MOnitoring Crops in Continental Climates through Assimilation of Satellite INformation

    Project: EU research project

    Project Details

    Description

    Information on the outlook of yield and production of crops over large regions is essential for government services, food relief agencies, and international organizations monitoring the world food production and trade. In 2007, unbalances in the global production of agricultural commodities caused the marked prices of agricultural commodities to peak. Given this background the need for a global monitoring system for agricultural production is undisputed and included in the priority themes for GEO.
    In Europe, agricultural monitoring has been implemented through the MARS Crop Yield Forecasting System (MCYFS) operated by JRC and also embedded in the European Union’s GMES initiative. Recently, the MCYFS was extended and now includes the monitoring of crops in all of Russia, Central-Asia and China. These regions are characterized by harsh winter conditions and warm and dry summer conditions. Particularly winter-crops are affected by low temperatures during the winter which determine whether rapid regrowth is possible in spring. However, the effects of winter-kill are poorly described in the crop models used by MCYFS. Earth observation data provide an opportunity to derive such information and can form a basis for real-time updating of wheat growth parameters in the MCYFS.
    Within the proposed project we plan to combine earth observation capabilities as well as improvements in crop modeling approaches. Data from the Russian RESURS-DK and KMSS sensors complemented with observations from the MERIS and MODIS sensors should allow to obtain: 1) masks of winter-wheat planting; 2) time-series of crop-specific variables and 3) maps of (relative) winter kill damage. Moreover, recent advances in crop modeling allow to take EO data into account through data assimilation techniques. Validation of these principles must be carried out both at local scale with observed field data, as well as on regional scale by evaluating the adapted and default models with regional statistics.
    AcronymMOCCCASIN
    StatusFinished
    Effective start/end date1/12/1031/05/13