Comparison Of Sentinel-2 And Isaria Winter Wheat Mapping For Variable Rate Application Of Nitrogen Fertilizers

MENDELNET 2019: PROCEEDINGS OF 26TH INTERNATIONAL PHD STUDENTS CONFERENCE(2019)

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摘要
Correct application of nitrogen fertilizers in crop management on arable land has high importance both from economic and environmental point of view, mainly in the condition of higher spatial variability of farm fields. One of the solutions could be a variable rate application (VRA) of nitrogen fertilizers based on the spatial variability and identification of management zones, which addresses site specific crop changes, soil characteristics and crop requirements. The aim of the study was to compare proximal and remote sensing systems of winter wheat mapping for variable rate application of nitrogen fertilizers. The methodology of the work was based on the collection of spectral data from the ISARIA proximal crop sensor system, represented by the vegetation indices IRMI and IBI, and set of vegetation indices indices from Sentinel-2 satellite imagery, both on the trial fields. All experimental work was carried out at the farm company SALIX MORAVA a.s. (locality Zdounky, Kromeriz, Czech Republic) during the year 2018 on the selected fields with winter wheat and total area of 355 ha. Spatial data were processed and analyzed by using geographic information systems and then statistically evaluated the relationships between variables. The study has shown higher level of correlation between the ISARIA vegetation indices and all evaluated indices from Sentinel-2. Highest values of correlation with Sentinel-2 indices were achieved for the ISARIA's IRMI index. The most sensitive Sentinel-2 vegetation indices were EVI, GNDVI and SRI indices, while the lowest correlation values were found for the NRERI, REIP, RENDVI and S2REP indices. Overall, the results of this study indicate high relationship between proximal and remote sensing, thus the use by farmers depends on the practical aspects of their in crop management practices.
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关键词
precision agriculture, crop sensing, Sentinel-2, vegetation indices
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