Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data

By Yaotong Cai, Hui Lin, Meng Zhang. 2019

135 citations recorded by the source corpus.

Abstract Rice is one of the world’s major staple foods, especially in China. In this study, we proposed an object-based random forest (RF) method for paddy rice mapping using time series Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) was used to blend MODIS and Sentinel-2 data for achieving multi-temporal Sentinel-2 data. Subsequently, the Savitzky-Golay filter (S-G) was applied to smooth the time series Sentinel-2 NDVI data. And the phenological parameters were derived from the filtered time series NDVI using the threshold method. Then, the optimum feature combination for paddy mapping was formed on the basis of Sentinel-2 MSI images, time series Sentinel-2 NDVI, phenology data and time series Sentinel-1 SAR backscattering images by using the JBh distance. Finally, an object-based Random Forest classifier was used to extract paddy rice with the optimum feature combination. The result showed that fused Sentinel-2 NDVI time series using RASTFM has a high correlation with the original Sentinel-2 image. The overall accuracy and Kappa coefficient of the classification results are higher than 95% and 0.93, respectively, when use the optimum feature combination and object-based RF method. The proposed method can provide technology support for rice mapping in areas with a lot of cloudy and rainy weathers.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/2a7e8c96e0f6e5d9d172a57126b79dabe3ffe2b2

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Citations in the corpus

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review (2020, methodology): “[182] proposed an object-based method for paddy rice mapping using S1 and S2/MSI time series deriving phenological parameters.”
Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications (2020, methodology): “Regarding classification techniques, beside pixel based, object based has showed to be an accurate classification approach using Sentinel-2 imagery [51].”