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Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R 2 ) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m 2 ) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R 2  = 0.63, MAE = 7.04 gC/m 2 , and RMSE = 8.51 gC/m 2 ), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R 2  = 0.72, MAE = 8.09 gC/m 2 , and RMSE = 9.89 gC/m 2 ) was higher than that of the LR method (R 2  = 0.70, MAE = 8.99 gC/m 2 , and RMSE = 10.69 gC/m 2 ). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R 2  = 0.69, MAE = 7.40 gC/m 2 , and RMSE = 9.01 gC/m 2  ,  while the LR method reaches the highest model accuracy of R 2  = 0.53, MAE = 9.20 gC/m 2 , and RMSE = 11.10 gC/m 2 . The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 10 4  Mg C, and the mean carbon stock density was 47.44 gC/m 2 . The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.

Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging †