**1. Introduction**

Land surface phenology is the assessment of seasonal vegetation growth at a large scale using satellite remote sensing and has been widely used to quantify the response of terrestrial ecosystems to climate change [1–3]. Alpine ecosystems, characterized by high elevations, low temperatures, snows, and short growing seasons, are very sensitive to climate change and are regarded as "climate change hot spots". The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is critical for understanding the dynamics of alpine vegetation and climate change. As the third pole of the earth and the largest alpine pasture in Asia, the Tibetan Plateau is a research focus in land surface phenology [4]. However, the prevalent and seasonal snow cover, one of the major features of alpine ecosystems, increases the complexity of monitoring vegetation phenology from satellites [5]. Especially in the Tibetan Plateau, existing studies have yielded inconsistent results on the SOS changes, and snow cover has been attributed as a major cause [6,7].

Phenological transitions are generally detected from the seasonal dynamics of the satellite-derived vegetation index (VI). The VI measures the greenness of vegetation through

**Citation:** Wang, Y.; Chen, Y.; Li, P.; Zhan, Y.; Zou, R.; Yuan, B.; Zhou, X. Effect of Snow Cover on Detecting Spring Phenology from Satellite-Derived Vegetation Indices in Alpine Grasslands. *Remote Sens.* **2022**, *14*, 5725. https://doi.org/ 10.3390/rs14225725

Academic Editors: Kenji Omasa, Shan Lu and Jie Wang

Received: 3 October 2022 Accepted: 8 November 2022 Published: 12 November 2022

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algebraic combinations of the multiband reflectance of satellite data and is closely related to the biophysical and structural properties of the canopy [8]. From the VI's trajectory, the SOS is detected as the point in time when the VI reaches a threshold, the growth gradient reaches its maximum, or the VI exceeds the moving average VI curve, corresponding to the threshold method, the derivative method, and the moving average methods, respectively [9]. While each method has its own advantages and shortcomings, there is no consensus on which method performs best [10,11]. The dynamic threshold method, achieving a balance between simplicity, universality, and robustness to noise, is one of the most commonly used methods, especially in the latest MODIS phenology product MCD12Q2 C006 [12]. Among the various VIs, the normalized difference vegetation index (NDVI) [13] is the earliest and most commonly used VI in SOS detection due to its simplicity and long records of historical data [14–16], but it suffers from saturation in densely vegetated areas and interference from soil backgrounds. To reduce the sensitivity of VI to the soil background and atmosphere, the two-band enhanced vegetation index (EVI2) [17] was proposed and has been widely used in SOS detection, such as in the VIIRS phenology product VNP12Q2 [18].

Although various satellite-derived VIs have been successfully applied in phenology detection [19–21], they face major limitations in alpine grasslands due to snow's interference with satellite signals [22,23]. The presence of snow can significantly affect the VI's value and change the VI's trajectory, while snowmelt can cause a rapid increase in the VI's trajectory [24,25]. If the effect of snow cover is not considered, the detected SOS may be a snowmelt date instead of the SOS [26,27], which will further cause bias in our understanding of vegetation phenology trends and climate change [28]. For example, preseason snow was found to cause the SOS detected by NDVI to advance compared to snow-free cases [20,21].

Previous studies have attempted to eliminate the effect of snow cover on SOS detection from satellite data. Some studies introduced auxiliary information on snow, precipitation, and temperature to replace the SOS of snow-covered pixels with those of snow-free background pixels [18,29]. However, auxiliary data are not always available in large alpine areas, and additional data can also add bias and uncertainty [30]. Alternative approaches have attempted to propose new snow-free VIs, such as the normalized difference phenology index (NDPI) [31] and the normalized difference greenness index (NDGI) [21], which were recently developed to eliminate the effects of snow and soil. Both VIs were found to have better correlation with the in situ measurements and outperform the traditional VIs under snow conditions, such as NDVI and EVI [20,21]. In some studies, the SOS dates detected by NDPI or NDGI were used as the SOS detected under snow-free conditions to evaluate the advancement or delay of the SOS under snow conditions [20,32]. In addition, the near-infrared reflectance of vegetation (NIRv) [33] and solar-induced chlorophyll fluorescence (SIF), as direct indicators of vegetation photosynthesis, are promising indicators for phenological monitoring [33,34]. Both SIF and NIRv are physiological-based VIs and overcome the saturation problem of NDVI. Existing studies verified the good consistency of the SOS detected by NIRv and SIF with the SOS measured by flux towers [20,35].

Despite recent progress in eliminating the effect of snow cover on SOS detection, the mechanism of how snow cover affects VI values and subsequent SOS detection remains unclear. Existing studies have attempted to find evidence from satellite data or in situ measurements [36–39], yet it is challenging to compare snow-free and snow-covered areas directly. Vegetation growth on snow-free pixels cannot simply represent the growth on the snow-covered pixels due to the confounding effects of snow cover on SOS detection and on SOS itself. Furthermore, although several new snow-free VIs and SIF-related VIs have been proposed [31,35,40], there are no definitive answers as to how they are affected by snow cover and which VI performs best for alpine ecosystems. Direct evidence on how snow cover affects SOS detected from satellite-derived VIs is urgently needed to enhance our understanding of vegetation phenology changes on the Tibetan Plateau.

To address the above issues, this study combined simulation experiments and satellite data to investigate the effect of snow cover on VI values and subsequent SOS detection, aiming to clarify the mechanism of how snow cover affects SOS detection from satellite-derived VIs. Four snow parameters were adopted to describe the coverage and phonological characteristics of snow, including snow cover fractions (SCFs), snow cover duration (i.e., consecutive days with snow and the ratio of days with snow to total days, hereafter referred to as SCDc and SCDr), and the end of the snow season (ESS). Five different VIs were used and compared in this study, including four structure-based (i.e., NDVI, EVI2, NDPI, NDGI) VIs and one physiological-based (i.e., NIRv) VI. Simulation experiments were carefully designed to model the time series of different VIs under different snow scenarios to investigate the difference in SOS between snow and snow-free conditions. Then, the variations in the SOS under different snow conditions were analyzed using satellite data. The main objectives of this study are: (1) to elucidate the effect of snow cover on the five VIs and subsequent SOS detection through simulation experiments; (2) to analyze the spatial and temporal patterns of snow cover and investigate the effect of snow cover on the detected SOS using real satellite data from 2020; and (3) to compare the performance of the five VIs in detecting the SOS under snow-covered conditions.
