the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling of Multi-Frequency Microwave Backscatter and Emission of Land Surface by a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP)
Abstract. Emission and backscattering signals of land surfaces at different frequencies have distinctive responses to soil and vegetation physical states. The use of multi-frequency combined active and passive microwave signals provides complementary information to better understand and interpret the observed signals in relation to surface states and the underlying physical processes. Such a capability also improves our ability to retrieve surface parameters and states such as soil moisture, freeze-thaw dynamics and vegetation biomass and vegetation water content (VWC) for ecosystem monitoring. We present here a prototype Community Land Active Passive Microwave Radiative Transfer Modelling platform (CLAP) for simulating both backscatter (σ0) and emission (TB) signals of land surfaces, in which the CLAP is backboned by an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering, and the Tor Vergata model for modelling vegetation scattering and the interaction between vegetation and soil parts. The CLAP was used to simulate both ground-based and space-borne multi-frequency microwave measurements collected at the Maqu observatory on the eastern Tibetan plateau. The ground-based systems include a scatterometer system (1–10 GHz) and an L-band microwave radiometer. The space-borne measurements are obtained from the X-band and C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) radiation observations. The impacts of different vegetation properties (i.e., structure, water and temperature dynamics) and soil conditions (i.e., different moisture and temperature profiles) on the microwave signals were investigated by CLAP simulation for understanding factors that can account for diurnal variations of the observed signals. The results show that the dynamic VWC partially accounts for the diurnal variation of the observed signal at the low frequencies (i.e., S- and L-bands), while the diurnal variation of the observed signals at high frequencies (i.e., X- and C-bands) is more due to vegetation temperature changing, which implies the necessity to first disentangle the impact of vegetation temperature for the use of high frequency microwave signals. The model derived vegetation optical depth τ differs in terms of frequencies and different model parameterizations, while its diurnal variation depends on the diurnal variation of VWC regardless of frequency. After normalizing τ at multi-frequency by wavenumber, difference is still observed among different frequencies. This indicates that τ is indeed frequency-dependent, and τ for each frequency is suggested to be applied in the retrieval of soil and vegetation parameters. Moreover, τ at different frequencies (e.g., X-band and L-band) cannot be simply combined for constructing accurate long time series microwave-based vegetation product. To this purpose, it is suggested to investigate the role of the leaf water potential in regulating plant water use and its impact on the normalized τ at multi-frequency. Overall, the CLAP is expected to improve our capability for understanding and applying current and future multi-frequency space-borne microwave systems (e.g. those from ROSE-L and CIMR) for vegetation monitoring.
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RC1: 'Comment on hess-2022-333', David Chaparro, 29 Nov 2022
Please, see my comments in the attached document.
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AC1: 'Reply on RC1', Hong Zhao, 13 Jan 2023
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2022-333/hess-2022-333-AC1-supplement.pdf
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AC1: 'Reply on RC1', Hong Zhao, 13 Jan 2023
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RC2: 'Comment on hess-2022-333', Anonymous Referee #2, 12 Dec 2022
The authors present a radiative transfer modelling platform for simulating backscatter and brightness temperature emission. They apply this model platform to simulate in-situ backscatter and emission data from a grassland site on the Tibetian plateau based on observed and simulated data for soil moisture and temperature. The authors investigate multiple different parameterizations target variables, and frequencies. A particular focus is put on explaining diurnal variability, where the authors try to disentangle the effects of diurnal variations of temperature and vegetation water content.
I believe that a better understanding of backscatter and brightness temperature and how they are influenced by diurnal variations of VWC and temperature can advance the scientific progress of the hydrological community. However, in the current state, I would advise the editor to reject the manuscript.
The manuscript has 3 major general issues, and also some other major issues with specific analyses performed, as listed below.
Major general issues:
- The article is too long, and there are too many figures (19 figures in the article, 27 figures in the supplement). The authors should put more effort
into presenting their results in fewer figures using higher-level summaries of the obtained results.
- Throughout the manuscript, the authors state that CLAP can reproduce the observed signals. Based solely on the results shown, I am not convinced whether this is true. The authors should at least discuss in more detail why such large deviations as shown in the plots still qualify as "reproducing the observed signal".
