The Arctic is undergoing unprecedented climatic and terrestrial environmental changes. The Arctic primary industry has experienced a wide and significant effect from these shifting environmental factors, such as rising temperatures, greening vegetation, thawing permafrost, and frequent wildfires. It is essential to integrate the knowledge of impacts caused by climatic and terrestrial environmental changes on Arctic primary production in order to support the sustainable development of primary industry and inform the formulation of industrial policies. The four main sectors of primary industry (cultivation, livestock husbandry, forestry, fishery and aquaculture) were discussed in this work. This study synthesized the types and extent of the impacts caused by climatic and terrestrial environmental changes on each sector, and provided a summary and outlook from five aspects with a cross-sector perspective: (1) the specific ways of climate changes affecting primary production; (2) the challenges of integrating knowledge from local researches; (3) the enactment and implementation of adaptation strategies; (4) the demand for integration and innovation in data and methods; and (5) the inspiration of the climate change-induced alteration in Arctic primary industry for China. This study concluded the priorities of researching the impact of climate changes on Arctic primary industry, and the results would be capable to aid China's participation in the sustainable development of Arctic primary producing activities.

River width, as one of the basic parameters of river morphology, is very important to understand the hydrological process and ecosystem function on Earth. The Yarlung Zangbo River is not only a global plateau river with typical regional characteristics, but also an important international river. However, due to its remote location, mountainous terrains, and harsh environmental conditions, the gauge stations are sparsely distributed in the Yarlung Zangbo River, which greatly impedes a better understanding of its hydrological dynamic characteristics. Satellite remote sensing makes it possible to monitor and analyze the wide and long-term dynamic changes and to examine the hydrological characteristics of the Yarlung Zangbo River by providing multi-temporal image data. Based on the Global Land Analysis and Discovery (GLAD) dataset and the threshold segmentation method of water boundary inundation frequency, this study reconstructed the complete monthly water area of the study area, and then estimated the spatial and temporal distribution of the river width from 2000 to 2020. The results indicate that due to the seasonal differences in temperature and precipitation, the width of the river showed a significant seasonal variation from 2000 to 2020. The seasonal variation in the middle reaches was the largest (453.6 m), while that of the downstream was the smallest (90.3 m). Influenced by climate and topography, the spatial distribution of river width in the whole course of the Yarlung Zangbo River varies greatly. The narrowest part of the observed river channel is about 30 m, i.e., a spatial resolution of Landsat images. The maximum river width in the middle and lower reaches of braided rivers can reach 5935.7 m. About 50% of the river segments of the Yarlung Zangbo River are narrower than 150 m, and only 2.0% of the river segments are wider than 2000 m. From 2000 to 2020, the average river width of the main stream first decreased, then increased and finally decreased. There was a very significant positive correlation between the mean river width of the mainstream and precipitation and air temperature, and the correlation coefficient R was 0.7, which passed the confidence test of 0.01. In addition, the monthly river width based on GLAD was also evaluated by the river width results derived from 10 m resolution Sentinel-2 data. The results showed that both of the relative deviations were less than 2%, indicating that the estimation results were relatively reliable in general.

The landform of the Yellow River source zone is diverse, leading to various drainage patterns. To understand the drivers of planform geometry of river networks in the Yellow River source zone, 83 representative sub-basins, including dendritic, pinnate, rectangular and symmetric pinnate patterns are selected for studies. Attributes to characterize the planform geometry of river networks are calculated. The relationships between river network attributes and environmental factors are examined. The results show that the differences in the characteristics of the 4 drainage patterns are well reflected by the aspect ratio, drainage density and the maximum frequency of flow directions. Changes in drainage density and the maximum frequency of flow directions are well expressed by slope and precipitation variation. Aspect ratio is significantly influenced by precipitation. The pinnate networks are mainly distributed at the northern edge of the upper plateau where the climate is arid and the surface is bare. The mean basin slope of this pattern is 4.5o, and the mean relief is 730 m. The rectangular networks are concentrated in the Zoige basin where the mean basin slope and relief are 2.3o and 177 m, respectively. The climate of the Zoige basin is relatively humid and there are plenty of swamps and wetlands. The symmetric pinnate networks are more likely to occur in the high-relief valleys where the precipitation varies greatly. The average slope and relief of the symmetric pinnate networks are 16.9o and 1167 m, respectively. The dendritic networks are distributed mainly in mountainous areas of the middle reaches and fluvial plains in the northeast part of the source zone. The average slope and relief of the dendritic networks are 15.4o and 968 m, respectively. The vegetation coverage is better than that of the upper plateau. Our analysis suggests that topography is the main factor that leads to the differences of planform geometry among various drainage patterns. Climate and vegetation coverage play an important role in the development of river networks when the constraints of topography are reduced.

