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


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 2, pp. 39-50

Design of satellite sensing data classification algorithm based on machine learning using the example of granulometric composition of soils in agricultural landscapes of Western Siberia

V.V. Chursin 1, 2 , I.V. Kuzhevskaia 1 , O.E. Merzliakov 1 , T.V. Valevich 1 , K.V. Ruchkina 1 
1 National Research Tomsk State University, Tomsk, Russia
2 Siberian Center of SRC Planeta, Novosibirsk, Russia
Accepted: 25.02.2021
DOI: 10.21046/2070-7401-2021-18-2-39-50
The possibility of using Sentinel-2 images and machine learning algorithms to identify and map the spatial heterogeneity of ground cover from the PSD (particle size distribution) of agricultural land, along with the use of precise farming data is discussed. An array was obtained on the basis of field data comprising satellite images with NDVI values <0.3 and additionally computed indices, including those related to spectral brightness (sensitive to PSD), for training and evaluating binary classification models based on solution trees. The XGBoost algorithm was used to train four binary classification models. For these models, the optimum hyperparameter values were chosen and the most important variables for the classification of each type of soil were determined. The architecture of the neural network, including spectral reflectivity values, calculated indices and effects of binary classification, was suggested as input data. The precision of the designed procedure on the validation set reached 76 %.
Keywords: soil texture, clay content, Copernicus mission, Sentinel, multispectral imagery, gradient boosting, mapping of soils
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