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Remote sensing imagery derived from different sensors has different crop identification capability. due to the different spatial and temporal resolution, availability of certain spectral bands. Landsat OLI and RapidEye provide an important part of data sources for agricultural monitoring with a relative long historic records. As the availability of the data from these two sensors is totally different, an analysis of the crop identification capability using these sensors could provide the stakeholders data source selection for the operational perspectives. This paper took Bei'an city, Heilongjiang province as the study area, where major planted crops are maize and soybean. The authors used maximum likelihood classification methods to try tried to identify best registration phase, best spectral band combination for crop type identification in the study region. Especially, the authors evidenced the identification improvement contributed by the unique short infrared wave band from LANDSAT OLI and red edge band from RapidEye.

Morocco has an agricultural production system for cereals which is dominated by rainfed, low yielding cereal production which is highly vulnerable to fluctuations in rainfall. Crop yield forecasting systems could play a significant role to reduce the vulnerability of the Moroccan agriculture to weather risks in the framework of a food security strategy. This paper describes the efforts that were carried out for adapting the European Crop Growth Monitoring System to Moroccan conditions for crop monitoring and regional yield forecasting. Results demonstrate that the correlations between CGMS output and regional reported yields can be strongly improved by relatively simple changes to the parameterization of the crop model and a different strategy for the initialization of the soil water balance.

Doctorat en Sciences, info:eu-repo/semantics/nonPublished