ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 131-141

Method for automated crop types mapping using remote sensing data and a plant growth simulation model

D.E. Plotnikov 1 , S.A. Khvostikov 1 , S.A. Bartalev 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 01.08.2018
DOI: 10.21046/2070-7401-2018-15-4-131-141
This paper presents a method for automatic generation of representative and unbiased training set for in-season crop types mapping, based on WOFOST crop simulation model, previously parameterized using ground truth data, as well as meteodata and satellite historical data. The method provided confident mapping of agricultural fields, planted with five different crop types using no ground truth data or a-priori information about their in-season phenology. Calibration fields, most typical for each specific crop, were identified through comparison of their remote sensing-based seasonal time series with modeled LAI values, used as in-season temporal reference. Relatively simple criteria were then used to generate a training set of required fidelity. Overall mapping accuracy calculated using relevant ground truth data has reached 85 %. This kind of approach may be versatile for automated mapping of broad variety of crop types over large heterogeneous areas in cases when in-season ground truth timely information or other kind of calibration data is not available.
Keywords: crop mapping, remote sensing, automated training set building, object-oriented classification, simulation modeling, WOFOST
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