LAUSR

ABSTRACT The relationship between vegetation index (VI) and climatic variables such as temperature (TEP) and precipitation (PRE) at local, regional and global scales are conventionally analysed to understand the responses of vegetation to climate change. Those unique responses also afford opportunities for using climate variables to discriminate vegetation types. This paper presents a data-driven analysis to explore correlations between ten monthly climatic variables (temperature, precipitation, potential evapotranspiration (PET), vapour pressure (VAP), wet days (WET), and others) and monthly VIs of four different crop types (maize, rice, soybeans, and wheat) at global scale. The purpose is to show the VI–climate correlations in a spatially explicit way, laying the foundation for better crop type mapping by integrating climatic variables and remote sensing. The results show large variations in VI–climate correlation for different crop types and regions. Most cropland areas in the world show strong correlations between VI and VAP, and other variables such as WET, PET, and monthly average daily minimum temperature (TMN). This result encourages future studies using additional climate variables (in addition to TMP and PRE) for detailed vegetation/crop-type mapping.