Chin J Plant Ecol ›› 2022, Vol. 46 ›› Issue (10): 1234-1250.DOI: 10.17521/cjpe.2022.0104

• Research Articles • Previous Articles     Next Articles

Remotely sensed monitoring method of grassland plant functional diversity and its relationship with productivity based on Sentinel-2 satellite data

ZHAO Yan-Ping1, WANG Zhong-Wu1, WENDU Rigen3, ZHAO Yu-Jin2,*(), BAI Yong-Fei2,*()   

  1. 1College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010000, China
    2State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinses Academy of Science, Beijing 100093, China
    3Grassland Workstation of Zhenglan Banner, Xilinhot, Nei Mongol 027200, China
  • Received:2022-03-23 Accepted:2022-07-06 Online:2022-10-30 Published:2022-09-28
  • Contact: ZHAO Yu-Jin,BAI Yong-Fei
  • Supported by:
    Key Science & Technology Special Program of Inner Mongolia(2021ZD0011-04);National Natural Science Foundation of China(41801230);Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080303)


Aims The relationship between biodiversity and ecosystem function is an important ecological issue that is increasingly receiving global attention. Plant functional diversity, as one of the most important components of biodiversity, is directly linked to ecosystem functions. Traditional in-situ monitoring of grassland plant functional diversity is not only time-consuming and laborious, but also difficult to expand to large-scale research due to the limitations of time and space. The development of remote sensing technology provides an economical and effective means for assessing the grassland functional diversity over large areas. We estimated functional diversity and aboveground biomass based on Sentinel-2 satellite images and field data across the meadow steppe in the Ulgai Management Area of Xilin Gol League in Nei Mongol.

Methods We selected 46 spectral feature variables from the Sentinel-2 satellite imagery in the study area. Next, three methods, including stepwise regression, partial least squares regression (PLSR), and random forest regression (RFR) were applied to retrieve the grassland functional richness (FRic), functional evenness (FEve) and functional divergence (FDiv). Finally, the grassland aboveground biomass was also estimated using PLSR method, and the relationships between remotely sensed grassland functional diversity and grassland aboveground biomass were analyzed.

Important findings Our results showed that: (1) Band 11, optimized soil adjusted vegetation index (OSAVI), water band index (WBI) were the most important predictor of FRic; Band 6, Band 10, Band 12, carotenoid reflectance index 1 (CRI1), double-peak optical index (D), normalized difference index 45 (NDI45) were significantly related to FEve; and Band 5, Band 9, Band10, Band11, weighted difference vegetation index (WDVI), convex hull area played a critical role in predicting FDiv. (2) Based on 10-fold cross-validation, the retrieval accuracies of FRic and FEve estimated by stepwise regression were much higher than that of the other two regression methods, with R2 of 0.52 and 0.44, respectively. However, the FDiv was best estimated by PLSR (R2 = 0.61). (3) Grassland aboveground biomass was estimated with an accuracy of R2 = 0.61, and FRic was the best indicator of aboveground biomass (R2 = 0.40), followed by FDiv (R2 = 0.28) and FEve (R2 = 0.27). Our findings indicated the ability of Sentinel-2 satellite images to estimate grassland plant functional diversity, providing reference and basis for grassland plant functional diversity estimation at a large regional scale.

Key words: grassland, plant functional diversity, Sentinel-2, functional diversity index, stepwise regression, partial least squares regression, random forest regression