the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Mar 2024
SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data
Abstract. The Southeast Asia region has a vast expanse with diverse tropical climates, making it a prominent centre of rice cultivation, contributing to about 20 % of the world’s rice production and contributes 29 % of global rice methane emissions. As a staple food for many countries, accurate and up-to-date information on the rice harvested area is crucial for addressing food security issues, predicting rice yield and methane emissions, and formulating effective government policies. This paper presents the first detailed study of rice cropping intensity and harvested areas throughout Southeast Asia. Current remote sensing products have not been able to produce up-to-date cropping intensity information due to the variability of local cultivation practices. To address this problem, we integrated Sentinel-1A and Sentinel-2A/B time series data from 2020 to 2021 and developed a local unsupervised classification with phenological labelling (LUCK-PALM) method. We implemented the system on the Google Earth Engine (GEE) cloud-based platform to produce geospatial products of rice cropping intensity and harvested area at a spatial resolution of 10 m called SEA-Rice-Ci10s. The results show that Southeast Asia's total rice growing area in 2020–2021 was 28.5 Mha, with 51 % single cropping, 47 % double cropping, and 2 % triple cropping. These were equivalent to 42.9 Mha of annual harvested area, consisting of Thailand (11.2 Mha), Indonesia (8.4 Mha), Myanmar (8.4 Mha), Vietnam (6.3 Mha), Cambodia (3.9 Mha), the Philippines (3.3 Mha), Laos (0.8 Mha), Malaysia (< 0.5 Mha), and Timor-Leste (0.01 Mha). We compared our rice maps to agricultural statistics data at the district and province levels and existing rice maps for some Southeast Asian countries. The results demonstrate that our map agreed well with countries’ statistics with a linear coefficient of determination (R2) from 0.85 to 0.97. Compared to existing products, our map can resolve small paddy fields of about 0.2 ha in the hilly areas. This information will be useful in addressing food security challenges and improving estimates of methane emissions from rice cultivation. The 10 m paddy rice cropping intensity map for Southeast Asia, SEA-Rice-Ci10, is available on the GEE App (https://rudiyanto.users.earthengine.app/view/seariceci2021), the Climate TRACE platform (https://climatetrace.org/) and the Zenodo repository (https://doi.org/10.5281/zenodo.10707621) (Frisa Irawan et al., 2024).
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RC1: 'Comment on essd-2024-90', Anonymous Referee #1, 25 Apr 2024
The SEA-Rice-Ci10 study devised a novel approach called the Local Unsupervised Classification with Phenological Labeling Method (LUCK-PALM). This method aimed to accurately quantify and map rice cropping intensity and harvested areas across Southeast Asia from 2020 to 2021, with a 10-meter resolution using time-series inputs from Sentinel-1 and Sentinel 2A/B imagery. The LUCK-PALM method is able to resolve the challenge due to the discrepancy in rice cropping calendar among the country and regions. Compared to agricultural statistics and existing rice maps, the study demonstrates its strength in retrieving field paddy details. The study, being open-source and featuring an open dataset, offers valuable insights into agricultural decision-making and management practices, such as methane emissions from rice cultivation, but the quality of the manuscript could be enhanced by addressing the following major and minor comments regarding methodology and content organization, etc. Please refer to the detailed feedback provided for further improvement.
Major comments:
- As shown in Fig.2 and Section 2.3.5, the “Google Map Very High Resolution and Street View” is the data source for exports to label rice and non-rice areas. Although examples and locations of such street view imagery are given in Fig.3, Fig.17 and Figure.S2/Table.S4, it would be good if more details about the street view collection for validations are provided. For example, how was each street view image acquired and how many validation points did the experts label in each grid or each country/province/district? Were those points selected randomly or the selection process was the same as the 2,000 random samples from each defined area?
- The manuscript provided an assumption (line 219-220) that 25 to 30 clusters output from the unsupervised K-Means classification method would sufficiently represent the spectral data variations in each grid – with 2,000 points and 72 bands. Further explicit explanations or justifications for this assumption should be provided to offer readers a clearer understanding of its methods and enhance transferability.
- The study trained local K-Means models for each grid using 2,000 randomly sampled points, and then assessed the accuracy of the produced maps with respect to agricultural statistics and existing products. However, descriptions about how the study applied the locally trained models in combination with expert knowledge to produce grid-wise maps as well as the compilation of maps into national/provincial/regency scales were missing from the manuscript. Also, as seen from the workflow of the study presented in Fig.2, the “Accuracy Assessment” was before the “Map of rice field extent and cropping intensity,” which could potentially lead to the confusion that if the accuracy assessment was conducted with the 2,000 samples each grid or with the final mapping product.
