The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the 2001–2004 period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses.

As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences.

The frequency and intensity of extreme heat wave events have increased in the past several decades and are likely to continue to increase in the future under the influence of human-induced climate change. Exposure refers to people, property, systems, or other elements present in hazard zones that are thereby subject to potential losses. Exposure to extreme heat and changes therein are not just determined by climate changes but also population changes. Here we analyze output for three scenarios of greenhouse gas emissions and socio-economic growth to estimate future exposure change taking account of both climate and population factors. We find that for the higher emission scenario (RCP8.5-SSP3), the global exposure increases nearly 30-fold by 2100. The average exposure for Africa is over 118 times greater than it has been historically, while the exposure for Europe increases by only a factor of four. Importantly, in the absence of climate change, exposure is reduced by 75–95% globally and across all geographic regions, as compared with exposure under the high emission scenario. Under lower emission scenarios RCP4.5-SSP2 and RCP2.6-SSP1, the global exposure is reduced by 65% and 85% respectively, highlighting the efficacy of mitigation efforts in reducing exposure to extreme heat.

This research is motivated by the increasing threat of urban heat waves that are likely worsened by pervasive global warming and urbanization. Different regions of the city including urban, borderland and rural area will experience different levels of heat health risk. In this paper, we propose an improved approach to quantitatively assess Beijing’s heat health risk based on three factors from hazard, vulnerability and especially environment which is considered as an independent factor because different land use/cover types have different influence on ambient air temperatures under the Urban Heat Island effect. The results show that the heat health risk of Beijing demonstrates a spatial-temporal pattern with higher risk in the urban area, lower risk in the borderland between urban and rural area, and lowest risk in the rural area, and the total risk fluctuated dramatically during 2008–2011. To be more specific, the heat health risk was clearly higher in 2009 and 2010 than in 2008 and 2011. Further analysis with the urban area at sub-district level signifies that the impervious surface (urban area such as buildings, roads, et al.) ratio is of high correlation with the heat health risk. The validation results show that the proposed method improved the accuracy of heat health risk assessment. We recommend that policy makers should develop efficient urban planning to accomplish Beijing’s sustainable development.

Motivated by growing heat-related morbidity and mortality in a warming climate, this paper assesses global heat health risk in order to understand the challenges to sustainability in the 21st century, using four Representative Concentration Pathways (RCPs) of the HadGEM2-ES climate model and five Shared Socio-Economic Pathways (SSPs). Factors influencing global heat health risk were reviewed and risks were estimated based on heat hazard and socio-economic vulnerability. Hazard, vulnerability, risk and in particular, populations at different risk levels, were analyzed quantitatively at both global and regional scales. The results show that under an RCP8.5-SSP3 scenario, the world will be subject to the highest heat health risk, with rapidly increasing hazard levels and vulnerability over the century. Less developed or developing regions, such as Africa and Southeast Asia, are at the highest risk. The heat risk under an RCP2.6-SSP1 scenario will first increase and then fall, resulting in the lowest heat-health-risk pattern. We found that heat health risk will increase during the century under all RCP-SSP scenarios, with a higher frequency, higher intensity, longer duration and expanding spatial reach. Significant differences were observed across regions. The results make clear that the increasing risk poses significant challenges to sustainable human health. To meet these challenges, more attention and effective actions are urgently needed from both policy makers and individuals.

Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA–MLC (Vertex Component Analysis–Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12Q1 during 2001–2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from 9.02 × 10⁻⁴ m²m⁻²yr⁻¹ in shrubland subregions to 2.34 × 10⁻² m²m⁻²yr⁻¹ in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle.