the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Jan 2022
A Novel Global Freeze-Thaw State Detection Algorithm Based on Passive L-Band Microwave Remote Sensing
Abstract. Knowing the Freeze-Thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Near-surface air temperature and land surface temperature are usually used in meteorology to infer the FT-state. However, the uncertainty is large because both temperatures can hardly be distinguished from remote sensing. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not known sufficiently well.
We present a new FT-state detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. The exploitation of the daily variation signal allows for a more reliable state detection, particularly during the transitions periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98 % in summer and up to 75 % in winter. Compared to the FT state inferred indirectly from the 2-m air temperature of the ERA5-land reanalysis, the new FT algorithm has a similar performance as the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s 2-m air temperature estimate. This study is expected to extend L-band microwave remote sensing data for improved FT detection.
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Interactive discussion
Status: closed
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RC1: 'Review of Lv et al.', Simon Zwieback, 15 Jan 2022
Lv et al. present a new remote sensing algorithm for identifying the land surface freeze/thaw state using SMAP passive microwave observations. The central premise of the algorithm is that landscapes that remain frozen on daily to synoptic time scales are characterized by small diurnal differences in brightness temperature. The authors compare the results obtained with their algorithm with the SMAP product and various reanalysis-based freeze/thaw-related temperature indices over the Northern Hemisphere. They further derive common F/T metrics such as the length of the frozen period, but they do not analyze them in depth.
The manuscript's content is relevant to The Cryosphere, as it covers a topic that is of interest to the journal's audience and also to applied remote sensing scientists. However, it suffers from several major weaknesses and inconsistencies that will require extensive revision or rewriting before publication. The main shortcomings are:
1) opaque writing that curtails comprehension (paper structure, poorly structured paragraphs, figures)
2) inconsistencies between the author's claims that their algorithm is globally applicable and the assumptions underlying the algorithm
3) multiple claims that are not backed up by evidence or references
4) limited scrutiny of the algorithm and its output1) Lack of clarity
The manuscript is difficult to read. In addition to many poorly worded phrases that detract from the content, the paper structure, the paragraph structure and the figures are challenging to follow.
The paper structure is unusual in that the authors do not include a proper discussion (see below). Furthermore, the introduction does a poor job of conveying the main ideas and findings. For instance, the variance-based filtering that is central to the algorithm is not mentioned. The reader is caught by surprise in the methods section. More broadly, I suggest the authors clarify what they mean by the estimand, i.e., the freeze/thaw state. Is it defined instantaneously (with the variance-based filtering just a convenient means to stabilize the estimation), or is it aggregated on daily or synoptic time scales?
The paragraphs often appear to be haphazardly put together, thus greatly limiting the readability of the manuscript. The introduction serves as a good example. The paragraph starting at line 47 opens by highlighting the limitations of temperature-based indicators for freeze/thaw state estimation. The second sentence states that "[i]n contrast, more direct state information results from the very different microwave dielectric constant for frozen and unfrozen soil. However, the reader has to guess that this sentence and the paragraph it is contained in are about microwave remote sensing of the freeze/thaw state, as the expressions "remote sensing" and "freeze/thaw" are not mentioned once. The remainder of the paragraph talks about emissivities without referring to the frequency and polarization. At some point, the reader stumbles upon L-band observations from SMAP and SMOS, which, however, are of central importance to the manuscript and to the introduction. I suggest the authors identify one theme for each paragraph and structure the paragraph such that the reader can easily follow.
Another example of opaque writing is furnished by lines 177-190. The authors first propose their own definition of the beginning/end of "the annual freezing", but the subsequent algorithm is seemingly at odds with the definition. For instance, a brief cold spell in summer would meet the definition but would in most cases be screened by the variance criterion. The authors further claim that their variance screening using a window length beta of 7 (implicit unit: days) "optimally" filters out brief events. However, it is not at all clear how optimality is defined and what the evidence is that optimality is achieved.
