20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data
- 1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- 2International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
- 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- 1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- 2International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
- 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract. Over 90 % of the world’s rice is produced in the Asia-Pacific Region. Synthetic aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. Rice growth patterns in tropical and subtropical regions are complex, and it is difficult to construct representative rice growth patterns, which makes it much more difficult to extract rice distribution based on SAR data. To address this problem, a rice mapping method based on time-series Sentinel-1 SAR data is proposed in this study for large regional tropical or subtropical areas. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatio-temporal statistical features with thegeneralization ability to complex rice growth patterns was selected, then input into the U-Net semantic segmentation model and combined with WorldCover data to eliminate false alarms, and finally the 20-meter resolution rice map of five countries in mainland Southeast Asia in 2019 was obtained. On the validation sample set, the proposed method achieved an accuracy of 92.20 %. Good agreement was obtained when comparing our rice map with statistical data and other rice maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice extraction with complex cropping patterns and the reliability of the generated rice maps. The 20 m annual paddy rice map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun, 2022).
Chunling Sun et al.
Status: open (until 11 Feb 2023)
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RC1: 'Comment on essd-2022-392', Anonymous Referee #1, 28 Jan 2023
reply
The manuscript proposes a rice mapping method to construct representative rice growth patterns based on time-series Sentinel-1 SAR data. Apparently, use of Sentinel-1 SAR data for rice mapping is very useful and seems the work is ongoing in the region for some time. Such methodology is useful, especially if it can give the rice area in advance which can help estimate the production helping in making timely trade/consumption plans.
Below are few comments that should be helpful to improve the clarity and completeness of the manuscript.
- What does this statement, .. rice growth patterns in Southeast Asia are too complex…means? Complexity refers to what? Is it not complex in other regions?
- This is nice to have high-precision rice area mapping but why is it needed. Please explain for the benefit of readers in section 1. Also is it only high precision or high resolution too?
- Were the data downloaded for whole year of 2019 of specific season? If so, the seasonal difference in rice area matters although there may be irrigation in some areas of some countries with 2 to 3 rice crops in irrigated areas and one crop in non-irrigated areas (as mentioned as Rice 1 through 4 in the manuscript). How was this seasonal difference considered in analysis in mapping the rice area? Because rice area per seasons will be different for the same country.
- Line 207-208, …the high heterogeneity of rice backscattering coefficients in Southeast Asia is caused by the high heterogeneity in climate and topography…what does mean that? Climate is obvious but what is heterogeneity in topography?
- Figure 3 is not so nice. It should be improved to be more clear to read.
- Line 223, if all flooded, why rice will differ significantly from other crops as other crops will also be flooded and possible similar backscatter, unlike for example sugarcane field which certainly may have different backscatter as they may not submerged. Please clarify the statement – the other crops.
- Figure 4, the term ‘Building’ does not reflect that square. Appropriate name is settlement. Rice is also vegetation, so vegetation may better be called as non-rice vegetation. What is band combination for optical image?
- How was number of (sample)plots of 1913 and 2032 for validation determined, any basis?
- Country’s statistics on area (despite may not align with data collection cycle) are most authentic. Table 5: the extracted rice area is only 44% of the statistics of rice cultivation area. This is rather huge difference. How to explain the feasibility of using the rice mapping method recommended by this study because of that discrepancy? It has now only Vietnam in the table. It is better to show all the countries data in the table.
- The method itself of rice mapping using Sentinel-1 SAR Data is major output of the study. If the proposed method is referring to Figure 2 flowchart, then, it still shows the need of using statistical data and available rice maps. Hence, better discussion with rationale is needed whether proposed method is to replace the existing system as efficient, accurate and even pre-harvest season rice mapping method or just additional task of mapping the rice area. Ideally, the new method is to replace or improve the existing method. It would be nice to indicate with the proposed method whether existing statistical data collection on rice is still needed. Also, advise (recommend) how this new method can be made available for the country to use? Afterall the best use of method if adopted will be for the country.
Chunling Sun et al.
Data sets
20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, Chao Wang https://doi.org/10.5281/zenodo.7315076
Chunling Sun et al.
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