg/hm<sup>2</sup>). (2) The R<sup>2</sup>, RMSE, and RPD of the improved AGB estimation model, based on AGB/H<sub>dsm</sub>, are 0.88, 2291.90 kg/hm<sup>2</sup>, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm<sup>2</sup>) is more similar to the measured mean AGB (17,222.59 kg/hm<sup>2</sup>). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the H<sub>dsm</sub>. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios. |
| first_indexed | 2024-04-24T18:40:10Z |
| format | Article |
| id | doaj.art-b00fdbf0a92b4705a96b23e1fd67c084 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-04-24T18:40:10Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-b00fdbf0a92b4705a96b23e1fd67c0842024-03-27T13:15:54ZengMDPI AGAgriculture2077-04722024-02-0114337810.3390/agriculture14030378Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral ImagesYan Guo0Jia He1Huifang Zhang2Zhou Shi3Panpan Wei4Yuhang Jing5Xiuzhong Yang6Yan Zhang7Laigang Wang8Guoqing Zheng9Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, ChinaAboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (H<sub>dsm</sub>) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R<sup>2</sup>, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the H<sub>dsm</sub>, are 0.58, 4528.23 kg/hm<sup>2</sup>, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm<sup>2</sup>) is slightly smaller than the measured mean AGB (16,960.23 kg/hm<sup>2</sup>). (2) The R<sup>2</sup>, RMSE, and RPD of the improved AGB estimation model, based on AGB/H<sub>dsm</sub>, are 0.88, 2291.90 kg/hm<sup>2</sup>, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm<sup>2</sup>) is more similar to the measured mean AGB (17,222.59 kg/hm<sup>2</sup>). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the H<sub>dsm</sub>. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.https://www.mdpi.com/2077-0472/14/3/378aboveground biomassUAVheighttransferabilityBP neural networkmachine learning |
| spellingShingle | Yan Guo Jia He Huifang Zhang Zhou Shi Panpan Wei Yuhang Jing Xiuzhong Yang Yan Zhang Laigang Wang Guoqing Zheng Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images Agriculture aboveground biomass UAV height transferability BP neural network machine learning |
| title | Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images |
| title_full | Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images |
| title_fullStr | Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images |
| title_full_unstemmed | Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images |
| title_short | Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images |
| title_sort | improvement of winter wheat aboveground biomass estimation using digital surface model information extracted from unmanned aerial vehicle based multispectral images |
| topic | aboveground biomass UAV height transferability BP neural network machine learning |
| url | https://www.mdpi.com/2077-0472/14/3/378 |
| work_keys_str_mv | AT yanguo improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT jiahe improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT huifangzhang improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT zhoushi improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT panpanwei improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT yuhangjing improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT xiuzhongyang improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT yanzhang improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT laigangwang improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages AT guoqingzheng improvementofwinterwheatabovegroundbiomassestimationusingdigitalsurfacemodelinformationextractedfromunmannedaerialvehiclebasedmultispectralimages |