A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
<p>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-...
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Copernicus Publications
2023-04-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/1577/2023/essd-15-1577-2023.pdf |
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| author | S. Tao Z. Ao J.-P. Wigneron S. Saatchi P. Ciais J. Chave T. Le Toan P.-L. Frison X. Hu C. Chen L. Fan M. Wang J. Zhu X. Zhao X. Li X. Liu Y. Su T. Hu Q. Guo Q. Guo Z. Wang Z. Tang Y. Y. Liu J. Fang |
| author_facet | S. Tao Z. Ao J.-P. Wigneron S. Saatchi P. Ciais J. Chave T. Le Toan P.-L. Frison X. Hu C. Chen L. Fan M. Wang J. Zhu X. Zhao X. Li X. Liu Y. Su T. Hu Q. Guo Q. Guo Z. Wang Z. Tang Y. Y. Liu J. Fang |
| author_sort | S. Tao |
| collection | DOAJ |
| description | <p>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 (<span class="inline-formula">∼8.9</span> km instead of the commonly used <span class="inline-formula">∼25</span> 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<span id="page1578"/> 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.</p>
<p>The quality of the merged radar signal was assessed by computing the Pearson <span class="inline-formula"><i>r</i></span>, 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 <span class="inline-formula"><i>r</i></span> values and low RMSE values at both the regional (<span class="inline-formula"><i>r</i>≥0.92</span>, RMSE <span class="inline-formula">≤</span> 0.11 dB, and rRMSE <span class="inline-formula">≤</span> 0.38) and pixel levels (median <span class="inline-formula"><i>r</i></span> across pixels <span class="inline-formula">≥</span> 0.64, median RMSE <span class="inline-formula">≤</span> 0.34 dB, and median rRMSE <span class="inline-formula">≤</span> 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 <span class="inline-formula"><i>r</i></span> values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic.</p>
<p>The LHScat data set (<a href="https://doi.org/10.6084/m9.figshare.20407857">https://doi.org/10.6084/m9.figshare.20407857</a>; 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.</p> |
| first_indexed | 2024-04-09T18:23:53Z |
| format | Article |
| id | doaj.art-084e4a6b830f4e77b8b2487b1ca9ae68 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-09T18:23:53Z |
| publishDate | 2023-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-084e4a6b830f4e77b8b2487b1ca9ae682023-04-12T04:44:10ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162023-04-01151577159610.5194/essd-15-1577-2023A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCATS. Tao0Z. Ao1J.-P. Wigneron2S. Saatchi3P. Ciais4J. Chave5T. Le Toan6P.-L. Frison7X. Hu8C. Chen9L. Fan10M. Wang11J. Zhu12X. Zhao13X. Li14X. Liu15Y. Su16T. Hu17Q. Guo18Q. Guo19Z. Wang20Z. Tang21Y. Y. Liu22J. Fang23Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, ChinaBeidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528000, ChinaINRAE, UMR1391 ISPA, Université de Bordeaux, 33140 Villenave d'Ornon, FranceJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USALaboratoire des Sciences du Climat et de l'Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, Gif-sur-Yvette, FranceCNRS, Université Toulouse 3 Paul Sabatier, IRD, UMR 5174, Evolution et Diversité Biologique (EDB), 31062 Toulouse, FranceCentre d'Etudes Spatiales de la Biosphère, CNRS-CNES-UPS-IRD, Toulouse, FranceLaSTIG, Université Gustave Eiffel, ENSG, IGN, 77420 Champs-sur-Marne, FranceInstitute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, ChinaClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USAChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou, Henan 450001, ChinaInstitute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, ChinaState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, ChinaINRAE, UMR1391 ISPA, Université de Bordeaux, 33140 Villenave d'Ornon, FranceINRAE, UMR1391 ISPA, Université de Bordeaux, 33140 Villenave d'Ornon, FranceState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, ChinaState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, ChinaInstitute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, ChinaInstitute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaInstitute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, ChinaInstitute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, ChinaSchool of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, AustraliaInstitute 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<p>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 (<span class="inline-formula">∼8.9</span> km instead of the commonly used <span class="inline-formula">∼25</span> 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<span id="page1578"/> 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.</p> <p>The quality of the merged radar signal was assessed by computing the Pearson <span class="inline-formula"><i>r</i></span>, 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 <span class="inline-formula"><i>r</i></span> values and low RMSE values at both the regional (<span class="inline-formula"><i>r</i>≥0.92</span>, RMSE <span class="inline-formula">≤</span> 0.11 dB, and rRMSE <span class="inline-formula">≤</span> 0.38) and pixel levels (median <span class="inline-formula"><i>r</i></span> across pixels <span class="inline-formula">≥</span> 0.64, median RMSE <span class="inline-formula">≤</span> 0.34 dB, and median rRMSE <span class="inline-formula">≤</span> 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 <span class="inline-formula"><i>r</i></span> values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic.</p> <p>The LHScat data set (<a href="https://doi.org/10.6084/m9.figshare.20407857">https://doi.org/10.6084/m9.figshare.20407857</a>; 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.</p>https://essd.copernicus.org/articles/15/1577/2023/essd-15-1577-2023.pdf |
| spellingShingle | S. Tao Z. Ao J.-P. Wigneron S. Saatchi P. Ciais J. Chave T. Le Toan P.-L. Frison X. Hu C. Chen L. Fan M. Wang J. Zhu X. Zhao X. Li X. Liu Y. Su T. Hu Q. Guo Q. Guo Z. Wang Z. Tang Y. Y. Liu J. Fang A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT Earth System Science Data |
| title | A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT |
| title_full | A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT |
| title_fullStr | A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT |
| title_full_unstemmed | A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT |
| title_short | A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT |
| title_sort | global long term high resolution satellite radar backscatter data record 1992 2022 merging c band ers ascat and ku band qscat |
| url | https://essd.copernicus.org/articles/15/1577/2023/essd-15-1577-2023.pdf |
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