Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms
Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning a...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/750 |
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| author | Savittri Ratanopad Suwanlee Dusadee Pinasu Jaturong Som-ard Enrico Borgogno-Mondino Filippo Sarvia |
| author_facet | Savittri Ratanopad Suwanlee Dusadee Pinasu Jaturong Som-ard Enrico Borgogno-Mondino Filippo Sarvia |
| author_sort | Savittri Ratanopad Suwanlee |
| collection | DOAJ |
| description | Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R<sup>2</sup>) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes. |
| first_indexed | 2024-04-25T00:21:22Z |
| format | Article |
| id | doaj.art-ac1d94148674461da233249496a65e00 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:21:22Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ac1d94148674461da233249496a65e002024-03-12T16:53:55ZengMDPI AGRemote Sensing2072-42922024-02-0116575010.3390/rs16050750Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning AlgorithmsSavittri Ratanopad Suwanlee0Dusadee Pinasu1Jaturong Som-ard2Enrico Borgogno-Mondino3Filippo Sarvia4Department of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, ThailandTechnology and Informatics Institute for Sustainability, National Metal and Materials Technology Center, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani 12120, ThailandDepartment of Geography, Faculty of Humanities and Social Sciences, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Agricultural, Forest and Food Sciences, University of Turin, 10095 Torino, ItalyDepartment of Agricultural, Forest and Food Sciences, University of Turin, 10095 Torino, ItalyAccurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R<sup>2</sup>) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes.https://www.mdpi.com/2072-4292/16/5/750sugarcaneaboveground biomasscarbon stockremote sensingearth observationtime series |
| spellingShingle | Savittri Ratanopad Suwanlee Dusadee Pinasu Jaturong Som-ard Enrico Borgogno-Mondino Filippo Sarvia Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms Remote Sensing sugarcane aboveground biomass carbon stock remote sensing earth observation time series |
| title | Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms |
| title_full | Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms |
| title_fullStr | Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms |
| title_full_unstemmed | Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms |
| title_short | Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms |
| title_sort | estimating sugarcane aboveground biomass and carbon stock using the combined time series of sentinel data with machine learning algorithms |
| topic | sugarcane aboveground biomass carbon stock remote sensing earth observation time series |
| url | https://www.mdpi.com/2072-4292/16/5/750 |
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