the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GLOBMAP SWF: a global annual surface water cover frequency dataset during 2000–2020 for change analysis of inland water bodies
- 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- 2Key Laboratory for Humid Subtropical Eco-geographical Process of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350007, China
- 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
- 2Key Laboratory for Humid Subtropical Eco-geographical Process of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350007, China
Abstract. The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes of inland water bodies due to their high seasonal variation and abrupt change. In this paper, a global annual surface water cover frequency dataset (GLOBMAP SWF) was generated from the MODIS land surface reflectance products during 2000–2020 to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water bodies and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. Regional analysis demonstrates that our estimation results show better performances on frozen water, saline lake, bright surface and cloud-frequent regions compared with the two high-frequent surface water datasets derived from MODIS data. Compared with the high-resolution SWF maps extracted from Sentinel-1 data in four regions that cover lake, river and wetland, the R2 reaches 0.83 to 0.97, RMSE is ranging from 7.24 % to 11.28 %, and MAE is between 2.07 % and 7.15 %. In 2020, the area of global maximum surface water extent is 3.38 million km2, of which the permanent surface water accounts for approximately 54 % (1.83 million km2) and the other 46 % is intermittent surface water (1.55 million km2). The area of global maximum and permanent surface water has been shrinking since 2001, with a change rate of -7577 km2/yr and -4315 km2/yr (p < 0.05), respectively; while the intermittent surface water with the SWF above 50 % has been expanding (1368 km2/yr, p < 0.01). This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland waters extent with consideration of its seasonal variation. The GLOBMAP SWF data are available at https://doi.org/10.5281/zenodo.6462883 (Liu and Liu, 2022).
Yang Liu et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2022-138', Anonymous Referee #1, 01 Jul 2022
Interesting paper with an approach to surface water detection globally. In general, the method works and generates results that are compelling enough to consider. However there are many significant holes in the logic that were not tested (or not proven) by the authors which could have significant impacts on their results and conclusions. Overall, much more detail is needed in the descriptions of how conditions (I noted several specific things in my comments below) were handled and tested for validity. While I accept the premise that frequent MODIS observations are an advantage compared to frequency products using Landsat, which was stated by the authors in the introduction, it is still necessary to compare the frequency results from GLOBMAP to one or more of the frequency maps from Landsat (Pekel or Pickens at the very least). The comparisons that were done were with other MODIS derived products. This is ok for a first look but if you are trying to claim that you have a better approach than the Landsat products you must test this and show the results so the reader can decide for themselves. For this paper to be published in context these evaluations must be performed and reported. Beyond that it is important to clarify for the reader how the following things were handled so that the reader can trust the results. How did you delineate the oceans? Where did you cut off rivers where they meet the oceans? How did you handle extensive burned areas globally which would effect your low NIR values?
Line Comment
50 “Surface water was also mapping” needs revision for English grammar
99 determinate should be determine
101 why did you not use the finer resolution GMTED which was designed for use with MODIS data?
116 the sentence starting with “The cloud, ice…” appears to end abruptly or otherwise be an incomplete sentence
136 all of your validation sites are in the tropics, this is not a best practice. For a global product you need to have validation from northern latitudes as well as mid latitudes to assess performance everywhere
173 If you use MOD09A1 you have a total of 46 possible observations in a year. In most cases at least half (probably more) are not usable due to clouds or other data problems. Using the “six lowest NIR” values, could be that you only have six total observations for a pixel. This is a questionable method for a global product.
194 less than 15 percent or less than 15 count? What do you do in the frequent case where Nland is < 15? In northern latitudes you won’t get that many snow free observations, the method seems to ignore the typical case of snow surrounding ice covered lakes.
230 does your definition of intermittent include the fact that high latitude lakes are frozen for a large part of the year? Much more clarity is needed on your definitions.
308 this statement confirms my earlier comments. Many of your assumptions about the availability of clear sky observations are invalid for many places in the world. Unless you provide a companion product describing the per pixel reliability (based on the number of observations available) users are likely to draw incorrect conclusions in many cases.
352 should be Carroll not Carrell
378 for these evaluations to be understood it is essential to know how many clear observations there were in each year. How can the reader know that the variation you are reporting is not simply due to differences in the number of observations for a given year?
395 largest variation by total area or by percent change? Total area would limit this to only very large lakes…
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RC2: 'Comment on essd-2022-138', Anonymous Referee #2, 29 Jul 2022
The dataset provides a MODIS-derived annual surface water frequency dataset, which can help analyze the dynamics of surface water. The manuscript is well structured and written! My major comments are:
I agree with the authors that the daily MODIS observations have value in capturing the variations of surface water; however, its limitation is also obvious. The 500-m resolution is too coarse for capturing the abundant small water bodies as well as the subtle changes of surface water. Moreover, the Sinusoidal projection of MODIS caused a considerable distortion in high latitudes, worsening this omission, particularly in North America and Eastern Russia, where a large portion of the global small water bodies are located. It seems that the authors have been mainly focused on China and a few low-mid latitudes, but did not assess the performance in high latitudes, which seems to require more attention during validation.
The authors reported the areas of global inland surface water, including permanent and maximum areas; however, I think the numbers could be biased by failing to capture the small water bodies as commented in the above paragraph. As many much finer surface water datasets have already been produced, I would suggest the authors clarify the conditions of these reported areas, such as water bodies larger than a certain size; otherwise, the areas would not be valid.
I am not convinced why the authors did not compare the results to the global water dataset produced by Pekel et al. and the GLAD (Pickens et al., 2020), which all provide permanent and seasonable water cover that can be comparable to this dataset.
Specific comments:
I would suggest removing “for change analysis of inland water bodies” in the title.
Line 172, why six observations?
Line 179, does the slope criteria also remove water in a sloppy area that is outside of shadow? Also, did the variation of solar angles along latitudes and seasons considered in estimating shadows?
Line 200, please clarify what resample method was adopted.
Line 427-448, I am not fully agreeing with the novelty of the method as mentioned here. The method still identifies water cover as explained in the methodology, so the statement of the advancement here does not seem to be a valid point. Also, the method seems to be a very simple one without considering calculating water index or machine learning-based models. I am honestly surprised that it was robust enough for producing a reasonable global result.
Figure 8 is hard to interpret because most of the pixels showing positive trends also show negative trends. I think that the authors need to come up with a better way of presenting the results.
Yang Liu et al.
Data sets
GLOBMAP SWF: a global annual surface water cover frequency dataset since 2000 for change analysis of inland water bodies Ronggao Liu, Yang Liu https://doi.org/10.5281/zenodo.6462883
Yang Liu et al.
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