Soil particle-size fractions (PSFs), including sand, silt, and clay, are key parameters for land-surface process simulation and ecosystem service evaluation. More accurate interpolation of soil PSFs can help better understand the simulation of the above models. As compositional data, soil PSFs have special demands of the constant sum (1 or 100%) in the interpolation process, and the spatial distribution accuracy is mostly affected by the performance of spatial prediction methods. Here, we provided a framework for the spatial prediction of soil PSFs, and reviewed a series of methods in the steps of this framework, including methods of log-ratio transformation of soil PSFs (additive log-ratio, centered log-ratio, symmetry log-ratio, and isometric log-ratio methods), spatial interpolators of soil PSFs (geostatistical methods, regression models, and machine learning models), validation methods (probability sampling, data splitting, and cross-validation) and indices for accuracy assessments in soil PSF interpolation and soil texture classification (rank correlation coefficient, mean error, root mean square error, mean absolute error, coefficient of determination, Aitchison distance, standardized residual sum of squares, overall accuracy, Kappa coefficient, and precision-recall curve) and uncertainty analysis (prediction interval, confidence interval, standard deviation, and confusion index). In addition, we summarized several ways to improve the prediction accuracy of soil PSF, such as normalizing the data distributions through effective data transformation, choosing suitable prediction methods based on the data distribution characteristics, improving mapping accuracy and distribution reasonability through the combination of auxiliary data, improving interpolation accuracy through hybrid models or joint modeling for multi-components. Finally, we proposed the future research fields of the spatial prediction methods of soil PSFs, including considering the principles and mechanisms of data transformation, developing joint simulation models and high accuracy surface modeling methods for multi-components, and combining soil particle size curves with stochastic simulations. Our review highlights the importance of spatial prediction methods for soil PSFs, and also provides a clear framework for improving the performance of these methods for other researchers in this field.

Guided by the conviction that "mountains-rivers-forests-farmlands-lakes-grasslands is a life community", we should optimize the ecological construction of the "Beautiful China" vision. This is a key scientific issue, and as well as an essential resolution to meet the huge demands of the national ecological civilization. Furthermore, it is important to realize the vision of "Beautiful China" via the establishment of ecological regionalization. However, ecological regionalization studies have not considered systematic ecological construction or the spatial heterogeneity of ecosystems, which are affected by changes in global climate and human activities. In this study, a holistic "beautiful landscape, beautiful ecosystem services, and beautiful ecological security" theoretical framework of ecological construction regionalization was constructed, taking into account the spatial heterogeneity of ecosystem structure, pattern, quality, services, and pressure; then, we built three grades of ecological construction regionalization indicators, an integrated ecological construction index, and an integrated ecological degradation index for ecosystem status and assessed the ecosystem degradation. Then, spatial cluster analysis based on the national third watershed was performed and the whole country was divided into five first-grade and 22 second-grade ecological construction regions, respectively. Then, we chose one typical region and established the third-grade ecological construction region. These ecological regionalization-associated results can serve as important application supports for the ecological construction of "Beautiful China". Future ecological regionalization should emphasize the correlation between the indicators and the diversity of ecosystem functions and consider coupling the interference from changes in global climate and human activities.

Accurately identifying the dominant factor of karst ecosystem services is a prerequisite for the rocky desertification control. However, the explanatory power of environmental factors for the spatial distribution of ecosystem services is affected by scaling, and the quantitative research on the scale effect still needs to be further strengthened. This study used Geodetector to access the explanatory power of environmental factors on soil erosion and water yield at different spatial resolutions, and then explored their differences in three geomorphological types. Results showed that slope and vegetation coverage were the dominant factors of soil erosion, and the interactive explanatory power between the two factors was stronger. Affected by the universality of topographic relief and landscape fragmentation in the study area, the explanatory of slope and land use type on soil erosion was optimal at a low resolution. Precipitation, elevation, and land use type were the dominant factors for the spatial heterogeneity of water yield, and the interaction between precipitation and land use type could explain more than 95% of water yield. The spatial variability of elevation in different geomorphological types affected its optimal explanatory power, especially in the terrace and hill type areas. The spatial variability of elevation was weak, and its explanatory power was optimal at a high resolution. However, in the mountainous areas, the spatial variability of elevation was strong, and its explanatory power was optimal at a low resolution. This study quantitatively identified the optimal explanatory power of ecosystem service variables through multi-scale analysis, which aimed to provide a way and basis for accurate identification of the dominant factors of karst mountain ecosystem services and zoning optimization.