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Estimation of the spatial distribution of spring barley yield using ground-based and satellite spectrophotometric data / A. P. Shevyrnogov [et al.] // IOP Conference Series: Earth and Environmental Science : Institute of Physics Publishing, 2019. - Vol. 315: International Scientific Conference on Agribusiness, Environmental Engineering and Biotechnologies, AGRITECH 2019 (20 June 2019 through 22 June 2019, ) Conference code: 152072, Is. 3. - Ст. 032023, DOI 10.1088/1755-1315/315/3/032023
Кл.слова (ненормированные):
Biotechnology -- Environmental technology -- Photomapping -- Seed -- Crop development -- Field experience -- Optical characteristics -- Precision agriculture technology -- Resource-saving technologies -- Spatial resolution -- Spring barley yields -- Vegetation index -- Spatial distribution
Аннотация: The article presents a method for estimating the spatial distribution of spring barley yield, based on the use of optical ground and satellite spectral data (PlanetScope data with a spatial resolution of 3 meters). This approach is highly relevant for the development of precision agriculture technologies. Yield mapping is carried out on the basis of data on the spatial distribution of the actual yield and the spatial distribution of the spectral optical characteristics. The method's characteristic feature is the use of the integral values of vegetation indices (NDVI, MSAVI2, ClGreen) at various stages of crop development. The method was tested on the basis of stationary field experience, where traditional agriculture (deep plowing) is compared with resource-saving technologies (subsurface and surface plowing, and direct seeding with zero tillage). © 2019 IOP Publishing Ltd. All rights reserved.

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Держатели документа:
Institute of Biophysics SB RAS, Federal Research Center, Krasnoyarsk Science Center, SB RAS, Krasnoyarsk, Akademgorodok, 660036, Russian Federation
Krasnoyarsk State Agrarian University, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Shevyrnogov, A. P.; Yu Botvich, I.; Yemelianov, D. V.; Larko, A. A.; Ivchenko, V. K.; Demianenko, T. N.

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Use of spectral surface characteristics for mapping soil cover structure under Krasnoyarsk forest-steppe conditions / V. Latysheva, T. Demyanenko, I. Botvich [et al.] // E3S Web of Conferences : EDP Sciences, 2020. - Vol. 223: 2020 Regional Problems of Earth Remote Sensing, RPERS 2020 (29 September 2020 through 2 October 2020, ) Conference code: 166122. - Ст. 03003, DOI 10.1051/e3sconf/202022303003
Кл.слова (ненормированные):
Humus -- Multiple regression -- Particle-size distribution -- Soil cover structure -- Spectral brightness coefficient -- Carbon dioxide -- Forestry -- Particle size -- Photomapping -- Remote sensing -- Soil testing -- Field spectrometry -- Informative parameters -- Particle-size fractions -- Regression equation -- Soil cover -- Soil property -- Surface characteristics -- Test parameters -- Soils
Аннотация: The relations between the spectral surface characteristics of the elements of the soil cover structure and soil properties in the Krasnoyarsk forest-steppe of Central Siberia were investigated. It was revealed that the most informative parameters for field spectrometry are the content of humus, carbonate carbon dioxide and the prevailing particle-size fractions. A statistically significant relationship between the elements of the soil cover structure and the reflectivity of soils has been confirmed by means of multidimensional statistics. The wave lengths with the greatest coupling force are highlighted. Regression equations for remote study of soil cover structure have been obtained, which can be used if additional point studies are carried out in a wider range of test parameters. © The Authors, published by EDP Sciences, 2020.

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Держатели документа:
Fed. State Budget Educational Institution of Higher Education Krasnoyarsk State Agrarian University, 90 Mira Avenue, Krasnoyarsk City, 660049, Russian Federation
Institute of Biophysics SB RAS, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Latysheva, V.; Demyanenko, T.; Botvich, I.; Emelyanov, D.; Khizhnyak, S.

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