The figures are exceedingly difficult to interpret. For example, figure 3 is a cornucopia of lines and markers, with poor contrast (the yellow line is almost invisible) and most items being obscured by others that are plotted on top. The choice of colours (rainbow scale) and line weights (the grid obscures a good part of Fig. 11) is questionable in almost all maps. The captions contain insufficient information to interpret the figures. For instance, Fig. 11 does not explain how the fraction of agreement (negative in the figure) was computed. Is it a difference? The caption of figure 2 shows a histogram of the beta-windowed variance, but it is not explained what input data were used and what value of beta was used.
2) Globally applicable algorithm?
The authors claim in the title that their algorithm is globally applicable, but the limited applicability of the underlying assumptions casts doubt on this claim. The authors do little to dispel these concerns, as they do not include a separate discussion section where associated limitations ought to be scrutinized. I also note that despite the word global in the title, no results for the southern hemisphere are provided. However, my two biggest concerns in this respect are the assumption of 6 am / 6 pm overpasses and the assumption of elevated variability of the dielectric characteristics of thawed landscapes on synoptic scales. Neither of these two assumptions are very accurate on a global scale.
The assumption of 6 am / 6 pm overpasses is difficult to defend at high latitudes, where the temporal sampling deviates substantially from that at the equator. The authors neglect this issue completely, although negative repercussions on their algorithm's performance are not too difficult to imagine.
The assumption of elevated variability of the dielectric characteristics of thawed landscapes on synoptic scales is not subjected to any scrutiny. The authors acknowledge that the Rossby wave time scale that serves as foundation for the beta parameter is relevant to mid-latitudes, but they do not discuss their variance-based filtering within a time window of length beta affects the results elsewhere. Among the regions of particular concern, I list cold arid regions (mentioned by the authors as presenting challenges to microwave F/T algorithms in the introduction), bedrock-dominated areas, and regions with extended periods of stable anticyclone.3) Unsubstantiated claims
The authors make numerous claims that are not backed up by evidence or references. An excellent example is furnished by the paragraph starting on line 153, whose intent is to provide a rationale for the new algorithm. There, the authors make numerous such claims. For instance, they state that brightness temperature changes during freeze/thaw transitions are at least as large as those associated with precipitation "because of the huge epsilon difference between frozen and unfrozen soil". They do not provide a reference or evidence for this claim, nor do they state when this may not be the case (e.g., certain arid landscapes). A further issue is that the language is inappropriate and vague ("huge"). There are numerous similar claims in this paragraph alone, and not a single piece of evidence or reference is provided.4) Very limited scrutiny
The authors do not subject their algorithm and its underlying assumptions to the level of scrutiny that a reader of The Cryosphere may expect. There is no discussion section that assesses failure cases or that establishes a link between potentially inappropriate assumptions and questionable results. Furthermore, general issues with the "validation" strategy employed here (e.g., scale and commensurability with reanalysis-derived temperature metrics) should be incorporated.Minor comments
l 37: suggest replacing solar with shortwave and terrestrial with longwave
l 40: Potentially inappropriate reference (Schuur et al.): How does the surface freeze/thaw state relate to permafrost carbon
l 94: "replaying": odd choice of word
l 171: That \Delta TB_i will be smaller than \Delta T_i does not follow from the provided inequalities because (7) is a sum. A mathematically sound argument is needed to substantiate the claim.Citation: https://doi.org/10.5194/tc-2021-369-RC1 -
AC1: 'Reply on RC1', Shaoning Lv, 22 Mar 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-369/tc-2021-369-AC1-supplement.pdf
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AC1: 'Reply on RC1', Shaoning Lv, 22 Mar 2022
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RC2: 'Comment on tc-2021-369', Anonymous Referee #2, 17 Jan 2022
Review on A novel global freeze-thaw state detection algorithm based on passive L-band microwave remote sensing, by Lv et al., (tc-2021-369).