- As has also been pointed out by the other reviewer, the used metrics are not suitable to evaluate the diurnal or day-to-day variations. Metrics like correlation, unbiased RMSE, or even a detailed analysis of the mean diurnal cycles regarding magnitude or phase shift should be included.Major specific issues:
- The authors compare the use of in-situ SM from 2.5cm depth with modelled SM from 1mm depth, and from this draw conclusions about the sensing depths of the different bands. The effect mainly shows as a bias component in the simulated backscatter, while the differences in the diurnal patterns are visible, but small. However, as shown in Fig. S2, there is a bias between modelled and in-situ soil moisture even at similar depths. The different depths in the model all show the same "base level" and differ only in the magnitude of their diurnal variations. The difference in absolute value between using in-situ 2.5cm depth and modelled 1mm SM (e.g. in Fig. 11) therefore seems to be more related to this bias, than to difference in depth. Additionally, there is also a difference in the magnitude of diurnal variations between model and observations. The variations in the model at similar depths are much more pronounced, which furthermore makes the model simulations and the in-situ observations hard to compare. I would therefore be careful about drawing any conclusions about sensing depths from these comparisons.
- Fig. S12: I have some trouble understanding why Case 3 and Case 4 show such strong differences in mean and do not even cross. Since Case 4 uses the same air temperature, but imposes a diurnal pattern on VWC, I would guess that they have to be the same at least twice a day. All other plots also show that Case 3 and Case 4 are closely together and only show diurnal differences.
- In lines 508-510 the authors claim that observed winter-period VV diurnal variations of backscatter can be reproduced with CLAP. With some imagination I can see this in C-band, but not in the other bands. Please rephrase or show other plots or metrics supporting this statement.
Minor comments:- Setting the phase shift of the VWC curve to $\pi/2$ means that the minimum is reached at noon, and the maximum at midnight, but according to the description in section 2.2, VWC is replenished until early morning. This implies that the maximum should be at early morning. I don't think this is a large problem per se, because currently inferences are only made about the impact of dynamic VWC on modelled backscatter. But it might be something to consider in case a more detailed analysis of the mean diurnal cycles reveals a phase shift.
- Change "the CLAP" to "CLAP" throughout the manuscript. I also encourage the authors to google for "the clap".
- If feasible, it would be helpful to have (i) an overview table of all parameters going into the model (maybe in the appendix) and (ii) a short summary of the main model equations of the used models (TVG, AIEM, ATS), also in the appendix.
- l. 55: "instruments ... measure"
- l. 63: the presence of vegetation
- l. 64: needed -> necessary
- l. 77: noticeable -> noteworthy?
- l. 78: effect of vegetation on *the* microwave signal
- l. 102: "the ground truth observations" is a bit vague, do you mean in-situ
backscatter/emission as you used in this paper?
- l. 132: how the vegetation plays the role in -> the role vegetation plays in
- l. 133: is not explored yet -> has not been explored yet
- l. 140: "imposes great impacts on variations of sampling depth": unclear what you want to say here
- l. 147: maybe start a new paragraph here with "It is well established..."
- l. 153: temeprature -> temperature
- l. 158: forward simultaneous simulations -> simultaneous forward simulations
- l. 190: they exhibit *a* difference
- l. 201: for use -> used
- l. 217: is valued at -> is set to (also l. 219)
- l. 229: "comparable to the in situ measurements": See my comment above, there are biases and it is not clear how these influence your results. Did you calibrate the soil parameters for the site?
- l. 240-244: This sentence is very long, it would help readability to split it into multiple sentences.
- l. 248: for obtaining *the* soil scattering matrix
- l. 260: remove "below (Eq. (3))"
- l. 286-287: remove "please also refer to"
- l. 312: The role of the low air pressure in the approximation is not clear to me, can you make this more detailed?
- l. 330: What is the reason for the low value? Does this indicate a problem with the model or the model parameters in this setup?
- l. 333: simulated at X-band
- l. 338-341: You show that both vary at similar frequencies, but is there a correlation between wind and X-band signal?
- l. 398: despite -> except for?
- l. 458: while those simulated at L-band to not
- l. 458: Is Figure 4 a wrong reference? Which figure should this refer to?
