Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction
Remote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation...
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2024-12-01
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| author | Jian Li Kewen Shao Jia Du Kaishan Song Weilin Yu Zhengwei Liang Weijian Zhang Jie Qin Kaizeng Zhuo Cangming Zhang Yu Han Yiwei Zhang Bingrun Sui |
| author_facet | Jian Li Kewen Shao Jia Du Kaishan Song Weilin Yu Zhengwei Liang Weijian Zhang Jie Qin Kaizeng Zhuo Cangming Zhang Yu Han Yiwei Zhang Bingrun Sui |
| author_sort | Jian Li |
| collection | DOAJ |
| description | Remote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. Model performance was compared using the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE). The potential for conservation tillage was explored. The results showed that the R<sup>2</sup> values of the seven tillage indices in the study area exceeded 0.5, with particularly high correlations for NDTI and STI, with R<sup>2</sup> values of 0.755 and 0.751, respectively. When using machine learning algorithms to construct models, the Stacking2 model exhibited the highest estimation accuracy, with an R<sup>2</sup> of 0.923, RMSE of 3.32%, and MAE of 0.025, while Stacking1 also demonstrated robust performance, with an R<sup>2</sup> of 0.910, RMSE of 3.45%, and MAE of 0.029. Among the base models, XGBoost achieved the highest estimation performance and the lowest error, with R<sup>2</sup>, RMSE, and MAE values of 0.884, 4.77%, and 0.031, respectively. The R<sup>2</sup> values of RF, SVR, and BPNN were 0.865, 0.859, and 0.842, respectively, with RMSE values of 4.06%, 4.76%, and 5.91%, and MAE values of 0.039, 0.047, and 0.059, respectively. These results indicate that the Stacking2 model demonstrates a significant advantage in prediction accuracy. Geostatistical analysis of the inversion results of the Stacking2 model revealed that the proportions of farmland with MRC values exceeding 30% in Changchun, Songyuan, and Siping were 81.90%, 77.96%, and 83.58%, respectively. This indicates that Changchun and Siping have greater potential for implementing conservation tillage. This study demonstrates that the stacking ensemble learning model significantly improves the predictive performance by leveraging the strengths of multiple base models and accurately monitoring the spatial distribution of MRC. |
| format | Article |
| id | doaj-art-ec37337697434bddb5c20798d6a340a9 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Remote Sensing |
| spelling | doaj-art-ec37337697434bddb5c20798d6a340a92025-01-10T13:20:14ZengMDPI AGRemote Sensing2072-42922024-12-0117110510.3390/rs17010105Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover PredictionJian Li0Kewen Shao1Jia Du2Kaishan Song3Weilin Yu4Zhengwei Liang5Weijian Zhang6Jie Qin7Kaizeng Zhuo8Cangming Zhang9Yu Han10Yiwei Zhang11Bingrun Sui12College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaRemote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. Model performance was compared using the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE). The potential for conservation tillage was explored. The results showed that the R<sup>2</sup> values of the seven tillage indices in the study area exceeded 0.5, with particularly high correlations for NDTI and STI, with R<sup>2</sup> values of 0.755 and 0.751, respectively. When using machine learning algorithms to construct models, the Stacking2 model exhibited the highest estimation accuracy, with an R<sup>2</sup> of 0.923, RMSE of 3.32%, and MAE of 0.025, while Stacking1 also demonstrated robust performance, with an R<sup>2</sup> of 0.910, RMSE of 3.45%, and MAE of 0.029. Among the base models, XGBoost achieved the highest estimation performance and the lowest error, with R<sup>2</sup>, RMSE, and MAE values of 0.884, 4.77%, and 0.031, respectively. The R<sup>2</sup> values of RF, SVR, and BPNN were 0.865, 0.859, and 0.842, respectively, with RMSE values of 4.06%, 4.76%, and 5.91%, and MAE values of 0.039, 0.047, and 0.059, respectively. These results indicate that the Stacking2 model demonstrates a significant advantage in prediction accuracy. Geostatistical analysis of the inversion results of the Stacking2 model revealed that the proportions of farmland with MRC values exceeding 30% in Changchun, Songyuan, and Siping were 81.90%, 77.96%, and 83.58%, respectively. This indicates that Changchun and Siping have greater potential for implementing conservation tillage. This study demonstrates that the stacking ensemble learning model significantly improves the predictive performance by leveraging the strengths of multiple base models and accurately monitoring the spatial distribution of MRC.https://www.mdpi.com/2072-4292/17/1/105tillage indicesmachine learningmaize residue coverstacking ensemble learningsentinel-2 remotely sensed data |
| spellingShingle | Jian Li Kewen Shao Jia Du Kaishan Song Weilin Yu Zhengwei Liang Weijian Zhang Jie Qin Kaizeng Zhuo Cangming Zhang Yu Han Yiwei Zhang Bingrun Sui Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction Remote Sensing tillage indices machine learning maize residue cover stacking ensemble learning sentinel-2 remotely sensed data |
| title | Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction |
| title_full | Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction |
| title_fullStr | Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction |
| title_full_unstemmed | Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction |
| title_short | Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction |
| title_sort | comparative analysis of tillage indices and machine learning algorithms for maize residue cover prediction |
| topic | tillage indices machine learning maize residue cover stacking ensemble learning sentinel-2 remotely sensed data |
| url | https://www.mdpi.com/2072-4292/17/1/105 |
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