Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model
Abstract Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-09535-9 |
| _version_ | 1811272434778636288 |
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| author | Chunyan Ma Mingxing Liu Fan Ding Changchun Li Yingqi Cui Weinan Chen Yilin Wang |
| author_facet | Chunyan Ma Mingxing Liu Fan Ding Changchun Li Yingqi Cui Weinan Chen Yilin Wang |
| author_sort | Chunyan Ma |
| collection | DOAJ |
| description | Abstract Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation. |
| first_indexed | 2024-04-12T22:40:14Z |
| format | Article |
| id | doaj.art-266150018fbe454ba94c0313dc7c9b08 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T22:40:14Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-266150018fbe454ba94c0313dc7c9b082022-12-22T03:13:46ZengNature PortfolioScientific Reports2045-23222022-03-0112111610.1038/s41598-022-09535-9Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth modelChunyan Ma0Mingxing Liu1Fan Ding2Changchun Li3Yingqi Cui4Weinan Chen5Yilin Wang6School of Surveying and Land Information Engineering, Henan Polytechnic UniversityZhangzhou Institute of Surverying and MappingSchool of Surveying and Land Information Engineering, Henan Polytechnic UniversitySchool of Surveying and Land Information Engineering, Henan Polytechnic UniversitySchool of Surveying and Land Information Engineering, Henan Polytechnic UniversitySchool of Surveying and Land Information Engineering, Henan Polytechnic UniversitySchool of Surveying and Land Information Engineering, Henan Polytechnic UniversityAbstract Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.https://doi.org/10.1038/s41598-022-09535-9 |
| spellingShingle | Chunyan Ma Mingxing Liu Fan Ding Changchun Li Yingqi Cui Weinan Chen Yilin Wang Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model Scientific Reports |
| title | Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model |
| title_full | Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model |
| title_fullStr | Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model |
| title_full_unstemmed | Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model |
| title_short | Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model |
| title_sort | wheat growth monitoring and yield estimation based on remote sensing data assimilation into the safy crop growth model |
| url | https://doi.org/10.1038/s41598-022-09535-9 |
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