Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models
Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential of time-series S-2 data still remains unclear. To fill this gap, this study introduced an innovative approach for mining time-series data. Using 200 top soil organic carbon samples a...
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MDPI AG
2025-03-01
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| author | Zhibo Cui Songchao Chen Bifeng Hu Nan Wang Chunhui Feng Jie Peng |
| author_facet | Zhibo Cui Songchao Chen Bifeng Hu Nan Wang Chunhui Feng Jie Peng |
| author_sort | Zhibo Cui |
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| description | Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential of time-series S-2 data still remains unclear. To fill this gap, this study introduced an innovative approach for mining time-series data. Using 200 top soil organic carbon samples as an example, we revealed temporal variation patterns in the correlation between SOC and time-series S-2 data and subsequently identified the optimal monitoring time window for SOC. The integration of environmental covariates with multiple ensemble models enabled precise mapping of SOC in the arid region of southern Xinjiang, China (6109 km<sup>2</sup>). Our results indicated the following: (a) the correlation between SOC and time-series S-2 data exhibited both interannual and monthly variations, while July to August is the optimal monitoring time window for SOC; (b) adding soil properties and S-2 texture information could greatly improve the accuracy of SOC prediction models. Soil properties and S-2 texture information contribute 8.85% and 61.78% to the best model, respectively; (c) among different ensemble models, the stacking ensemble model outperformed both the weight averaging and sample averaging ensemble models in terms of prediction performance. Therefore, our study proved that mining spectral and texture information from the optimal monitoring time window, integrated with environmental covariates and ensemble models, has a high potential for accurate SOC mapping. |
| format | Article |
| id | doaj-art-fbdb70b6244f44a9ac912e7d90fcc1c8 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fbdb70b6244f44a9ac912e7d90fcc1c82025-08-20T03:03:23ZengMDPI AGSensors1424-82202025-03-01257218410.3390/s25072184Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble ModelsZhibo Cui0Songchao Chen1Bifeng Hu2Nan Wang3Chunhui Feng4Jie Peng5College of Agriculture, Tarim University, Alar 843300, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, ChinaDepartment of Land Resource Management, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, ChinaCollege of Horticulture and Forestry, Tarim University, Alar 843300, ChinaCollege of Agriculture, Tarim University, Alar 843300, ChinaDespite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential of time-series S-2 data still remains unclear. To fill this gap, this study introduced an innovative approach for mining time-series data. Using 200 top soil organic carbon samples as an example, we revealed temporal variation patterns in the correlation between SOC and time-series S-2 data and subsequently identified the optimal monitoring time window for SOC. The integration of environmental covariates with multiple ensemble models enabled precise mapping of SOC in the arid region of southern Xinjiang, China (6109 km<sup>2</sup>). Our results indicated the following: (a) the correlation between SOC and time-series S-2 data exhibited both interannual and monthly variations, while July to August is the optimal monitoring time window for SOC; (b) adding soil properties and S-2 texture information could greatly improve the accuracy of SOC prediction models. Soil properties and S-2 texture information contribute 8.85% and 61.78% to the best model, respectively; (c) among different ensemble models, the stacking ensemble model outperformed both the weight averaging and sample averaging ensemble models in terms of prediction performance. Therefore, our study proved that mining spectral and texture information from the optimal monitoring time window, integrated with environmental covariates and ensemble models, has a high potential for accurate SOC mapping.https://www.mdpi.com/1424-8220/25/7/2184Sentinel-2soil organic carbontextural informationensemble model |
| spellingShingle | Zhibo Cui Songchao Chen Bifeng Hu Nan Wang Chunhui Feng Jie Peng Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models Sensors Sentinel-2 soil organic carbon textural information ensemble model |
| title | Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models |
| title_full | Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models |
| title_fullStr | Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models |
| title_full_unstemmed | Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models |
| title_short | Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models |
| title_sort | mapping soil organic carbon by integrating time series sentinel 2 data environmental covariates and multiple ensemble models |
| topic | Sentinel-2 soil organic carbon textural information ensemble model |
| url | https://www.mdpi.com/1424-8220/25/7/2184 |
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