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Preventing soil salinization is key to the healthy development of agroecosystems in arid regions. Notably, long‐term anthropogenic irrigation accelerates secondary salinity accumulation in arid areas; however, the regional‐scale spatial patterns of soil salinity pre‐ and post‐irrigation season remain unclear. Accordingly, field observations over two periods (n = 182 samples), along with environmental covariates and the randomForest algorithm, were used to estimate the horizontal and vertical spatial distributions (12 soil depths) of soil salinity in the Weiku Oasis (0–60 cm) at a 30 m spatial resolution. The results showed that the prediction accuracy was greater for the non‐irrigated season than for the irrigated season. Moreover, soil salinity was low in the interior of the oasis, high in the desert oasis interlacing zone, and significantly decreased with increasing soil depth. Variable contributions indicated that the effects of SoilGrids variables on soil salinity increased with greater soil depth. During the irrigation season, an inflection point for salt aggregation was observed at ~55 cm for farmlands, grasslands, and shrubs, with the greatest variation seen in farmlands. The findings here provide insights into the improvement of irrigation techniques in arid zone agroecosystems. [ABSTRACT FROM AUTHOR]

Soil is the largest carbon reservoir in terrestrial ecosystems, and thus minor changes in it can dramatically affect atmospheric CO2 concentrations. In the northwestern arid zone of China, the prediction of soil organic carbon (SOC) changes is often limited by the scarcity of soil samples and the scale and depth of research, which limit the understanding of carbon cycling processes in arid zone terrestrial ecosystems. Therefore, this study produced digital soil maps of SOC stocks (SOCS) for two periods (1980‐1990 and 2010‐2020) at a 90‐m resolution based on historical soil profile data and a random forest model. The results showed that the prediction accuracy for SOCS in the topsoil (0–30 cm) was superior to that of the subsoil (30–100 cm). Among them, the mean annual evapotranspiration, normalized difference vegetation index during the growing season, multi‐year mean temperature, and clay content were the main environmental factors affecting the spatial distribution of SOCS. In the past 30 years, the SOCS of the northwestern arid zone has decreased by 585.50 Tg, with a mean decline of 19.52 Tg C yr−1. The changes in SOCS caused by land‐use conversion and reductions in SOCS were further shown to be attributable to grassland desertification and agricultural reclamation. These findings are valuable for exploring the carbon cycle in terrestrial ecosystems in the context of global climate change and for achieving China's goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

• A spatio-temporal prediction framework was developed to map SOC. • The inter-decadal maps of China SOC were updated to the 2010s. • Effects of climatic factors for SOC variability are location- and scale- specific. • The soils in China function as carbon sinks at national scale from 1980s to 2010s. Spatiotemporal variability in soil organic carbon (SOC) plays key roles in ecosystem stability and terrestrial carbon cycling. However, the inter-decadal characteristics of SOC variability and its climate drivers in China from the 1980s to 2010s remain unclear. In this study, a modeling framework for combining spatiotemporal prediction models was proposed. Within this model, soil samples from each of the two periods were combined and calibrated to obtain three models, and their prediction outputs were selectively combined to obtain SOC maps for the different inter-decadal periods. A total of 8473 soil samples from three periods (1980s, 2000s and 2010s) were used for model validation. The results suggested that the SOC prediction accuracy in the top 0–20 cm of soil (SOC 020) was better than that of the first meter of soil (SOC 0100), and the predicted spatial patterns shared characteristics with findings from previous studies. Predictions for the 1990s exhibited relatively high uncertainty, especially in the Tibetan Plateau, Tien Shan, and Northeast China. Both individual and coupled climatic factors exerted scale- and location-specific effects on the SOC. Coupled climatic factors accounted for more than 65% of the SOC variability in both soil layers. The regional SOC in North and Northeast China exhibited inter-decadal decline. Soil in China functioned as carbon sinks from the 1980s to 2010s at a national scale, with SOC 020 increasing from 30.40 Pg to 33.97 Pg and SOC 0100 from 79.29 Pg to 82.26 Pg. Overall, our results provide reliable baseline data for optimizing carbon cycle models of terrestrial ecosystems, and to guide future carbon sequestration policies. [ABSTRACT FROM AUTHOR]

