Relationships between meridional profiles of satellite-derived vegetation index (NDVI) and climate over Siberia

Abstract

This study investigates the regional relationship between the satellite‐derived vegetation index (Normalized Difference Vegetation Index, NDVI) and climatological parameters (temperature and precipitation) over Siberia on a 5‐year (1986–1990) annual mean basis. The NDVI in Siberia shows a large value around the 60°N zone, and it gradually decreases toward the southern arid region and the low temperature polar region. This meridional profile (south–north regionality) of the NDVI was analysed in two meridional transects, along 75°E (arid–forest transect) and 110°E (forest–tundra transect). A modified warmth index (WI(0)) was utilized as a temperature index. In the 75°E transect, high positive (0.79) and negative (−0.58) correlations were found in the NDVI–precipitation and the NDVI–WI(0) meridional profiles, respectively. This fact implies that precipitation plays a role in providing water and dominates the vegetation distribution, while high temperature induces dryness and water shortages, i.e. the critical factor for the vegetation meridional profile is available water in the arid–forest transect. In the 110°E transect, a high positive (0.92) correlation was found, which suggests that the dominant factor for the NDVI profile is temperature in the forest–tundra transect, i.e. the critical factor for the vegetation meridional profile here may be temperature. Furthermore, these meridional profiles were scrutinized in terms of the station altitude and it was suggested that the NDVI tends to change depending on the WI(0) and precipitation at regional variations, which are basically the result of the altitude difference between the stations. The comprehensive relationship between the NDVI, WI(0) and precipitation regionalities is discussed in terms of six vegetation types. The result shows that high NDVI (>0.2) is observed when the WI(0) is over 40°C and precipitation is more than 2.5×WI(0)+50. Copyright © 2000 Royal Meteorological Society

Satellite Observations of El Niño Impacts on Eurasian Spring Vegetation Greenness during the Period 1982–2015

“…The original NDVIsn is downscaled from 0.144 • grid cells to 1 • using bilinear interpolation [50]. Considering that springtime is the most sensitive season for vegetation growth to climate change [66][67][68] and March is still too cold to enable vegetation growth over Eurasia [56], the NDVI seasonal mean in this study is averaged for the two months of April and May to represent boreal spring Eurasian vegetation activities, which is consistent with previous studies [69,70]. In total, each 1 • × 1 • cell has 34 years of spring mean NDVI (n = 34, which represents the sample size) for the period 1982−2015.…”

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

“…Use of remote-sensing data for inferring phenological characteristics of vegetation is becoming popular due to its multi-temporal, multi-spectral, synoptic and repetitive coverage capabilities. Several studies utilized information from vegetation indices to capture phenology changes Myneni et al, 1997;Suzuki et al, 2000). One of the important indicators of vegetation presence, abundance and vigor is the Normalized Difference Vegetation Index (NDVI).…”

Roles of Eastern Siberian Mountain Ranges in Precipitation -Verkhoyansk, Dzhugdzhur and Stanovoy Mountain Ranges-

“…Positive correlation was found between precipitation and the Normalized Difference Vegetation Index (NDVI) from 60°N to 75°N along 130°E (Suzuki et al 2000). High levels of precipitation in the southern area engendered large NDVI.…”