MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000&ndash;2022)

Pan, Fangbo; Jiang, Lingmei; Wang, Gongxue; Pan, Jinmei; Huang, Jinyu; Zhang, Cheng; Cui, Huizhen; Yang, Jiangwei; Zheng, Zhaojun; Wu, Shengli; Shi, Jiancheng

doi:https://doi.org/10.5194/essd-2023-250

Preprints

https://doi.org/10.5194/essd-2023-250

Preprints

27 Nov 2023

| 27 Nov 2023

Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000–2022)

Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jiangwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi

Abstract. Accurate long-term daily Cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asia Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC product have a spatial resolution of 0.005°, and spans from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal scale accuracy assessment. The fractional snow cover accuracy metrics, including coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer’s accuracy (PA), and user’s accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 %, and a cohen’s kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information of snowpack for mountain hydrological models, land surface models, and numerical weather prediction in the Asia Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Centre at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://zenodo.org/doi/10.5281/zenodo.10005826.

Received: 29 Jun 2023 – Discussion started: 27 Nov 2023

Download & links

Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jiangwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi

Status: closed

RC1:
'Comment on essd-2023-250', Anonymous Referee #1, 08 Jan 2024

1. Please add a reference for Table 2, and justify why the thresholds are reasonable and reliable in the study area.
2. Figure 3. It is true that the NDSI and NDVI of vegetation, soil, and snow endmembers show much difference. However, I am curious about whether the approach works well for some vertically mixed pixels. For example, forest or shrub-covered snow represent a special mixture of spectral information. Please discuss the performance of the algorithm and products in these regions. Some studies have suggested that NDSI does not show many changes with or without snow under the canopy of boreal forest.
3. L212-214, L230-232. The authors indicated that the snow cover in the area change rapidly, and thus selected a small time window for the temporal interpolation (3 day). However, in the following step, they applied a 9-day window to remove the rest cloud pixels (PCHIP method). It does not make sense to me. Did this PCHIP approach brings too much errors?
4. L363-365. The authors argued that increase in the amount of station data and observations lead to better accuracy assessment. It does not make sense. Were the station data only used for verification of the FSC products? Were they used for the training of the algorithm? If not, I think more station data for verification would not increase the accuracy of the developed products. Declined cloud cover should have contributed to the better verification as MODIS has higher accuracy in cloud-free days. I am not sure whether changes of snow cover days also affected the accuracy indices. It seems the high OA and CK values after 2015 were mainly driving by higher PA index. It likely means the omission errors decreased. Was there less snow cover in the AWT area in these years?
5. L492-494. It is true that there are many cloud/snow confusion errors in MODIS data. Some researchers (e.g. Dong and Menzel, 2016, Journal of Hydrology; Remote Sensing of Enviroment) have conducted some research on this topic and developed some algorithm to remove overestimated (misclassified) snow pixels on MODIS snow maps using station data. It seems the proposed algorithm here did not consider this problem. It would be helpful to add some discussions about this.

Citation: https://doi.org/10.5194/essd-2023-250-RC1
- AC2: 'Reply on RC1', Fangbo Pan, 08 Mar 2024
  
  We would like to thank the reviewers for their careful and constructive reviews. These comments have played a pivotal role in enhancing the overall quality of this work. We have carefully revised our manuscript and adequately addressed all the questions and concerns that the referees have raised, and you can find our detailed responses in the attached document.
  
  Citation: https://doi.org/10.5194/essd-2023-250-AC2
RC2:
'Comment on essd-2023-250', Anonymous Referee #2, 06 Feb 2024

Pan’s paper produced a daily cloud-free daily Cloud-gap-filled fractional snow cover products with 0.005°spatial resolution based on MODIS surface reflectance data in the Asia Water Tower (AWT) region. The core of the algorithm contains two parts, one part is the automatic endmember extraction (MESMA-AGE) technique for producing FSC, and the other part is the multistep spatiotemporal interpolation algorithm (MSTI) for filling gaps. However, before this article is accepted by ESSD, the following questions are still required.

General comments:

1. The author conducted a comprehensive evaluation of the product using Landsat8 and sites, but comparison with existing products was lacking. It is recommended to increase comparison with other 500-meter fractional snow cover products. The author also mentioned that SNOW CCI data can be compared before going to the cloud to clarify the accuracy of the MESMA-AGE algorithm. In particular, the MOD10A1F product is global daily Cloud-gap-filled fractional snow cover dataset. This study does not mention or compare with it.

2. The selection of pure end-menmber is the key to the MESMA-AGE algorithm. What is the basis for selecting pure end-menmber in TABLE2? Qinghai-Tibet soil and rocks are quite different. Is the current selection very representative? Further explanation is needed.

