## 1 Introduction  
Vegetation phenology refers to the periodic phenomena such as germination, leaf spreading, flowering, and defoliation of vegetation in the process of growth influenced by climate and other driving factors [1], which can reflect the interaction of vegetation phenological parameters in the abiotic environment [2; 3]. By controlling seasonal changes in vegetation and physiological functions such as photosynthesis, changes in vegetation phenology can affect the structure and function of ecosystems [4]. The change in vegetation phenology affects the structure and function of ecosystems and climate change. Therefore, it is crucial to monitor vegetation phenology changes, due to the variety of vegetation types, high plant coverage, and sensitivity to climate change in Northeastern China. Thus, the study of vegetation phenology in this region is of great significance for understanding the response of phenology to climate change. Satellite remote sensing data can provide effective information on vegetation phenology at multiple scales, and have become an outstanding tool for monitoring surface phenology. Traditional vegetation indices [5; 6], such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI), are widely used in vegetation phenology research [7; 8]. However, vegetation indices have some uncertainties in characterizing vegetation phenology. On the one hand, it is difficult to accurately capture the greenness of the vegetation canopy due to the influence of snow and bare rock on the vegetation index signal [7; 9; 10; 11]. On the other hand, the vegetation index is usually related to the morphological characteristics of the vegetation canopy. There is an inconsistency between the morphological characteristics of the vegetation canopy and the physiological characteristics of the vegetation, which results in the accuracy of phenology analysis based on the morphological changes in the vegetation canopy [12; 13; 14]. As a complement to the vegetation index, Solar-induced chlorophyll fluorescence (SIF) offers new possibilities for monitoring vegetation function spatially. SIF is the spectral signals emitted by vegetation under light conditions through photosynthesis at wavelengths of 650-800 nm in the red band (around 690 nm). There are two peaks in the near-infrared band (around 740 nm), and the data are insensitive to both atmospheric and cloud scattering, which can directly reflect the dynamic changes in the photosynthesis of vegetation. Thus, SIF has a stronger physiological basis compared with traditional vegetation indices. With the continuous development of observation satellites such as GOSAT, GOME-2, and OCO-2, it is possible to invert SIF and monitor vegetation phenology studies on a global scale. However, the current studies based on SIF mainly focus on the consistency analysis between SIF and Gross Primary Productivity (GPP) and the comparison of SIF with other indicators [15; 16], rather than the calculation of vegetation phenology values using SIF. In addition, previous studies [17] have been conducted mainly on a global scale and in larger study areas. The relationship between SIF and vegetation indices changes with the change in spatial resolution and the increase in global temperature. Vegetation phenology in the high latitudes of the northern hemisphere shows a trend of earlier rejuvenation and later yellowing in recent years. In addition, the stress of abiotic factors (temperature, precipitation, relative humidity, etc.) leads to a complex spatiotemporal pattern of vegetation phenology. Located in the middle and high latitudes, Northeastern China has high vegetation cover and is highly sensitive to climate change, which is an ideal region for studying vegetation growth and phenological changes.