Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran?

ABSTRACT

Satellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R². Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R² = 0.47 and R² = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands.

Acknowledgements

The authors would like to thank our field assistants, Davood Akhzari, Mohsen Sherafatmandrad, Abdollah Kaboly and bachelor students for assisting with biomass data collection. Further, we thank two anonymous reviewers and the editor in chief and the associate editor for their valuable comments on this manuscript. This study was part of a Ph.D. thesis, which was supported by the Ph.D. programme of the Rangeland and Watershed Department, Gorgan University of Agricultural Sciences and Natural Resources.

Disclosure statement

No potential conflict of interest was reported by the authors.