| Summary: | Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>σ</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> </inline-formula>) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>σ</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> </inline-formula> and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.
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