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September 17, 2025SensorsOpen Access

Multi-Sensor NDVI Time Series for Crop and Fallow Land Classification in Khabarovsk Krai, Russia

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Authors

LILyubov IllarionovaKDKonstantin DubrovinEFElizaveta Fomina

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Overview

This research demonstrates improved crop classification accuracy using NDVI data from Sentinel-2 and Meteor-M, indicating reliable monitoring techniques in variable conditions.

Key Points

  • Combining NDVI data from multiple satellites increased overall classification accuracy to 97% in 2023.
  • Classification accuracies averaged 87% for Landsat 8/9 and 93% for Sentinel-2, showcasing the effectiveness of varied data sources.
  • Random forest classification was applied for five land cover categories, including soybean and fallow land.
  • This study highlights the value of advanced multi-sensor techniques in agricultural monitoring amidst challenges like cloud cover.

Cite This Study

Illarionova et al. (2025) studied this question.

synapsesocial.com/papers/68d45e4431b076d99fa5e2b2https://doi.org/10.3390/s25185746
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