# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 1 Introduction  
Drought is a natural disaster with high frequency, wide range of influence, and serious losses in the world [1, 2, 3, 4]. Under the background of global warming, the drought trend will be strengthened due to the lack of precipitation in a long time and expansive space [5, 6, 7]. The increasing drought event has seriously threatened the biodiversity of terrestrial ecosystem [8, 9]. Because drought occurs frequently in most climatic zones, drought research has been a concern for scholars [10, 11]. In the context of global climate change, drought not only significantly reduces the productivity of vegetation, but also causes large-scale death of vegetation. As an important part of terrestrial biosphere, vegetation plays an irreplaceable role in regulating the global carbon balance, maintaining the stability of global climate and water resources, but it is vulnerable to drought [12, 13]. Drought is considered to be one of the important factors affecting vegetation growth and net primary productivity of terrestrial ecosystems [13, 14]. Different drought indices were used for drought monitoring, including the Standardized Precipitation Evapotranspiration Index (SPEI) [15], the Palmer Drought Severity Index (PDSI) [16], the Standardized Precipitation Index (SPI) [17]. However, SPIE is obtained by quantifying precipitation, potential evaporation, and temperature. Compared with PDSIand SPI, SPEI consideration of drought-related climatic factors has the characteristics of multi-timescale [18; 19; 20]. Therefore, studying the response characteristics of vegetation to drought is the key basis for analyzing the impact of drought and predicting the drought risk, which is of great significance to the biodiversity protection of terrestrial ecosystem [21]. The developed remote sensing technology is used to monitor the changes of vegetation cover. EVI (Enhanced Vegetation Index), NDVI (Normalized Difference Vegetation Index) and SIF (Sun/Solar-induced Chlorophyll Fluorescence) have been gradually applied to evaluate the response of vegetation growth to drought [22; 23]. SIF is a new remote sensing method developed in recent years to study vegetation photosynthesis, and it is one of the main tasks of the European Space Agency's future Earth exploration program. Compared with traditional optical reflection vegetation remote sensing, SIF has great advantages in vegetation stress detection and carbon cycle research [24; 25]. [PERSON] et al. [26] evaluated the correlation between SIF and four commonly used drought indexes of SPI, SPEI, TCL, and PDSI in different climate regions in the U.S. mainland. They believe that SIF is highly sensitive to meteorological drought in arid areas, SIF of different vegetation types has different response to meteorological drought, and farmland is more sensitive to short-term drought. [PERSON] et al. [23] used NDVI and SIF from 2009 to 2018 to study the impact of extreme drought events on the growth of Australian vegetation. The study found that the average NDVI and SIF values in 2018 were significantly lower than the average values in the past decade. Abnormal drought greatly affected the growth of farmland and grassland, and farmland SIF was more sensitive to drought. [PERSON] et al. [25] used SIF and Traditional Vegetation Index (NDVI and EVI) to study the high temperature stress faced by winter wheat in Northwest India. Its research shows that the sudden rise of temperature since March 2010 has significantly affected wheat growth, and the yield predicted by SIF satellite observation has significantly decreased, and the response is earlier than that of NDVI and EVI. In the context of climate change, an in-depth understanding of the impact of drought on vegetation is particularly needed, which is conducive to restricting land carbon absorption and scientific climate policy-making in the future. The Yellow River Basin (YRB) is located in the arid, semi-arid, and semi-humid areas in northern China, with a total drainage area of \(79.5\times 10^{4}\) km\({}^{2}\).