In the spring of 2020, lockdown measures were adopted to control the spread of novel coronavirus, and these measurements had large impacts on air quality, regional climate, and vegetation growth. Accurately capturing the response of vegetation to environmental changes is crucial for ecosystem research. However, there might be some divergences in vegetation growth trends from different satellite datasets. To investigate the response of vegetation growth to environmental changes from January to April 2020, we employed two reconstructed solar-induced chlorophyll fluorescence (SIF) products, continuous SIF (CSIF) and global OCO-2 SIF (GOSIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI). Results shows that CSIF, GOSIF, NDVI, EVI, and LAI captured enhanced vegetation growth during January–March 2020 compared with 2015–2019. However, CSIF and GOSIF show an opposite trend to vegetation indices (VIs) and LAI in April. The trends from CSIF and GOSIF also differed with those from TROPOspheric Monitoring Instrument (TROPOMI) SIF and OCO-2 SIF. Biases were attributed to the input MODIS surface reflectance data for reconstructing SIF in the algorithms of GOSIF and CSIF.

中文翻译：

---

CSIF和GOSIF未能准确捕捉2020年春季植被绿化情况

2020年春天，为控制新型冠状病毒的传播采取了封锁措施，这些措施对空气质量、区域气候和植被生长产生了很大影响。准确捕捉植被对环境变化的响应对于生态系统研究至关重要。然而，不同卫星数据集的植被生长趋势可能存在一些差异。为了研究2020年1月至4月植被生长对环境变化的响应，我们采用了两种重建的太阳诱导叶绿素荧光（SIF）产品，连续SIF（CSIF）和全球OCO-2 SIF（GOSIF），归一化差异植被指数（ NDVI)、增强植被指数 (EVI) 和叶面积指数 (LAI)。结果显示，与 2015-2019 年相比，CSIF、GOSIF、NDVI、EVI 和 LAI 捕捉到了 2020 年 1 月至 3 月植被生长增强的情况。然而，CSIF 和 GOSIF 与 4 月份植被指数（VI）和 LAI 的趋势相反。 CSIF 和 GOSIF 的趋势也与对流层监测仪器 (TROPOMI) SIF 和 OCO-2 SIF 的趋势不同。 GOSIF和CSIF算法中重建SIF的输入MODIS表面反射率数据存在偏差。