A new method for monitoring start of season (SOS) of forest based on multisource remote sensing

Abstract

As a sensitive indicator of climate change, forest phenology (e.g., the start of season (SOS)) has profound impacts on the global carbon cycle. Traditional phenological observations are based on surface observation networks. Generally, the outcomes of this manner are less representative and hard to implement in wide forested regions. Remote sensing based observation of forest SOS has currently been a popular way. Those sensors with coarse spatial resolution have been widely used to estimate forest SOS, but they create serious estimation errors in areas of high heterogeneity. Medium-resolution sensors, such as Landsat, face significant challenges in SOS monitoring due to the long revisit period. In this study, we aimed to develop a new method to estimate forest SOS from 2013 to 2019. First, we collected all available Landsat and Sentinel-2 images, and then redefined the linear regression coefficients for the bandpass adjustment to weaken the surface reflectance (SR) differences in different sensors. Subsequently, we improved and developed the modified continuous change detection and classification (MCCDC) model to generate daily vegetation index curves. Finally, we adopted the logistic regression model to test the potential of the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI) and land surface water index (LSWI) in evaluating the annual SOS. The reduced root mean square error (RMSE) for all bands after the integration indicated that the adjustment was successful. We visually compared Landsat’s synthetic images with the actual acquired images and found that their respective false-colour composites were highly similar. Assessing the SOS from the EVI, NDVI and LSWI showed different estimated results. By comparing the annual SOS derived from the three indices with the field observations, it was found that the SOS based on the EVI maintained a low consistency with the field observations. The SOS accuracy from LSWI was the highest and most forest SOS from LSWI in the study area were mainly concentrated in 80–150 days. These three indices all showed that the SOS always fluctuated by ± 4 days from 2013 to 2019. Facing the lack of clear remote sensing images with medium spatial resolution in cloudy and rainy areas, this study proposes an improved method to generate clear daily vegetation index images at a spatial resolution of 30 m, making annual SOS monitoring promising and feasible.