Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons
Crop acreage analysis and yield estimation are of prime importance in field-level agricultural monitoring and management. This enables prudent decision making during any crop failure event and for ensuing crop insurance. The free availability of the high resolution Sentinel-2 satellite datasets has...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | Egyptian Journal of Remote Sensing and Space Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110982322000059 |
| _version_ | 1828880162394996736 |
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| author | Manas Hudait Priyank Pravin Patel |
| author_facet | Manas Hudait Priyank Pravin Patel |
| author_sort | Manas Hudait |
| collection | DOAJ |
| description | Crop acreage analysis and yield estimation are of prime importance in field-level agricultural monitoring and management. This enables prudent decision making during any crop failure event and for ensuing crop insurance. The free availability of the high resolution Sentinel-2 satellite datasets has created new possibilities for mapping and monitoring agricultural lands in this regard. In the present study conducted on the Tamluk Subdivision of the Purba Medinipur District of West Bengal, the heterogeneous crop area was mapped according to the respective crop type, using Sentinel-2 multi-spectral images and two machine learning algorithms- K Nearest Neighbour (KNN) and Random Forest (RF). Plot-level field information was collected from different cropland types to frame the training and validation datasets (comprising 70% and 30% of the total dataset, respectively) for cropland classification and accuracy assessment. Through this, the major summer crop acreage was identified (Boro rice, vegetables and betel vine- the three main crops in the study area). The extracted maps had an overall accuracy of 97.16% and 97.22%, respectively, in the KNN and RF classifications, with respective Kappa index values of 95.99% and 96.08%, and the RF method proved to be more accurate. This study was particularly useful in mapping the betel leaf acreage herein since scant information exists for this crop and it is cultivated by many smallholder farmers in the region. The methods used in this paper can be readily applied elsewhere for accurately enumerating the respective crop acreages. |
| first_indexed | 2024-12-13T09:42:26Z |
| format | Article |
| id | doaj.art-f85d1e7958c04ea993630ad9002ec1fb |
| institution | Directory Open Access Journal |
| issn | 1110-9823 |
| language | English |
| last_indexed | 2024-12-13T09:42:26Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Journal of Remote Sensing and Space Sciences |
| spelling | doaj.art-f85d1e7958c04ea993630ad9002ec1fb2022-12-21T23:52:10ZengElsevierEgyptian Journal of Remote Sensing and Space Sciences1110-98232022-02-01251147156Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisonsManas Hudait0Priyank Pravin Patel1Department of Geography, Presidency University, Kolkata. IndiaCorresponding author at: Department of Geography, Presidency University, 86/1, College Street, Kolkata 700 073, West Bengal, India.; Department of Geography, Presidency University, Kolkata. IndiaCrop acreage analysis and yield estimation are of prime importance in field-level agricultural monitoring and management. This enables prudent decision making during any crop failure event and for ensuing crop insurance. The free availability of the high resolution Sentinel-2 satellite datasets has created new possibilities for mapping and monitoring agricultural lands in this regard. In the present study conducted on the Tamluk Subdivision of the Purba Medinipur District of West Bengal, the heterogeneous crop area was mapped according to the respective crop type, using Sentinel-2 multi-spectral images and two machine learning algorithms- K Nearest Neighbour (KNN) and Random Forest (RF). Plot-level field information was collected from different cropland types to frame the training and validation datasets (comprising 70% and 30% of the total dataset, respectively) for cropland classification and accuracy assessment. Through this, the major summer crop acreage was identified (Boro rice, vegetables and betel vine- the three main crops in the study area). The extracted maps had an overall accuracy of 97.16% and 97.22%, respectively, in the KNN and RF classifications, with respective Kappa index values of 95.99% and 96.08%, and the RF method proved to be more accurate. This study was particularly useful in mapping the betel leaf acreage herein since scant information exists for this crop and it is cultivated by many smallholder farmers in the region. The methods used in this paper can be readily applied elsewhere for accurately enumerating the respective crop acreages.http://www.sciencedirect.com/science/article/pii/S1110982322000059Machine learning algorithmsSmallholder agricultureCrop acreageBetel vine cultivation |
| spellingShingle | Manas Hudait Priyank Pravin Patel Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons Egyptian Journal of Remote Sensing and Space Sciences Machine learning algorithms Smallholder agriculture Crop acreage Betel vine cultivation |
| title | Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons |
| title_full | Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons |
| title_fullStr | Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons |
| title_full_unstemmed | Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons |
| title_short | Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons |
| title_sort | crop type mapping and acreage estimation in smallholding plots using sentinel 2 images and machine learning algorithms some comparisons |
| topic | Machine learning algorithms Smallholder agriculture Crop acreage Betel vine cultivation |
| url | http://www.sciencedirect.com/science/article/pii/S1110982322000059 |
| work_keys_str_mv | AT manashudait croptypemappingandacreageestimationinsmallholdingplotsusingsentinel2imagesandmachinelearningalgorithmssomecomparisons AT priyankpravinpatel croptypemappingandacreageestimationinsmallholdingplotsusingsentinel2imagesandmachinelearningalgorithmssomecomparisons |