Reconstructing Long-Term Synthetic Aperture Radar Backscatter in Urban Domains Using Landsat Time Series Data: A Case Study of Jing-Jin-Ji Region

Synthetic aperture radar (SAR) records important information about the interaction of electromagnetic waves with the Earth's surface. However, long-term and high-resolution backscatter coefficient data are still lacking in many urban studies (e.g., building height estimation). Here, we proposed a framework to reconstruct the 1-km backscatter coefficient in 1990-2022 utilizing the Sentinel-1 Ground Range Detected data and Landsat time series data in the Jing-Jin-Ji (JJJ) region. First, we developed a regression model to convert the optical signals from Landsat into backscatter coefficients as the Sentinel-1 data, using observations from 2015 to 2022. Then, we reconstructed backscatter coefficients from 1990 to 2022 using the long-term Landsat data. Using the reconstructed backscatter coefficients, we analyzed the dynamic patterns of building height over the past decades. The proposed approach performs well on estimating the backscatter coefficient and its spatial pattern, with the annual mean absolute error, root mean square error, and R2 of 1.10 dB, 1.50 dB, and 0.64, respectively. The temporal trends revealed from the reconstructed backscatter data are reliable compared with satellite observations at a relatively coarse resolution, with Pearson's coefficients above 0.92 in 6 sample cities. The derived building height from the reconstructed SAR data indicates that the JJJ region experienced a noticeable upward expansion in 1990-2022, e.g., Beijing has the fastest growth rate of 0.420 km3/decade regarding the total building volumes. The proposed framework of reconstructing SAR data from optical satellite images provides a new insight to complement the long-term and high-resolution backscatter from local to global scales.