**1. Introduction**

Vegetation phenology is the study of the timing of recurring biological events of plants and their interactions among periodic changes in the natural environment [1]. It indicates the response and adaptation of vegetation ecosystems to seasonal and interannual environmental change [2,3]. Since the industrial revolution, climate change (e.g., global warming) induced by human activities has had a profound impact on vegetation phenology; at the same time, changes in vegetation phenology have been regarded as a sensitive indicator of climate change and the carbon cycle [4]. Information on vegetation phenology is playing an increasingly important role in global change monitoring, ecological environment simulation and climate change response [2,4].

**Citation:** Wang, C.; Wu, Y.; Hu, Q.; Hu, J.; Chen, Y.; Lin, S.; Xie, Q. Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations. *Remote Sens.* **2022**, *14*, 3018. https://doi.org/10.3390/ rs14133018

Academic Editor: Sofia Bajocco

Received: 26 April 2022 Accepted: 20 June 2022 Published: 23 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Remote sensing provides a useful approach to characterizing seasonal and interannual changes in land surface vegetation from regional to global scales [5–7]. Land surface phenology is mainly extracted based on satellite vegetation datasets using the key phenological metrics, i.e., the start of season (SOS) and the end of season (EOS) [8], to characterize the timing of vegetation dynamics during the growing season. Vegetation indices (VIs), calculated from land surface reflectance, are widely used to extract land surface phenology and analyze its response to climate change in various studies from the leaf and canopy greenness perspective. For example, the global land surface phenology product MCD12Q2 was generated by Zhang et al. [6,8] using the EVI (Enhanced Vegetation Index) time series, which is the only global land surface phenology product available in recent years. However, as VIs are not capable of providing us with a direct proxy of physiological processes, they cannot be perfectly applied to modeling frameworks [9]. In this case, some studies have explored the potential of vegetation phenology extraction from an photosynthetic perspective. Solar-induced chlorophyll fluorescence (SIF), as a new physiological proxy for photosynthesis activity [10], presenting a weak signal emitted by green plants during photosynthesis [11–14]. Compared to traditional VIs, SIF provides a direct indicator for monitoring vegetation physiological functioning [15,16], and has a close relationship with carbon uptake of vegetation. Some studies have indicated that satellite-based SIF observations are highly correlated with in situ Gross Primary Productivity (GPP) over flux towers, and thus have the potential to reveal GPP dynamics under environmental changes over a large scale [15,17].

Some studies have reported that phenology derived from SIF and EVI were different across various vegetation types [18,19], such as coniferous forests, deciduous forests, grasslands and croplands [15–17,20,21]. For example, Wang et al. [14] revealed that EVI-based EOS could be later than SIF-based EOS for more than two weeks in grasslands in Australia, and such differences would be larger when plants are stressed with decreasing soil moisture. Moreover, for different phenological metrics, i.e., SOS and EOS, SIF and EVI also performed differently. For example, Walther et al. [15] indicated that the EVI-based SOS of boreal evergreen coniferous forest was much later (about a month) than the SIF-based SOS, but the EVI-based EOS was slightly advanced (about 1 to 2 weeks) to the SIF-based EOS. Although some studies have revealed differences in phenology derived from SIF and EVI among land cover types, the driving factors and underlying mechanisms are less known.

Except for croplands, which could be largely affected by human activities, the dynamics of land surface phenology are driven by the physical characteristics of the vegetation itself and the external climate environment [22]. The external climate factors that affect vegetation phenology mainly include temperature, precipitation and radiation, which interact to promote or limit natural vegetation growth [23,24]. For example, Ma et al. [25] revealed that 80% of EVI-based phenology dynamics in dryland ecosystems are driven by the variability of annual precipitation. In contrast, recent studies have indicated that SIF has quicker responses to external environmental stress information (e.g., water stress) than EVI did [26,27], as SIF contains additional information on stress conditions that reflects fluorescence efficiency [11]. However, climate controls on EVI-based phenology (greenness) and SIF-based phenology (photosynthesis) have not been compared, and a comprehensive analysis across different climatic conditions is still scarce.

In this study, we defined the climatic limiting controls on vegetation growth as climatic limitations, which include temperature-limiting, water-limiting, and radiation-limiting factors. We focus on naturally vegetated areas in China and divide them into climatelimited areas (i.e., temperature, water, and radiation limitations). We then employed SIF and EVI to extract phenology from photosynthesis and greenness perspectives, respectively, and compared their characteristics across climate-limited areas. We further explored the underlying mechanisms by investigating the relationships between the differences in phenology derived from SIF and EVI and climatic limiting factors. This work can provide insights into the mechanistic differences between SIF and EVI in characterizing land surface

phenology to improve our understanding of vegetation dynamics from greenness and photosynthesis perspectives and their interactions with climate conditions.

#### **2. Materials and Methods**

*2.1. Data Sources and Reprocessing*

## 2.1.1. SIF Datasets

The GOSIF (Global OCO-2 SIF) is a reconstructed SIF product based on Orbiting Carbon Observatory-2 (OCO-2) observations, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation data, and meteorological reanalysis data. The GOSIF datasets from 2003 to 2016 were downloaded from http://globalecology.unh.edu/ (accessed on 10 February 2021), which were globally spatio-temporal continuous at 0.05◦ and 8-day resolution derived with a machine learning algorithm trained with OCO-2 SIF [28]. The datasets had a good performance validated by original SIF observations (RMSE = 0.07 W m<sup>−</sup><sup>2</sup> μm<sup>−</sup><sup>1</sup> sr<sup>−</sup>1) and also showed a good correlation with the in-situ GPP over flux sites (R<sup>2</sup> = 0.73, *p* < 0.001) [28].

## 2.1.2. EVI Datasets

The MODIS Terra/Aqua Vegetation Indices (MOD13C1/MYD13C1, V006) were combined to generate EVI time series from 2003 to 2016 at 8-day interval and 0.05◦ spatial resolution, which were available at https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 15 March 2021). Global MOD13C1 and MYD13C1 are cloud-free spatial composites of MOD13A2 and MYD13A2 at 16-day intervals and 1 km spatial resolutions, respectively.

#### 2.1.3. Land Cover Map

We utilized the global land cover product (GLC), freely available at http://data.ess. tsinghua.edu.cn/index.html (accessed on 22 September 2021) to map the natural vegetated areas and mask croplands that are vulnerable to human interference [29]. This product consists of 17 land cover types, among which the developed land types and non-vegetated land types were masked to generate natural vegetated areas. The accuracy for 2010, 2015 and 2020 are 86.39% ± 9.05%, 86.44% ± 8.99% and 84.83% ± 10.19%, respectively [29]. We aggregated the original land cover dataset from 2015 to 0.05◦ to match the spatial resolution of the SIF and EVI datasets in this study.