- The methods presented in the manuscript featured the workflow from Sentinel time series to rice / non-rice crop mapping (in terms of spatial distribution), but it appears that descriptions about how the authors retrieve the cropping intensity of rice (from time series-based spectral profiles?) were less elaborated in methods.
Minor comments:
- In Fig.2, it is good to be concise in workflow illustrations, but how high is “Google Maps Very High Resolution” presented here? Also, the capitalization styles of words in Fig.2. could be more consistent.
- In Section 2.3.2 (line 192-195), what is the difference between using different landcovers from the WorldCover dataset to “filter out” non-croplands and using waterbody, tree, and built-up layers from the same dataset to “mask” non-cropland areas? The goal to facilitate computation and processing, as well as improving model performance is clear here, but the description of this step could be clearer.
- 3 appears to be coarser than other figures provided in the manuscript in terms of resolution.
- 16 could have legends and classification accuracy and/or coefficients of determinant labeled on the map for each region.
- Line 642 has a typo: “penological mapping.”
Citation: https://doi.org/10.5194/essd-2024-90-RC1 -
AC1: 'Reply on RC1', Rudiyanto Rudiyanto, 28 May 2024
Dear Editors and Reviewers,
We very much appreciate the constructive comments from the reviewers, which have helped improve our manuscript, "SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data" (MS No: essd-2024-90). Our detailed responses to the comments are included in the supplement with the following notes:
- The original review comments (in black)
- Our response on how the manuscript was revised (in red) and
- Revised paragraphs in the new manuscript (in blue)
Most or all suggestions are included in the revised manuscript. We are also submitting an annotated version of the revised manuscript.
Sincerely,
Rudiyanto, on behalf of all co-authors
Email: rudiyanto@umt.edu.my
Citation: https://doi.org/10.5194/essd-2024-90-RC1
-
RC2: 'Comment on essd-2024-90', Anonymous Referee #2, 30 Apr 2024
This manuscript identifies the distribution of different maturation types of rice in Southeast Asia at a 10m resolution, generating the up-to-date maps of planting intensity and area in Southeast Asia, which is interesting. However, there are some non-negligible flaws in the paper. The study period is too short, covering only 2020-2021, so the data used for validation and comparison do not align with the study period perfectly. Comparisons with existing data lack a unified standard, and the description of the core methodology is not clear enough. Below are specific comments:
Comment 1: The results of R2 through the entire MS should be checked. Section 2.3.6 illustrated that R2 is determined by mapping results and statistics (or areas from existing datasets), however, the R2 values presented in the results seem to denote the goodness of fit, that is, the correlation with fitting lines. For instance, the declared consistency R2 for Fig.15(b) is 0.86, which is more like the correlation degree with the fitting line, not with the NESEA. RMSE values should also be confirmed.
Comment 2: Lines 22-23: The statement, "This paper presents the first detailed study of rice cropping intensity and harvested areas throughout Southeast Asia," requires further qualification in terms of temporal and spatial resolution. This is because the study is not the first of its kind when considering coarser spatial resolutions.
Comment 3: Lines 56-57: In many regions of China, rice fields undergo two harvests per season.
Comment 4: Line 76-77: What exactly does “requiring in-depth expertise for labelling time series of vegetation indices for rice growth stages” mean?
Comment 5: Why did the study only consider 2020-2021? Both sentinel-1 and sentinel-2 data from 2017 onwards are available. The datasets available for comparison extend only up to 2019; however, this study did not generate a map for that year.
Comment 6: Table 1 does not need to be listed separately.
Comment 7: In Table 2, some links to the statistical data are not functioning. Please check all links provided for accessing statistical data to ensure they directly lead to the specific webpage where the data can be accessed, not to the website's homepage.
Comment 8: The flow and details of the core method were confused.
(1) What is the scope of the unsupervised classification? Does it only include the crop layer, or the entire area? I was confused about whether to group the crop layers into rice and non-rice clusters, or group the whole area into 25 ~ 30 clusters.
(2) How many labels are available for each cluster? Besides rice, water, trees, and built-up areas, there should be some other categories. Do the available labels cover all the major land types?
(3) Following on question (2), how to distinguish rice from other crops?
(4) If not binary classification, how to determine all rice pixels or clusters according to the representative profiles? Are there any quantitative standards?
(5) For the clustering results in hundreds of grids, did the author manually label each cluster?
