Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features
The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC)...
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MDPI AG
2015-11-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/7/11/14559 |
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| author | Xiuliang Jin Jianhang Ma Zhidan Wen Kaishan Song |
| author_facet | Xiuliang Jin Jianhang Ma Zhidan Wen Kaishan Song |
| author_sort | Xiuliang Jin |
| collection | DOAJ |
| description | The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R2 = 0.88 and RMSE = 11.34%) or textural feature (R2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale. |
| first_indexed | 2024-12-10T19:43:28Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T19:43:28Z |
| publishDate | 2015-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-a66d3724f93d433cb398e5ede89255dd2022-12-22T01:35:57ZengMDPI AGRemote Sensing2072-42922015-11-01711145591457510.3390/rs71114559rs71114559Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural FeaturesXiuliang Jin0Jianhang Ma1Zhidan Wen2Kaishan Song3Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaKey Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaKey Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaKey Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaThe application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R2 = 0.88 and RMSE = 11.34%) or textural feature (R2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale.http://www.mdpi.com/2072-4292/7/11/14559maize residue coverLandsat-8 OLI imagespectral informationtextural featuresestimation |
| spellingShingle | Xiuliang Jin Jianhang Ma Zhidan Wen Kaishan Song Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features Remote Sensing maize residue cover Landsat-8 OLI image spectral information textural features estimation |
| title | Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features |
| title_full | Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features |
| title_fullStr | Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features |
| title_full_unstemmed | Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features |
| title_short | Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features |
| title_sort | estimation of maize residue cover using landsat 8 oli image spectral information and textural features |
| topic | maize residue cover Landsat-8 OLI image spectral information textural features estimation |
| url | http://www.mdpi.com/2072-4292/7/11/14559 |
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