Evidence map›Paper›PMID 38311765›Full record

ArticlePlant methods2024

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images.

Lihua Wang, Hao Ma, Yanghua Gao, Shengbo Chen, Songling Yang, Peng Lu, Li Fan, Yumiao Wang

Abstract read
In one paragraph

Article in Plant methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Lihua WangChongqing Institute of Meteorological Sciences, Chongqing, 401147, China. wanglihua1@nbu.edu.cn.
Hao MaDepartment of Geography and Spatial Information Techniques, Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.
Yanghua GaoChongqing Institute of Meteorological Sciences, Chongqing, 401147, China.
Shengbo ChenCollege of geoexploration science and technology, Jilin university, Changchun, 130026, China.
Songling YangDepartment of Geography and Spatial Information Techniques, Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.
Peng LuCollege of geoexploration science and technology, Jilin university, Changchun, 130026, China.
Li FanChongqing Institute of Meteorological Sciences, Chongqing, 401147, China.
Yumiao WangDepartment of Geography and Spatial Information Techniques, Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.

Funding

Key projects of major scientific and technological tasks in Ningbo No. 2021Z107Natural Science Foundation of Chongqing Municipality No. CSTB2022NSCQ-MSX0588Natural Science Foundation of Zhejiang Province No.LY22D010002Ningbo Municipal Bureau of Science and Technology No.2022S101Ningbo Municipal Bureau of Science and Technology No. 2023S102
6 · The paper itself

Abstract

backgroundMastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition.

methodsIn the paper, we proposed a solution based on Sentinel-1 SAR, Sentinel-2 MSI, DEM, and rice calendar data to focus on the rice fields identification in hilly areas. This solution mainly included the construction of rice feature dataset at four crucial phenological periods, the generation of rice standard spectral curve, and the proposal of spectral similarity algorithm for rice identification.

resultsThe solution, integrating topographical and rice phenological characteristics, manifested its effectiveness with overall accuracy exceeding 0.85. Comparing the results with UAV, it presented that rice fields with an area exceeding 400 m

conclusionsThe study illustrated that the proposed solution, integrating topographical and rice phenological characteristics, has the capability for charting various rice field sizes with fragmented and dispersed distribution. It also revealed that the synergy of Sentinel-1 SAR and Sentinel-2 MSI data significantly enhanced the recognition ability of rice paddy fields ranging from 400 m

Indexed as

Multi spectral instrumentRice phenological periodRice standard spectral curveSARSpectral similarity vector algorithm

Identifiers

PMID38311765
PMCPMC10840294

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