Estimation Model For Dust-Retention Content Of Main Green Plants In South China Based On The Red Edge Of Reflectance

Estimating dust-retention content (DRC) on plants leaves is important to the protection and improvement of the atmospheric environment, which can be helpful to the human health and social development. Due to lacking of well-used estimation model, the current studies about dust-retention based on remote sensing are scarce. This paper aims to establish an estimation model for DRC of main green plants based on 56 in situ samples collected in 2014-2016 from Guangzhou, South China. After pre-processing and laboratory analysis, the correlation coefficient between leaf reflectance spectral data and DRC was calculated under MATLAB environment. The results showed that the relationship between red edge (720 nm) of reflectance spectra and DRC is relatively higher. Therefore, a model for estimating DRC based on red edge position was established. It was found that the exponential model showed a high calibration accuracy (DRC: 0.019-1.1 g/m(2), R-2=0.71, N=40, P-value < 0.001) and had an acceptable validation accuracy (DRC: 0.154-0.978 g/m(2), RMSE =0.157 g/m(2), MRE=33.8%, N=16). Although the red edge of vegetation was generally used to monitor the health status of plants, the results of this study indicated that red edge of reflectance can be applied for estimating the DRC on plants leaves as well. Based on the DRC retrieval model, there is potential to monitor and estimate the dynamic changes of dust-retention in a large region from multi-source satellite and unnamed aerial vehicle platform.