Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
International audience Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely suppo...
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Online Access: | https://hal.inrae.fr/hal-04118354 https://hal.inrae.fr/hal-04118354/document https://hal.inrae.fr/hal-04118354/file/Peng%20J-2023.pdf https://doi.org/10.3390/rs15092350 |
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ftccsdartic:oai:HAL:hal-04118354v1 2024-02-27T08:43:55+00:00 Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes Peng, Junxiang Zeiner, Niklas Parsons, David Féret, Jean-Baptiste Söderström, Mats Morel, Julien Swedish University of Agricultural Sciences (SLU) Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) 2023-05 https://hal.inrae.fr/hal-04118354 https://hal.inrae.fr/hal-04118354/document https://hal.inrae.fr/hal-04118354/file/Peng%20J-2023.pdf https://doi.org/10.3390/rs15092350 en eng HAL CCSD MDPI info:eu-repo/semantics/altIdentifier/doi/10.3390/rs15092350 hal-04118354 https://hal.inrae.fr/hal-04118354 https://hal.inrae.fr/hal-04118354/document https://hal.inrae.fr/hal-04118354/file/Peng%20J-2023.pdf doi:10.3390/rs15092350 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2072-4292 Remote Sensing https://hal.inrae.fr/hal-04118354 Remote Sensing, 2023, 15 (9), pp.2350. ⟨10.3390/rs15092350⟩ Forage Dry matter yield Machine learning regression Sentinel-2 High latitudes [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2023 ftccsdartic https://doi.org/10.3390/rs15092350 2024-01-28T00:44:03Z International audience Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance. Article in Journal/Newspaper Northern Sweden Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Sutcliffe ENVELOPE(-81.383,-81.383,50.683,50.683) Remote Sensing 15 9 2350 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
Forage Dry matter yield Machine learning regression Sentinel-2 High latitudes [SDE]Environmental Sciences |
spellingShingle |
Forage Dry matter yield Machine learning regression Sentinel-2 High latitudes [SDE]Environmental Sciences Peng, Junxiang Zeiner, Niklas Parsons, David Féret, Jean-Baptiste Söderström, Mats Morel, Julien Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
topic_facet |
Forage Dry matter yield Machine learning regression Sentinel-2 High latitudes [SDE]Environmental Sciences |
description |
International audience Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance. |
author2 |
Swedish University of Agricultural Sciences (SLU) Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
format |
Article in Journal/Newspaper |
author |
Peng, Junxiang Zeiner, Niklas Parsons, David Féret, Jean-Baptiste Söderström, Mats Morel, Julien |
author_facet |
Peng, Junxiang Zeiner, Niklas Parsons, David Féret, Jean-Baptiste Söderström, Mats Morel, Julien |
author_sort |
Peng, Junxiang |
title |
Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
title_short |
Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
title_full |
Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
title_fullStr |
Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
title_full_unstemmed |
Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
title_sort |
forage biomass estimation using sentinel-2 imagery at high latitudes |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.inrae.fr/hal-04118354 https://hal.inrae.fr/hal-04118354/document https://hal.inrae.fr/hal-04118354/file/Peng%20J-2023.pdf https://doi.org/10.3390/rs15092350 |
long_lat |
ENVELOPE(-62.350,-62.350,-74.233,-74.233) ENVELOPE(-81.383,-81.383,50.683,50.683) |
geographic |
Nash Sutcliffe |
geographic_facet |
Nash Sutcliffe |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
ISSN: 2072-4292 Remote Sensing https://hal.inrae.fr/hal-04118354 Remote Sensing, 2023, 15 (9), pp.2350. ⟨10.3390/rs15092350⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs15092350 hal-04118354 https://hal.inrae.fr/hal-04118354 https://hal.inrae.fr/hal-04118354/document https://hal.inrae.fr/hal-04118354/file/Peng%20J-2023.pdf doi:10.3390/rs15092350 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.3390/rs15092350 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
9 |
container_start_page |
2350 |
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1792052000776519680 |