Estimating Farm Wheat Yields from NDVI and Meteorological Data
Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/5/946 |
| _version_ | 1797534648321966080 |
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| author | Astrid Vannoppen Anne Gobin |
| author_facet | Astrid Vannoppen Anne Gobin |
| author_sort | Astrid Vannoppen |
| collection | DOAJ |
| description | Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (<i>Triticum aestivum</i>) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R<sup>2</sup> = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment. |
| first_indexed | 2024-03-10T11:32:31Z |
| format | Article |
| id | doaj.art-36c5068f18464a2e9777fa6a4c4e784e |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T11:32:31Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-36c5068f18464a2e9777fa6a4c4e784e2023-11-21T19:05:53ZengMDPI AGAgronomy2073-43952021-05-0111594610.3390/agronomy11050946Estimating Farm Wheat Yields from NDVI and Meteorological DataAstrid Vannoppen0Anne Gobin1Vlaamse Instelling voor Technologisch Onderzoek NV, 2400 Mol, BelgiumVlaamse Instelling voor Technologisch Onderzoek NV, 2400 Mol, BelgiumInformation on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (<i>Triticum aestivum</i>) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R<sup>2</sup> = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.https://www.mdpi.com/2073-4395/11/5/946yield estimationNDVIwinter wheat<i>Triticum aestivum</i>Belgiumweather impact |
| spellingShingle | Astrid Vannoppen Anne Gobin Estimating Farm Wheat Yields from NDVI and Meteorological Data Agronomy yield estimation NDVI winter wheat <i>Triticum aestivum</i> Belgium weather impact |
| title | Estimating Farm Wheat Yields from NDVI and Meteorological Data |
| title_full | Estimating Farm Wheat Yields from NDVI and Meteorological Data |
| title_fullStr | Estimating Farm Wheat Yields from NDVI and Meteorological Data |
| title_full_unstemmed | Estimating Farm Wheat Yields from NDVI and Meteorological Data |
| title_short | Estimating Farm Wheat Yields from NDVI and Meteorological Data |
| title_sort | estimating farm wheat yields from ndvi and meteorological data |
| topic | yield estimation NDVI winter wheat <i>Triticum aestivum</i> Belgium weather impact |
| url | https://www.mdpi.com/2073-4395/11/5/946 |
| work_keys_str_mv | AT astridvannoppen estimatingfarmwheatyieldsfromndviandmeteorologicaldata AT annegobin estimatingfarmwheatyieldsfromndviandmeteorologicaldata |