the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ChinaRiceCalendar-Seasonal Crop Calendars for Early, Middle, and Late Rice in China
Hui Li
Xiaobo Wang
Shaoqiang Wang
Yuanyuan Liu
Zhenhai Liu
Shiliang Chen
Qinyi Wang
Tongtong Zhu
Lunche Wang
Lizhe Wang
Abstract. Long-time series and large-scale crop calendars provide valuable information for rational crop cultivation and management under climate change scenarios as a prerequisite for ensuring regional food security. Although some Chinese rice phenological products exist, there is a lack of studies on the long rice cropping calendar for early, middle, and late rice according to the actual cropping dates. Unlike in-situ monitoring and statistical rice phenology information, long-time series medium-resolution remote sensing satellite images provide the possibility and accuracy of real-time crop phenology monitoring. Based on MODIS data products and the improved PhenoRice algorithm, this study obtained phenological information on rice in seven major agricultural zones in China from 2003 to 2020. First, to effectively explore the changes in rice growing seasons over 18 years, we identified early, middle, and late rice according to specific cropping dates. Second, we selected 30 % of the recorded phenology data from Agricultural Meteorological Stations (AMSs) for parameter calibration and added a season division module to the PhenoRice algorithm to obtain a 250 m resolution raster dataset of rice crop calendars named ChinaRiceCalendar. However, it is consistent with the station data, RiceAtlas, and ChinaCropPhen1km. The validation accuracy R2 exceeded 0.95, 0.75, and 0.7 with the data recorded from AMSs, RiceAtlas, and ChinaCropPhen1km, respectively. In addition, we observed that the rice planting dates in China were delayed by 2.4 days/10a while the maturity dates were earlier by 5.5 days/10a during 2003–2020. ChinaRiceCalendar provides insights into practical rice farming measures and the response of rice cropping dates to environmental conditions across China.
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Hui Li et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2023-125', Chen Zhang, 20 Aug 2023
This manuscript introduces a new dataset for seasonal crop calendar for major agricultrual regions in China. The paper is easy to follow. The methods and results are well presented. The data are accessible. The authors may address the following issues to further improve the paper:
1. The map in Figure 1 needs to be improved. Please add a description to note source of the agricultural region boundary data. I suggest using the solid color for each region and adding a province-level boundary layer on the map.
2. The flowchart illustrated in Figure 2 is kind of confusing for readers. What does the input (the map) mean? What is the meaning of different shapes for each process? Is the output data (2003-2020) produced recursively or at one time? What does the two-way arrow mean?
3. Please use a high-resolution image for Figure 9.
4. The discussion section is quite long and hard to follow. Please divide the discussions into several subsections (e.g., advantages, uncertainty, limitations, and future works) to help readers follow the content more smoothly.
5. The conclusion section must be improved. It currently looks like the repeat of abstract. Avoid repetition of information already presented in the abstract. Instead, focus on summarizing the findings and emphasizing the scientific contributions your work offers.
6. The paper lacks a data sustainability plan, which is crucial for readers intending to reuse the data. The data only available through 2003-2020. What about the following years? Are there plans to continue the project in subsequent years? It is unfortunate that many projects and data were discontinued after the paper was published. I am looking forward a data management plan given the manuscript is submitting to a scientific data journal. For instance, if the project is to continue, what is the operational plan? If not, how can users reproduce the data independently?Citation: https://doi.org/10.5194/essd-2023-125-RC1 -
RC2: 'Comment on essd-2023-125', Anonymous Referee #2, 21 Aug 2023
This study presents an interesting topic to generate a rice calendar dataset that depicts planting, flowering, and maturity in China from 2003 to 2020. While this study has some merits, some places need improvement.
- In the abstract, it said that “there is a lack… cropping dates.” However, there are several studies addressing this issue. For example: Liu, Y., Zhou, W. & Ge, Q. Spatiotemporal changes of rice phenology in China under climate change from 1981 to 2010. Climatic Change 157, 261–277 (2019). https://doi.org/10.1007/s10584-019-02548-w; Bai, H., Xiao, D. Spatiotemporal changes of rice phenology in China during 1981–2010. Theor Appl Climatol 140, 1483–1494 (2020). https://doi.org/10.1007/s00704-020-03182-8; Li S H, Xiao J T, Ni P, Zhang J, Wang H S, Wang J X. Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. Int J Agric & Biol Eng, 2014; 7(6): 28－ Therefore, the contributions of this study need to be reconstructed.
