Digital terrain model (DTM) generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN)-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation.

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.

Extremely high fine particulate matter (PM_2.5) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM_2.5 exposure assessments have practical limitations, due to the assumption that population distribution or air pollution levels are spatially stationary and temporally constant and people move within regions of generally the same air quality throughout a day or other time periods. To deal with this challenge, we propose a novel method to achieve the real-time estimation of population exposure to PM_2.5 in China by integrating mobile-phone locating-request (MPL) big data and station-based PM_2.5 observations. Nationwide experiments show that the proposed method can yield the estimation of population exposure to PM_2.5 concentrations and cumulative inhaled PM_2.5 masses with a 3-h updating frequency. Compared with the census-based method, it introduced the dynamics of population distribution into the exposure estimation, thereby providing an improved way to better assess the population exposure to PM_2.5 at different temporal scales. Additionally, the proposed method and dataset can be easily extended to estimate other ambient pollutant exposures such as PM₁₀, O₃, SO₂, and NO₂, and may hold potential utilities in supporting the environmental exposure assessment and related policy-driven environmental actions.

Crop residue burning, which is a convenient approach to process excessive crop straws, has a negative impact on local and regional air quality and soil structures. China, as a major agricultural country with a large population, should take more effective measures to control crop residue burning. In this case, a better understanding of long-term spatio-temporal variations of crop residue burning in China is required. The MODIS products MOD14A1/MYD14A1 were employed in this research. Meanwhile, due to the vast territory of China, we divided the study area into seven regions based on the national administrative divisions to examine crop residue burning in each region, respectively. The temporal analysis of crop residue burning in different regions demonstrates a fluctuated, but generally upward, trend from 2003 to 2017. For monthly variations of crop residue burning in different regions, detected fire spots in June mainly concentrated in Central China (CC), East China (EC), and North China (NC). A majority of detected fire spots in Northeast China (NEC) and Northwest China (NWC) appeared in April and October. For other months, a small number of fire spots were distributed in all regions in a scattered manner. Furthermore, from a spatio-temporal perspective, this research revealed that crop residue burning in NEC was the most active among all regions both in spring and autumn. For summer, EC holds a larger proportion of burning spots than other regions. For winter, the number of burning spots in most regions was close. This research conducts a comprehensive analysis of crop residue burning in China at both a national and regional scale. The methodology and results from this research provide useful reference for better monitoring and controlling crop residue burning in China.

Vegetation‐climate relationships have been extensively investigated in previous studies, yet the critical seasons and corresponding climate factors influencing annual vegetation growth in a changing climate remain unclear. Relying on Global Inventory Modeling and Mapping Studies NDVI3g data and climate data from 1982 to 2013, this study explored the responses of vegetation growth to climate changes in the Northern Hemisphere (NH), with an emphasis on vegetation changes and legacy effects during different seasons. We further identified the most critical season in terms of vegetation growth. We found an apparent seasonal shift in the influence of climate change on vegetation growth in the NH. In addition, we observed legacy effects of climate change on vegetation growth throughout the seasons. Winter temperatures exerted a positive impact on vegetation growth during the following spring in Europe and central North America. Spring precipitation was an important factor for summer vegetation growth in dry areas. Using partial least squares regression, the relative importance of seasonal climatic factors on annual vegetation growth conditions were determined. This study finds that the vegetation in more than 40% of the NH is controlled by temperature, especially spring temperatures (12.8%). Precipitation and solar radiation were the dominant climate factors influencing vegetation growth in 29.9% and 29.1% of vegetated regions, respectively. Furthermore, compared with the period from 1983 to 1997, the influence of seasonal solar radiation on vegetation growth increased in the NH during the period from 1998 to 2013. This phenomenon was accompanied by reductions in the influence of spring and summer temperatures.

Ozone formation regimes are closely related to the ratio of volatile organic compounds (VOCs) to NO_x. Different ranges of $\mathrm{HCHO}/{\mathrm{NO}}_{\mathrm{2}}$ indicate three formation regimes, including VOC-limited, transitional, and NO_x-limited regimes. Due to the unstable interactions between a diversity of precursors, the range of the transitional regime, which plays a key role in identifying ozone formation regimes, remains unclear. To overcome the uncertainties from single models and the lack of reference data, we employed two models, polynomial simulation and convergent cross-mapping (CCM), to identify the ranges of $\mathrm{HCHO}/{\mathrm{NO}}_{\mathrm{2}}$ across China based on ground observations and remote sensing datasets. The ranges of the transitional regime estimated by polynomial simulation and CCM were [1.0, 1.9] and [1.0, 1.8]. Since 2013, the ozone formation regime has changed to the transitional and NO_x-limited regime all over China, indicating that ozone concentrations across China were mainly controlled by NO_x. However, despite the NO₂ concentrations, HCHO concentrations continuously exert a positive influence on ozone concentrations under transitional and NO_x-limited regimes. Under the circumstance of national NO_x reduction policies, the increase in VOCs became the major driver for the soaring ozone pollution across China. For an effective management of ozone pollution across China, the emission reduction in VOCs and NO_x should be equally considered.