[1] XIANG MINGTAO, WEI WEI, WU WENBIN. Review on remote sensing extraction of vegetation phenological parameters. China Agricultural Information, 30, 55-66(2018).

[2] LIU Y, WANG J, DONG J et al. Variations of vegetation phenology extracted from remote sensing data over the Tibetan Plateau Hinterland during 2000-2014. Journal of Meteorological Research, 34, 786-797(2020).

[3] WANG BEIBEI, ZHOU SHUQIN, JING YAODONG et al. Vegetation phenology change and its response to climate in Luliangshan region. Chinese Agricultural Science Bulletin, 37, 102-107(2021).

[4] GIAN-RETO W, ERIC P, PETER C et al. Ecological res-ponses to recent climate change. Nature, 416, 389-395(2002).

[5] XIA CHUANFU, LI JING, LIU QINHUO. Research progress of vegetation phenology monitoring by remote sensing. Journal of Remote Sensing, 17, 1-16(2013).

[6] TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing and Environment, 8, 127-150(1979).

[7] HUETE A, DIDAN K, MIURA T et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195-213(2002).

[8] PORCAR-CASTELL A, TYYSTJÄRVI E, ATHERTON J et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications： Mechanisms and challenges. Journal of Experimental Botany, 65, 4065-4095(2014).

[9] JOINER J, YOSHIDA Y, VASILKOV A P et al. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8(2011).

[10] WANG X, DANNENBERG M P, YAN D et al. Globally consistent patterns of asynchrony in vegetation phenology derived from optical， microwave， and fluorescence satellite data. Journal of Geophysical Research： Biogeosciences, 125(2020).

[11] GUO M, LI J, HUANG S et al. Feasibility of using MODIS products to simulate Sun-Induced Chlorophyll Fluorescence （SIF） in boreal forests. Remote Sensing, 12, 680(2020).

[12] WANG Le, DONG Lei, HU Tianyu et al. History and prospect of vegetation mapping research in China. Science China Life Sciences, 51, 219-228(2021).

[13] LI X, XIAO J. A global， 0.05-degree product of Solar-Induced Chlorophyll Fluorescence derived from OCO-2， MODIS， and reanalysis data. Remote Sensing, 11(2019).

[14] GAO K, XU J et al. Rising CO2 and increased light exposure synergistically reduce marine primary productivity. Nature Publishing Group(2012).

[15] ZHUANG Q, BALDOCCHI D et al. Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data. Agricultural & Forest Meteorology Amsterdam Elsevier, 148, 1827-1847(2008).

[16] PENG S, GANG C, CAO Y et al. Assessment of climate change trends over the Loess Plateau in China from 1901 to 2100. International Journal of Climatology, 38, 2250-2264(2018).

[17] PENG S, DING Y, LIU W et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 11, 1931-1946(2019).

[18] PENG S, DING Y, WEN Z et al. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agricultural and Forest Meteorology, 233, 183-194(2017).

[19] DING D, PENG S. Spatiotemporal trends and attribution of drought across China from 1901-2100. Sustainability, 12, 477(2020).

[20] PENG Songzhang. Monthly precipitation data set with 1 km resolution in China （1901-2020）. National Tibetan Plateau Scientific Data Center. National Data Center for Tibetan Plateau Science(2020).

[21] PENG Songzhang. Data set of monthly mean temperature with 1 km resolution in China （1901-2020）. National Tibetan Plateau Scientific Data Center. National Data Center for Tibetan Plateau Science(2019).

[22] GUAN K, WOOD E F, MEDVIGY D et al. Terrestrial hydrological controls on land surface phenology of African savannas and woodlands. Journal of Geophysical Research： Biogeosciences, 119, 1652-1669(2014).

[23] LI Guangyong, LI Xiaoyan, ZHAO Guoqin et al. Spatio-temporal characteristics of grassland phenology under the dynamic change trend of grassland vegetation in Qinghai Lake Basin. Acta Ecologica Sinica, 34, 3038-3047(2014).

[24] LI Weijia. Temporal and Spatial Trends of vegetation phenology over the Tibetan Plateau from 1982 to 2017(2020).

[25] SOPHIA W, MAXIMILIAN V et al. Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Global Change Biology, 22, 2979-2996(2016).

[26] XIE Y, WANG X, SILANDER J. Deciduous forest responses to temperature， precipitation， and drought imply complex climate change impacts. Proceedings of the National Academy of Sciences of the United States of America, 2015, 13585-13590.

[27] LIU L, ZHANG X. Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016. Scientific Reports, 10, 1-14(2020).

[28] YU L, HAN Y, JIANG Y et al. Sex-specific responses of bud burst and early development to nongrowing season warming and drought in populus cathayana. Canadian Journal of Forest Research, 48, 68-76(2017).

[29] CONSTANT S, ESTER T, DE CARCER PAULA S et al. Asymmetric effects of cooler and warmer winters on beech phe-nology last beyond spring. Global Change Biology, 23, 4569-4580(2017).

[30] SCHIEBER B, KUBOV M, JANK R. Effects of climate warming on vegetative phenology of the common beech fagus sylvatica in a submontane forest of the Western Carpathians： Two-decade analysis. Polish Journal of Ecology, 65, 339-351(2017).

[31] RUONAN Q, XING L, GE H et al. Monitoring drought impacts on crop productivity of the U. S. Midwest with solar-induced fluorescence： GOSIF outperforms GOME-2 SIF and MODIS NDVI， EVI， and NIRv. Agricultural and Forest Meteorology, 323, 109038(2022).