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Estimating the above ground biomass of winter wheat using the Sentinel-2 data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhengyang@radi.ac.cn
  • Introduction：郑阳(1988—　)，男，博士研究生，研究方向为农业遥感。E-mail：zhengyang@radi.ac.cn
ZHENG Yang12,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

WU Bingfang1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Miao1

resumen

Crop biomass plays an important role in food security and global carbon cycle, and the timely and efficient monitoring of biomass is crucial for precise and reasonable agricultural management. Recently, remote sensing technique has been proven to be an effective tool for biomass estimation and it can decrease the conduct of field surveys. The European Sentinel-2A satellite was successfully launched in late June 2015.This satellite can provide high spatial resolution (10 m, 20 m, and 60 m) data freely. It uses a thirteen-band spectrum ranging from the visible region to the short-wave infrared region and thus is useful in imaging planted regions with high fragmentation. For this reason, the main objective of this paper is to explore the potential of winter wheat biomass estimation based on the new Sentinel data.In this study, 17 Vegetation Indices (VIs) based on the combinations of canopy reflectance in blue, green, red, red-edge, and near-infrared bands were first derived from the Sentinel-2A imagery in April and May 2016. The Above Ground Biomass (AGB) data collected during the same period were then used for constructing the best-fit relationships between the selected VIs and AGB. The correlation and sensitivity of the relationships between them were then analyzed. Finally, the spatial distributions of the biomass in the study area were mapped through the estimation models.All the tested VIs were nonlinearly and significantly correlated with AGB and generatedR2 ranging from 0.59 to 0.83 and RMSE ranging from 180.29 g·m–2 to 0.289.79 g·m–2. Among these VIs, the red-edge chlorophyll index exhibited superior performance on AGB estimation (R2=0.83, RMSE=180.29 g·m–2), whereas the green chlorophyll index presented the highest estimation accuracy when the red-edge bands were not available (R2=0.81, RMSE=191.15 g·m–2). The scatter-plots between the VIs and AGB showed that several VIs, such as the widely used normalized difference vegetation index, saturate at moderate-to-high biomass stages (higher than 1000g·m–2) mainly because of the strong light absorption of the red band and scattering of the near-infrared band at high LAI levels. In addition, the indices incorporated red-edge bands and thus were more closely related to the biomass compared with the original indices and were able to disrupt the saturation. Sensitivity analysis results indicated that although theR2 and RMSE values of some VIs were similar, the Vis had different sensitivities. For example, the normalized difference indices and ratio indices were more sensitive to biomass variations in the low and moderate-to-high biomass stages, respectively. On the basis of their high predictive ability, high sensitivity, and high degree of linearity, we consider the red-edge simple ratio and MERIS terrestrial chlorophyll index as a stable index for AGB estimation covering the entire growing season.Our research provides a reliable approach for winter wheat biomass estimation using the Sentinel-2A data. Given that the repeat cycle will be shortened to five days when the Sentinel-2B is launched, the Sentinel data with high spatial resolution and enhanced spectral information (including threered-edge bands) is meaningful in precision agriculture, especially in yield and production prediction.

palabra clave

Sentinel-2;winter wheat;vegetation indices;aboveground biomass;red-edge bands

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