ChinaCropSM1 km: a fine 1&thinsp;km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018

Cheng, Fei; Zhang, Zhao; Zhuang, Huimin; Han, Jichong; Luo, Yuchuan; Cao, Juan; Zhang, Liangliang; Zhang, Jing; Xu, Jialu; Tao, Fulu

doi:https://doi.org/10.5194/essd-15-395-2023

Articles | Volume 15, issue 1

https://doi.org/10.5194/essd-15-395-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-395-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 1

Data description paper

|

23 Jan 2023

Data description paper |

| 23 Jan 2023

ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018

Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Jialu Xu, and Fulu Tao

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Final revised paper (published on 23 Jan 2023)
Supplement to the final revised paper
Preprint (discussion started on 01 Sep 2022)
Supplement to the preprint

Interactive discussion

Status: closed

EC1:
'Comment on essd-2022-254', Hao Shi, 04 Sep 2022

Please carefully check the citations and references. For example, the references repeat with each other in lines 345-360.

Citation: https://doi.org/10.5194/essd-2022-254-EC1
- AC1: 'Reply on EC1', zhao zhang, 05 Sep 2022
  
  Many thanks for your careful check. We have followed your advice and checked the all citations and references. We have deleted duplicate references (Line 345-360) and modified citations in the text (Line 51-53, 54-56, 130-131, 254-255).
  1) Line 51-53: As one part of the Climate Change Initiative (CCI), the European Space Agency (ESA) published a long-term surface SM dataset, and the latest version (v06.1) covered the period of 1978–2020 (https://www.esa-soilmoisture-cci.org/, last access: 10 Apr. 2022) (Dorigo et al., 2017a; Gruber et al., 2019; Preimesberger et al., 2021).
  we have modified the citations to “As one part of the Climate Change Initiative (CCI), the European Space Agency (ESA) published a long-term surface SM dataset, and the latest version (v06.1) covered the period of 1978–2020 (https://www.esa-soilmoisture-cci.org/, last access: 10 Apr. 2022) (Dorigo et al., 2017; Gruber et al., 2019; Preimesberger et al., 2021).”.
  2) Line 54-56: The ESA CCI SM products are consistent with the observed values at some grassland and farmland sites in China (Liu et al., 2011; Albergel et al., 2013; Dorigo et al., 55 2015, 2017b), however, they have a coarse spatial resolution (~27 km) and lots of coverage gaps (Llamas et al., 2020; Guevara et al., 2021).
  we have modified the citations to “The ESA CCI SM products are consistent with the observed values at some grassland and farmland sites in China (Liu et al., 2011; Albergel et al., 2013; Dorigo et al., 2015, 2017), however, they have a coarse spatial resolution (~27 km) and lots of coverage gaps (Llamas et al., 2020; Guevara et al., 2021).”.
  3) Line 130-131：We used the v05.2 product for comparison because of its advantages comparing with active/passive products (Liu et al., 2012; Dorigo et al., 2017c).
  we have modified the citations to “We used the v05.2 product for comparison because of its advantages comparing with active/passive products (Liu et al., 2012; Dorigo et al., 2017).”.
  3) Line 254-255：We ascribed such improvement partly into some corrections based on in situ observations for ESA 255 CCI SM (Dorigo et al., 2017b).
  we have modified the citations to “We ascribed such improvement partly into some corrections based on in situ observations for ESA CCI SM (Dorigo et al., 2017).”.
  4) Line 345-360：Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017a.
  Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017b.
  Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017c.
  we have modified the references to “Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.”.
  
  Citation: https://doi.org/10.5194/essd-2022-254-AC1
RC1:
'Comment on essd-2022-254', Anonymous Referee #1, 09 Sep 2022

I was very impressed by such valuable daily SM for more than 20 years over the whole mainland of China. Comparing with quantities of public products retrieved from remote sensing or downscaling into fine resolution, Chinacropland really open a new window for us to provide key parameters on earth observations. Irrigation practices do play more significances on crop production in China, especially for dryland crop. Therefore, no any doubt will be shown on the values of irrigation sub-model. Such novelty imply a potential way for applying irrigation sub-model into other areas and crops in the world. The study is fallen closely within the scope of ESSD. However, the authors should consider my several concerns below before their submission being accepted

(1) I am wondering how they obtain the crop dryland maps. For wheat or maize, it seem to me the location is constant. I need more detailed information to better understand their study.

(2) I do not think RF is a new method to retrieve SM. That is to say, more interesting findings have ascribed from combining irrigation module into SM estimation model. However, the authors have not specified the point. I am looking forward to more information on it, e.g. the accuracy comparison between with irrigation module and without it.

(3) Deeper and more extent discussions will further expand the reputation and influence of their study.

(4) Generally, the English writing is Ok. But typo can be observed sometimes, a careful check should be conducted throughout their manuscript.

