Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.
Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, high...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277425 |
| _version_ | 1797959494636929024 |
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| author | Yin Liu Preeti Rao Weiqi Zhou Balwinder Singh Amit K Srivastava Shishpal P Poonia Derek Van Berkel Meha Jain |
| author_facet | Yin Liu Preeti Rao Weiqi Zhou Balwinder Singh Amit K Srivastava Shishpal P Poonia Derek Van Berkel Meha Jain |
| author_sort | Yin Liu |
| collection | DOAJ |
| description | Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes. |
| first_indexed | 2024-04-11T00:33:36Z |
| format | Article |
| id | doaj.art-328b2cc1fcce4f1a9b7359ecac442c7f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T00:33:36Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-328b2cc1fcce4f1a9b7359ecac442c7f2023-01-07T05:31:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711e027742510.1371/journal.pone.0277425Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.Yin LiuPreeti RaoWeiqi ZhouBalwinder SinghAmit K SrivastavaShishpal P PooniaDerek Van BerkelMeha JainRemote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.https://doi.org/10.1371/journal.pone.0277425 |
| spellingShingle | Yin Liu Preeti Rao Weiqi Zhou Balwinder Singh Amit K Srivastava Shishpal P Poonia Derek Van Berkel Meha Jain Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. PLoS ONE |
| title | Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. |
| title_full | Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. |
| title_fullStr | Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. |
| title_full_unstemmed | Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. |
| title_short | Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems. |
| title_sort | using sentinel 1 sentinel 2 and planet satellite data to map field level tillage practices in smallholder systems |
| url | https://doi.org/10.1371/journal.pone.0277425 |
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