Journal Article

A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT

Guo, Qinghua, Tao, Shengli, Zhu, Jiangling, Fang, Jingyun, Tang, Zhiyao, Saatchi, Sassan, Frison, Pierre-Louis, Su, Yanjun, Wang, Zhiheng, Hu, Tianyu, Chen, Chi, Zhao, Xia, Le Toan, Thuy, Ao, Zurui, Wang, Mengjia, Liu, Yi Y., Wigneron, Jean-Pierre, Ciais, Philippe, Hu, Xiaomei, Fan, Lei, Li, Xiaojun, Chave, Jérôme, Liu, Xiangzhuo

2023

Overview

Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions.

• Publisher: Copernicus GmbH,Copernicus Publications
• Subject: Backscatter / Backscattering / C band / Climate change / Datasets / Decision trees / Environmental Sciences / Error correction / ERS-2 / Gyroscopes / High resolution / Image acquisition / Long-term changes / Median (statistics) / Modelling / Moisture effects / Monthly / Monthly precipitation / Pixels / Precipitation (Meteorology) / Radar / Radar backscatter / Radar data / Radar systems / Radiation / Radiometers / Remote sensing / Remote sensing systems / Resolution / Root-mean-square errors / Satellite radar / Satellites / Scatterometers / Sensors / Skin temperature / Snow accumulation / Snow depth / Soil moisture / Spatial data / Spatial discrimination / Spatial resolution / Superhigh frequencies / Surface roughness / Vegetation / Wavelength