3. In 3.2 "Multistep spatiotemporal interpolation algorithm", the author's last two steps are "piecewise cubic hermite interpolating polynomial (PCHIP) for the 19-day period, and further spatial interpolation using a 10×10 window." However, the snow cover on the Tibetan Plateau changes rapidly and has strong spatial heterogeneity. The author also mentioned in "4.2.3 Performance of spatiotemporal reconstruction algorithm" that "The results show that the accuracy of the fractional snow cover based on clear sky observations is significantly better than that of the spatiotemporal reconstruction. "The 19-day time series and the 10 ×10 window” interpolation may not be suitable for the Tibetan Plateau, and the author needs to further consider the rationality of the Multistep spatiotemporal interpolation algorithm.

4. In previous studies, FSC set thresholds greater than 10, 15... to consider snow, and site verification set thresholds greater than 0cm, 1cm, 2cm, 3, cm, 4cm, 5cm... both, this study Please explain further the reasons for using FSC>15 and sd>3cm

5. It is recommended that the author add a schematic diagram of Asian Water Tower Region's products so that readers can more intuitively understand the algorithm effect of each step.

Minor comments:

1. There are two RMSE indicators in Figure 2. It is recommended to modify one of them to MAE.

2. Line 207 mentioned “further spatial interpolation using a 10×10 window”, and Line 241 mentioned “In this study, the observation information from the 11*11 interpolation window centered on the cloud pixel was used based on the inverse distance weight ", did the author use "10×10" or "11×11" window in the fourth step of the multistep spatiotemporal interpolation algorithm?

Citation: https://doi.org/10.5194/essd-2023-250-RC2
- AC1: 'Reply on RC2', Fangbo Pan, 08 Mar 2024
  
  We would like to thank the reviewers for their careful and constructive reviews. These comments have played a pivotal role in enhancing the overall quality of this work. We have carefully revised our manuscript and adequately addressed all the questions and concerns that the referees have raised, and you can find our detailed responses in the attached document.
  
  Citation: https://doi.org/10.5194/essd-2023-250-AC1

Status: closed

RC1:
'Comment on essd-2023-250', Anonymous Referee #1, 08 Jan 2024

1. Please add a reference for Table 2, and justify why the thresholds are reasonable and reliable in the study area.
2. Figure 3. It is true that the NDSI and NDVI of vegetation, soil, and snow endmembers show much difference. However, I am curious about whether the approach works well for some vertically mixed pixels. For example, forest or shrub-covered snow represent a special mixture of spectral information. Please discuss the performance of the algorithm and products in these regions. Some studies have suggested that NDSI does not show many changes with or without snow under the canopy of boreal forest.
3. L212-214, L230-232. The authors indicated that the snow cover in the area change rapidly, and thus selected a small time window for the temporal interpolation (3 day). However, in the following step, they applied a 9-day window to remove the rest cloud pixels (PCHIP method). It does not make sense to me. Did this PCHIP approach brings too much errors?
4. L363-365. The authors argued that increase in the amount of station data and observations lead to better accuracy assessment. It does not make sense. Were the station data only used for verification of the FSC products? Were they used for the training of the algorithm? If not, I think more station data for verification would not increase the accuracy of the developed products. Declined cloud cover should have contributed to the better verification as MODIS has higher accuracy in cloud-free days. I am not sure whether changes of snow cover days also affected the accuracy indices. It seems the high OA and CK values after 2015 were mainly driving by higher PA index. It likely means the omission errors decreased. Was there less snow cover in the AWT area in these years?
5. L492-494. It is true that there are many cloud/snow confusion errors in MODIS data. Some researchers (e.g. Dong and Menzel, 2016, Journal of Hydrology; Remote Sensing of Enviroment) have conducted some research on this topic and developed some algorithm to remove overestimated (misclassified) snow pixels on MODIS snow maps using station data. It seems the proposed algorithm here did not consider this problem. It would be helpful to add some discussions about this.

Citation: https://doi.org/10.5194/essd-2023-250-RC1
- AC2: 'Reply on RC1', Fangbo Pan, 08 Mar 2024
  
  We would like to thank the reviewers for their careful and constructive reviews. These comments have played a pivotal role in enhancing the overall quality of this work. We have carefully revised our manuscript and adequately addressed all the questions and concerns that the referees have raised, and you can find our detailed responses in the attached document.
  