- l. 462: the volume scatterin effect *is* present
- l. 480: remove "While"
- l. 495: as mentioned above, I can not see that the diurnal variations agree with the observations from the plots
- l. 517-518: The cylinder parameterization outperforms the disc parameterization in simulating ...
- l. 523: deep -> low
- l. 535: despite -> with?
- l. 543: Are the values for L-band exactly zero or just very small?
- section 4.2: a more verbose naming scheme for the different cases might help understanding your argument here
- l. 664: To me it seems not really useful to use CLAP for detecting rainfall events, if they could also be detected directly from the data going into CLAP (e.g. the soil moisture data)
- l. 689: While as -> Whereas
- l. 691: varied -> different
- l. 740: observed signal -> modelled signal, since your dynamic VWC only influences the modelled signalCitation: https://doi.org/10.5194/hess-2022-333-RC2 -
AC2: 'Reply on RC2', Hong Zhao, 13 Jan 2023
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2022-333/hess-2022-333-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Hong Zhao, 13 Jan 2023
Status: closed
-
RC1: 'Comment on hess-2022-333', David Chaparro, 29 Nov 2022
Please, see my comments in the attached document.
-
AC1: 'Reply on RC1', Hong Zhao, 13 Jan 2023
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2022-333/hess-2022-333-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Hong Zhao, 13 Jan 2023
-
RC2: 'Comment on hess-2022-333', Anonymous Referee #2, 12 Dec 2022
The authors present a radiative transfer modelling platform for simulating backscatter and brightness temperature emission. They apply this model platform to simulate in-situ backscatter and emission data from a grassland site on the Tibetian plateau based on observed and simulated data for soil moisture and temperature. The authors investigate multiple different parameterizations target variables, and frequencies. A particular focus is put on explaining diurnal variability, where the authors try to disentangle the effects of diurnal variations of temperature and vegetation water content.
I believe that a better understanding of backscatter and brightness temperature and how they are influenced by diurnal variations of VWC and temperature can advance the scientific progress of the hydrological community. However, in the current state, I would advise the editor to reject the manuscript.
The manuscript has 3 major general issues, and also some other major issues with specific analyses performed, as listed below.
Major general issues:
- The article is too long, and there are too many figures (19 figures in the article, 27 figures in the supplement). The authors should put more effort
into presenting their results in fewer figures using higher-level summaries of the obtained results.
- Throughout the manuscript, the authors state that CLAP can reproduce the observed signals. Based solely on the results shown, I am not convinced whether this is true. The authors should at least discuss in more detail why such large deviations as shown in the plots still qualify as "reproducing the observed signal".
- As has also been pointed out by the other reviewer, the used metrics are not suitable to evaluate the diurnal or day-to-day variations. Metrics like correlation, unbiased RMSE, or even a detailed analysis of the mean diurnal cycles regarding magnitude or phase shift should be included.Major specific issues:
- The authors compare the use of in-situ SM from 2.5cm depth with modelled SM from 1mm depth, and from this draw conclusions about the sensing depths of the different bands. The effect mainly shows as a bias component in the simulated backscatter, while the differences in the diurnal patterns are visible, but small. However, as shown in Fig. S2, there is a bias between modelled and in-situ soil moisture even at similar depths. The different depths in the model all show the same "base level" and differ only in the magnitude of their diurnal variations. The difference in absolute value between using in-situ 2.5cm depth and modelled 1mm SM (e.g. in Fig. 11) therefore seems to be more related to this bias, than to difference in depth. Additionally, there is also a difference in the magnitude of diurnal variations between model and observations. The variations in the model at similar depths are much more pronounced, which furthermore makes the model simulations and the in-situ observations hard to compare. I would therefore be careful about drawing any conclusions about sensing depths from these comparisons.
- Fig. S12: I have some trouble understanding why Case 3 and Case 4 show such strong differences in mean and do not even cross. Since Case 4 uses the same air temperature, but imposes a diurnal pattern on VWC, I would guess that they have to be the same at least twice a day. All other plots also show that Case 3 and Case 4 are closely together and only show diurnal differences.
