the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
20 m Africa Rice Distribution Map of 2023
Abstract. In recent years, the demand for rice in Africa has been growing rapidly, and in order to meet this demand, the rice cultivation area is also expanding rapidly, thus it is of great significance to monitor the rice cultivation in Africa. The spatial and temporal distribution of rice cultivation in Africa is complex, making it difficult to use a climate-based rice identification method, and the existing rice distribution products are all grid based statistical data with low resolution, unable to obtain accurate rice field location and available labels. To address these two difficulties, based on time-series optical and dual-polarisation SAR data, this study proposes a sample set construction method by fast coarse positioning assisted visual interpretation, and a feature importance guided supervised classification combining multiple temporal optical and SAR features to reduce the impact of rice diversity in Africa. Firstly, we use the time-series statistical features of VH data for fast coarse positioning and screening of possible rice areas, and combine multiple auxiliary data for visual interpretation to make sample set; secondly, based on the complementary information in SAR data and optical data, the 20 meter Africa rice distribution map of 2023 was completed by combining the object-oriented segmentation results of temporal optical images and the pixel based classification results of temporal SAR data features after feature selection. The average classification accuracy of the proposed method on the validation set is more than 85 %, and the R2 of the linear fit to various existing statistical data is more than 0.9, which proves that the proposed method can achieve the spatial distribution mapping of rice under the complex climatic conditions in a large region, providing crucial data support for rice monitoring and agricultural policy development. The dataset is available at https://doi.org/10.5281/zenodo.13729353 (Jiang, Zhang et al. 2024).
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RC1: 'Comment on essd-2024-402', Anonymous Referee #1, 22 Oct 2024
This study presents a high-resolution rice distribution map in Africa using an innovative approach that combines time-series optical and SAR data. Given the limitations of current rice distribution products in this region, this study will provide a valuable product for monitoring rice cultivation in African. This product can contribute to assessing food security and the sustainability of rice production in African, such as the evaluation of rice yield and GHGs emission. However, there are several major comments that needed to be addressed:
- Please provide the full form of all abbreviations when they first appear, such as SAR, to ensure clarity for readers.
- Section 2.2.1: More details are needed on the criteria used for image screening.
- Section 2.2.4: Is it appropriate to distinguish the distribution of single- and double-season rice in 2023 using a crop type dataset in 2017? My main concern is that the planting area of single- and double-season rice in Africa have expanded rapidly in recent years.
- Section 3.1: How was the quality of screened samples assessed, and how are these samples distributed?
- The accuracy of the rice distribution map highly depends on image segmentation. Please explain the reason for choosing bands such as B3, B4, B8 and B8A for image segmentation, and provided results demonstrating the image segmentation.
- If more reliable rice samples can be obtain based on fast coarse positioning and ancillary data, is it possible to map rice distribution directly using this method? Additionally, after SAR features screening, can rice paddy be distinguished from similar elements such as wetland?
- The structure of the methods is confusing, and the description of the methodology is unclear. This section required further improved.
- The figures and tables need better organization. For instance, there are overlaps between Table5 and Fig7, and some figures, such as fig.12, are missing horizontal or vertical axes.
- The discussion section should be strengthened, especially by comparing the methods in this study with those used in other studies, and the implications of the rice distribution map for Africa should also be emphasized further.
- Some references are incorrect, such as those in lines 61 and 189, and should be corrected.
Citation: https://doi.org/10.5194/essd-2024-402-RC1 -
AC1: 'Reply on RC1', Hong Zhang, 02 Nov 2024
Dear the reviewer and the editor,
Manuscript ID ESSD-2024-402 entitled “20 m Africa Rice Distribution Map of 2023.”
