FUNRESO · FUNctional-structural plant models for improved estimation of crop and soil status based on REmote Sensing Observations

FUNctional-structural plant models for improved estimation of crop and soil status based on REmote Sensing Observations

This report outlines the activity carried out at the UMR (Unité Mixte de Recherche) EMMAH (Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes) of the INRA (Institut National pour la Recherche Agronomique) at Avignon (France) in the period 22 August 2008 - 21 August 2009 with the support of the EC Programme PEOPLE-2007-2-1-IEF, contract No. PIEF-GA-2008-2204, by the grant holder Raffaele Casa. The reports is structured in two parts: the first one concerns the research activity and the second one the training and complementary skills development activities carried out during the fellowship. The research proposal aimed at developing a novel approach for improving the accuracy of estimation of crop and soil variables from remote sensing. As a matter of fact, the number of agronomic and environmental variables that can be estimated from remote sensing, as well as the accuracy of estimation, constitute today limiting factors for the development of several applications in the domain of precision agriculture and of regional agronomic and environmental assessments and forecasting. The approach proposed in the project was based on the coupling of a dynamic functional crop model to a 3D canopy structure model, i.e. the development of a Functional structural plant model (FSPM) capable of taking into account in particular water and nitrogen stress responses at the canopy level. The hypothesis that was made was that improvements in remote sensing information retrieval accuracy could be expected by: a) implicit and explicit inclusion of prior knowledge of vegetation properties, allowing attenuation of problems due to the ill-posed nature of information retrieval from remote sensing; and b) better accuracy of canopy reflectance simulations due to the improved realism of such models as compared to classical turbid medium approaches. In this context, the canopy biophysical and biochemical properties that can be retrieved by remote sensing, exploiting the inversion of such more detailed 3D canopy models, would be used for example by data assimilation into dynamic crop functioning models in order to estimate additional crop and soil agronomic and environmental variables which are unattainable by direct estimation from remote sensing. Prior to the development of a proper FSPM, it was considered important to analyse the impact of the accuracy of estimation of canopy properties by remote sensing, as well as of other sources of error and incertitude, on the retrieval of additional agronomic and environmental variables through forcing or assimilation of remote sensing data into crop functioning models. The questions that were raised were: what would be the impact of improvements in the accuracy of retrieval of canopy properties from remote sensing, such as those that can be expected by the development of a coupling scheme between a radiative transfer model and a FSPM, on the estimation of additional agronomic and environmental variables? What are the sources of errors and incertitude having a major impact on such estimation? Consequently, a large part of the research was devoted to a specific activity aimed at answering to such questions, considered as a preliminary and essential step to the subsequent phases. For such activity a comprehensive study based on a database of wheat field trials and simulations with the crop model STICS was put into place. In parallel, the possibility of improving the estimation of canopy properties by remote sensing, offered by the possibility to use FSPM, or 4D models, was assessed by using experimental data obtained on maize, and comparing the inversion of a classical turbid medium model with that of a maize 4D model.

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