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Remote Sens. 2015, 7(11), 14559-14575; doi:10.3390/rs71114559

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Department of Cartography and Geographical Information System, University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Academic Editors: Mutlu Ozdogan, Sreekala Bajwa and Prasad S. Thenkabail
Received: 25 August 2015 / Revised: 26 October 2015 / Accepted: 29 October 2015 / Published: 4 November 2015
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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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. View Full-Text
Keywords: maize residue cover; Landsat-8 OLI image; spectral information; textural features; estimation maize residue cover; Landsat-8 OLI image; spectral information; textural features; estimation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jin, X.; Ma, J.; Wen, Z.; Song, K. Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features. Remote Sens. 2015, 7, 14559-14575.

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