Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables
A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2022-01-01
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| Series: | Revista de Teledetección |
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| Online Access: | https://polipapers.upv.es/index.php/raet/article/view/15099 |
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| author | Cristian Iranzo Raquel Montorio Alberto García-Martín |
| author_facet | Cristian Iranzo Raquel Montorio Alberto García-Martín |
| author_sort | Cristian Iranzo |
| collection | DOAJ |
| description | A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models. |
| format | Article |
| id | doaj-art-42f74f0af0454677a8dc4906dff82e19 |
| institution | Kabale University |
| issn | 1133-0953 1988-8740 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Revista de Teledetección |
| spelling | doaj-art-42f74f0af0454677a8dc4906dff82e192024-12-02T01:49:53ZengUniversitat Politècnica de ValènciaRevista de Teledetección1133-09531988-87402022-01-01059597010.4995/raet.2022.150999238Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variablesCristian Iranzo0Raquel Montorio1Alberto García-Martín2Universidad de ZaragozaUniversidad de ZaragozaCentro Universitario de la Defensa de Zaragoza, Academia General Militar ; Universidad de ZaragozaA precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models.https://polipapers.upv.es/index.php/raet/article/view/15099agriculturaíndices de vegetacióncalendario agronómicoregresión múltiplegoogle earth engine |
| spellingShingle | Cristian Iranzo Raquel Montorio Alberto García-Martín Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables Revista de Teledetección agricultura índices de vegetación calendario agronómico regresión múltiple google earth engine |
| title | Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables |
| title_full | Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables |
| title_fullStr | Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables |
| title_full_unstemmed | Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables |
| title_short | Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables |
| title_sort | estimation of barley yield from sentinel 1 and sentinel 2 imagery and climatic variables |
| topic | agricultura índices de vegetación calendario agronómico regresión múltiple google earth engine |
| url | https://polipapers.upv.es/index.php/raet/article/view/15099 |
| work_keys_str_mv | AT cristianiranzo estimationofbarleyyieldfromsentinel1andsentinel2imageryandclimaticvariables AT raquelmontorio estimationofbarleyyieldfromsentinel1andsentinel2imageryandclimaticvariables AT albertogarciamartin estimationofbarleyyieldfromsentinel1andsentinel2imageryandclimaticvariables |