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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4, pp. 195-203

Obtaining time series of LAI to predict crop yield

E.V. Fedotova 1, 2 , Yu.A. Maglinets 1 , R.V. Brezhnev 1 , A.G. Vyrvinsky 1 
1 Siberian Federal University, Krasnoyarsk, Russia
2 Sukachev Institute of Forest SB RAS, Krasnoyarsk Scientific Center SB RAS, Krasnoyarsk, Russia
Accepted: 05.08.2020
DOI: 10.21046/2070-7401-2020-17-4-195-203
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 forecasting 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.
Keywords: LAI, NDVI, Landsat-8 OLI, Sentinel-2, data fusion, yield forecast, Krasnoyarsk Krai
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