Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2
[EN] Accurate estimation of forage yield is essential, as forage plays an indirect yet vital role in the global food supply. Various methods were developed to obtain quantitative forage yield estimates from remotely sensed data. This study's main goal was to estimate forage yield from Sentinel-...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/167795 |
| Acceso en línea: | http://hdl.handle.net/10366/167795 |
| Access Level: | acceso abierto |
| Palabra clave: | Aqua Crop Forage yield estimation Satellite-based NDVI time series Second derivative Cultivos acuícolas Estimación del rendimiento de forraje Series temporales del NDVI basadas en satélite Segunda derivada 3103.07 Cultivos Forrajeros 5102.01 Agricultura 2506.16 Teledetección (Geología) |
| id |
ES_9b2c703f14ae53cb7ff13af24d4e6b72 |
|---|---|
| oai_identifier_str |
oai:gredos.usal.es:10366/167795 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| title |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| spellingShingle |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 Pouyez, Léa Aqua Crop Forage yield estimation Satellite-based NDVI time series Second derivative Cultivos acuícolas Estimación del rendimiento de forraje Series temporales del NDVI basadas en satélite Segunda derivada 3103.07 Cultivos Forrajeros 5102.01 Agricultura 2506.16 Teledetección (Geología) |
| title_short |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| title_full |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| title_fullStr |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| title_full_unstemmed |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| title_sort |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2 |
| dc.creator.none.fl_str_mv |
Pouyez, Léa Sánchez Martín, Nilda Plaza Martín, Javier Palacios Riocerezo, Carlos |
| author |
Pouyez, Léa |
| author_facet |
Pouyez, Léa Sánchez Martín, Nilda Plaza Martín, Javier Palacios Riocerezo, Carlos |
| author_role |
author |
| author2 |
Sánchez Martín, Nilda Plaza Martín, Javier Palacios Riocerezo, Carlos |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Aqua Crop Forage yield estimation Satellite-based NDVI time series Second derivative Cultivos acuícolas Estimación del rendimiento de forraje Series temporales del NDVI basadas en satélite Segunda derivada 3103.07 Cultivos Forrajeros 5102.01 Agricultura 2506.16 Teledetección (Geología) |
| topic |
Aqua Crop Forage yield estimation Satellite-based NDVI time series Second derivative Cultivos acuícolas Estimación del rendimiento de forraje Series temporales del NDVI basadas en satélite Segunda derivada 3103.07 Cultivos Forrajeros 5102.01 Agricultura 2506.16 Teledetección (Geología) |
| description |
[EN] Accurate estimation of forage yield is essential, as forage plays an indirect yet vital role in the global food supply. Various methods were developed to obtain quantitative forage yield estimates from remotely sensed data. This study's main goal was to estimate forage yield from Sentinel-2 time series imagery from 2021 and 2022. The novelty of the approach lies in adapting a method originally derived from the AquaCrop model by replacing the crop transpiration coefficient with interpolated values of the normalised difference vegetation index (NDVI). This method, previously implemented with drone imagery, enables rapid, large-scale estimation of forage production. The experiment was conducted across seven agricultural fields in the province of Salamanca, Spain. NDVI values were extracted from Sentinel-2A and Sentinel-2B bottom-of-atmosphere reflectance (BOA) at different dates that span the growing period. The sum of NDVI values between selected thresholds was calculated and multiplied by a standard water productivity coefficient. Final yield estimates were validated against direct forage yield measurements collected by farmers at the seven sites. Results demonstrate good performance of the Sentinel-2 time series in estimating forage yield, with correlation coefficients exceeding 0.75 and errors below 30% when compared to observed yields. This method offers a fast, scalable, and effective alternative to traditional yield measurement techniques. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2025 2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10366/167795 |
| url |
http://hdl.handle.net/10366/167795 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.rights.none.fl_str_mv |
Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Wiley |
| publisher.none.fl_str_mv |
Wiley |
| dc.source.none.fl_str_mv |
reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca instname:Universidad de Salamanca (USAL) |
| instname_str |
Universidad de Salamanca (USAL) |
| reponame_str |
GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| collection |
GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869414482768297984 |
| spelling |
Estimation of Forage Yield Using the Second Derivative of Normalized Difference Vegetation Index Time Series From Sentinel‐2Pouyez, LéaSánchez Martín, NildaPlaza Martín, JavierPalacios Riocerezo, CarlosAqua CropForage yield estimationSatellite-based NDVI time seriesSecond derivativeCultivos acuícolasEstimación del rendimiento de forrajeSeries temporales del NDVI basadas en satéliteSegunda derivada3103.07 Cultivos Forrajeros5102.01 Agricultura2506.16 Teledetección (Geología)[EN] Accurate estimation of forage yield is essential, as forage plays an indirect yet vital role in the global food supply. Various methods were developed to obtain quantitative forage yield estimates from remotely sensed data. This study's main goal was to estimate forage yield from Sentinel-2 time series imagery from 2021 and 2022. The novelty of the approach lies in adapting a method originally derived from the AquaCrop model by replacing the crop transpiration coefficient with interpolated values of the normalised difference vegetation index (NDVI). This method, previously implemented with drone imagery, enables rapid, large-scale estimation of forage production. The experiment was conducted across seven agricultural fields in the province of Salamanca, Spain. NDVI values were extracted from Sentinel-2A and Sentinel-2B bottom-of-atmosphere reflectance (BOA) at different dates that span the growing period. The sum of NDVI values between selected thresholds was calculated and multiplied by a standard water productivity coefficient. Final yield estimates were validated against direct forage yield measurements collected by farmers at the seven sites. Results demonstrate good performance of the Sentinel-2 time series in estimating forage yield, with correlation coefficients exceeding 0.75 and errors below 30% when compared to observed yields. This method offers a fast, scalable, and effective alternative to traditional yield measurement techniques.[ES] La estimación precisa del rendimiento de forraje es esencial, ya que este desempeña un papel indirecto pero vital en el suministro mundial de alimentos. Se han desarrollado diversos métodos para obtener estimaciones cuantitativas del rendimiento de forraje a partir de datos de teledetección. El objetivo principal de este estudio fue estimar el rendimiento de forraje a partir de imágenes de series temporales de Sentinel-2 de 2021 y 2022. La novedad del enfoque radica en la adaptación de un método derivado originalmente del modelo AquaCrop, sustituyendo el coeficiente de transpiración del cultivo por valores interpolados del índice de vegetación de diferencia normalizada (NDVI). Este método, previamente implementado con imágenes de drones, permite una estimación rápida y a gran escala de la producción de forraje. El experimento se llevó a cabo en siete campos agrícolas de la provincia de Salamanca, España. Los valores de NDVI se extrajeron de la reflectancia en la base de la atmósfera (BOA) de Sentinel-2A y Sentinel-2B en diferentes fechas que abarcan el período de crecimiento. Se calculó la suma de los valores de NDVI entre umbrales seleccionados y se multiplicó por un coeficiente estándar de productividad del agua. Las estimaciones finales de rendimiento se validaron mediante mediciones directas del rendimiento de forraje recopiladas por los agricultores en los siete sitios. Los resultados demuestran el buen desempeño de la serie temporal Sentinel-2 en la estimación del rendimiento de forraje, con coeficientes de correlación superiores a 0,75 y errores inferiores al 30 % en comparación con los rendimientos observados. Este método ofrece una alternativa rápida, escalable y eficaz a las técnicas tradicionales de medición del rendimiento.Wiley202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/167795reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1677952026-06-07T06:28:51Z |
| score |
15,811543 |