- In lines 508-510 the authors claim that observed winter-period VV diurnal variations of backscatter can be reproduced with CLAP. With some imagination I can see this in C-band, but not in the other bands. Please rephrase or show other plots or metrics supporting this statement.
Minor comments:- Setting the phase shift of the VWC curve to $\pi/2$ means that the minimum is reached at noon, and the maximum at midnight, but according to the description in section 2.2, VWC is replenished until early morning. This implies that the maximum should be at early morning. I don't think this is a large problem per se, because currently inferences are only made about the impact of dynamic VWC on modelled backscatter. But it might be something to consider in case a more detailed analysis of the mean diurnal cycles reveals a phase shift.
- Change "the CLAP" to "CLAP" throughout the manuscript. I also encourage the authors to google for "the clap".
- If feasible, it would be helpful to have (i) an overview table of all parameters going into the model (maybe in the appendix) and (ii) a short summary of the main model equations of the used models (TVG, AIEM, ATS), also in the appendix.
- l. 55: "instruments ... measure"
- l. 63: the presence of vegetation
- l. 64: needed -> necessary
- l. 77: noticeable -> noteworthy?
- l. 78: effect of vegetation on *the* microwave signal
- l. 102: "the ground truth observations" is a bit vague, do you mean in-situ
backscatter/emission as you used in this paper?
- l. 132: how the vegetation plays the role in -> the role vegetation plays in
- l. 133: is not explored yet -> has not been explored yet
- l. 140: "imposes great impacts on variations of sampling depth": unclear what you want to say here
- l. 147: maybe start a new paragraph here with "It is well established..."
- l. 153: temeprature -> temperature
- l. 158: forward simultaneous simulations -> simultaneous forward simulations
- l. 190: they exhibit *a* difference
- l. 201: for use -> used
- l. 217: is valued at -> is set to (also l. 219)
- l. 229: "comparable to the in situ measurements": See my comment above, there are biases and it is not clear how these influence your results. Did you calibrate the soil parameters for the site?
- l. 240-244: This sentence is very long, it would help readability to split it into multiple sentences.
- l. 248: for obtaining *the* soil scattering matrix
- l. 260: remove "below (Eq. (3))"
- l. 286-287: remove "please also refer to"
- l. 312: The role of the low air pressure in the approximation is not clear to me, can you make this more detailed?
- l. 330: What is the reason for the low value? Does this indicate a problem with the model or the model parameters in this setup?
- l. 333: simulated at X-band
- l. 338-341: You show that both vary at similar frequencies, but is there a correlation between wind and X-band signal?
- l. 398: despite -> except for?
- l. 458: while those simulated at L-band to not
- l. 458: Is Figure 4 a wrong reference? Which figure should this refer to?
- l. 462: the volume scatterin effect *is* present
- l. 480: remove "While"
- l. 495: as mentioned above, I can not see that the diurnal variations agree with the observations from the plots
- l. 517-518: The cylinder parameterization outperforms the disc parameterization in simulating ...
- l. 523: deep -> low
- l. 535: despite -> with?
- l. 543: Are the values for L-band exactly zero or just very small?
- section 4.2: a more verbose naming scheme for the different cases might help understanding your argument here
- l. 664: To me it seems not really useful to use CLAP for detecting rainfall events, if they could also be detected directly from the data going into CLAP (e.g. the soil moisture data)
- l. 689: While as -> Whereas
- l. 691: varied -> different
- l. 740: observed signal -> modelled signal, since your dynamic VWC only influences the modelled signalCitation: https://doi.org/10.5194/hess-2022-333-RC2 -
AC2: 'Reply on RC2', Hong Zhao, 13 Jan 2023
The comment was uploaded in the form of a supplement: https://hess.copernicus.org/preprints/hess-2022-333/hess-2022-333-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Hong Zhao, 13 Jan 2023
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Cited
4 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
- An improved change detection method for high-resolution soil moisture mapping in permafrost regions S. Du et al. 10.1080/15481603.2024.2310898
- Digital twin approach for the soil-plant-atmosphere continuum: think big, model small Y. Zeng & Z. Su 10.3389/fsci.2024.1376950
- Analysis of land-atmosphere interactions and their influence on the energy and water cycle over the Tibetan Plateau Y. Ma et al. 10.1080/10095020.2024.2372504