the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jan 2024
A dataset of 190 lakes' ice phenology on the Tibetan Plateau extracted from AMSR2 data
Abstract. The utilization of passive microwave land‒water mixed pixels in extracting lake ice phenology has been underestimated. There are still many small and medium-sized lakes whose ice phenology has not been recorded, especially in regions such as the Tibetan Plateau, for which there are very few in situ observations. In this study, the changing characteristics of mixed pixels during freeze‒thaw processes are discussed. Using air temperature to reduce the seasonal variation in brightness temperature series, Advanced Microwave Scanning Radiometer 2 (AMSR2) data were used to extract the ice phenology of 194 lakes that could represent 78 % of the total lake area on the Tibetan Plateau from September 2012 to August 2022. The lake ice phenology results show high consistency compared with the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived lake ice phenology dataset and demonstrate better performance than the existing AMSR2-derived dataset. This ice phenology dataset covers the largest number of lakes in the Tibetan Plateau region, fills a gap or increases the integrity of ice phenology records for at least one hundred small lakes and provides complete pixel-scale freeze‒thaw processes on the lake surface. The dataset will provide valuable information to the user community about the spatial distribution of and changes in ice cover in lakes, especially small lakes, over the last decade. The dataset is available at https://doi.org/10.11888/Cryos.tpdc.300796 (Cai and Ke, 2023).
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Status: closed
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RC1: 'Comment on essd-2023-441', Anonymous Referee #1, 17 Feb 2024
- AC2: 'Reply on RC1', Yu Cai, 22 Apr 2024
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RC2: 'Comment on essd-2023-441', Anonymous Referee #2, 18 Mar 2024
The study proposed a new method to extract lake ice phenology in passive microwave data by combining ERA-5 air temperature. This is an interesting exploration for passive microwave data, However, as a dataset article, the applicability, advantage and necessity of the dataset still need further consideration and discussion.
Detail questions below:
(1) In the introduction, authors list previous research in Table 1. But as far as I know, there are also some other dataset or studies on lake ice phenology on Tibetan Plateau or even larger area, such as Wu et al. (2022), Feng et al. (2021), and Qiu et al. (2017)…
Qiu Y., Guo H., et al. A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region (2002-2016). Science Data Bank. 2017. doi: 10.11922/sciencedb.374
Wang X., Feng L., et al. High-resolution mapping of ice cover changes in over 33,000 lakes across the North Temperate Zone. Geophysical Research Letters, 2021.
Wu Y., Guo L., et al., Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau. Scientific Data, 2022, 9(1):743.
(2) Line 73-75 “we drew a 20 km… freeze-thaw information.” How did authors determine whether the AMSR2 pixels within the buffer contained extractable freeze‒thaw information? By visual check? Or some automatically method?
(3) The title of section 2.2 is “Input data”, but validation data is also included in section 2.2. I suggest as “2.2 Data…2.2.1 Input data…2.2.2 validation data…” and describe AMSR2 and ERA5 data in 2.2.1.
(4) What is the accuracy of the two validation data? Has the reliability or robustness of the two dataset been validated?
(5) As the authors mentioned in the manuscript that optical products (MODIS) and microwave products (PMW) have their own problems and shortcomings, it is not enough to compare the results with these two types of data. Some in-situ data should be collected as much as possible to validate the accuracy of dataset. Or at least, using high-resolution optical data (Landsat, Sentinel…) to visually interpret and obtain reliable lake ice coverage to validate.
(6) As I understand, the new method eliminates the influence of land in land‒water mixed pixels by EAR Ta. Is the method adaptable to all land‒water mixed pixels? Even for those pixels that are primarily contained by land.
(7) I’m confused in how the threshold are determined. There are 4 phenology indicators (FUS, FUE, BUS, BUE) need to be extracted, which should correspond to 4 THs. In Line 161-162 “we obtained … averaging the two mean values”. The average value of two groups mean value is the threshold. Which indicator is this threshold for? A clearer description is needed here.
(8) In section 2.3.4, Is the lake group comparable to a single lake? Because even lakes that are very close in distance may have different freeze-thaw properties. Perhaps comparing the LIP of the same single lake is more convincing.
(9) Line 224-225, so how to determine the freeze-thaw information in pixels with high proportion of land? By visual interpretation?
