Net primary productivity (NPP) is an important component of the terrestrial carbon cycle. In this study, NPP was estimated based on two models and Moderate Resolution Imaging Spaectroradiometer (MODIS) data. The spatiotemporal patterns of NPP and the correlations with climate factors and vegetation phenology were then analyzed. Our results showed that NPP derived from MODIS performed well in China. Spatially, NPP decreased from the southeast toward the northwest. Temporally, NPP showed a nonlinear increasing trend at a national scale, but the magnitude became slow after 2004. At a regional scale, NPP in Northern China and the Tibetan Plateau showed a nonlinear increasing trend, while the NPP decreased in most areas of Southern China. The decreases in NPP were more than offset by the increases. At the biome level, all vegetation types displayed an increasing trend, except for shrub and evergreen broad forests (EBF). Moreover, a turning point year occurred for all vegetation types, except for EBF. Generally, climatic factors and Length of Season were all positively correlated with the NPP, while the relationships were much more diverse at a regional level. The direct effect of solar radiation on the NPP was larger (0.31) than precipitation (0.25) and temperature (0.07). Our results indicated that China could mitigate climate warming at a regional and/or global scale to some extent during the time period of 2001–2014.

Fine particulate matter (PM_2.5) pollution’s passive effects on public health have attracted a great deal of attention. Extensive studies have tried to uncover the impacts of external drivers on PM_2.5 pollution variation; however, the topography’s effects on PM_2.5 pollution remain poorly understood. Using annual high-resolution concentration estimates for PM_2.5, this paper quantitatively disentangled the geographical impacts of topography on the PM_2.5 pollution in China and quantified the mountain blocking effects on the PM_2.5 pollution dispersion on a macro scale. The results show that, in China, the plains and surrounding platforms and hills tend to suffer from long-term severe PM_2.5 pollution. The most polluted topography type is the plains. In comparison, regions such as high-altitude mountains and plateaus are less affected by PM_2.5 pollution. Mountains have significant and evident blocking effects on the cross-regional spread of PM_2.5 pollution. Generally, Level I mountains (high elevation, density and coverage mountains) provide better blocking effects than Level II (middle elevation, density and coverage mountains) mountains and Level III mountains (low elevation, density and coverage mountains). The mountains’ blocking effects begin to play an efficient role when their altitudes reach a certain value; however, the exact altitude values vary by different mountains with a value of 163 m for all typical mountains with absolute PM_2.5 concentration differences between their two sides greater than 10 μg/m³. In heavily polluted areas, PM_2.5 pollution may overflow where the surrounding mountains are not high enough or the mountains’ stretch breaks. This study can provide key theoretical support for air pollution modelling and regional air pollution joint prevention and control.

The terrestrial ecosystem is the cradle of energy and material basis for human survival and development. However, there are large research deficits in accurately and finely depicting the quality of the terrestrial ecosystem (QTE) and assessing its changing triggers' contribution. Here, we summarized three major principles for selecting image sources in remote sensing data fusion. A continuous 30-m net vegetation productivity (NPP) dataset during 2000-2019 for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was derived by using the Carnegie-Ames-Stanford approach model and pre-fused normalized difference vegetation index. The factors' contributions to the QTE changes were quantitatively assessed. The role of the QTE in affecting the socio-economic and its behind mechanisms was quantitatively investigated. The results showed that: (1) High-quality images sources are the preference for spatio-temporal fusion of remote sensing data. Images with close month, the same season and year, and sensors should be then selected. Images of different sensors with similar spectral bandwidth, the ones from adjacent years and seasons, can be alternately considered. (2) Fine-resolution NPP has higher accuracy than coarse-resolution NPP and has marked advantages in finely characterizing the QTE. In the past 20 years, the QTE in the GBA has shown a fluctuating increasing trend (0.20 Tg C/yr). (3) Human activities contributed 54.19% of the QTE changes in the GBA, and dominates the QTE changes in the central rapidly urbanizing areas. Residual factors accounted for an overall contribution ratio of 35.71%. Climate change dominants the peripheral forest variations in the GBA. (4) In the GBA, the improvement of QTE has a significant positive socio-economic impact, it contributes to the GDP increment firstly then the GDP aggregate indirectly. Our results highlight that it is of great urgent to estimate long-term continuous NPP with high spatio-temporal resolution globally. Controlling strategies should be implemented to reduce factitious impacts on QTE. High level of ecological and environmental protection promotes the sustainable development.

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future.

Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation’s tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future “vegetation-climate” models.

Chlorophyll-a plays an essential biochemical role in the eutrophication process, and is widely considered an important water quality indicator for assessing human activity’s effects on aquatic ecosystems. Herein, 20 years of moderate resolution imaging spectroradiometer (MODIS) data were applied to investigate the spatiotemporal patterns and trends of chlorophyll-a concentration (Chla) in the eutrophic Lake Taihu, based on a new empirical model. The validated results suggested that our developed model presented appreciable performance in estimating Chla, with a root mean square error (MAPE) of 12.95 μg/L and mean absolute percentage error (RMSE) of 29.98%. Long-term MODIS observations suggested that the Chla of Lake Taihu experienced an overall increasing trend and significant spatiotemporal heterogeneity during 2002–2021. A driving factor analysis indicated that precipitation and air temperature had a significant impact on the monthly dynamics of Chla, while chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were important driving factors and together explained more than 81% of the long-term dynamics of Chla. This study provides a 20 year recorded dataset of Chla for inland waters, offering new insights for future precise eutrophication control and efficient water resource management.