Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning.
Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yie...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0309982 |
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| author | Varun Tiwari Kelly Thorp Mirela G Tulbure Joshua Gray Mohammad Kamruzzaman Timothy J Krupnik A Sankarasubramanian Marcelo Ardon |
| author_facet | Varun Tiwari Kelly Thorp Mirela G Tulbure Joshua Gray Mohammad Kamruzzaman Timothy J Krupnik A Sankarasubramanian Marcelo Ardon |
| author_sort | Varun Tiwari |
| collection | DOAJ |
| description | Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yield estimation at a sub-district scale (1,000-meter spatial resolution). However, a significant gap exists in the application of remote sensing methods for government-reported rice yield estimation for food security management at high spatial resolution. Current methods are limited to specific regions and primarily used for research, lacking integration into national reporting systems. Additionally, there is no consistent yearly boro rice yield map at a sub-district scale, hindering localized agricultural decision-making. This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. Additionally, the estimated yield of boro rice varies with an uncertainty range between 0.40 and 0.45 tons per hectare across Bangladesh. Furthermore, a trend analysis was performed on the estimated boro rice yield data from 2002 to 2021 using the modified Mann-Kendall trend test with a 95% confidence interval (p < 0.05). In Bangladesh, 23% of the rice area exhibits an increasing trend in boro rice yield, 0.11% shows a decreasing trend, and 76.51% of the area demonstrates no trend in rice yield. Given that this is the first attempt to estimate boro rice yield at 1,000-meter spatial resolution over two decades in Bangladesh, the estimated mid-season boro rice yield estimates are scalable across space and time, offering significant potential for strengthening food security management in Bangladesh. Furthermore, the proposed workflow can be easily applied to estimate rice yields in other regions worldwide. |
| format | Article |
| id | doaj-art-fa66a8075825413695ed231a16a017e7 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-fa66a8075825413695ed231a16a017e72025-08-20T01:57:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e030998210.1371/journal.pone.0309982Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning.Varun TiwariKelly ThorpMirela G TulbureJoshua GrayMohammad KamruzzamanTimothy J KrupnikA SankarasubramanianMarcelo ArdonTimely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yield estimation at a sub-district scale (1,000-meter spatial resolution). However, a significant gap exists in the application of remote sensing methods for government-reported rice yield estimation for food security management at high spatial resolution. Current methods are limited to specific regions and primarily used for research, lacking integration into national reporting systems. Additionally, there is no consistent yearly boro rice yield map at a sub-district scale, hindering localized agricultural decision-making. This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. Additionally, the estimated yield of boro rice varies with an uncertainty range between 0.40 and 0.45 tons per hectare across Bangladesh. Furthermore, a trend analysis was performed on the estimated boro rice yield data from 2002 to 2021 using the modified Mann-Kendall trend test with a 95% confidence interval (p < 0.05). In Bangladesh, 23% of the rice area exhibits an increasing trend in boro rice yield, 0.11% shows a decreasing trend, and 76.51% of the area demonstrates no trend in rice yield. Given that this is the first attempt to estimate boro rice yield at 1,000-meter spatial resolution over two decades in Bangladesh, the estimated mid-season boro rice yield estimates are scalable across space and time, offering significant potential for strengthening food security management in Bangladesh. Furthermore, the proposed workflow can be easily applied to estimate rice yields in other regions worldwide.https://doi.org/10.1371/journal.pone.0309982 |
| spellingShingle | Varun Tiwari Kelly Thorp Mirela G Tulbure Joshua Gray Mohammad Kamruzzaman Timothy J Krupnik A Sankarasubramanian Marcelo Ardon Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. PLoS ONE |
| title | Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. |
| title_full | Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. |
| title_fullStr | Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. |
| title_full_unstemmed | Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. |
| title_short | Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning. |
| title_sort | advancing food security rice yield estimation framework using time series satellite data amp machine learning |
| url | https://doi.org/10.1371/journal.pone.0309982 |
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