Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data
Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The...
| Huvudupphovsmän: | , , , , , |
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| Materialtyp: | Artikel |
| Språk: | English |
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MDPI AG
2016-11-01
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| Serie: | Remote Sensing |
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| Länkar: | http://www.mdpi.com/2072-4292/8/12/972 |
| _version_ | 1831640294148276224 |
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| author | Xiuliang Jin Lalit Kumar Zhenhai Li Xingang Xu Guijun Yang Jihua Wang |
| author_facet | Xiuliang Jin Lalit Kumar Zhenhai Li Xingang Xu Guijun Yang Jihua Wang |
| author_sort | Xiuliang Jin |
| collection | DOAJ |
| description | Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R2), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data. |
| first_indexed | 2024-12-19T12:04:24Z |
| format | Article |
| id | doaj.art-c6c250090b7e4873b78f94e7ea337b91 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-19T12:04:24Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c6c250090b7e4873b78f94e7ea337b912022-12-21T20:22:24ZengMDPI AGRemote Sensing2072-42922016-11-0181297210.3390/rs8120972rs8120972Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral DataXiuliang Jin0Lalit Kumar1Zhenhai Li2Xingang Xu3Guijun Yang4Jihua Wang5Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaEcosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, AustraliaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaKnowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R2), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data.http://www.mdpi.com/2072-4292/8/12/972biomassyieldAquaCrop modelspectral indexparticle swarm optimizationwinter wheat |
| spellingShingle | Xiuliang Jin Lalit Kumar Zhenhai Li Xingang Xu Guijun Yang Jihua Wang Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data Remote Sensing biomass yield AquaCrop model spectral index particle swarm optimization winter wheat |
| title | Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data |
| title_full | Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data |
| title_fullStr | Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data |
| title_full_unstemmed | Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data |
| title_short | Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data |
| title_sort | estimation of winter wheat biomass and yield by combining the aquacrop model and field hyperspectral data |
| topic | biomass yield AquaCrop model spectral index particle swarm optimization winter wheat |
| url | http://www.mdpi.com/2072-4292/8/12/972 |
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