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Estimation of winter wheat residue cover using spectral and textural information from Sentinel-2 data

  • role： First author第一作者
  • Affiliation：

    School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

    Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

  • Email：cwt52@foxmail.com
  • Introduction：蔡文婷，1995年生，女，硕士研究生，研究方向为资源环境遥感。E-mail:cwt52@foxmail.com
CAI Wenting123,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

    Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

  • Email：zhaosh@nju.edu.cn
  • Introduction：赵书河，1971年生，男，副教授，研究方向为陆表参数获取与反演、土地覆盖与全球变化、农业灾害与粮食安全。E-mail:zhaosh@nju.edu.cn
ZHAO Shuhe123*,  
  • Affiliation：

    School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

    Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

WANG Yamei123,  
  • Affiliation：

    School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

    Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

PENG Fanchen123

résumé

As an important element of farmland ecosystems, Crop Residues Cover (CRC) provides a barrier against water erosion and improves soil structure and organic matter content. Timely and accurate estimation of CRC at regional scale is essential for understanding the ecosystem condition and interactions with the surrounding environment. Satellite remote sensing is an effective method of regional CRC estimation. Tillage indices based on multi-spectral satellite imagery data are commonly used in CRC estimation. However, this method is ineffective in high coverage areas due to “saturation”. Previous studies have shown that a combination of image spectral and textural information can solve saturation problems to a certain extent. Sentinel-2 is a new satellite mission that can provide observations at multi-spectral bands with spatial resolutions of 10, 20, and 60 m. Sentinel-2 can provide more information about texture compared with the commonly used multi-spectral satellite Landsat-8 Operational Land Imager. Therefore, exploring the potential of combining spectral and textural information from Sentinel-2 data is an important task in CRC estimation.The objectives of this study are to (1) analyze correlation between field measured CRC and satellite-derived variables such as Sentinel-2 band reflectance, tillage indices, and gray-level co-occurrence matrix statistics in different windows, and (2) determine the optimal CRC estimation method from optimal subset regression with various combinations of tillage indices and image textural features.The results showed that the Normalized Difference Tillage Index (NDTI), B12_CO (contrast of band12, B12 in window 5×5), and B12_DI (dissimilarity of B12 in window 5×5) were significantly correlated with the measured CRC with correlation coefficient R values of 0.765, –0.641, –0.553. The estimation model based on NDTI outperformed the models based on other single variables. The model that combined the spectral and textural information in an optimal window (R=0.869， RMSE=11.0%) provided a more precise result than that based solely on spectral information (R=0.775 and RMSE=14.5%). The results demonstrated that a combination of spectral and textural information can improve the accuracy of CRC estimation.

mots-clés

Sentinel-2;crop residue coverage;crop residue indices;gray-level co-occurrence matrix;texture window;optimal subset regression method;Landsat OLI

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