Comment 9: Why does the clustering utilize a two-year continuous time series as input features rather than a one-year time series? To which year does the rice mapping area pertain when using this clustering method based on two-year time series data? I am concerned that this approach may overlook variations in rice cultivation area across different years, which could affect the accuracy of subsequent comparisons between rice maps and statistical data.
Comment 10: To my knowledge, even with monthly composites, the Sentinel-2 time series data exhibits significant gaps in Southeast Asia. How do the authors address the issue of these data gaps?
Comment 11: The manuscript presents numerous curves of NDVI, VH, and MNDWI for rice. It is necessary to clearly specify the samples these curves are based on.
Comment 12: Are there any directly available sources for Statistics rice growing area in Table 3? Or is it calculated indirectly by using the harvested area of different cropping patterns?
Comment 13: The statistics referenced in Section 3.2 include data from years other than 2020 and 2021. How to address the discrepancies between the mapping year and the statistical year?
Comment 14: Table 5 stated that the consistency of Timor-Leste with the statistical area is compared using “growing area” statistics. Why are there no consistency results for NESEA?
Comment 15: Is Figure 15 a comparison based on “growing area” or “harvested area”? How to determine the relative accuracy of areas that differ greatly from existing datasets? Without a unified standard of reference, such comparisons are meaningless because it is uncertain which is more accurate.
Comment 16: Similarly, the spatial comparison lacks a real reference and is not based on the same year products.
Comment 17: To my understanding, the NESEA-Rice10 rice map is generated based on flood signals, primarily focusing on irrigated rice fields and scarcely including rainfed types. In this study, according to the authors, the rice map includes rainfed rice (although it remains unclear how the authors determine which category within the clustering represents rice), which may account for the main difference in area statistics between NESEA-Rice10 and this work. The authors need to clearly describe the differences in rice types included in the different data products when making comparisons.
Comment 18: The study lacks individual comparisons of areas with different rice cropping intensities. Therefore, further validation of the accuracy of rice intensity measurements is necessary.
Comment 19: How is the rice map validated using Google Street View data? There is a lack of more extensive validation based on sample points.
Citation: https://doi.org/10.5194/essd-2024-90-RC2 -
AC2: 'Reply on RC2', Rudiyanto Rudiyanto, 28 May 2024
Dear Editors and Reviewers,
We greatly appreciate the constructive comments provided by the reviewers, which have significantly improved our manuscript, "SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data" (MS No: essd-2024-90). Our detailed responses to these comments are included in the supplement, organized as follows:
• The original review comments (in black)
• Our response on how the manuscript was revised (in red) and
• Revised paragraphs in the new manuscript (in blue)We have incorporated most or all of the reviewers' suggestions into the revised manuscript. Additionally, we are submitting an annotated version of the revised manuscript for your reference.
Sincerely,Rudiyanto, on behalf of all co-authors
Email: rudiyanto@umt.edu.my
Citation: https://doi.org/10.5194/essd-2024-90-RC2
-
AC2: 'Reply on RC2', Rudiyanto Rudiyanto, 28 May 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on essd-2024-90', Anonymous Referee #1, 25 Apr 2024
The SEA-Rice-Ci10 study devised a novel approach called the Local Unsupervised Classification with Phenological Labeling Method (LUCK-PALM). This method aimed to accurately quantify and map rice cropping intensity and harvested areas across Southeast Asia from 2020 to 2021, with a 10-meter resolution using time-series inputs from Sentinel-1 and Sentinel 2A/B imagery. The LUCK-PALM method is able to resolve the challenge due to the discrepancy in rice cropping calendar among the country and regions. Compared to agricultural statistics and existing rice maps, the study demonstrates its strength in retrieving field paddy details. The study, being open-source and featuring an open dataset, offers valuable insights into agricultural decision-making and management practices, such as methane emissions from rice cultivation, but the quality of the manuscript could be enhanced by addressing the following major and minor comments regarding methodology and content organization, etc. Please refer to the detailed feedback provided for further improvement.
Major comments:
- As shown in Fig.2 and Section 2.3.5, the “Google Map Very High Resolution and Street View” is the data source for exports to label rice and non-rice areas. Although examples and locations of such street view imagery are given in Fig.3, Fig.17 and Figure.S2/Table.S4, it would be good if more details about the street view collection for validations are provided. For example, how was each street view image acquired and how many validation points did the experts label in each grid or each country/province/district? Were those points selected randomly or the selection process was the same as the 2,000 random samples from each defined area?
