A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment
With a growing global population and intensifying regional conflicts, the need for food is more urgent than ever. Rice, as one of the world's major staple crops especially in Asia, sustains over 50 percent of the global population. Accurate rice mapping is fundamental to ensuring global food se...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017224000567 |
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| author | Husheng Fang Shunlin Liang Yongzhe Chen Han Ma Wenyuan Li Tao He Feng Tian Fengjiao Zhang |
| author_facet | Husheng Fang Shunlin Liang Yongzhe Chen Han Ma Wenyuan Li Tao He Feng Tian Fengjiao Zhang |
| author_sort | Husheng Fang |
| collection | DOAJ |
| description | With a growing global population and intensifying regional conflicts, the need for food is more urgent than ever. Rice, as one of the world's major staple crops especially in Asia, sustains over 50 percent of the global population. Accurate rice mapping is fundamental to ensuring global food security and sustainable agricultural development. Remote sensing has become an essential tool for mapping rice cultivation due to its ability to cover large areas and provide timely observation. Existing reviews mainly focus on the paddy rice mapping methods. However, it lacks a comprehensive understanding on the quality of different paddy rice maps from regional to global scales. This paper provides a comprehensive review of existing satellite-based rice mapping methods and products. Firstly, we categorized all previous methods into four classes: 1) spatial statistical method; 2) traditional machine learning method; 3) phenology-based method; and 4) deep learning method. Secondly, we summarized 25 products, including 3 global products and 22 regional products. Furthermore, we examined the consistency and discrepancy among different products in China, Heilongjiang China and Vietnam respectively and explored the underlying reasons. We found that 1) rice fields with simple cropping patterns and intensive cultivation can be correctly recognized using various algorithms; 2) different products share low consistency in fragmented rice fields 3) the prevalence of clouds and complicated rice cropping patterns or diverse growing environments in subtropical and tropical regions poses challenges to accurate rice mapping. Due to these challenges, currently it still lacks paddy rice maps with both large spatial coverage, high spatial resolution, and long time series. Moreover, deficiency of ground-truth samples impedes product development and validation. For improved paddy rice mapping at large scale, we suggest to apply sample-free rice mapping techniques and remote sensing foundation models to leverage the strengths of phenology-based methods and deep learning methods. |
| format | Article |
| id | doaj-art-2c0213cf966d4d2ba14643a10a4b9d20 |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-2c0213cf966d4d2ba14643a10a4b9d202024-12-12T05:23:09ZengElsevierScience of Remote Sensing2666-01722024-12-0110100172A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessmentHusheng Fang0Shunlin Liang1Yongzhe Chen2Han Ma3Wenyuan Li4Tao He5Feng Tian6Fengjiao Zhang7Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaJockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, 999077, China; Corresponding author.Jockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, 999077, ChinaJockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, 999077, ChinaJockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, 999077, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, ChinaWith a growing global population and intensifying regional conflicts, the need for food is more urgent than ever. Rice, as one of the world's major staple crops especially in Asia, sustains over 50 percent of the global population. Accurate rice mapping is fundamental to ensuring global food security and sustainable agricultural development. Remote sensing has become an essential tool for mapping rice cultivation due to its ability to cover large areas and provide timely observation. Existing reviews mainly focus on the paddy rice mapping methods. However, it lacks a comprehensive understanding on the quality of different paddy rice maps from regional to global scales. This paper provides a comprehensive review of existing satellite-based rice mapping methods and products. Firstly, we categorized all previous methods into four classes: 1) spatial statistical method; 2) traditional machine learning method; 3) phenology-based method; and 4) deep learning method. Secondly, we summarized 25 products, including 3 global products and 22 regional products. Furthermore, we examined the consistency and discrepancy among different products in China, Heilongjiang China and Vietnam respectively and explored the underlying reasons. We found that 1) rice fields with simple cropping patterns and intensive cultivation can be correctly recognized using various algorithms; 2) different products share low consistency in fragmented rice fields 3) the prevalence of clouds and complicated rice cropping patterns or diverse growing environments in subtropical and tropical regions poses challenges to accurate rice mapping. Due to these challenges, currently it still lacks paddy rice maps with both large spatial coverage, high spatial resolution, and long time series. Moreover, deficiency of ground-truth samples impedes product development and validation. For improved paddy rice mapping at large scale, we suggest to apply sample-free rice mapping techniques and remote sensing foundation models to leverage the strengths of phenology-based methods and deep learning methods.http://www.sciencedirect.com/science/article/pii/S2666017224000567Rice mappingSatellite remote sensingSummary of existing algorithmsConsistency assessment of existing products |
| spellingShingle | Husheng Fang Shunlin Liang Yongzhe Chen Han Ma Wenyuan Li Tao He Feng Tian Fengjiao Zhang A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment Science of Remote Sensing Rice mapping Satellite remote sensing Summary of existing algorithms Consistency assessment of existing products |
| title | A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment |
| title_full | A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment |
| title_fullStr | A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment |
| title_full_unstemmed | A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment |
| title_short | A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment |
| title_sort | comprehensive review of rice mapping from satellite data algorithms product characteristics and consistency assessment |
| topic | Rice mapping Satellite remote sensing Summary of existing algorithms Consistency assessment of existing products |
| url | http://www.sciencedirect.com/science/article/pii/S2666017224000567 |
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