This paper used Diurnal Amplitude Variation (DAV) to detect the landscape FT status over Northern hemisphere using SMAP L-band H-pol brightness temperatures. The performance of the FT classification was assessed using ERA5 2m air temperature and other global SMAP FT data records. The paper covers a topic that is suitable to readers of The Cryosphere and should be of particular interest to those interested in FT classification algorithm development and FT dynamics under climate change. However, the manuscript has concluded with lack of detail in describing method and FT classification algorithm, and insufficient FT agreement assessment. Additional analysis on relationship between L-band signal and soil temperature should be added to improve the conclusion (See “line 329-331” below). The suggested major revisions are as follows:
- Major concern is FT agreement assessment. Authors used air and skin temperatures, and soil temperatures at several depths from a single site (Xilinhot). Agreement assessment from only one site is not enough for global scale FT validation. FT sensitivity to L-band Tb signal varies on land cover type and climate regions.
- In the accuracy agreement at global domain, ERA5 is a model reanalysis data with uncertainty as well. Authors should include additional global FT agreement assessment instead of using only ERA5 data.
- Additional analysis on relationship between L-band signal (FT dynamics as well) and soil temperature should be added to improve the conclusion. That would be the possible reason why L-band microwave remote sensing can be used for better penetration depth monitoring.
- Although this study provided better overall FT classification accuracy, it is not clear that what factors (or which land cover type?) contribute to improve FT classification accuracy or degrade. Other landscape factors affect FT classification accuracy. The factors include sub-grid open water fraction, terrain heterogeneity, tree cover, precipitation and snowmelt and on. To improve the quality of the paper, additional analysis and discussion on this should be required.
Additional edits are noted below:
Line 66: Are the limitations not clearly described? Authors should include what the limitations are in more details.
Line 72-74: This is not clear to me. Author should clarify it.
Line 87: Authors should justify why you used 36km instead of 9km brightness temperature (Tb) data records. Indeed, SMAP data are provided at both 36-km and 9-km spatial resolution. The 9-km spatial resolution is closer to 0.1 degree ERA5.
Line 89: This study used older version of SMAP data.
Line 92, 98: ERA5 data provide hourly. What time did authors use for agreement assessment? Is it 6PM or 6AM? Authors should include data source (e.g., web link).
Line 118-123: The relevant citation should be included (Xu?, Derksen? Kim?).
Line 134: Surface air temperature from global weather stations were used for landscape FT classification accuracy assessment, not for validation. Authors should check and revise it.
Line 165: Why did you use H-pol? Is there any justification?
Line 178-179: Is this your assumption?
Line 212: Authors should include in-situ data description in Data sections (e.g., relevant references, data source (web site)).
Line 222: Figure 4 does not show soil moisture variations. How did you provide the influence of soil moisture on Tb? If it is soil moisture influence, how much variation in soil moisture?
Line 263: The geographic location of Xilinhot site should be provided to check if this site is within a domain applied to SCV algorithm in SMAP FT Prpdocuts.
Line 286: SMAP FT sate products were compared new FT data. Authors should provide more details on SMAP FT state products used in this validation. Which overpass time did you use? (e.g., 6am or 6pm?).
Line 293: Authors compared two FT state data with different spatial resolution. You should include how to reproject one data from another in method sections. Is it from 0.1 degree to 36km?
Line 296: Why was it worse in latitudes above 60N and low latitudes below 30N? Is it false frozen or thawing? What if you use skin or/and soil temperature? Could it be a better agreement?
Line 324: Some studies reported the results on FT accuracy assessment with soil temperature derived FT state. Authors should discuss the results from previous studies.
Line 329-331: Because you did not use soil temperature (indeed, soil temperature from one site only), this statement is not clear conclusion.
Line338: Is spatial resolution of ERA5 1degree? In data section, the resolution is 0.1 degree.
Figure 1: It would be great to include the latitude/longitude of Xilinhot site.
Figure 3: It is too complicated. Author could remove unnecessary time-series lines.
Figure 4: Where (or what) is Maqu?
Figure 5: Authors should describe study domain in details. E.g., how to define your domain?
