the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China
- 1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
- 2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
- 3Collaborative Innovation Centre of Geospatial Technology, Wuhan 430079, China
- 1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
- 2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
- 3Collaborative Innovation Centre of Geospatial Technology, Wuhan 430079, China
Abstract. Snow dynamics are crucial in ecosystems, affecting radiation balance, hydrological cycles, biodiversity, and human activities. Snow areas with notably diverse characteristics are extensively distributed in China, mainly including Northern Xinjiang (XJ), Northeast China (NC), and Tibetan Plateau (TP). Spatio-temporal continuous snow monitoring is indispensable for ecosystem maintenance. Nevertheless, the formidable challenge of cloud obscuration severely impedes data collection. In the past decades, abundant binary snow cover area (SCA) maps have been retrieved from moderate resolution imaging spectroradiometer (MODIS) datasets. However, the integrated normalized difference snow index (NDSI) maps containing additional details on snow cover extent are still extremely scarce. In this study, a recent 20-year stretch seamless MODIS NDSI collection in China is generated for the first time using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR), which comprehensively considers spatial and temporal contextual information. Evaluation tests confirm that the gap-filled STAR NDSI collection is highly consistent with the Landsat NDSI dataset, with an average correlation coefficient of approximately 0.84. Consequently, this collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues. This collection is available from https://doi.org/10.5281/zenodo.5644386 (Jing et al., 2021).
Yinghong Jing et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2021-395', Anonymous Referee #1, 16 Mar 2022
The Normalized Difference Snow Index (NDSI) is vital in snow cover extent, snow cover fraction, and snow depth retrieving in case of using optical satellite observations. In this study, the authors developed an integrated cloud-free MODIS NDSI over China with the help of a spatio-temporal Adaptive fusion method. However, the following global and regional cloud-free gap-filled NDSI and snow cover extent dataset are excluded in this study, neither in the introduction section, nor in the cross-comparison section. Therefore, it is difficult for me to evaluate whether this dataset is uniqueness or usefulness.
The existing cloud-free NDSI dataset and snow cover extent datasets including:
- MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61 (https://doi.org/10.5067/MODIS/MOD10A1F.061), which provide a cloud-gap-filled daily MODIS NDSI dataset at 500m spatial resolution.
- The NIEER AVHRR snow cover extent product over China (https://essd.copernicus.org/articles/13/4711/2021/, https://doi.org/10.5194/essd-13-4711-2021), which provide a cloud-gap-filled daily AVHRR snow cover extent dataset over China.
- The daily MODIS 500m snow cover extent over China (http://data.casnw.net/portal/metadata/be3a4134-2e5c-467f-8a5e-b1c0ed6cc341, doi:10.12072/ncdc.I-SNOW.db0001.2020)
- Daily fractional snow cover dataset over High Asia during 2002 to 2018 (http://www.ncdc.ac.cn/portal/metadata/0e277d66-d89b-4e54-8a75-fe22fcc3adee, doi: 10.11922/sciencedb.457)
Although the study may content material worthy of publication, the paper in its current version needs major revision and resubmission to meet the level expected of ESSD, for the following reasons.
- Literature review in Introduction disregards former studies on NDSI retrievals from satellite data, including both datasets retrieval methods. Thus, the contribution of the current study in accordance to existing knowledge and methods is not clear. Please add the above listed cloud-free NDSI dataset and snow cover extent datasets in the introduction section.
- The lack of innovation in accordance to existing knowledge. Please add the comparison between NDSI dataset in present study and MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61, both in fusion method and results.
- The lack of depth in the result analysis that makes the study inconclusive. Please emphasize the unique contributions in the present study in the comparison with the above listed cloud-free NDSI dataset and snow cover extent datasets over China.
- AC1: 'Reply on RC1', Xinghua Li, 22 Mar 2022
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RC2: 'Comment on essd-2021-395', Anonymous Referee #2, 25 Mar 2022
Comments to authors
The manuscript titled “STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China” estimates cloud-free snow data for China. The authors use Spatio-Temporal Adaptive fusion method with erroR correction (STAR) to derive snow cover. Cloud cover is the main obstacle in passive remote sensing snow monitoring and is important to overcome. The study is important but there are few major issues in the present form which needs to be addressed.
Major comments
- The authors use combined Terra and Aqua MODIS data in this manuscript. They combine the data first and then use STAR method consisting of spatio-temporal adaptive fusion (STAF) and error correction (EC). Combining Terra and Aqua this way potentially overestimates snow (Muhammad and Thapa, 2020, 2021). The authors are suggested to either revise the TAC or explain the potential uncertainty.
- The authors missed to share the code to generate STAR NDSI dataset. It is incomplete without sharing the code. The code is also required to evaluate the methodology as well.
- The C6 snow is in NDSI ranging between 0 and 100. It is not explained how the authors reconstructed the snow data. It is a challenge to improve the data on how to replace the cloudy pixel, so it is significant to understand the way the value is replaced.
- The authors indicate they have derived data between 2000 and 2020. The Aqua data is available from July 2002, the authors should clearly mention the observed period. As the data is combined Terra and Aqua, therefore, it should be between 2002 and 2020 not starting from the year 2000.
- One of the major issues is the remaining overestimation. The authors have to consider the existence of overestimation mainly due to the larger solar zenith angle. It is, therefore, necessary to estimate the overestimation in the combined Terra and Aqua as in the combined product the uncertainty increases.
Minor comments
Line 45-65: The authors missed to point out one of the most recent cloud-free 8-day (Muhammad and Thapa, 2020 - https://doi.org/10.5194/essd-12-345-2020) and daily (Muhammad and Thapa 2021 - https://doi.org/10.5194/essd-13-767-2021) snow data, combining Terra and Aqua satellites data, reducing up to 50% of uncertainty. These datasets uniquely combine Terra and Aqua, to avoid overestimation after temporal and spatial filters are applied to individual products for clouds removal. The authors are advised to add these important papers.
MODIS is onboard on Terra and Aqua satellites, the authors are advised to clearly mention which constellation they use in e.g. to clearly mention in “A daily spatio-temporal continuous MODIS C6 NDSI dataset with a spatial resolution of 500 m for China (Fig. 1) from 2001 to 2020 is generated for the first time.”
- AC2: 'Reply on RC2', Xinghua Li, 01 Apr 2022
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RC3: 'Comment on essd-2021-395', Anonymous Referee #3, 08 May 2022
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2021-395/essd-2021-395-RC3-supplement.pdf
- AC3: 'Reply on RC3', Xinghua Li, 15 May 2022
Yinghong Jing et al.
Data sets
STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China Yinghong Jing, Xinghua Li, and Huanfeng Shen https://doi.org/10.5281/zenodo.5644386
Yinghong Jing et al.
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