We would like to express our sincere gratitude to the editor and the reviewer for their constructive feedback and thorough review of our manuscript. We have carefully considered all suggestions and have made the corresponding revisions to the manuscript. In addition to addressing the reviewer’s comments, we have also refined the overall language to enhance the quality of the paper, and redrawn some of the figures for greater clarity. Below, we provide detailed responses to the reviewer's comments, including clarifications where necessary. We hope these revisions address the concerns and uncertainties raised by the reviewer.
Sincerely,
Hong Zhang
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RC2: 'Comment on essd-2024-402', Anonymous Referee #2, 03 Nov 2024
This article develops a 20m-resolution rice map for Africa by combining time-series SAR and optical data. It is a pioneering effort involving Africa, as there are few high-resolution rice maps in Africa, and it is quite a challenge to map rice at a continental scale.
However, the data quality is still questionable and subject to further validation and improvement.
The Authors admitted that large areas of rainfed rice cultivation in Africa lack the distinct flooding signals typical of irrigated rice, but the methodology is based on the detection of flooding signals. How can you then map rainfed rice fields? More importantly, it does not seem the author’s product can differentiate irrigated rice and rainfed rice, which is important to support rice monitoring and agricultural and climate mitigation policy development.
The authors also admitted that the main challenge is constructing a training/validation sample set. However, the method used in this study is not convincing, as there is no real “ground-truth” data.
Line 55: so spatial distribution map is not gridded maps? This sentence is not accurate.
Figure 5: how do you know which are rice fields, which are wetlands, which are other land covers?
I have a big concern about the procedure of constructing the training/validation sample set. The first step is ok and fine, which uses some image signal to find potential rice fields, but the second step is questionable: cross-referencing the intersections of the rice grid map from CROPGRIDS
and Cropland distribution maps with corresponding optical imagery. CROPGRIDS is very coarse, with each grid including multiple land covers, and I do not know how you can confirm whether a location within that grid is a rice field or not. If this works, I can simply make a map of rice fields by cross-overlaying Cropland distribution with CROPGRIDS.
The negative samples, which are randomly sampled based on World Cover products, are also questionable. World Cover Product is subject to errors (omission and commission), how can you guarantee your samples are correct and accurate?
There is no demonstration/validation of the performance of the image segmentation. Shall at least use some known crop field (must include rice fields) to demonstrate the segmentation can reasonably divide different fields.
The division between single-season and double-season rice fields based on crop calendar from riceAltas is too simple. I hope the authors can do better based on time series inundation/phenological data. riceAltas’s crop calendar is country/county-based and we know there is much variation within a country and county.
Look at Table 8: if assume these survey statistics are right, your estimate overestimates a lot for many countries such as Angola, Burundi, Cameroon, Cameroon, and the Gambia, suggesting possible large commission errors. The high R2 score in Figure 12 can only suggest that your product generally captured the continental-scale distribution pattern, and does not directly approve a high-quality high-resolution map.
Even based on the current accuracy assessment, many countries still have over accuracy ~69.76%, which is too low to accept based on the current technology of rice-paddy mapping.
Citation: https://doi.org/10.5194/essd-2024-402-RC2 -
AC2: 'Reply on RC2', Hong Zhang, 12 Nov 2024
Dear the reviewers and the editor,
Manuscript ID ESSD-2024-402 entitled “20 m Africa Rice Distribution Map of 2023.”
We would like to express our sincere gratitude to the editor and the reviewers for your constructive feedback and thorough review of our manuscript. We have carefully considered all suggestions and have made the corresponding revisions to the manuscript. In addition to addressing the reviewer’s comments, we have also refined the overall language to enhance the quality of the paper, and redrawn some of the figures for greater clarity. Below, we provide detailed responses to the reviewer's comments, including clarifications where necessary. We hope these revisions address the concerns and uncertainties raised by the reviewer.
Sincerely,
Hong Zhang
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AC2: 'Reply on RC2', Hong Zhang, 12 Nov 2024
Data sets
20m Africa rice distribution map in 2023 Jiang Jingling et al. https://doi.org/10.5281/zenodo.13729353
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