Citation: https://doi.org/10.5194/tc-2021-369-RC2 -
AC2: 'Reply on RC2', Shaoning Lv, 22 Mar 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-369/tc-2021-369-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Shaoning Lv, 22 Mar 2022
Interactive discussion
Status: closed
-
RC1: 'Review of Lv et al.', Simon Zwieback, 15 Jan 2022
Lv et al. present a new remote sensing algorithm for identifying the land surface freeze/thaw state using SMAP passive microwave observations. The central premise of the algorithm is that landscapes that remain frozen on daily to synoptic time scales are characterized by small diurnal differences in brightness temperature. The authors compare the results obtained with their algorithm with the SMAP product and various reanalysis-based freeze/thaw-related temperature indices over the Northern Hemisphere. They further derive common F/T metrics such as the length of the frozen period, but they do not analyze them in depth.
The manuscript's content is relevant to The Cryosphere, as it covers a topic that is of interest to the journal's audience and also to applied remote sensing scientists. However, it suffers from several major weaknesses and inconsistencies that will require extensive revision or rewriting before publication. The main shortcomings are:
1) opaque writing that curtails comprehension (paper structure, poorly structured paragraphs, figures)
2) inconsistencies between the author's claims that their algorithm is globally applicable and the assumptions underlying the algorithm
3) multiple claims that are not backed up by evidence or references
4) limited scrutiny of the algorithm and its output1) Lack of clarity
The manuscript is difficult to read. In addition to many poorly worded phrases that detract from the content, the paper structure, the paragraph structure and the figures are challenging to follow.
The paper structure is unusual in that the authors do not include a proper discussion (see below). Furthermore, the introduction does a poor job of conveying the main ideas and findings. For instance, the variance-based filtering that is central to the algorithm is not mentioned. The reader is caught by surprise in the methods section. More broadly, I suggest the authors clarify what they mean by the estimand, i.e., the freeze/thaw state. Is it defined instantaneously (with the variance-based filtering just a convenient means to stabilize the estimation), or is it aggregated on daily or synoptic time scales?
The paragraphs often appear to be haphazardly put together, thus greatly limiting the readability of the manuscript. The introduction serves as a good example. The paragraph starting at line 47 opens by highlighting the limitations of temperature-based indicators for freeze/thaw state estimation. The second sentence states that "[i]n contrast, more direct state information results from the very different microwave dielectric constant for frozen and unfrozen soil. However, the reader has to guess that this sentence and the paragraph it is contained in are about microwave remote sensing of the freeze/thaw state, as the expressions "remote sensing" and "freeze/thaw" are not mentioned once. The remainder of the paragraph talks about emissivities without referring to the frequency and polarization. At some point, the reader stumbles upon L-band observations from SMAP and SMOS, which, however, are of central importance to the manuscript and to the introduction. I suggest the authors identify one theme for each paragraph and structure the paragraph such that the reader can easily follow.
Another example of opaque writing is furnished by lines 177-190. The authors first propose their own definition of the beginning/end of "the annual freezing", but the subsequent algorithm is seemingly at odds with the definition. For instance, a brief cold spell in summer would meet the definition but would in most cases be screened by the variance criterion. The authors further claim that their variance screening using a window length beta of 7 (implicit unit: days) "optimally" filters out brief events. However, it is not at all clear how optimality is defined and what the evidence is that optimality is achieved.
The figures are exceedingly difficult to interpret. For example, figure 3 is a cornucopia of lines and markers, with poor contrast (the yellow line is almost invisible) and most items being obscured by others that are plotted on top. The choice of colours (rainbow scale) and line weights (the grid obscures a good part of Fig. 11) is questionable in almost all maps. The captions contain insufficient information to interpret the figures. For instance, Fig. 11 does not explain how the fraction of agreement (negative in the figure) was computed. Is it a difference? The caption of figure 2 shows a histogram of the beta-windowed variance, but it is not explained what input data were used and what value of beta was used.
2) Globally applicable algorithm?
