Insuring forage through satellites: testing alternative indices against grassland production estimates for France. Issue 7 (3rd April 2017)

• Record Type: Journal Article
• Authors: Roumiguié, Antoine; Sigel, Grégoire; Poilvé, Hervé; Bouchard, Bruno; Vrieling, Anton; Jacquin, Anne
• Is Part Of: International journal of remote sensing. Volume 38:Issue 7(2017)
• Page Start: 1912
• Page End: 1939
• Publication Date: 2017-04-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1230288
• Languages: English
• ISSNs: 0143-1161

Abstract

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 ( f APAR); (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 ( f Cover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error – SRMSE = 0.57 and coefficient of determination – R 2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R 2 = 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 R 2 = 0.83) in the insurance product. This model integrates daily f Cover 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 f Cover annual profile.