Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East
Crop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation In...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7902 |
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| author | Konstantin Dubrovin Alexey Stepanov Andrey Verkhoturov |
| author_facet | Konstantin Dubrovin Alexey Stepanov Andrey Verkhoturov |
| author_sort | Konstantin Dubrovin |
| collection | DOAJ |
| description | Crop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation Index (DpRVI), which was calculated based on data acquired by the Sentinel-1B satellite between May and October 2021, as the main characteristic. Radar images of the Khabarovskiy District of the Khabarovsk Territory, as well as those of the Arkharinskiy, Ivanovskiy, and Oktyabrskiy districts in the Amur Region (Russian Far East), were obtained and processed. The identifiable classes were soybean and oat crops, as well as fallow land. Classification was carried out using the Support Vector Machines, Quadratic Discriminant Analysis (QDA), and Random Forest (RF) algorithms. The training (848 ha) and test (364 ha) samples were located in Khabarovskiy District. The best overall accuracy on the test set (82.0%) was achieved using RF. Classification accuracy at the field level was 79%. When using the QDA classifier on cropland in the Amur Region (2324 ha), the overall classification accuracy was 83.1% (F1 was 0.86 for soybean, 0.84 for fallow, and 0.79 for oat). Application of the Radar Vegetation Index (RVI) and VV/VH ratio enabled an overall classification accuracy in the Amur region of 74.9% and 74.6%, respectively. Thus, using DpRVI allowed us to achieve greater performance compared to other SAR data, and it can be used to identify crops in the south of the Far East and serve as the basis for the automatic classification of cropland. |
| first_indexed | 2024-03-10T22:01:08Z |
| format | Article |
| id | doaj.art-23363720a6974cb1a2a16c769844a1d2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:08Z |
| publishDate | 2023-09-01 |
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| series | Sensors |
| spelling | doaj.art-23363720a6974cb1a2a16c769844a1d22023-11-19T12:55:49ZengMDPI AGSensors1424-82202023-09-012318790210.3390/s23187902Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far EastKonstantin Dubrovin0Alexey Stepanov1Andrey Verkhoturov2Computing Center Far Eastern Branch of the Russian Academy of Sciences, 680000 Khabarovsk, RussiaFar Eastern Agriculture Research Institute, Vostochnoe, 680521 Khabarovsk, RussiaKhabarovsk Federal Research Center of the Far Eastern Branch of the Russian Academy of Sciences, 680000 Khabarovsk, RussiaCrop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation Index (DpRVI), which was calculated based on data acquired by the Sentinel-1B satellite between May and October 2021, as the main characteristic. Radar images of the Khabarovskiy District of the Khabarovsk Territory, as well as those of the Arkharinskiy, Ivanovskiy, and Oktyabrskiy districts in the Amur Region (Russian Far East), were obtained and processed. The identifiable classes were soybean and oat crops, as well as fallow land. Classification was carried out using the Support Vector Machines, Quadratic Discriminant Analysis (QDA), and Random Forest (RF) algorithms. The training (848 ha) and test (364 ha) samples were located in Khabarovskiy District. The best overall accuracy on the test set (82.0%) was achieved using RF. Classification accuracy at the field level was 79%. When using the QDA classifier on cropland in the Amur Region (2324 ha), the overall classification accuracy was 83.1% (F1 was 0.86 for soybean, 0.84 for fallow, and 0.79 for oat). Application of the Radar Vegetation Index (RVI) and VV/VH ratio enabled an overall classification accuracy in the Amur region of 74.9% and 74.6%, respectively. Thus, using DpRVI allowed us to achieve greater performance compared to other SAR data, and it can be used to identify crops in the south of the Far East and serve as the basis for the automatic classification of cropland.https://www.mdpi.com/1424-8220/23/18/7902remote sensingcrop identificationtime series classificationSAR dataDpRVImachine learning |
| spellingShingle | Konstantin Dubrovin Alexey Stepanov Andrey Verkhoturov Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East Sensors remote sensing crop identification time series classification SAR data DpRVI machine learning |
| title | Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East |
| title_full | Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East |
| title_fullStr | Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East |
| title_full_unstemmed | Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East |
| title_short | Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East |
| title_sort | cropland mapping using sentinel 1 data in the southern part of the russian far east |
| topic | remote sensing crop identification time series classification SAR data DpRVI machine learning |
| url | https://www.mdpi.com/1424-8220/23/18/7902 |
| work_keys_str_mv | AT konstantindubrovin croplandmappingusingsentinel1datainthesouthernpartoftherussianfareast AT alexeystepanov croplandmappingusingsentinel1datainthesouthernpartoftherussianfareast AT andreyverkhoturov croplandmappingusingsentinel1datainthesouthernpartoftherussianfareast |