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 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 deep Auto-encoder show considerable relationship between the LSE and Short-Wave Infrared bands which not be seen using simple linear correlation. In cases of satellite/simulated data of bare soils, the developed network showed superior performance compared to NDVI-based method.

中文翻译：

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使用 Landsat 数据中的深层条件生成对抗网络基于 SWIR 估计裸露土壤表面的 TIR 发射率

 目标

地表发射率（LSE）是土壤研究中的一个重要变量。尽管有多种遥感方法来估算LSE，但准确预测LSE仍然是一个重大挑战。通常，LSE 和可见近红外波段之间的相关性用于 LSE 估计。然而，一些研究对这种相关性提出了一些担忧，特别是在裸土地区。因此，有必要进行进一步的研究，以确定LSE与其他光谱带之间是否存在简单的线性相关/回归无法检测到的非线性关系。

 方法

在本研究中，首先，使用深度自动编码器网络来研究 LSE 与其他光谱带之间的相关性。随后，我们应用条件生成对抗网络（CGAN）来估计 LSE。拟议的 CGAN 使用 Landsat 和 ECOSTRESS 卫星数据集进行训练。然后将所开发网络的性能与基于卫星/模拟裸土像素的 NDVI 方法进行比较。

 结果

对于卫星数据，使用所提出的 CGAN 和 ECOSTRESS LSE 估计的 LSE 之间的 RMSE（均方根误差）和相关系数 (R) 分别为 0.005 和 0.97。对于模拟数据，估计LSE 与模拟数据之间的RMSE 和R 分别为0.01 和0.92。

 结论

深度自动编码器的结果显示了 LSE 和短波红外波段之间的相当大的关系，而使用简单的线性相关性是看不到这种关系的。在裸土卫星/模拟数据的情况下，与基于 NDVI 的方法相比，开发的网络表现出优越的性能。