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

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.

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

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.