Aims
Land surface emissivity (LSE) is an important variable in soil studies. Although there are various remote sensing methods to estimate LSE, accurately predicting LSE still remains a major challenge. Typically, the correlation between LSE and the Visible Near Infrared bands is employed for LSE estimation. However, some studies have raised some concerns about this correlation, especially in bare soil areas. Therefore, it is necessary to conduct further investigation to determine if there exists a nonlinear relationship between the LSE and other spectral bands, which was not detected by simple linear correlation/regression.
Methods
In this study, firstly, a deep Auto-encoder (DAE) network has been used to investigate the correlation between LSE and other spectral bands. Subsequently, we have applied a Conditional Generative Adversarial Network (CGAN) to estimate the LSE. The proposed CGAN was trained using the Landsat and ECOSTRESS satellite datasets. The performance of the developed network was then compared with NDVI-based method on satellite/simulated-based bare soil pixels.
Results
For satellite data, the RMSE (Root Mean Squared Error) and correlation coefficient (R) between the estimated LSE using proposed CGAN and ECOSTRESS LSE are 0.005 and 0.97, respectively. For the simulated data, the RMSE and R between the estimated LSE and the simulated one are 0.01 and 0.92, respectively.
Conclusion
The results of the DAE show considerable relationship between the LSE and Short-Wave Infrared bands which not be seen using simple linear correlation.