(10) I’m confused how much lake ice indicator are extracted. Two or four? Please state in Section 2.3. If only two indices are considered, how to compare them with dataset that contained 4 indicators (Such as Figure 6)?
(11) In conclusion, line 338-340. The authors mentioned the dataset contained more small lakes. How small lakes that the LIP can be extracted? Because the resolution of passive microwave data is coarse, is the dataset more accurate than that based on optical data in small lakes?
(12) The applicability, and applicable scenarios of the dataset need to be further clarified in the conclusion
Citation: https://doi.org/10.5194/essd-2023-441-RC2 - AC1: 'Reply on RC2', Yu Cai, 22 Apr 2024
Status: closed
-
RC1: 'Comment on essd-2023-441', Anonymous Referee #1, 17 Feb 2024
- AC2: 'Reply on RC1', Yu Cai, 22 Apr 2024
-
RC2: 'Comment on essd-2023-441', Anonymous Referee #2, 18 Mar 2024
The study proposed a new method to extract lake ice phenology in passive microwave data by combining ERA-5 air temperature. This is an interesting exploration for passive microwave data, However, as a dataset article, the applicability, advantage and necessity of the dataset still need further consideration and discussion.
Detail questions below:
(1) In the introduction, authors list previous research in Table 1. But as far as I know, there are also some other dataset or studies on lake ice phenology on Tibetan Plateau or even larger area, such as Wu et al. (2022), Feng et al. (2021), and Qiu et al. (2017)…
Qiu Y., Guo H., et al. A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region (2002-2016). Science Data Bank. 2017. doi: 10.11922/sciencedb.374
Wang X., Feng L., et al. High-resolution mapping of ice cover changes in over 33,000 lakes across the North Temperate Zone. Geophysical Research Letters, 2021.
Wu Y., Guo L., et al., Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau. Scientific Data, 2022, 9(1):743.
(2) Line 73-75 “we drew a 20 km… freeze-thaw information.” How did authors determine whether the AMSR2 pixels within the buffer contained extractable freeze‒thaw information? By visual check? Or some automatically method?
(3) The title of section 2.2 is “Input data”, but validation data is also included in section 2.2. I suggest as “2.2 Data…2.2.1 Input data…2.2.2 validation data…” and describe AMSR2 and ERA5 data in 2.2.1.
(4) What is the accuracy of the two validation data? Has the reliability or robustness of the two dataset been validated?
(5) As the authors mentioned in the manuscript that optical products (MODIS) and microwave products (PMW) have their own problems and shortcomings, it is not enough to compare the results with these two types of data. Some in-situ data should be collected as much as possible to validate the accuracy of dataset. Or at least, using high-resolution optical data (Landsat, Sentinel…) to visually interpret and obtain reliable lake ice coverage to validate.
(6) As I understand, the new method eliminates the influence of land in land‒water mixed pixels by EAR Ta. Is the method adaptable to all land‒water mixed pixels? Even for those pixels that are primarily contained by land.
(7) I’m confused in how the threshold are determined. There are 4 phenology indicators (FUS, FUE, BUS, BUE) need to be extracted, which should correspond to 4 THs. In Line 161-162 “we obtained … averaging the two mean values”. The average value of two groups mean value is the threshold. Which indicator is this threshold for? A clearer description is needed here.
(8) In section 2.3.4, Is the lake group comparable to a single lake? Because even lakes that are very close in distance may have different freeze-thaw properties. Perhaps comparing the LIP of the same single lake is more convincing.
(9) Line 224-225, so how to determine the freeze-thaw information in pixels with high proportion of land? By visual interpretation?
(10) I’m confused how much lake ice indicator are extracted. Two or four? Please state in Section 2.3. If only two indices are considered, how to compare them with dataset that contained 4 indicators (Such as Figure 6)?
(11) In conclusion, line 338-340. The authors mentioned the dataset contained more small lakes. How small lakes that the LIP can be extracted? Because the resolution of passive microwave data is coarse, is the dataset more accurate than that based on optical data in small lakes?
(12) The applicability, and applicable scenarios of the dataset need to be further clarified in the conclusion
Citation: https://doi.org/10.5194/essd-2023-441-RC2 - AC1: 'Reply on RC2', Yu Cai, 22 Apr 2024
Data sets
Lake ice phenology on the Tibetan Plateau extracted from AMSR2 data (2013-2022) Yu Cai and Chang-Qing Ke https://doi.org/10.11888/Cryos.tpdc.300796
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