- The introduction did not well review previous research. Several classic phenological extraction methods are missing. Such as: Jönsson, P. and Eklundh, L., 2004, TIMESAT - a program for analysing time-series of satellite sensor data, Computers and Geosciences, 30, 833-845; Zhang, X., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, and A. Huete (2003), Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471–475.
- The advantages and disadvantages of curve-based and trend-based methods are not clear, therefore the benefits of PhenoRice are uncertain.
- Line 85, please define the early, middle, and later rice here, as they are the first time mentioned.
- In the Study area section, the abbreviations for the seven zones are not appropriate. They are not related to the full name. Rather than using DB for Northeast Plain, it may be better to use NP. Meanwhile, for readers who are not familiar with China, it will be better to put all the place names that are mentioned in the text on a map.
- Lines 127-128, if MODIS cannot provide cloud-free images of the study area for the entire period, why did authors use it?
- Line 136, please clarify DOY, is it the day of year?
- Line 148, in addition to using R2 and RMSE, authors may also want to use bias, as it can tell whether the areas are underestimated or overestimated.
- Line 159, the purpose of using elevation data needs to be mentioned.
- Line 200, to determine the rice pixel, I am not sure if the threshold method is a good option especially since EVI values may be influenced by several factors in different years, such as climate. Why not consider using some advanced method, such as the one mentioned here: Shen, R., Pan, B., Peng, Q., Dong, J., Chen, X., Zhang, X., Ye, T., Huang, J., and Yuan, W.: High-resolution distribution maps of single-season rice in China from 2017 to 2022, Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, 2023. This method may improve the accuracy of late rice.
- There are many grammar issues. For example, line 48: it needs a space between ‘scales’ and ‘(Bondeau et al., 2007…’; Line 78-70: the sentence is duplicated; Line 85: there is no verb; Line 121, what is 800-2700 m. Please carefully polish the article.
Citation: https://doi.org/10.5194/essd-2023-125-RC2 -
RC3: 'Comment on essd-2023-125', Anonymous Referee #3, 09 Sep 2023
Utilizing MODIS satellite data and an adapted PhenoRice algorithm, the study creates the "ChinaRiceCalendar," a dataset capturing rice phenology in seven primary agricultural zones in China from 2003 to 2020. While it successfully validates this dataset against established data sources with high accuracy, and identifies temporal changes in rice cultivation, the research falls short in clearly articulating its unique contributions. As it stands, the study appears to primarily extend the application of PhenoRice to China without significant innovation. To truly validate its worth, further elucidation of its specific advancements over existing methods is essential. Additionally, the writing needs to be improved for enhanced clarity. Detailed suggestions for improvement are provided below.
Major comments:
- To align with the journal's standards, it is encouraged that the authors could enhance the clarity and coherence of the writing. For example, lines 88-91, the sentence, “However, we observed that the differences in rice types due to ignoring the actual cropping dates in previous studies made the current rice crop calendar 90 focus on the key phenological dates in mono seasons (Luo et al., 2020; Qiu et al., 2017) and did not explore the spatial and temporal distribution for different rice varieties in China,” seems to lack a clear subject in its second segment. This could potentially confuse readers and reduce the quality of paper.
- The manuscript proposed an "enhanced PhenoRice algorithm", yet a distinct clarification of the modifications compared to the original PhenoRice is missing. From the current description, it appears that the primary enhancement of the refined version seems to just add the prediction of maturity. It's essential for the authors to provide more detailed info about the modifications, explaining the rationale behind each and how they contribute to the study's objectives. This will offer readers a clearer understanding of the research's unique contributions and its potential implications.
