OICC Press Journal of Rangeland Science 2423-642X 16 3 2026 09 30 Estimation of Chlorophyll Content and Leaf Area Index of Vegetation Cover Using Remote Sensing in Malayer Rangeland, Iran 10.57647/JRS.2026.1603.23 EN Behnaz Attaeian Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran Mahnaz Mohammadi Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran Behrooz Mohammadparast Faculty of Science, Malayer University, Malayer, Iran Journal Article 2026 09 30 Accurate estimation of plant biophysical and biochemical parameters is essential for sustainable rangeland management. This study aimed to investigate the potential of Landsat 8 satellite imagery in estimating LAI and chlorophyll content of mountain rangeland species in Malayer, Iran. Field sampling was conducted on May 23, 2016. Forty plots, each corresponding to the 30×30 m spatial resolution of Landsat pixels, were selected. Within each plot, five 1 m² subplots were sampled, resulting in 200 plant samples. LAI was measured using a WINAREA-UT-10 leaf area meter, and chlorophyll content was determined by the Arnon method using spectrophotometric analysis. A cloud-free Landsat 8 image dated May 1, 2016, was preprocessed to calculate vegetation indices and extract reflectance values from various spectral bands. Among the tested indices, the Chlorophyll Index green (CI Green) had a strong correlation with measured chlorophyll content (r=0.61, p<0.01). Similarly, the LAI index showed a significant correlation with field-measured LAI (r = 0.63, p < 0.01). Regression models showed that CI and the short-wavelength infrared (SWIR band) were included in the final regression model, with positive and negative effects, respectively. These variables explained 42% of the total variance in the field-measured chlorophyll content, while the LAI index explained 36% of the variation in field-measured LAI. Other indices, such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI), Transformed Soil Adjusted Vegetation Index (TSAVI), and Ratio Vegetation Index (RVI) showed weak or non-significant correlations with both variables. These results indicate that CI Green and LAI are more reliable indices than conventional vegetation indices for evaluating plant biochemical and structural properties in semi-arid, mountainous rangelands. The findings confirm the potential of remote sensing techniques, especially CI and LAI indices, for cost-effective and scalable vegetation monitoring in semi-arid rangelands.