Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia

Hongrui Ren,Guangsheng Zhou,Xinshi Zhang,Feng Zhang

doi:10.1007/s11434-012-5016-3

Abstract

Accurate estimation of non-photosynthetic biomass is critical for modeling carbon dynamics within grassland ecosystems. We evaluated the cellulose absorption index (CAI), widely used for monitoring non-photosynthetic vegetation coverage, for non-photosynthetic biomass estimation. Our analysis was based on in situ hyperspectral measurements, during the growing seasons of 2009 and 2010, in the desert steppe of Inner Mongolia. ASD (Analytical Spectral Device)-derived and Hyperion-derived CAI were found to be effective for non-photosynthetic biomass estimation, yielding relative error (RE) values of 26.4% and 26.6%, respectively. The combination of MODIS (Moderate Resolution Imaging Spectroradiometer)-derived (MODIS2-MODIS5)/(MODIS2+MODIS5) and (MODIS6-MODIS7)/(MODIS6+MODIS7) showed a high multiple correlation (multiple correlation coefficient, r= 0.884) with ASD-derived CAI. A predictive model involving the two MODIS indices gave greater accuracy (RE=28.9%) than the TM (Landsat Thematic Mapper)-derived indices. The latter were the normalized difference index (NDI), the soil adjusted corn residue index (SACRI), and the modified soil adjusted crop residue index (MSACRI). These indices yielded RE values of more than 42%. Our conclusions have great significance for the estimation of regional non-photosynthetic biomass in grasslands, based on remotely sensed data.

Highlights

Accurate estimation of non-photosynthetic biomass is critical for modeling carbon dynamics within grassland ecosystems
The sampled area was small compared with the pixel size of spaceborne Hyperion hyperspectral data, and atmospheric conditions differed from plot-scale field observations
We have demonstrated the utility of cellulose absorption index (CAI) based on hyperspectral and multispectral remotely sensed data for estimating the non-photosynthetic biomass of desert steppe in Inner Mongolia

Summary

Introduction

Accurate estimation of non-photosynthetic biomass is critical for modeling carbon dynamics within grassland ecosystems. Efforts to enhance the discrimination of non-photosynthetic vegetation from soil have led to numerous spectral indices that incorporate the Landsat Thematic Mapper (TM) shortwave infrared bands, such as the normalized difference index (NDI) [14], the soil adjusted corn residue index (SACRI) [7], and the modified soil adjusted crop residue index (MSACRI) [21]. These broadband spectral indices were only weakly correlated to non-photosynthetic vegetation cover [12,20]

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Conclusion