Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
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 in...
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ftmdpi:oai:mdpi.com:/2072-4292/15/9/2350/ 2023-08-20T04:08:47+02:00 Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes Junxiang Peng Niklas Zeiner David Parsons Jean-Baptiste Féret Mats Söderström Julien Morel agris 2023-04-29 application/pdf https://doi.org/10.3390/rs15092350 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Agriculture and Vegetation https://dx.doi.org/10.3390/rs15092350 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 9; Pages: 2350 forage dry matter yield machine learning regression Sentinel-2 high latitudes Text 2023 ftmdpi https://doi.org/10.3390/rs15092350 2023-08-01T09:54:14Z 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. Text Northern Sweden MDPI Open Access Publishing 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 |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
forage dry matter yield machine learning regression Sentinel-2 high latitudes |
spellingShingle |
forage dry matter yield machine learning regression Sentinel-2 high latitudes Junxiang Peng Niklas Zeiner David Parsons Jean-Baptiste Féret Mats Söderström Julien Morel Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes |
topic_facet |
forage dry matter yield machine learning regression Sentinel-2 high latitudes |
description |
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. |
format |
Text |
author |
Junxiang Peng Niklas Zeiner David Parsons Jean-Baptiste Féret Mats Söderström Julien Morel |
author_facet |
Junxiang Peng Niklas Zeiner David Parsons Jean-Baptiste Féret Mats Söderström Julien Morel |
author_sort |
Junxiang Peng |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15092350 |
op_coverage |
agris |
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 |
Remote Sensing; Volume 15; Issue 9; Pages: 2350 |
op_relation |
Remote Sensing in Agriculture and Vegetation https://dx.doi.org/10.3390/rs15092350 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
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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