• The SOC in two depths were mapped at a resolution of 1 km and five-year interval during 1980–2100. • Three climate scenarios of SSP 1–1.9, SSP2-4.5 and SSP5-8.5 in CMIP6 were considered. • The top 20 cm and 100 cm SOC of China both acted as carbon sinks during 1980–2020. • The SOC will fluctuate significantly in the future under different climate scenarios. • Under the SSP2-4.5 scenario, SOC presented a larger likelihood of being a carbon source in most region. Revealing historical changes in soil organic carbon (SOC) and exploring its future status are important for safeguarding soil health and food security, giving full play to the service function of soil ecosystems, and coping with climate change. However, there is still a gap in our understanding of the SOC stocks in China and their spatial patterns in response to future climate change. Therefore, we attempted to fill this knowledge gap using a large amount of soil observation data, digital soil mapping technology, and global circulation models from Coupled Model Inter-comparison Project phase 6 (CMIP6). In this study, a random forest model was selected to construct the spatial relationship between SOC in the top 0–20 cm (SOC020) and 0–100 cm (SOC0100) of the soil and 21 environmental factors. Spatiotemporal changes in SOC from 1980 to 2100 were revealed at a resolution of 1 km and five-year interval for three climate scenarios in CMIP6. The cross-validation results indicated acceptable predictions for both depths of SOC; however, relatively large prediction uncertainties were observed for SOC0100 in the Tibetan Plateau and northeastern China. The mean values of SOC020 and SOC0100 over the last four decades were 35.77 and 84.62 Tg, respectively, and showed carbon sinks at the national scale, with accumulation rates of 0.05 Pg yr-1 and 0.036 Pg y-1 for the two depths. Compared with historical values (1980–2020), SOC will fluctuate significantly in the future under different climate scenarios. Among them, SOC showed a slow increasing trend at both depths under the SSP1-1.9 low emission scenario, while SOC presented a decreasing trend under the SSP2-4.5 and SSP5-8.5 medium–high emission scenarios. In particular, in most of China under the SSP2-4.5 scenario, SOC0100 presented a larger likelihood of being a carbon source. This study provides a reference for exploring the response of soil carbon pools to climate change and evaluating the effectiveness of land management and ecological protection. [ABSTRACT FROM AUTHOR]

• GMM can distinguish similar spectral features in vegetation. • After spectral feature partition, the difference modeling is superior to whole-sample modeling. • UAV hyperspectral can indirectly reveal salinization patterns by inversion of saline vegetation SPAD. The chlorophyll content of saline vegetation can indirectly reflect salinization. Rapid and non-destructive capture of chlorophyll content of saline vegetation at a regional scale is essential for saline soil improvement and sustainability of saline agriculture. However, traditional soil–plant analyzer development (SPAD) monitoring based on SPAD-502 is carried out at the leaf scale, which does not allow rapid access to SPAD information for the whole region. In this study, we proposed that post-hyperspectral classification based on spectral differences that could contribute to enhanced estimation of SPAD in saline vegetation. To test this proposal, we partitioned the hyperspectral images using a Gaussian mixture model. Then, estimation models based on spectral partition and full-sample SPAD were developed using in situ observation data and a random forest model, respectively. Finally, the unmanned aerial vehicles (UAV) hyperspectral images were used as the input data source to digitally map the SPAD of saline vegetation in the region, using the prediction model. The results indicated that there were significant intensity and shape differences in the spectral reflectance characteristics of saline vegetation under different clusters. The SPAD prediction model, based on spectral feature partition, performed significantly better than the full sample. The SPAD maps of saline vegetation before and after clustering displayed similar spatial distribution models, but the prediction uncertainty of the models, based on spectral feature partition, was relatively low. Our results confirm the effectiveness and stability of UAV hyperspectral and spectral partition-based modeling in developing SPAD spatial distribution estimation models for saline vegetation. [ABSTRACT FROM AUTHOR]