Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals
Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder f...
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| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/9/1666 |
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| author | Zinhle Mashaba-Munghemezulu George Johannes Chirima Cilence Munghemezulu |
| author_facet | Zinhle Mashaba-Munghemezulu George Johannes Chirima Cilence Munghemezulu |
| author_sort | Zinhle Mashaba-Munghemezulu |
| collection | DOAJ |
| description | Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs. |
| first_indexed | 2024-03-10T11:59:50Z |
| format | Article |
| id | doaj.art-5d20aba94ef349b8a8c9ba841b9eb4a0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:59:50Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5d20aba94ef349b8a8c9ba841b9eb4a02023-11-21T17:01:58ZengMDPI AGRemote Sensing2072-42922021-04-01139166610.3390/rs13091666Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development GoalsZinhle Mashaba-Munghemezulu0George Johannes Chirima1Cilence Munghemezulu2Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South AfricaDepartment of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South AfricaGeoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South AfricaReducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.https://www.mdpi.com/2072-4292/13/9/1666sustainable development goalssmallholdermaizeSentinel-1principal component analysisSVM |
| spellingShingle | Zinhle Mashaba-Munghemezulu George Johannes Chirima Cilence Munghemezulu Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals Remote Sensing sustainable development goals smallholder maize Sentinel-1 principal component analysis SVM |
| title | Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals |
| title_full | Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals |
| title_fullStr | Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals |
| title_full_unstemmed | Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals |
| title_short | Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals |
| title_sort | mapping smallholder maize farms using multi temporal sentinel 1 data in support of the sustainable development goals |
| topic | sustainable development goals smallholder maize Sentinel-1 principal component analysis SVM |
| url | https://www.mdpi.com/2072-4292/13/9/1666 |
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