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Accurately understanding the distribution and changing trends of Center Pivot Irrigation (CPI) farmland in the Mu Us region and exploring the impact of CPI farmland construction on sandy land vegetation growth hold significant importance for local sustainable development. By using Landsat images to extract CPI farmland information and applying buffer zone analysis to explore the impact of CPI farmland construction on the surrounding vegetation growth, the results revealed the following key findings: (1) The number and area of CPI farmland units showed a continuous growth trend from 2008 to 2022. Spatially, Etoke Front Banner was the focal point of the CPI farmland unit construction, gradually expanding outward. In terms of scale, small-scale CPI farmland units (0–0.2 km2) dominated, while large-scale CPI farmland units (>0.4 km2) were primarily distributed in Yulin City (Mu Us). (2) The growth rate of CPI farmland units in Yulin City gradually slowed down, while that in Ordos City (Mu Us) continued to exhibit a high growth trend. Affected by water-resource pressure and policies, CPI farmland units in Ordos City may continue to increase in the future, while they may stop growing or even show a downward trend in Yulin City. (3) CPI farmland mainly came from the conversion of cultivated land, but over time, more and more grassland was reclaimed as CPI farmland. The absence of cover planting after crop harvesting and the lack of shelterbelt construction may exacerbate land desertification in the region. (4) Within the typical region, CPI farmland unit construction promoted vegetation growth within the CPI units and the 500 m buffer zone but had an inhibitory effect on vegetation growth within the 500–3000 m buffer zone and no significant effect on vegetation growth within the 3000–5000 m buffer zone. (5) The decrease in groundwater reserves caused by CPI farmland unit construction was the primary reason for inhibiting the vegetation growth within the 500–3000 m buffer zone of CPI farmland units in the Mu Us region. This study can provide a scientific basis for the sustainable development of CPI farmland in semi-arid areas. [ABSTRACT FROM AUTHOR]

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 105 hm2, with the predominant decline occurring in the 0–1 hm2 and 1–3 hm2 size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors. [ABSTRACT FROM AUTHOR]

The biophysical effect of land cover changes (LCC) on local temperature is currently a hot topic. This work selects one of the nine agricultural divisions in China, the Sichuan Basin and surrounding regions, as the study area. By combining long-term series satellite remote sensing products with the space-and-time method, the spatial and temporal variations of the actual biophysical effects of LCC on land surface temperature (LST) are obtained. The results show that: (1) From 2001 to 2020, LCCs from Savannas to Cropland, from Cropland to Savannas, and from Savannas to Mixed Forest occurred frequently within the study area, and their area proportions of the total conversions are 21.7%, 18.5%, and 17.6%, respectively. (2) The biophysical feedback of LCC in the study area led to a LST increase of 0.01 ± 0.004 K at annual scale, which presents a seasonal pattern of "strong warming in summer and autumn yet weak cooling in winter". It can exacerbate 14.3% or alleviate 8.3% of the background climate warming effect, illustrating the importance of biophysical effects on local climate change. The interaction between savannas and cropland or mixed forest and urbanizations formed the main driver for the above patterns. (3) Both the occurrence area of LCC and the warming effects at annual or seasonal scale show a trend of "first rising and then declining", whereas the cooling effect in winter exhibits continuous enhancement over time. The monodirectional or mutual conversion between cropland and savannas is the dominant conversion responsible for these temporal patterns. The findings can provide realistic scientific guidance for informing rational policies on land management and targeted strategies for climate change response in the study area. [ABSTRACT FROM AUTHOR]

The accurate estimation of a regional ecosystem's carbon storage and the exploration of its spatial distribution and influencing factors are of great significance for ecosystem carbon sink function enhancements and management. Using the Yellow River Basin as the study area, we assessed the changes in regional terrestrial ecosystem carbon storage through geographically weighted regression modeling based on a large number of measured sample sites, explored the main influencing factors through geographic probe analysis, and predicted the carbon sequestration potentials under different scenarios from 2030 to 2050. The results showed that (1) the total carbon storage in the Yellow River Basin in 2020 was about 8.84 × 109 t. Above-ground biological carbon storage, below-ground biological carbon storage, and soil carbon storage accounted for 6.39%, 5.07%, and 89.70% of the total ecosystem carbon storage, respectively. From 2000 to 2020, the carbon storage in the basin showed a trend in decreasing and then increasing, and the carbon storage in the west was larger than in the east and larger in the south than in the north. (2) Forest ecosystem was the main contributor to the increase in carbon storage in the Yellow River Basin. Elevation, temperature, and precipitation were the main factors influencing the spatial pattern of carbon storage. (3) The ecological conservation scenario had the best carbon gain effect among the four future development scenarios, and appropriate ecological conservation policies could be formulated based on this scenario in the future to help achieve the goals of carbon sequestration and sink increase. [ABSTRACT FROM AUTHOR]

