Chinese Geographical Science

, Volume 29, Issue 1, pp 166–180 | Cite as

Comparative Analysis of Fractional Vegetation Cover Estimation Based on Multi-sensor Data in a Semi-arid Sandy Area

  • Qiuyu Liu
  • Tinglong Zhang
  • Yizhe Li
  • Ying Li
  • Chongfeng Bu
  • Qingfeng Zhang


The estimation of fractional vegetation cover (FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A (S2) multispectral instrument (MSI) and Landsat 8 (L8) operational land imager (OLI) data regarding the retrieval of FVC in a semi-arid sandy area (Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle (UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index (NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination (R2) of S2 increased by 26.0%, and the root mean square error (RMSE) and the sum of absolute error (SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index (RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors (especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters (FVC).


fractional vegetation cover (FVC) Sentinel-2A (S2) unmanned aerial vehicle (UAV) image pixel dichotomy model regression model 


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Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Natural Resources and EnvironmentNorthwest A&F UniversityYangling ShaanxiChina
  2. 2.Institute of Soil and Water ConservationChinese Academy of Sciences and Ministry of Water ResourcesYanglingChina

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