Citation: https://doi.org/10.5194/essd-2022-254-RC1
- AC2: 'Reply on RC1', zhao zhang, 03 Oct 2022
  
  Dear Reviewer,
  Thank you a lot for the constructive comments. We have responded to every question, indicating exactly how we addressed each concern.
  The point-to-point responses to the comments are attached to the supplement in PDF format.
  Thank you again for your reviewing and the insightful comments.
  Sincerely,
  Fei Cheng on behalf of all Co-Authors
  
  Citation: https://doi.org/10.5194/essd-2022-254-AC2
RC2:
'Comment on essd-2022-254', Anonymous Referee #2, 12 Sep 2022
This study provides a longer term soil moisture dataset (ChinaCropSM1km) for crop drylands across mainland of China. ChinaCropSM1km perform better than public product in both higher accuracy and more details (daily, more soil layers) by using machine learning technology. Such soil moisture dataset with higher resolutions is very valuable for the studies on crop model, yield estimation, and climate change impact assessment. Moreover, their methodology is robust, and their interesting results were well interpreted. The irrigation module is a novel way to improve highly moisture estimation. Therefore, I recommend it can be accepted after a minor revision.

Comments and suggestions:

There is a problem with the resolution. The ground observation data is point measurement data, how to match the resolution of 1km? Please explain this in the manuscript.

Section 2.1. The authors pointed out that the study area is dominated by dryland crops (i.e. wheat and maize) in China, how was the Chinacropland layer defined in Figure 1 according to the annual crop harvested area in mainland China from 2000 to 2015? please describe the details.

In (1), the author judges the irrigation factors by comparing the observed soil moisture and the soil moisture evaluation index (SMI) according to the corresponding soil depth and phenology of crops. However, I notice that the SMI in Table 2 is a range, rather than an exact number. Please give reasonable explanation for this.

In section 2.3.2, considering the new SM product has been derived by integrating the irrigation module into SM model, it is better to evaluate accuracy of the module (irrigation factor forecasting model) and supply such important information into new edition.

Some typos are found in manuscript, and check manuscript carefully and correct them. e.g. Line143: delete ‘in China’.

Figure 2 should be improved. Currently, some labels are too vague to clearly identify.

Please modify the line widths in Table 2.

Line257: insert blank between two words. ‘Figure8’ -> ‘Figure 8’.

Figure S5 was not used in the main text, please cite it in main text or delete it from supplemental material.
Citation: https://doi.org/10.5194/essd-2022-254-RC2
- AC3: 'Reply on RC2', zhao zhang, 03 Oct 2022
  
  Dear Reviewer,
  Thank you a lot for the constructive comments. We have responded to every question, indicating exactly how we addressed each concern.
  The point-to-point responses to the comments are attached to the supplement in PDF format.
  Thank you again for your reviewing and the insightful comments.
  Sincerely,
  Fei Cheng on behalf of all Co-Authors
  
  Citation: https://doi.org/10.5194/essd-2022-254-AC3
RC3:
'Comment on essd-2022-254', Anonymous Referee #3, 16 Oct 2022

This is an interesting effort in developing the SM product for crop dryland, which has potential for various applications. The paper is well written and organized. Taking the CIR as a predictor seems to be a useful way to predict SM in crop dryland. However, I have some concerns as following. Please pay more attention on the comments about line 174-175.

Why only mapping SM for dryland, not rice?

Line 110-115: there are two sources of FCï¼ which one is used?

Line 120: the short name “AMS” is used only one time. Consider full name. In addition, what is R4, R5 and R16? And it should not be calculated only for AMS but for each cell, as a predictor.

Line 171: Grammar error. Not a complete sentence.

Line 174-175: It should not be random splitting because SM of different time from the same site may be highly correlated. This will give a higher performance for the model. Instead, the splitting should be based on sites, i.e., data from a site should be all in the training set or all in test set. Note that the model is predicting unknown locations based on the observing sites, and the spatial interpolation ability should be evaluated by the site-based splitting.

Line 185: How many times do you run the model to get the importance, as the importance will be different each time. It should take the average importance of dozens of runs like 100.

Fig.6 and 7ï¼ what are the different boxes stand for?

Section 3.5ï¼ I do not think this comparison is fare. The evaluation using the test data for Cropland should be used instead of all in situ data because the model used them to establish leading to an independent evaluation.

Citation: https://doi.org/10.5194/essd-2022-254-RC3
- AC4: 'Reply on RC3', zhao zhang, 11 Nov 2022
  
  Dear Reviewer,
  Thank you a lot for the constructive comments. We have responded to every question, indicating exactly how we addressed each concern.
  The point-to-point responses to the comments are attached to the supplement in PDF format.
  Thank you again for your reviewing and the insightful comments.
  Sincerely,
  Fei Cheng on behalf of all Co-Authors
  
  Citation: https://doi.org/10.5194/essd-2022-254-AC4

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by zhao zhang on behalf of the Authors (11 Nov 2022) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (26 Nov 2022) by Hao Shi

AR by zhao zhang on behalf of the Authors (06 Dec 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (23 Dec 2022) by Hao Shi

AR by zhao zhang on behalf of the Authors (27 Dec 2022) Author's response Manuscript

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Short summary

We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.