The authors claim in the title that their algorithm is globally applicable, but the limited applicability of the underlying assumptions casts doubt on this claim. The authors do little to dispel these concerns, as they do not include a separate discussion section where associated limitations ought to be scrutinized. I also note that despite the word global in the title, no results for the southern hemisphere are provided. However, my two biggest concerns in this respect are the assumption of 6 am / 6 pm overpasses and the assumption of elevated variability of the dielectric characteristics of thawed landscapes on synoptic scales. Neither of these two assumptions are very accurate on a global scale.
The assumption of 6 am / 6 pm overpasses is difficult to defend at high latitudes, where the temporal sampling deviates substantially from that at the equator. The authors neglect this issue completely, although negative repercussions on their algorithm's performance are not too difficult to imagine.
The assumption of elevated variability of the dielectric characteristics of thawed landscapes on synoptic scales is not subjected to any scrutiny. The authors acknowledge that the Rossby wave time scale that serves as foundation for the beta parameter is relevant to mid-latitudes, but they do not discuss their variance-based filtering within a time window of length beta affects the results elsewhere. Among the regions of particular concern, I list cold arid regions (mentioned by the authors as presenting challenges to microwave F/T algorithms in the introduction), bedrock-dominated areas, and regions with extended periods of stable anticyclone.3) Unsubstantiated claims
The authors make numerous claims that are not backed up by evidence or references. An excellent example is furnished by the paragraph starting on line 153, whose intent is to provide a rationale for the new algorithm. There, the authors make numerous such claims. For instance, they state that brightness temperature changes during freeze/thaw transitions are at least as large as those associated with precipitation "because of the huge epsilon difference between frozen and unfrozen soil". They do not provide a reference or evidence for this claim, nor do they state when this may not be the case (e.g., certain arid landscapes). A further issue is that the language is inappropriate and vague ("huge"). There are numerous similar claims in this paragraph alone, and not a single piece of evidence or reference is provided.4) Very limited scrutiny
The authors do not subject their algorithm and its underlying assumptions to the level of scrutiny that a reader of The Cryosphere may expect. There is no discussion section that assesses failure cases or that establishes a link between potentially inappropriate assumptions and questionable results. Furthermore, general issues with the "validation" strategy employed here (e.g., scale and commensurability with reanalysis-derived temperature metrics) should be incorporated.Minor comments
l 37: suggest replacing solar with shortwave and terrestrial with longwave
l 40: Potentially inappropriate reference (Schuur et al.): How does the surface freeze/thaw state relate to permafrost carbon
l 94: "replaying": odd choice of word
l 171: That \Delta TB_i will be smaller than \Delta T_i does not follow from the provided inequalities because (7) is a sum. A mathematically sound argument is needed to substantiate the claim.Citation: https://doi.org/10.5194/tc-2021-369-RC1 -
AC1: 'Reply on RC1', Shaoning Lv, 22 Mar 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-369/tc-2021-369-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Shaoning Lv, 22 Mar 2022
-
RC2: 'Comment on tc-2021-369', Anonymous Referee #2, 17 Jan 2022
Review on A novel global freeze-thaw state detection algorithm based on passive L-band microwave remote sensing, by Lv et al., (tc-2021-369).
This paper used Diurnal Amplitude Variation (DAV) to detect the landscape FT status over Northern hemisphere using SMAP L-band H-pol brightness temperatures. The performance of the FT classification was assessed using ERA5 2m air temperature and other global SMAP FT data records. The paper covers a topic that is suitable to readers of The Cryosphere and should be of particular interest to those interested in FT classification algorithm development and FT dynamics under climate change. However, the manuscript has concluded with lack of detail in describing method and FT classification algorithm, and insufficient FT agreement assessment. Additional analysis on relationship between L-band signal and soil temperature should be added to improve the conclusion (See “line 329-331” below). The suggested major revisions are as follows:
- Major concern is FT agreement assessment. Authors used air and skin temperatures, and soil temperatures at several depths from a single site (Xilinhot). Agreement assessment from only one site is not enough for global scale FT validation. FT sensitivity to L-band Tb signal varies on land cover type and climate regions.