  Citation: https://doi.org/10.5194/essd-2023-250-AC2
RC2:
'Comment on essd-2023-250', Anonymous Referee #2, 06 Feb 2024

Pan’s paper produced a daily cloud-free daily Cloud-gap-filled fractional snow cover products with 0.005°spatial resolution based on MODIS surface reflectance data in the Asia Water Tower (AWT) region. The core of the algorithm contains two parts, one part is the automatic endmember extraction (MESMA-AGE) technique for producing FSC, and the other part is the multistep spatiotemporal interpolation algorithm (MSTI) for filling gaps. However, before this article is accepted by ESSD, the following questions are still required.

General comments:

1. The author conducted a comprehensive evaluation of the product using Landsat8 and sites, but comparison with existing products was lacking. It is recommended to increase comparison with other 500-meter fractional snow cover products. The author also mentioned that SNOW CCI data can be compared before going to the cloud to clarify the accuracy of the MESMA-AGE algorithm. In particular, the MOD10A1F product is global daily Cloud-gap-filled fractional snow cover dataset. This study does not mention or compare with it.

2. The selection of pure end-menmber is the key to the MESMA-AGE algorithm. What is the basis for selecting pure end-menmber in TABLE2? Qinghai-Tibet soil and rocks are quite different. Is the current selection very representative? Further explanation is needed.

3. In 3.2 "Multistep spatiotemporal interpolation algorithm", the author's last two steps are "piecewise cubic hermite interpolating polynomial (PCHIP) for the 19-day period, and further spatial interpolation using a 10×10 window." However, the snow cover on the Tibetan Plateau changes rapidly and has strong spatial heterogeneity. The author also mentioned in "4.2.3 Performance of spatiotemporal reconstruction algorithm" that "The results show that the accuracy of the fractional snow cover based on clear sky observations is significantly better than that of the spatiotemporal reconstruction. "The 19-day time series and the 10 ×10 window” interpolation may not be suitable for the Tibetan Plateau, and the author needs to further consider the rationality of the Multistep spatiotemporal interpolation algorithm.

4. In previous studies, FSC set thresholds greater than 10, 15... to consider snow, and site verification set thresholds greater than 0cm, 1cm, 2cm, 3, cm, 4cm, 5cm... both, this study Please explain further the reasons for using FSC>15 and sd>3cm

5. It is recommended that the author add a schematic diagram of Asian Water Tower Region's products so that readers can more intuitively understand the algorithm effect of each step.

Minor comments:

1. There are two RMSE indicators in Figure 2. It is recommended to modify one of them to MAE.

2. Line 207 mentioned “further spatial interpolation using a 10×10 window”, and Line 241 mentioned “In this study, the observation information from the 11*11 interpolation window centered on the cloud pixel was used based on the inverse distance weight ", did the author use "10×10" or "11×11" window in the fourth step of the multistep spatiotemporal interpolation algorithm?

Citation: https://doi.org/10.5194/essd-2023-250-RC2
- AC1: 'Reply on RC2', Fangbo Pan, 08 Mar 2024
  
  We would like to thank the reviewers for their careful and constructive reviews. These comments have played a pivotal role in enhancing the overall quality of this work. We have carefully revised our manuscript and adequately addressed all the questions and concerns that the referees have raised, and you can find our detailed responses in the attached document.
  
  Citation: https://doi.org/10.5194/essd-2023-250-AC1

Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jiangwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi

Data sets

MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022) Lingmei Jiang, Fangbo Pan, Gongxue Wang, Jinmei Pan, Jiancheng Shi, Cheng Zhang, and Jinyu Huang https://zenodo.org/doi/10.5281/zenodo.10005826

Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jiangwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi

Viewed

Total article views: 459 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
336	92	31	459	27	27

HTML: 336
PDF: 92
XML: 31
Total: 459
BibTeX: 27
EndNote: 27

Views and downloads (calculated since 27 Nov 2023)

Month	HTML	PDF	XML	Total
Nov 2023	16	3	1	20
Dec 2023	98	29	5	132
Jan 2024	61	22	4	87
Feb 2024	41	12	6	59
Mar 2024	66	18	10	94
Apr 2024	54	8	5	67

Cumulative views and downloads (calculated since 27 Nov 2023)

Month	HTML	PDF	XML	Total
Nov 2023	16	3	1	20
Dec 2023	98	29	5	132
Jan 2024	61	22	4	87
Feb 2024	41	12	6	59
Mar 2024	66	18	10	94
Apr 2024	54	8	5	67

Viewed (geographical distribution)

Total article views: 465 (including HTML, PDF, and XML) Thereof 465 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 27 Apr 2024

Short summary

It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple endmember spectral mixture analysis algorithm and the gap-filling algorithm. It can provide highly accurate, quantitative fractional snow cover information for subsequent studies such as hydrology and climate.


Total:	0
HTML:	0
PDF:	0
XML:	0