Abstract
The study was conducted on a test plot located in the Yasnogorodsky district of the Tula Region; With a camera with fisheye lens photographs were taken at different points of the plot, each point with a geographical reference. Subsequently, the possible relationship between the information extracted from the classification of the photographs using the CAN-EYE software (LAI, percentage of vegetation) and the vegetation indices (Ratio, NDVI, SAVI and EVI) calculated with spectral values obtained from the different channels of Sentinel-2 (B2, B3, B4, B5, B6, B7, B8, B8a) were evaluated. Finally, best regression models obtained for each phase of the winter wheat development were used to create the maps of LAI and percentage of vegetation. According to our results, Sentinel-2 can be successfully used to map LAI in the studied region at the shooting stage of winter wheat development with accuracy of 85 %. At other stages and for percentage of vegetation the accuracy of the models was below 50 %.
Highlights
Remote sensing of the Earth from space using digital methods and image processing techniques is widely used to monitor environmental changes; and in recent decades it has been widely used in agriculture (Martinez, 2005)
Photosynthesis is one of the key processes in plants that is responsible for the energy and carbon balance
Actual chlorophyll estimates are important for applications such as precision agriculture, since chlorophyll is the main plant constituent determining the reflectance in the visible region of the spectrum, optical remote sensing techniques have great potential in providing information on canopy chlorophyll
Summary
Remote sensing of the Earth from space using digital methods and image processing techniques is widely used to monitor environmental changes; and in recent decades it has been widely used in agriculture (Martinez, 2005). In vegetation studies actual information on canopy chlorophyll content, in addition to properties like leaf area index, biomass and fraction of absorbed photosynthetically active radiation, is important in understanding plant functioning and status (Clevers and Gitelson, 2013). Actual chlorophyll estimates are important for applications such as precision agriculture, since chlorophyll is the main plant constituent determining the reflectance in the visible region of the spectrum, optical remote sensing techniques have great potential in providing information on canopy chlorophyll. LAI is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. LAI is the main determinant of the processes of photosynthesis and the evapotranspiration of crops
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