- The manuscript provided an assumption (line 219-220) that 25 to 30 clusters output from the unsupervised K-Means classification method would sufficiently represent the spectral data variations in each grid – with 2,000 points and 72 bands. Further explicit explanations or justifications for this assumption should be provided to offer readers a clearer understanding of its methods and enhance transferability.
- The study trained local K-Means models for each grid using 2,000 randomly sampled points, and then assessed the accuracy of the produced maps with respect to agricultural statistics and existing products. However, descriptions about how the study applied the locally trained models in combination with expert knowledge to produce grid-wise maps as well as the compilation of maps into national/provincial/regency scales were missing from the manuscript. Also, as seen from the workflow of the study presented in Fig.2, the “Accuracy Assessment” was before the “Map of rice field extent and cropping intensity,” which could potentially lead to the confusion that if the accuracy assessment was conducted with the 2,000 samples each grid or with the final mapping product.
- The methods presented in the manuscript featured the workflow from Sentinel time series to rice / non-rice crop mapping (in terms of spatial distribution), but it appears that descriptions about how the authors retrieve the cropping intensity of rice (from time series-based spectral profiles?) were less elaborated in methods.
Minor comments:
- In Fig.2, it is good to be concise in workflow illustrations, but how high is “Google Maps Very High Resolution” presented here? Also, the capitalization styles of words in Fig.2. could be more consistent.
- In Section 2.3.2 (line 192-195), what is the difference between using different landcovers from the WorldCover dataset to “filter out” non-croplands and using waterbody, tree, and built-up layers from the same dataset to “mask” non-cropland areas? The goal to facilitate computation and processing, as well as improving model performance is clear here, but the description of this step could be clearer.
- 3 appears to be coarser than other figures provided in the manuscript in terms of resolution.
- 16 could have legends and classification accuracy and/or coefficients of determinant labeled on the map for each region.
- Line 642 has a typo: “penological mapping.”
Citation: https://doi.org/10.5194/essd-2024-90-RC1 -
AC1: 'Reply on RC1', Rudiyanto Rudiyanto, 28 May 2024
Dear Editors and Reviewers,
We very much appreciate the constructive comments from the reviewers, which have helped improve our manuscript, "SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data" (MS No: essd-2024-90). Our detailed responses to the comments are included in the supplement with the following notes:
- The original review comments (in black)
- Our response on how the manuscript was revised (in red) and
- Revised paragraphs in the new manuscript (in blue)
Most or all suggestions are included in the revised manuscript. We are also submitting an annotated version of the revised manuscript.
Sincerely,
Rudiyanto, on behalf of all co-authors
Email: rudiyanto@umt.edu.my
Citation: https://doi.org/10.5194/essd-2024-90-RC1
-
RC2: 'Comment on essd-2024-90', Anonymous Referee #2, 30 Apr 2024
This manuscript identifies the distribution of different maturation types of rice in Southeast Asia at a 10m resolution, generating the up-to-date maps of planting intensity and area in Southeast Asia, which is interesting. However, there are some non-negligible flaws in the paper. The study period is too short, covering only 2020-2021, so the data used for validation and comparison do not align with the study period perfectly. Comparisons with existing data lack a unified standard, and the description of the core methodology is not clear enough. Below are specific comments:
Comment 1: The results of R2 through the entire MS should be checked. Section 2.3.6 illustrated that R2 is determined by mapping results and statistics (or areas from existing datasets), however, the R2 values presented in the results seem to denote the goodness of fit, that is, the correlation with fitting lines. For instance, the declared consistency R2 for Fig.15(b) is 0.86, which is more like the correlation degree with the fitting line, not with the NESEA. RMSE values should also be confirmed.
Comment 2: Lines 22-23: The statement, "This paper presents the first detailed study of rice cropping intensity and harvested areas throughout Southeast Asia," requires further qualification in terms of temporal and spatial resolution. This is because the study is not the first of its kind when considering coarser spatial resolutions.
Comment 3: Lines 56-57: In many regions of China, rice fields undergo two harvests per season.
Comment 4: Line 76-77: What exactly does “requiring in-depth expertise for labelling time series of vegetation indices for rice growth stages” mean?
Comment 5: Why did the study only consider 2020-2021? Both sentinel-1 and sentinel-2 data from 2017 onwards are available. The datasets available for comparison extend only up to 2019; however, this study did not generate a map for that year.
Comment 6: Table 1 does not need to be listed separately.
