15 Aug 2022
15 Aug 2022
Status: a revised version of this preprint is currently under review for the journal ESSD.

C-band Scatterometer (CScat): the first global long-term satellite radar backscatter data set with a C-band signal dynamic

Shengli Tao1, Zurui Ao2, Jean-Pierre Wigneron3, Sassan Saatchi4, Philippe Ciais5, Jérôme Chave6, Thuy Le Toan7, Pierre-Louis Frison8, Xiaomei Hu1, Chi Chen9, Lei Fan10, Mengjia Wang11, Jiangling Zhu1, Xia Zhao12, Xiaojun Li3, Xiangzhuo Liu3, Yanjun Su12, Tianyu Hu12, Qinghua Guo13, Zhiheng Wang1, Zhiyao Tang1, Yi Liu14, and Jingyun Fang1 Shengli Tao et al.
  • 1Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
  • 2Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528000, China
  • 3ISPA, UMR 1391, Inrae Nouvelle-Aquitaine, Université de Bordeaux, Grande Ferrade, Villenave d’Ornon, France
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
  • 5Laboratoire des Sciences du Climat et de l’Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, Gif-surYvette, France
  • 6CNRS, Université Toulouse 3 Paul Sabatier, IRD, UMR 5174 Evolution et Diversité Biologique (EDB), 31062 Toulouse, France
  • 7Centre d'Etudes Spatiales de la Biosphère, CNRS-CNES-UPS-IRD, Toulouse, France
  • 8LaSTIG, Université Gustave Eiffel, ENSG, IGN, F-77420 Champs-sur-Marne, France
  • 9Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
  • 10Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
  • 11School of Geoscience and Technology, Zhengzhou University, 450001, China
  • 12State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
  • 13Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
  • 14School of Civil and Environmental Engineering, University of New South Wales, Sydney NSW 2052, Australia

Abstract. Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness, and can be acquired in all weather conditions, thus has been used in a range of earth science disciplines. However, there is no single global radar data set that spans more than two decades. This has limited the use of radar data for trend analysis over extended time intervals. We here provide the first long-term (since 1992), high resolution (~8.9 km) satellite radar backscatter data set over global land areas, the C-band Scatterometer (CScat) data set, by fusing signals from 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 six-year data gap between C-band ERS and ASCAT was filled out by modelling an equivalent C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. Towards this purpose, we first rescaled the signals from different sensors, pixel by pixel, using a new signal rescaling method that is robust to limited overlapping observations among sensors. 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 ≥ 0.93, RMSE ≤ 0.16, rRMSE ≤0.37) and pixel levels (median r across pixels ≥ 0.80, median RMSE ≤ 0.38, median rRMSE ≤ 0.64), suggesting high accuracy for the data merging procedure.

The merged radar signal was then validated with a continuous ERS-2 data set available between 1995 and 2011. ERS-2 stopped working in full mode after 2001 but observations are occasionally available for a subset of the pixels until 2011. Because the period of 1995–2011 fully overlaps with the working period of QSCAT (1999–2009), comparing the merged radar signal against the ERS-2 data in 1995–2011 is the most direct validation available. We found concordant monthly dynamics between the merged radar signals and the ERS-2 signals during 1995–2011, with Pearson r value ranging from 0.79 to 0.98 across regions. These results evidenced that our merged radar data have a consistent C-band signal dynamic.

The CScat data set (, Tao et al. 2022a) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture. The data set will be updated on a regular basis. 

Shengli Tao et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-264', Anonymous Referee #1, 12 Sep 2022
    • AC1: 'Reply on RC1', Shengli Tao, 14 Sep 2022
  • RC2: 'Comment on essd-2022-264', Anonymous Referee #2, 04 Oct 2022
    • AC2: 'Reply on RC2', Shengli Tao, 06 Oct 2022
  • RC3: 'Comment on essd-2022-264', Anonymous Referee #3, 25 Nov 2022

Shengli Tao et al.


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Short summary
We provide the first long-term (since 1992), high resolution (8.9 km) satellite radar backscatter data set with a C-band (5.3 GHz) signal dynamic for global lands, the C-band Scatterometer (CScat) data set. CScat was created by fusing signals from ERS (1992–2001, C-band), QSCAT (1999–2009, Ku-band, 13.4 GHz), and ASCAT (since 2007, C-band). CScat has been validated against independent ERS-2 signals. It could be used in a variety of studies such as vegetation monitoring and hydrological modeling.