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

• Published in: Plant methods Vol. 20; no. 1; p. 25
• Main Authors: Wang, Lihua, Ma, Hao, Gao, Yanghua, Chen, Shengbo, Yang, Songling, Lu, Peng, Fan, Li, Wang, Yumiao
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.02.2024; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Analysis; Case studies; Crop production; Growth; Identification; Land cover; Multi spectral instrument; Phenology; Precipitation; Recognition; Remote sensing; Rice; Rice fields; Rice phenological period; Rice standard spectral curve; Rivers; SAR; Seasons; Spatial distribution; Spectral similarity vector algorithm; Topography; Vegetation; Wavelet transforms; China; Rice standard spectral curve; Multi spectral instrument; Rice phenological period; SAR; Spectral similarity vector algorithm
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-024-01142-1

Mastering 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. In 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. The 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 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m . The 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 to 2000 m .