Autor/s: Kurz, F.*, Martínez, L.**, Palà, V.**, Arbiol, R.**, Villar, P.***, Calvet, F.***, Villar, P., Calvet, F. Títol: Assimilation of hyperspectral data into crop growth models: Precision farming application for maize in Catalonia Publicat a: Revista Catalana de Geografia IV època / volumn XII / núm. 31 / juliol 2007 Font: IGARSS 2005. Seoul (Corea), juliol 2005 URL: http://www.rcg.cat/articles.php?id=96 ASSIMILATION OF HYPERSPECTRAL DATA INTO CROP GROWTH MODELS. PRECISION FARMING APPLICATION FOR MAIZE IN CATALONIA Franz Kurz. Institute of Remote Sensing Technology, DLR – German Aerospace Center. Oberpfaffenhofen, Germany Franz.Kurz@dlr.de Lucas Martínez, Vicenç Palà, Roman Arbiol. Remote Sensing Department, ICC – Institut Cartogràfic de Catalunya. Barcelona, Spain lmartinez@icc.es , vicencp@icc.es , arbiol@icc.es Pere Villar, Ferran Calvet. LAF – Laboratori d'Anàlisi i Fertilitat dels Sòls Sidamon. Spain pvillar@lafsols.com , fcalvet@lafsols.com I. INTRODUCTION The agriculture in Catalonia (Spain) suffers from the excessive use of fertilization and the waste of irrigation water. Over-application of pig manure and flood irrigations can cause severe environmental damage. Thus, development of improved cultivation techniques, stricter controls, and gathering of more detailed information about the current crop state are necessary to solve these environmental problems. Recent developments in the agriculture tend to dose the amount of fertilizer according to the needs of the plants and the capacity of the soil. This has been made possible through a better understanding of complex mechanisms in the soil and their interactions with the plants. Besides, the remote sensing techniques have reached a development state which allows the derivation of important agricultural information with a reasonable temporal and spatial resolution in order to allow the control of the cultivation practice and to support the agriculturist decisions. The goal is the development of a system based on hyperspectral remote sensing data which provides agricultural relevant information about the crop fields at a reasonable level of costs. The information will be offered to farmers or governmental institutions interested in the monitoring of the cultivation to improve the environmental conditions. The paper is organized as follows: Chapter II describes the proposed method to derive agricultural relevant parameters from hyperspectral CASI data based on physical models. In Chapter III, the concept of data assimilation is explained in more detail. Chapter IV describes the measurement and flight campaigns, which took place at maize test sites in Catalonia. In chapter V, the results of calibration and validation of the proposed method and the crop growth models are described and discussed. II. DERIVATION OF CROP PARAMETERS FROMHYPERSPECTRAL DATA A. Radiative transfer models The radiative transfer plays an important role in modern remote sensing techniques, as optical remote sensing sensors measure the earth surface reflected solar irradiation. For this project, we choose the ACRM canopy spectral model (A twolayer Canopy Reflectance Model) [5] for the calculation of the radiative transfer within vegetation canopies. It is based on the homogeneous multispectral model MSRM [6] and the SAIL model, which models the diffuse radiative transfer. The ACRM model accounts for non-lambertian soil reflectance, specular reflection of direct sun rays on leaves, the hot spot effect, a two-parameter leaf angle distribution and the spectral effect of cultivation rows. For the spectral properties of leaves, we choose the PROSPECT model [4]. B. Inverse problem The derivation of crop parameters from hyperspectral remote sensing data can be regarded as an optimization problem, i.e. the goal is to search the set of crop and soil parameters, which are “best” suited to the observations. One possibility to solve this problem is to search the most probable set of parameters, which is the basis of the maximum likelihood methods. In our case, the goal is to find a set of unknown crop and soil parameters, for which the a posteriori probability of the parameters Vˆ with measured reflectances r* is maximal. A basic problem is that the number of parameters describing the scene exceeds the number of the independent observations available. The reasons for this are the complexity of the vegetated earth surface and the influences on the measured reflectances. Therefore, it is impossible to find unique estimators only by relying on the measured reflectances, because the inverse function is ill-posed. A first step to solve this problem is to reduce the number of variable parameters, i.e. the input parameters of the physical models are divided in variable V and constant c. Revista Catalana de Geografia Revista digital de geografia, cartografia i ciències de la Terra