ROUMIGUIE Antoine

This paper aims at comparing satellite-derived and in situ yearly variations of grassland production, over nine farms located in France. During three successive years (2016, 2017, 2018), a dense experimental device has allowed to characterize the variability of grassland production, through a regular survey conducted on more than 169 plots. Ranging from 4.1 to 11.2 t.ha -1 , the yearly production were derived from several tens of thousands of measurements, and combined to derive yearly variations of production. Such unique dataset was compared to a grassland production index, derived from the combined use of medium resolution satellite images and meteorological data. A high level of accuracy is observed between the in situ and the satellite-derived yearly variations of grassland production, with a R 2 of 0.76 and a maximum deviation of 16.3%. Finally, a focus on the maps obtained at the national level makes it possible to analyze the context of the 3 studied years.

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Grasslands can help provide solutions to the current economic and environmental challenges faced by livestock farmers. Farmers have high standards as they seek out methods for improving grassland management and forage system security. In the 1980s, a variety of tools were developed to facilitate and enhance grazing management. Today, they are little used because they are often time intensive, difficult to employ, and expensive. Recently developed measurement and communication tools (e.g., WIFI-enabled smartphones and tablets with integrated bluetooth and GPS technologies) show great potential in helping farmers manage their grasslands with greater efficacy and precision. In the intermediate term, remote sensing and access to high-resolution images from satellites or drones present new possibilities.

The index method is considered in the field of agricultural insurance to measure the impact of climate on agricultural production. It is likely to characterize the impact of the random event of drought or flooding. To a lesser extent, this method can be used for other climatic events such as frost. Until now, the deployments of these index methods have been based on data from the measuring stations of the meteorological network of a country. These were mainly rainfall, temperature and in some cases potential evapotranspiration. This type of measurement is technically well mastered and accepted by insurance professionals and their clients. However, these measurements are difficult to spatialize if the number of observation points is not sufficient or the environment is climatically complex. With the development of satellite remote sensing, we have a set of data with variable characteristics (spatial, spectral and temporal resolutions) and complementary allowing a continuous monitoring of the state of vegetation over large areas and at a more or less fine scale (greater than a kilometer to a few hundred meters). In the proposed thesis, the objective is to analyze how remote sensing can be integrated with an index-based insurance. The application framework of the thesis is the forage insurance in France.

An index insurance is proposed in response to the increase in droughts affecting grasslands. It is based on a forage production index (FPI) obtained from medium spatial resolution satellite images to estimate the impact of the hazard in a defined geographical area. The main issue related to the implementation of such an insurance lies in the correct estimation of the losses incurred. The work of this thesis is based on two objectives: the validation of the IPF and the proposal of improvement of this index. A validation protocol is built to limit the problems related to the use of medium resolution products and to the change of scale. The IPF, when compared to different types of reference data, shows good performance: in situ production measurements (R² = 0.81. R² = 0.71), high spatial resolution satellite images (R² = 0.78 - 0.84) and data from modelling (R² = 0.68). The work also allows us to identify ways to improve the IPF processing chain. A new index, based on semiempirical modeling combining satellite data with exogenous data on climatic conditions and grassland phenology, improves the accuracy of production estimates by 18.6%. All of the results obtained open up numerous research perspectives on the development of the IPF and its potential application in the insurance field.

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 - 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 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.

An index-based insurance is being developed to estimate and monitor forage production in France in near real-time based on a forage production index (FPI) derived from the fraction of green vegetation cover (fCover) integral, obtained from medium spatial resolution time series. This article presents the first step of the scientific validation implemented. The grassland parcels, the field protocol established to collect biomass production data, and the method used to get the fCover are described. Local ground measurements of biomass production are compared with FPI values obtained from high-resolution space-based images. Discrepancies between the two variables are quantified by the coefficient of determination, the mean square error and the normalised root mean square error. First, fCover derived from the four sensors are coherent demonstrating the ability of the algorithm used to provide a consistent way of calculating fCover. Second, for the whole data set, the scatter plot between FPI and biomass shows an acceptable correlation (R-2=0.75) improved when only taking into account data recorded up until the production maximum (R-2=0.81). Third, the analysis carried out on the scale of the parcels, grass species, period of mowing or climatic conditions reveals variability on the regression coefficients indicating that other explanatory variables should be integrated to better compute the FPI.

An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km x 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R-2 = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R-2 ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images.

Forage production is very sensitive to climate variability and change. In particular, the increase of extreme drought events makes livestock breeders' incomes unstable and unpredictable. Monitoring vegetation vigor and biomass during the growing season provides a good estimation of the final pastures production. Among the wide range of risk management practices, index-based insurance is a relevant tool to reduce economic shocks in agricultural livelihoods. This type of insurance is not indemnifying client's individually regarding to a loss on a production as usual, but payouts are indexed on an indicator which is correlated to the production. In this work, we present an index insurance solution designed to estimate and monitor the near real-time forage production at national scale. Losses are calculated using a biophysical characterization of the vegetation from remote sensing time series. Producers are indemnified based on the deviation from normal or reference within a defined geographic unit. This product is developed in the framework of a pilot project lead by Pacifica Crédit Agricole Assurances and Astrium GEO-Information Services and initiated in 2010. Pioneering products have been developed to provide insurance to farmers based on vegetation status indicators derived from remotely-sensed images time series. Their analysis highlights the existence of many remaining challenges: dealing with the spatial resolution of satellite images and the size of grassland parcels. managing the existence of missing data within the time series due to cloud contamination. calibrating the biophysical parameter taking into account local soil and climate conditions and also sensors effect. The biophysical parameter used is the fCover obtained from an inversion of radiative transfer models applied on a multi-sensor MODIS/MERIS 10-days images time series. The application of spectral unmixing model enables to determine a fCover time series for grassland signature in a 6*6km grid covering France. We used the annual fCover integral as a surrogate of annual forage production. Thanks to the MODIS/MERIS data archive, the annual forage production is computed since 2000. In case of drought event, farmers are indemnified based on the variation observed within a grid between the annual forage production and the average of annual forage production for the last five years. Compared with existing methods, this approach offers three major advances. Using a biophysical parameter such as the fCover enables a consistency between sensors and over time. Implementation of an automated detection of clouds to build 10-days images improve the constancy of the processing chain. The development of an algorithm to replace remaining missing data within the time series helps to improve the quality of annual forage production estimation. Finally, a validation protocol is conducted over different sites in order to check the reliability of the estimation of forage production. It consists in studying the relation of biomass ground measurements with grassland production estimated by fCover. To this end, a field experiment enables to collect production data on 275 grasslands in 2012 and 541 in 2013.

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