Obtaining time series of LAI to predict crop yield


Тип публикации: статья из журнала

Год издания: 2020

Идентификатор DOI: 10.21046/2070-7401-2020-17-4-195-203

Ключевые слова: data fusion, krasnoyarsk krai, lai, landsat-8 oli, ndvi, sentinel-2, yield forecast

Аннотация: Evaluation of vegetation bio-productivity, yield prediction, is effectively carried out using simulation models of plant growth. To calculate the value of the aboveground biomass in these models, the leaf area index (LAI) is used. In the agromonitoring service of the Institute of Space and Information Technologies, a productivity fПоказать полностьюorecasting component is being developed using available field map systems showing crops and remote sensing data in the public domain. In this paper, we propose an approach to solving the problem of obtaining the LAI time series during the growing season for agricultural objects. Landsat-8 OLI and Sentinel-2 medium resolution data are used. These data have time resolution restrictions. The use of daily MODIS data is not possible due to their low spatial resolution, taking into account the typical size of agricultural fields of Krasnoyarsk region central part. Algorithms for data fusion with low and medium spatial resolutions are considered to obtain NDVI with the necessary frequency in the absence of medium-resolution data. The construction of the NDVI using data from different systems for LAI estimation required the introduction of additive coefficients for time series alignment using the VEGA Pro service as the base values. The model of calculating LAI from NDVI in linear exponential form is used. The developed approach allows the LAI assessment with the frequency necessary for the work of the predictive model for yield estimating. © 2020 Space Research Institute of the Russian Academy of Sciences. All rights reserved.

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Журнал: Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa

Выпуск журнала: Vol. 17, Is. 4

Номера страниц: 195-203

ISSN журнала: 20707401

Издатель: Space Research Institute of the Russian Academy of Sciences


  • Fedotova E.V. (Siberian Federal University, Krasnoyarsk, 660074, Russian Federation, Sukachev Institute of Forest SB RAS, Krasnoyarsk Scientific Center SB RAS, Krasnoyarsk, 660036, Russian Federation)
  • Maglinets Yu.A. (Siberian Federal University, Krasnoyarsk, 660074, Russian Federation)
  • Brezhnev R.V. (Siberian Federal University, Krasnoyarsk, 660074, Russian Federation)
  • Vyrvinskiy A.G. (Siberian Federal University, Krasnoyarsk, 660074, Russian Federation)

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