- In the accuracy agreement at global domain, ERA5 is a model reanalysis data with uncertainty as well. Authors should include additional global FT agreement assessment instead of using only ERA5 data.
- Additional analysis on relationship between L-band signal (FT dynamics as well) and soil temperature should be added to improve the conclusion. That would be the possible reason why L-band microwave remote sensing can be used for better penetration depth monitoring.
- Although this study provided better overall FT classification accuracy, it is not clear that what factors (or which land cover type?) contribute to improve FT classification accuracy or degrade. Other landscape factors affect FT classification accuracy. The factors include sub-grid open water fraction, terrain heterogeneity, tree cover, precipitation and snowmelt and on. To improve the quality of the paper, additional analysis and discussion on this should be required.
Additional edits are noted below:
Line 66: Are the limitations not clearly described? Authors should include what the limitations are in more details.
Line 72-74: This is not clear to me. Author should clarify it.
Line 87: Authors should justify why you used 36km instead of 9km brightness temperature (Tb) data records. Indeed, SMAP data are provided at both 36-km and 9-km spatial resolution. The 9-km spatial resolution is closer to 0.1 degree ERA5.
Line 89: This study used older version of SMAP data.
Line 92, 98: ERA5 data provide hourly. What time did authors use for agreement assessment? Is it 6PM or 6AM? Authors should include data source (e.g., web link).
Line 118-123: The relevant citation should be included (Xu?, Derksen? Kim?).
Line 134: Surface air temperature from global weather stations were used for landscape FT classification accuracy assessment, not for validation. Authors should check and revise it.
Line 165: Why did you use H-pol? Is there any justification?
Line 178-179: Is this your assumption?
Line 212: Authors should include in-situ data description in Data sections (e.g., relevant references, data source (web site)).
Line 222: Figure 4 does not show soil moisture variations. How did you provide the influence of soil moisture on Tb? If it is soil moisture influence, how much variation in soil moisture?
Line 263: The geographic location of Xilinhot site should be provided to check if this site is within a domain applied to SCV algorithm in SMAP FT Prpdocuts.
Line 286: SMAP FT sate products were compared new FT data. Authors should provide more details on SMAP FT state products used in this validation. Which overpass time did you use? (e.g., 6am or 6pm?).
Line 293: Authors compared two FT state data with different spatial resolution. You should include how to reproject one data from another in method sections. Is it from 0.1 degree to 36km?
Line 296: Why was it worse in latitudes above 60N and low latitudes below 30N? Is it false frozen or thawing? What if you use skin or/and soil temperature? Could it be a better agreement?
Line 324: Some studies reported the results on FT accuracy assessment with soil temperature derived FT state. Authors should discuss the results from previous studies.
Line 329-331: Because you did not use soil temperature (indeed, soil temperature from one site only), this statement is not clear conclusion.
Line338: Is spatial resolution of ERA5 1degree? In data section, the resolution is 0.1 degree.
Figure 1: It would be great to include the latitude/longitude of Xilinhot site.
Figure 3: It is too complicated. Author could remove unnecessary time-series lines.
Figure 4: Where (or what) is Maqu?
Figure 5: Authors should describe study domain in details. E.g., how to define your domain?
Citation: https://doi.org/10.5194/tc-2021-369-RC2 -
AC2: 'Reply on RC2', Shaoning Lv, 22 Mar 2022
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2021-369/tc-2021-369-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Shaoning Lv, 22 Mar 2022
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Cited
3 citations as recorded by crossref.
- Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model H. Zhao et al. 10.3390/s23052620
- Quantitative Changes in the Surface Frozen Days and Potential Driving Factors in Northern Northeastern China D. Yang et al. 10.3390/land13030273
- Microwave Remote Sensing of Soil Moisture J. Zeng et al. 10.3390/rs15174243