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Estimation of rice Plant Nitrogen Content from MERIS using a field-based empirical model

Mirco Boschetti(1), Daniela Stroppiana(1), Alexandra Rosenmund(2) and Claudia Giardino(1)

(1) Consiglio Nazionale delle Ricerche - IREA, Via Bassini 15, 20133 Milano, Italy
(2) EC-JRC, MARS-STAT, Via Enrico Fermi 1, 21020 Ispra, Italy


Physiological crop models are able to describe growth and development of cultivated plants and are very useful to provide estimation of crop production. However, most crop growth models have been developed at the field scale and their performance is not satisfactory when they are extended to regional scales. The most critical issue is that they have been constrained by the lack of a description of the real spatial variability of the variables involved in modelling. Contribution of remote sensing in mechanicistic model applications relies on the possibility of providing useful information about the state variables that describe plant status. The WARM model, recently developed for rice crop yield simulation by MARS-STAT-JRC, considers Plant Nitrogen Content (PNC) to describe in a most realistic way changes in crop radiation use efficiency (RUE). Preliminary studies conducted on field data showed that it is possible to quantify PNC by exploiting a vegetation index that makes use of narrow bands in the visible (blue/green) region of the electromagnetic spectrum where nitrogen/chlorophyll compounds play a key role in radiation absorption. In this research we tested the possibility of using MERIS data to scale up an empirical model derived from field data. Three years of LP2 MERIS data (2004-2005-2006) were selected in the phenological period between panicle initiation and flowering. Images were visually analysed to discard cloudy data and atmospheric correction implemented using the BAER algorithm in VISAT software. The empirical model was rescaled on real MERIS data and PNC was estimated. The output maps provide reliable estimates of PNC in terms of absolute values and temporal trends. Finally, forcing the WARM model using these data provided significantly better estimates of rice yield for all the years analysed.


Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry