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

Remote Sensing

Xiuliang Jin²  Jianhang Ma²  Zhidan Wen²  Kaishan Song²  Mutlu Ozdogan¹  Sreekala Bajwa¹ 

[1] Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;;Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; E-Mails:

关键词: maize residue cover;    Landsat-8 OLI image;    spectral information;    textural features;    estimation;   

DOI  :  10.3390/rs71114559

来源: mdpi

【 摘 要 】

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. Band3_mean, Band4_mean, and Band5_mean 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.