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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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Estimation of the spatial distribution of spring barley yield (Krasnoyarsk Territory) from ground and satellite spectrophotometric data / I. Yu. Botvich [и др.] // Sovrem. Probl. Distancionnogo Zondirovania Zemli kosm. - 2019. - Vol. 16, Is. 5. - С. 183-193, DOI 10.21046/2070-7401-2019-16-5-183-193 . - ISSN 2070-7401
Кл.слова (ненормированные):
Barley -- Crop yield -- Growing season -- PlanetScope -- Precision farming -- Spectroradiometer -- Types of tillage
Аннотация: The paper presents a method for estimating the spatial distribution of spring barley yield, implemented based on the use of optical ground and satellite spectral data (PlanetScope with a spatial resolution of 3 meters). This approach is highly relevant for the development of precision farming technologies. Yield mapping is carried out on the basis of the data on spatial distribution of actual yield and spatial distribution of spectral optical characteristics. A feature of the method is the use of the integral values of vegetation indices (NDVI, MSAVI2, ClGreen) at various stages of crop development. Testing of the method was performed on the basis of stationary field experience, when traditional agriculture (deep plowing) was compared with resource-saving technologies (flat-cut, surface treatments and direct seeding at zero tillage). As a result, a method for estimating the spatial distribution of spring barley yield, implemented using optical ground and satellite spectral data (PlanetScope with a spatial resolution of 3 meters) was developed. The prediction of barley yields at the end of July on the basis of a linear regression model was performed, the values of the integral under the NDVI curve in different periods of time were used as parameters. The type of a multiple linear model for predicting barley with 7 variables was established (the coefficient of determination is 0.73; the root-mean-square error is 1.5). The spatial distribution of barley yield by satellite (PlanetScope) and ground data was mapped. The resulting yield maps will be used when planning work for the next year. © 2019 Space Research Institute of the Russian Academy of Sciences. All rights reserved.

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Держатели документа:
Institute of Biophysics SB RAS, Krasnoyarsk, 660036, Russian Federation
Krasnoyarsk State Agrarian University, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Botvich, I. Yu.; Emelyanov, D. V.; Larko, A. A.; Malchikov, N. O.; Ivchenko, V. K.; Demyanenko, T. N.; Shevyrnogov, A. P.

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The use of the data derived from the PlanetScope satellite and unmanned aerial vehicles to estimate crop yield as dependent on the amount of nitrogen fertilizer applied / N. O. Malchikov, T. I. Pisman, I. Y. Botvich [et al.] // IOP Conference Series: Earth and Environmental Science : IOP Publishing Ltd, 2021. - Vol. 839: 5th International Scientific Conference on Agribusiness, Environmental Engineering and Biotechnologies, AGRITECH-V 2021 (16 June 2021 through 19 June 2021, ) Conference code: 172484, Is. 2. - Ст. 022004, DOI 10.1088/1755-1315/839/2/022004
Аннотация: The purpose of the present study is to show the usefulness of the satellite data and the data derived from unmanned aerial vehicles (UAVs) for estimating the relationship between cereal grain crop yield and the amount of nitrogen fertilizer applied. The study was conducted on the land of the Kuraginskoye Research Farm. The study material was spring barley cv. Biom. Three test plots were studied; mineral fertilizer, urea, was used in different quantities for foliar application in June; applications were performed at equal intervals. Multispectral images were based on PlanetScope satellite data, with the 3 m spatial resolution, and the data derived from the DJI Phantom 4 Multispectral UAV, with the 10 cm resolution. The satellite and UAV data were used to calculate spectral vegetation index (NDVI) (Normalized Difference Vegetation Index). A high correlation was obtained between the NDVI values calculated using satellite data and UAV data. The satellite data provided the basis for assessing barley crop yield as dependent on the amount of foliar-applied urea during the growing season. Maps of the spatial distribution of barley NDVI were constructed using the Phantom UAV data; they showed that the third foliar application of the fertilizer was not economically justified. © Published under licence by IOP Publishing Ltd.

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Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, Russian Federation
Kuragino RF, FRC KSC SB RAS, Kuragino, Krasnoyarsk Krai, Russian Federation

Доп.точки доступа:
Malchikov, N. O.; Pisman, T. I.; Botvich, I. Y.; Emelyanov, D. V.; Vagner, V. V.; Shevyrnogov, A. P.

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Information Content of Spectral Vegetation Indices for Assessing the Weed Infestation of Crops Using Ground-Based and Satellite Data / T. I. Pisman, M. G. Erunova, I. Y. Botvich [et al.] // Izv. Atmos. Ocean. Phys. - 2021. - Vol. 57, Is. 9. - P1188-1197, DOI 10.1134/S0001433821090577. - Cited References:32 . - ISSN 0001-4338. - ISSN 1555-628X

РУБ Meteorology & Atmospheric Sciences + Oceanography
Рубрики:
DIFFERENTIATION
REFLECTANCE
Кл.слова (ненормированные):
vegetation indices -- PlanetScope -- ground-based spectrometry -- geobotanical -- studies -- wheat crops -- Krasnoyarsk krai
Аннотация: This paper presents the results of a study assessing the degree of weed infestation of wheat crops. They are obtained using optical ground-based and satellite spectral data with a 3-m spatial resolution from PlanetScope Dove satellites for 2019. The vegetation indices, including the normalized difference vegetation index (NDVI), the relative chlorophyll index (Chlorophyll Index Green-ClGreen or GCI), the modified soil-adjusted vegetation index (MSAVI2), and the visible atmospherically resistant index (VARI) are used in the interpretation of ground-based spectrometric and space images. This paper indicates the possibility of assessing the degree of weed infestation of agricultural fields. The higher the weed infestation, the lower the index values. The dynamics of VARI is found to be different from the dynamics of NDVI, ClGreen, and MSAVI2 during the growing season. The strong correlation between NDVI, ClGreen, and MSAVI2 and the weak correlation between VARI and other indices are observed. The possibility of identifying weedy sites in the agricultural fields is shown using the spatial distribution map of ClGreen dated August 2, 2019.

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Держатели документа:
Russian Acad Sci, Inst Biophys, Siberian Branch, Krasnoyarsk, Russia.
Russian Acad Sci, Fed Res Ctr, Siberian Branch, Krasnoyarsk Sci Ctr, Krasnoyarsk, Russia.
Russian Acad Sci, Fed Res Ctr, Krasnoyarsk Sci Res Inst Agr, Siberian Branch,Krasnoyarsk Sci Ctr, Krasnoyarsk, Russia.

Доп.точки доступа:
Pisman, T., I; Erunova, M. G.; Botvich, I. Yu; Emelyanov, D., V; Kononova, N. A.; Bobrovsky, A., V; Kryuchkov, A. A.; Shpedt, A. A.; Shevyrnogov, A. P.

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