A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Ma, Hao; Wang, Lihua; Sun, Weiwei; Yang, Songling; Gao, Yanghua; Fan, Li; Yang, Gang; Wang, Yumiao

doi:10.3389/fevo.2023.1093454

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…In addition, other SAR sensor data (such as GF-3 and RADARSAT, etc.) can be supplemented to enhance the density of time series datasets [69]. The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Image aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument aboard Europe's Copernicus Sentinel-2A and Sentinel-2B satellites.…”

Section: Uncertainty and Implication For Future Studiesmentioning

confidence: 99%

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Wang,

Meng,

Chen

et al. 2024

Remote Sensing

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Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructed and used to construct and optimize polarization characteristics. A new SAR index named VH-sum is built, which is defined as the summation of VH backscattering intensity for specific time periods based on the temporal changes in VH polarization characteristics of different land cover types. Then comes feature selection, optimization, and reconstruction of optical data. Finally, the PFO-HKMAR classification method is established based on Simple Non-Iterative Clustering. PFO-HKMAR can achieve early-season rice mapping one month before harvest, with overall accuracy, Kappa, and F1 score reaching 0.9114, 0.8240 and 0.9120, respectively (F1 score is greater than 0.9). Compared with the two crop distribution datasets in Northeast China and ARM-SARFS, overall accuracy, Kappa, and F1 scores of PFO-HKMAR are improved by 0.0507–0.1957, 0.1029–0.3945, and 0.0611–0.1791, respectively. The results show that PFO-HKMAR can be promoted in Northeast China to enable early-season rice mapping, and provide valuable and timely information to different stakeholders and decision makers.

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Section: Uncertainty and Implication For Future Studiesmentioning

confidence: 99%

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Wang,

Meng,

Chen

et al. 2024

Remote Sensing

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Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

Yang,

Wang,

Yuan

et al. 2024

Science of The Total Environment

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

Wang,

Ma,

Gao

et al. 2024

Plant Methods

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Background 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. Methods 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. Results 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 m2 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m2. Conclusions 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 m2 to 2000 m2.

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A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Cited by 3 publications

References 44 publications

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images

Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

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

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