Comment 7: In Table 2, some links to the statistical data are not functioning. Please check all links provided for accessing statistical data to ensure they directly lead to the specific webpage where the data can be accessed, not to the website's homepage.
Comment 8: The flow and details of the core method were confused.
(1) What is the scope of the unsupervised classification? Does it only include the crop layer, or the entire area? I was confused about whether to group the crop layers into rice and non-rice clusters, or group the whole area into 25 ~ 30 clusters.
(2) How many labels are available for each cluster? Besides rice, water, trees, and built-up areas, there should be some other categories. Do the available labels cover all the major land types?
(3) Following on question (2), how to distinguish rice from other crops?
(4) If not binary classification, how to determine all rice pixels or clusters according to the representative profiles? Are there any quantitative standards?
(5) For the clustering results in hundreds of grids, did the author manually label each cluster?
Comment 9: Why does the clustering utilize a two-year continuous time series as input features rather than a one-year time series? To which year does the rice mapping area pertain when using this clustering method based on two-year time series data? I am concerned that this approach may overlook variations in rice cultivation area across different years, which could affect the accuracy of subsequent comparisons between rice maps and statistical data.
Comment 10: To my knowledge, even with monthly composites, the Sentinel-2 time series data exhibits significant gaps in Southeast Asia. How do the authors address the issue of these data gaps?
Comment 11: The manuscript presents numerous curves of NDVI, VH, and MNDWI for rice. It is necessary to clearly specify the samples these curves are based on.
Comment 12: Are there any directly available sources for Statistics rice growing area in Table 3? Or is it calculated indirectly by using the harvested area of different cropping patterns?
Comment 13: The statistics referenced in Section 3.2 include data from years other than 2020 and 2021. How to address the discrepancies between the mapping year and the statistical year?
Comment 14: Table 5 stated that the consistency of Timor-Leste with the statistical area is compared using “growing area” statistics. Why are there no consistency results for NESEA?
Comment 15: Is Figure 15 a comparison based on “growing area” or “harvested area”? How to determine the relative accuracy of areas that differ greatly from existing datasets? Without a unified standard of reference, such comparisons are meaningless because it is uncertain which is more accurate.
Comment 16: Similarly, the spatial comparison lacks a real reference and is not based on the same year products.
Comment 17: To my understanding, the NESEA-Rice10 rice map is generated based on flood signals, primarily focusing on irrigated rice fields and scarcely including rainfed types. In this study, according to the authors, the rice map includes rainfed rice (although it remains unclear how the authors determine which category within the clustering represents rice), which may account for the main difference in area statistics between NESEA-Rice10 and this work. The authors need to clearly describe the differences in rice types included in the different data products when making comparisons.
Comment 18: The study lacks individual comparisons of areas with different rice cropping intensities. Therefore, further validation of the accuracy of rice intensity measurements is necessary.
Comment 19: How is the rice map validated using Google Street View data? There is a lack of more extensive validation based on sample points.
Citation: https://doi.org/10.5194/essd-2024-90-RC2 -
AC2: 'Reply on RC2', Rudiyanto Rudiyanto, 28 May 2024
Dear Editors and Reviewers,
We greatly appreciate the constructive comments provided by the reviewers, which have significantly improved our manuscript, "SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data" (MS No: essd-2024-90). Our detailed responses to these comments are included in the supplement, organized as follows:
• The original review comments (in black)
• Our response on how the manuscript was revised (in red) and
• Revised paragraphs in the new manuscript (in blue)We have incorporated most or all of the reviewers' suggestions into the revised manuscript. Additionally, we are submitting an annotated version of the revised manuscript for your reference.
Sincerely,Rudiyanto, on behalf of all co-authors
Email: rudiyanto@umt.edu.my
Citation: https://doi.org/10.5194/essd-2024-90-RC2
-
AC2: 'Reply on RC2', Rudiyanto Rudiyanto, 28 May 2024
Data sets
SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data Frisa Irawan Ginting, Rudiyanto Rudiyanto, Fatchurahman Fatchurahman, Ramisah Mohd Shah, Norhidayah Che Soh, Sunny Goh Eng Giap, Dian Fiantis, Budi Indra Setiawan, Sam Schiller, Aaron Davitt, and Budiman Minasny https://doi.org/10.5281/zenodo.10707621
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Frisa Irawan Ginting
Rudiyanto Rudiyanto
Fatchurahman
Ramisah Mohd Shah
Norhidayah Che Soh
Sunny Goh Eng Giap
Dian Fiantis
Budi Indra Setiawan
Sam Schiller
Aaron Davitt
Budiman Minasny
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