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The warming world greatly suffers the increase of frequency, severity and duration of heat wave events, which would cause the significant societal and environmental damages and implications from local to global scales. In order to eliminate the limitations of site observations in accurate spatial extent identification and large scale monitoring, this study tried to investigate global heat waves in 2018 using spatial datasets of land surface temperature (LST) derived from AQUA TIR sensors. A 15-year daily maximum LST dataset was used to identify the hot days and heat wave events using the relative threshold method based on the probability density function analysis of LST sequences, then they were adopted in analyzing spatial patterns of duration, frequency and intensity of heat wave events using heat wave numbers (HWN), the frequency of the appeared heat wave days (HWF) and the amplitude of heat wave impacts (HWA) indices, respectively. Our results indicated that more heat wave events and longer heat wave duration happened in low and middle latitudes under the warmer land surface in 2018 than last decade, especially many heat wave events occurred in the environmentally fragile regions and the dense population area. Urban regions achieved the largest HWA by 45.5 ± 6.9℃. Both annual HWF and HWN highlighted seven hot spot regions influenced by heat waves, and most of them distributed around the latitude 30°N and the latitude 30°S. Extreme heat wave of hot spot regions in the Southern Hemisphere mainly happened in winter and spring in contrast to that in North Hemisphere appeared in summer and autumn. Dry climate could contribute to the occurrence of heat waves, and heat wave indices variations also implied the compound consequences between heat waves and drought. Our findings demonstrate that remote sensing datasets are capable of providing the continuous whole-Earth coverage of heat wave changes, and they would play more important roles in preventing or mitigating the impacts of extreme heat events on people and natural environment.

Soil bioavailable micronutrients have a significant impact on soil fertility, soil quality, maize production, and even environmental quality. However, minimal research has been conducted to characterize the spatial patterns of soil bioavailable micronutrients at the large spatial extent within the maize filed and to reveal the relative control of environmental factors on them at the global and local scale. In the study, topsoil samples (0–20 cm of plough depth, totaling 4,448) were collected from maize field to determine the spatial heterogeneity of soil bioavailable micronutrients including iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and boron (B), and to assess the relative effect of environmental indicators on them. Based on the 2-dimensional empirical mode decomposition (2D-EMD), the spatial pattern of micronutrients at the residues and their relationships with influencing indicators were explored. The results demonstrated that the distribution of Fe and Mn had the dominant longitudinal zonality, the distribution of Cu and Zn had the prominent vertical zonality, and the spatial characteristic of B did not exhibit any particular pattern at the large spatial extent. Based on the stepwise multivariate linear regression with the residues, environmental indicators had less global effect on the distribution of soil bioavailable micronutrients. However, based on the combination of geographically weighted regression (GWR) and 2D-EMD, environmental indicators had a good and significant interpretation on the dynamics of micronutrients bioavailability, which ranged from 53% to 75% in the study area. Specifically, the impact of environmental indicators on Fe, Mn, and Zn were greater, and the impact of human activities on B was greater. Our findings indicated that the local and scale effects on the soil available micronutrients should be integrated into the prediction methods, and the combined method of GWR and 2D-EMD would be a good choice for the high-quality digital mapping at large spatial extent.

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May–September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.