- The section titled "Extraction of rice growth information" (Line 194) lacks clarity and readability. As it currently stands, readers may need to refer to the original PhenoRice paper to understand the content fully. This is not ideal for an independent paper. Additionally, throughout the methodology section, various terms and methods, such as "wWHIT," are introduced without adequate context or explanations. To make this paper more reader-friendly and easy to understand, it's imperative for the authors to elaborate on these terminologies and provide a more thorough introduction to the methodologies employed. Specifically, highlighting the differences and innovations compared to previous works would greatly enhance the paper's clarity and value.
- The introduction section requires further refinement for clarity. For example, “A high-resolution rice calendar dataset (ChinaCropPhen1km) already exists, and the classification of rice types in this dataset is based on cropping frequency and cropping dates (early, late, and one-season rice). However, the actual cropping dates of some one-season rice, including the other two varieties, make it challenging to explore the spatial and temporal trends of different rice varieties.” The connection between the actual cropping dates and the challenges they present in understanding the spatial and temporal trends is not clearly elucidated. It is encouraged that the authors could provide a more detailed explanation or rephrase the statement to clarify why exactly these actual cropping dates introduce complications in analyzing the different rice varieties' trends.
- Following the previous question, I'm curious about the rationale behind the categorization of "early," "middle," and "late" rice. What is the significance of segmenting the rice calendar into these three distinct stages? How does your methodology specifically aid in distinguishing between early, middle, and late rice? While there are mentions related to this, they are somewhat scattered and not easily discernible. There's a need for more comprehensive information on this topic. It would be beneficial for readers if these points were more cohesively presented.
- To more compellingly establish the merits or improvements of your methodology, it is encouraged that the authors could directly compare the accuracy of your product against ground truth data, along with similar accuracy evaluations from existing or competing products. Instead of solely contrasting the final datasets or only showing the alignment of your product with ground truth data, incorporating a multi-faceted comparison using ground truth as a consistent benchmark would provide readers with a good understanding of your research's relative strengths and contributions.
- Another study titled "RICA: A Rice Crop Calendar for Asia based on MODIS multi-year data" has also proposed a methodology rooted in the PhenoRice algorithm in 2021. Notably, their output map resolution matches yours at 250 meters. It's essential to discuss the differences and unique aspects of your method in comparison to theirs. Specifically, how does your study differentiate and enhance the existing body of work?
Minor comment:
- In Fig.3, there's a noticeable discrepancy in the results for the late cultivars when compared to other varieties. It is encouraged that the authors could clarify about this difference. Is it due to limitations in the methodology, inherent variability in the late cultivars, or other external factors? A clear explanation would assist readers in better understanding the data and its implications.
- The method for evaluating the station's phenology appears to be inadequately described. Given that a station typically covers a limited region, it is crucial to elaborate on the evaluation process. The statement “Extraction results for planting, flowering, and maturity dates of different rice varieties were searched within 1 km of the remaining 70 % of AMSs and compared with recorded data to verify the accuracy of the extraction results at the site scale” provides some context but it would enhance clarity if the authors delved deeper into the specifics of this process.
- In Figure 7, it is unclear what the content in the bottom right corner box represents. Clarifying this element could improve the reader's understanding of the data presented in the figure.
- For Figure 9, it would be beneficial to enhance the resolution for better clarity.
- On line 184, the term "weight" is mentioned but lacks a clear definition or context. This ties back to Major Comment 2, where various technical details are introduced throughout the paper without sufficient elaboration.
- On line 188, the term "coarse fitting method" is mentioned, but there isn't a clear explanation provided for it.
- On line 220, there's an unnecessary space at the start of the paragraph that should be removed. Additionally, throughout the paper, there are instances where spaces seem to be missing. It's uncertain whether this issue arises from the original manuscript or due to PDF conversion. If the problem originates from the original version, it's recommended to address these formatting inconsistencies.
- On line 224, there appears to be a typo related to "R2." Please review the whole manuscript and correct all these typos to ensure accuracy and maintain the quality of the manuscript.
Citation: https://doi.org/10.5194/essd-2023-125-RC3
Hui Li et al.
Data sets
ChinaRiceCalendar Hui Li, Xiaobo Wang, Shaoqiang Wang, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, Lizhe Wang https://doi.org/10.7910/DVN/EUP8EY
Hui Li et al.
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