In semi‐arid central Asia, relief has a strong impact on the distribution of vegetation and discontinuous permafrost. Our aim was to analyse causal chains and inter‐relationships that control the spatial patterns of forest and permafrost in the forest‐steppe of the northern Khangai Mountains in Mongolia. For this purpose, we conducted soil‐profile descriptions, ground‐penetrating radar sounding, and vegetation mapping to gain information about forest and permafrost distribution. We integrated remote‐sensing analysis and field‐mapping data, including soil properties, vegetation cover, forest fires and anthropogenic forest use. We developed and applied a technique for spatial delineation of permafrost distribution, based on the parameters Topographic Wetness Index (TWI), incoming solar radiation and Normalized Difference Vegetation Index (NDVI). Key outcomes of this study are that the occurrence of discontinuous permafrost within 1 m depth is limited to forest stands larger than 100 ha on north‐facing slopes. Dense ground vegetation supports permafrost, whereas sandy soil texture leads to greater depth of the permafrost table. As the seasonal ice in the active layer progressively melts down during summer, meltwater interflow above the permafrost table provides additional soil moisture downslope. This process is reflected in enhanced vitality of the steppe vegetation on toe slopes below forests with permafrost. This effect can in turn be used to indirectly detect permafrost in forest stands by remote sensing. Permafrost mostly disappears after forest fires and other severe disturbances, but it may re‐establish during forest regrowth. However, climate warming is presently leading to a loss of permafrost regeneration potential after disturbance, and to a shift from climate‐induced and ecosystem‐driven permafrost to entirely ecosystem‐protected permafrost. These trends will result in a further decrease of permafrost area after forest disturbance.

Aim: Snow leopards are distributed across the mountains of 12 countries spread across 1.8 million km²in Central and South Asia. Previous efforts to map snow leopard distributions have relied on expert opinions and modelling of presence‐only data. Expert opinion is subjective and its reliability is difficult to assess, while analyses of presence‐only data have tended to ignore the imperfect detectability of this elusive species. The study was conducted to prepare the first ever probabilistic distribution map of snow leopards across Mongolia addressing the challenge of imperfect detection.Location: We conducted sign‐based occupancy surveys across 1,017 grid‐cells covering 406,800 km²of Mongolia's potential snow leopard range.Methods: Using a candidate model set of 31 ecologically meaningful models that used six site and seven sampling covariates, we estimate the probability of sites being used by snow leopards across the entire country.Results: Occupancy probability increased with greater terrain ruggedness, with lower values of vegetation indices, with less forest cover, and were highest at intermediate altitudes. Detection probability was higher for segments walked on foot, and for those in more rugged terrain. Our results showed broad agreement with maps developed using expert opinion and presence‐only data but also highlighted important differences, for example in northern areas of Mongolia deemed largely unfavourable by previous expert opinion and presence‐only analyses.Main conclusions: This study reports the first national‐level occupancy survey of snow leopards in Mongolia and highlights methodological opportunities that can be taken to scale and support national‐level conservation planning. Our assessments indicated that 0.5) probability of being used by snow leopards. We emphasize the utility of occupancy modelling, which jointly models detection and site use, in achieving these goals.

We investigate the spatiotemporal variability of near-surface CO₂ concentrations in Mongolia from 2010 to 2019 and the factors affecting it over four climate zones of Mongolia based on the Köppen-Geiger climate classification system, including arid desert climate (BWh), arid steppe climate (BSk), dry climate (Dw), and polar frost climate (ET). Initially, we validate the near-surface CO₂ datasets obtained from the Greenhouse Gases Observing Satellite (GOSAT) using ground-based CO₂ observations obtained from the World Data Center for Greenhouse Gases (WDCGG) and found good agreement. The results showed that CO₂ concentrations over Mongolia steadily increased from 389.48 ppmv in 2010 to 409.72 ppmv in 2019, with an annual growth rate of 2.24 ppmv/year. Spatially, the southeastern Gobi desert region has the highest annual average CO₂ concentration, while the northwestern Alpine and Meadow steppe region exhibits the most significant growth rate. Additionally, significant monthly and seasonal variations were observed in each climate zone, with CO₂ levels decreasing to a minimum in summer and reaching a maximum in spring. Furthermore, our findings revealed a negative correlation between CO₂ concentrations and vegetation parameters (NDVI, GPP, and LAI) during summer when photosynthesis is at its peak, while a positive correlation was observed during spring and autumn when the capacity for carbon sequestration is lower. Understanding CO₂ concentrations in different climate zones and the uptake capacity of vegetation may help improve estimates of carbon sequestration in ecosystems such as deserts, steppes and forests.

The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI.