The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model
The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data an...
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| Format: | Article |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2576 |
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| author | Bingxue Zhu Shengbo Chen Zhengyuan Xu Yinghui Ye Cheng Han Peng Lu Kaishan Song |
| author_facet | Bingxue Zhu Shengbo Chen Zhengyuan Xu Yinghui Ye Cheng Han Peng Lu Kaishan Song |
| author_sort | Bingxue Zhu |
| collection | DOAJ |
| description | The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R<sup>2</sup> greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (V<sub>LAI+LNA</sub>), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (V<sub>LAI</sub> or V<sub>LNA</sub>). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield. |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:55Z |
| publishDate | 2023-05-01 |
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| series | Remote Sensing |
| spelling | doaj.art-f68992f0d5be4fb08ee199cfc5bc5c8a2023-11-18T03:07:07ZengMDPI AGRemote Sensing2072-42922023-05-011510257610.3390/rs15102576The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT ModelBingxue Zhu0Shengbo Chen1Zhengyuan Xu2Yinghui Ye3Cheng Han4Peng Lu5Kaishan Song6College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaThe assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R<sup>2</sup> greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (V<sub>LAI+LNA</sub>), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (V<sub>LAI</sub> or V<sub>LNA</sub>). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield.https://www.mdpi.com/2072-4292/15/10/2576maizegrain protein contentyieldLAILNAdata assimilation |
| spellingShingle | Bingxue Zhu Shengbo Chen Zhengyuan Xu Yinghui Ye Cheng Han Peng Lu Kaishan Song The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model Remote Sensing maize grain protein content yield LAI LNA data assimilation |
| title | The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model |
| title_full | The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model |
| title_fullStr | The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model |
| title_full_unstemmed | The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model |
| title_short | The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model |
| title_sort | estimation of maize grain protein content and yield by assimilating lai and lna retrieved from canopy remote sensing data into the dssat model |
| topic | maize grain protein content yield LAI LNA data assimilation |
| url | https://www.mdpi.com/2072-4292/15/10/2576 |
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