Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method

Abstract

• Prediction accuracy of the proportion of vegetation cover (P V ) improves using an artificial neural network. • Variation in the accuracy of the P V affected calculation results of the land surface emissivity (LSE). • In addition to LSE, more variable should be considered to calculate land surface temperature. Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVI THM ), especially for agriculture and forest ecosystems. To apply NDVI THM , knowledge of the proportion of vegetation cover (P V ) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the P V on the estimation of LSE and LST when using NDVI THM . In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The P V was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate P V and to compute LSE and LST. The results showed that the prediction accuracy of P V improved using an artificial neural network (R 2 CV = 0.64, RMSE CV = 0.05) over classic vegetation indices (R 2 CV = 0.42, RMSE CV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated P V affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.