Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Huinan Yu, Yajie Yang, Changjing Wang, Rui Chen, Qiaoyun Xie, Guoxiang Liu, Gaofei Yin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Deciduous forests spring phenology plays a major role in balancing the carbon cycle. The cloud cover affects images acquired from optical sensors and reduces their performance in monitoring phenology. Synthetic aperture radar (SAR) can regularly acquire images day and night independent of weather conditions, which offers more frequent observations of vegetation phenology compared to optical sensors. However, it remains unclear how SAR data-derived indices vary across different growth stages of forests. Here, we explored the relationship between the cross ratio (CR) index derived from Sentinel-1 data and the deciduous forest growth process. We proposed a deciduous forests spring phenology extraction method using CR and compared the extracted start of growing season (SOS) with those extracted using normalized difference vegetation index (NDVI) derived from Sentinel-2 optical satellite data and green chromatic coordinate (GCC) derived from ground PhenoCam data. We extracted the SOS of 41 PhenoCam sites over the Continental United States in 2018 using the dynamic threshold method. Our results showed that the variations of CR time series are closely related to the phenological processes of deciduous forests. The SOS extracted using CR data showed high consistency with those extracted using GCC (R2 = 0.46), with slightly lower accuracy compared with NDVI-derived results (R2 = 0.62). Our study illustrates the value and mechanism of deciduous forests spring phenology extraction using SAR data and provides a reference for using SAR data to improve forest phenology extraction in addition to using optical remote sensing data, especially in rainy and cloudy regions.

Original languageEnglish
Pages (from-to)2841-2850
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume16
DOIs
Publication statusPublished - 2023
Externally publishedYes

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