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Volume 16
Issue 7
10.3390/rs16071144
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Article

Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria

by
Ilina Kamenova
1,
Milen Chanev
1,
Petar Dimitrov
1,
Lachezar Filchev
1,
Bogdan Bonchev
2,
Liang Zhu
3 and
Qinghan Dong
4,*
1
Department of Remote Sensing and GIS, Space Research and Technology Institute, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
2
Institute of Plant Genetic Resources “Konstantin Malkov”—Agricultural Academy, 4122 Sadovo, Bulgaria
3
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
4
Department of Remote Sensing, Flemish Institute of Technological Research, 2400 Mol, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(7), 1144; https://doi.org/10.3390/rs16071144
Submission received: 9 February 2024 / Revised: 17 March 2024 / Accepted: 22 March 2024 / Published: 25 March 2024
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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Abstract

The aim of this study is to predict and map winter wheat yield in the Parvomay municipality, situated in the Upper Thracian Lowland of Bulgaria, utilizing satellite data from Sentinel-2. The main crops grown in the research area are winter wheat, rapeseed, sunflower, and maize. To distinguish winter wheat fields accurately, we evaluated classification methods such as Support Vector Machines (SVM) and Random Forest (RF). These methods were applied to satellite multispectral data acquired by the Sentinel-2 satellites during the growing season of 2020–2021. In accordance with their development cycles, temporal image composites were developed to identify suitable moments when each crop is most accurately distinguished from others. Ground truth data obtained from the integrated administration and control system (IACS) were used for training the classifiers and assessing the accuracy of the final maps. Winter wheat fields were masked using the crop mask created from the best-performing classification algorithm. Yields were predicted with regression models calibrated with in situ data collected in the Parvomay study area. Both SVM and RF algorithms performed well in classifying winter wheat fields, with SVM slightly outperforming RF. The produced crop maps enable the application of crop-specific yield models on a regional scale. The best predictor of yield was the green NDVI index (GNDVI) from the April monthly composite image.
Keywords: Sentinel-2; crop mapping; machine learning; yield prediction; vegetation indices; winter wheat Sentinel-2; crop mapping; machine learning; yield prediction; vegetation indices; winter wheat
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MDPI and ACS Style

Kamenova, I.; Chanev, M.; Dimitrov, P.; Filchev, L.; Bonchev, B.; Zhu, L.; Dong, Q. Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria. Remote Sens. 2024, 16, 1144. https://doi.org/10.3390/rs16071144

AMA Style

Kamenova I, Chanev M, Dimitrov P, Filchev L, Bonchev B, Zhu L, Dong Q. Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria. Remote Sensing. 2024; 16(7):1144. https://doi.org/10.3390/rs16071144

Chicago/Turabian Style

Kamenova, Ilina, Milen Chanev, Petar Dimitrov, Lachezar Filchev, Bogdan Bonchev, Liang Zhu, and Qinghan Dong. 2024. "Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria" Remote Sensing 16, no. 7: 1144. https://doi.org/10.3390/rs16071144

APA Style

Kamenova, I., Chanev, M., Dimitrov, P., Filchev, L., Bonchev, B., Zhu, L., & Dong, Q. (2024). Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria. Remote Sensing, 16(7), 1144. https://doi.org/10.3390/rs16071144

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