Insuring forage through satellites: testing alternative indices against grassland production estimates for France

Antoine Roumiguié, Grégoire Sigel, Hervé Poilvé, Bruno Bouchard, A. Vrieling, Anne Jacquin

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

To mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling. All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies – ISOP – system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error - SRMSE = 0.57 and coefficient of determination – R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.
Original languageEnglish
Pages (from-to)1912-1939
JournalInternational journal of remote sensing
Volume38
Issue number7
DOIs
Publication statusPublished - 20 Feb 2017

Fingerprint

forage
grassland
photosynthetically active radiation
productivity
index
light use efficiency
senescence
water stress
prairie
vegetation cover
MODIS
modeling
satellite data
pasture
growing season
remote sensing
climate
monitoring

Keywords

  • ITC-ISI-JOURNAL-ARTICLE

Cite this

Roumiguié, Antoine ; Sigel, Grégoire ; Poilvé, Hervé ; Bouchard, Bruno ; Vrieling, A. ; Jacquin, Anne. / Insuring forage through satellites : testing alternative indices against grassland production estimates for France. In: International journal of remote sensing. 2017 ; Vol. 38, No. 7. pp. 1912-1939.
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abstract = "To mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling. All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies – ISOP – system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error - SRMSE = 0.57 and coefficient of determination – R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.",
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Insuring forage through satellites : testing alternative indices against grassland production estimates for France. / Roumiguié, Antoine; Sigel, Grégoire; Poilvé, Hervé; Bouchard, Bruno; Vrieling, A.; Jacquin, Anne.

In: International journal of remote sensing, Vol. 38, No. 7, 20.02.2017, p. 1912-1939.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Insuring forage through satellites

T2 - testing alternative indices against grassland production estimates for France

AU - Roumiguié, Antoine

AU - Sigel, Grégoire

AU - Poilvé, Hervé

AU - Bouchard, Bruno

AU - Vrieling, A.

AU - Jacquin, Anne

PY - 2017/2/20

Y1 - 2017/2/20

N2 - To mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling. All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies – ISOP – system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error - SRMSE = 0.57 and coefficient of determination – R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.

AB - To mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling. All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies – ISOP – system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error - SRMSE = 0.57 and coefficient of determination – R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.

KW - ITC-ISI-JOURNAL-ARTICLE

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