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|a Drought is one of the billion-dollar natural disasters and hard to trace and measure. In recent years drought monitoring becomes much easier with remote sensing. However, it is still difficult to pin vegetation variances on drought because of the delay of the caused vegetation stress. To assess vegetative drought, it is important to first understand the relationship between meteorological condition and vegetation condition, and measure the vegetation responses to meteorological drought. It would be very helpful for effective early warning about agricultural drought. This study uses the CONUS as the study area, and utilizes remote sensing products such as NDVI/VCI (normalized difference vegetation index/vegetation condition index) and SPI/SPEI (standardized precipitation index/standardized precipitation evapotranspiration index) to give a comparative evaluation to the vegetation's drought response. The used vegetation products and meteorological data are ensured to be consistent. The scale and lag of vegetation response to drought for various vegetation types and aridity levels were thoroughly investigated. The results show that: The AVHRR and MODIS NDVI series pairs and the meteorological drought index series pairs (SPEI and SPI) have fairly good consistencies. Among them, 69.5% and 84% have rho (correlation coefficient) values greater than 0.8 respectively. For the NDVI series pairs, the maximum rho value is 0.98, the minimum rho value is −0.47, and the mean rho value is 0.79, which are 0.97, 0.68, and 0.87 respectively for the meteorological index series pairs. Compared to rho values of the meteorological index series pairs, the rho values of the NDVI series pairs have more outliers indicating instability. The correlation between SPEI and VCI significantly relies on time lags and has high spatial inhomogeneity. 1- and 2-month lags of SPEI have more significant positive correlation with VCI in Arid, Semi-Arid and Dry sub-humid areas of central west CONUS with less precipitation and lower temperature. For various time scales (time scale is SPEI reference range), the most significant positive correlation between SPEI and VCI happens in the time scales of 6- to 12-month in summer, the time scales of 3- and 6-month in spring and autumn, and the time scales of 2- and 3-month in winter regardless of time lags. Despite of the different vegetation types and aridity levels, the maximum correlations between SPEI and VCI are observed in Hyper arid regions in January, Arid regions in April, Semi-arid regions in July, and Dry sub-humid regions in October. Shrub has prominent responses in January and April, grass responses appear in July and October, and Evergreen forest shows least responses in all seasons. The results add more insights of the connection between vegetation and climate, and guide the development of new technology leveraging remote sensing data for vegetation drought monitoring and early-warning. The results are also helpful to provide important references for studying large-scale physiological and phenological properties of the vegetation under different climate conditions. © 2021 The Authors
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