Crop type mapping at high resolution is crucial for various purposes related to agriculture and food security, including the monitoring of crop yields, evaluating the potential effects of natural disasters on agricultural production, analyzing the potential impacts of climate change on agriculture, etc. However, accurately mapping crop types and ranges on large spatial scales remains a challenge. For the accurate mapping of crop types at the regional scale, this paper proposed a crop type mapping method based on the combination of multiple single-temporal feature images and time-series feature images derived from Sentinel-1 (SAR) and Sentinel-2 (optical) satellite imagery on the Google Earth Engine (GEE) platform. Firstly, crop type classification was performed separately using multiple single-temporal feature images and the time-series feature image. Secondly, with the help of information entropy, this study proposed a pixel-scale crop type classification accuracy evaluation metric, i.e., the CA-score, which was used to conduct a vote on the classification results of multiple single-temporal images and the time-series feature image to obtain the final crop type map. A comparative analysis showed that the proposed classification method had excellent performance and that it can achieve accurate mapping of multiple crop types at a 10 m resolution for large spatial scales. The overall accuracy (OA) and the kappa coefficient (KC) were 84.15% and 0.80, respectively. Compared with the classification results that were based on the time-series feature image, the OA was improved by 3.37%, and the KC was improved by 0.03. In addition, the CA-score proposed in this study can effectively reflect the accuracy of crop identification and can serve as a pixel-scale classification accuracy evaluation metric, providing a more comprehensive visual interpretation of the classification accuracy. The proposed method and metrics have the potential to be applied to the mapping of larger study areas with more complex land cover types using remote sensing. [ABSTRACT FROM AUTHOR]

Impervious surfaces (IS) are one of the most important components of the earth's surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries. [ABSTRACT FROM AUTHOR]

Trajectory tracking is a critical problem in the field of mobile robotics. In this paper, a control scheme combined with backstepping and fractional-order PID is developed for the trajectory tracking of the differential-drive mobile robot. The kinematic and dynamic models of the mobile robot are described in detail for the trajectory tracking controller design. Then, based on the model of the mobile robot, the design of the trajectory tracking control system is addressed by combining backstepping with fractional-order PID. Moreover, to obtain an optimal control system, an improved beetle swarm optimization algorithm is presented to tune the parameters of the kinematic and dynamic controllers simultaneously. Finally, several simulations are implemented to the trajectory tracking of mobile robots in the cases with and without skidding and sliding, and the results can confirm the effectiveness and superiority of the combined control scheme. Abbreviations: FOPID: fractional-order PID; FOPD: fractional-order PD; DDMR:differential-drive mobile robot; BAS: beetle antennae search; BA: beetle antennae; PSO:particle swarm optimization; BSO: beetle swarm optimization. [ABSTRACT FROM AUTHOR]

High-resolution snow water equivalent studies are important for obtaining a clear picture of the potential of water resources in arid areas, and SAR-based sensors can achieve meter-level snow water equivalent inversion. The advanced C-band SAR satellite Gaofen-3 (GF-3) can now achieve meter-level observations of the same area within one day and has great potential for the inversion of the snow water equivalent. The EQeau model is an empirical method for snow water equivalent inversion using C-band SAR satellites, but the model has major accuracy problems. In this paper, the EQeau model is improved by using classification of underlying surface types and polarization decomposition, and the inversion of the snow water equivalent was also completed using the new data source GF-3 input model. The results found that: (1) the classification of underlying surface types can significantly improve the fit between the snow thermal resistance and the backscattering coefficient ratio; (2) the accuracy of the snow density extracted by the GF-3 satellite using the Singh–Cloude Three-Component Hybrid (S3H) decomposition is better than IDW spatial interpolation, and the overall RMSE can reach 0.005 g/cm3; (3) the accuracy of the optimized EQeau model is significantly improved, and the overall MRE is reduced from 27.4% to 10.3%. Compared with the original model, the optimized model is superior both in terms of verification accuracy and image detail. In the future, with the combination of advanced technologies such as the Internet of Things (IoT), long, gapless, all-weather, and high-resolution snow water equivalent inversion can be achieved, which is conducive to the realization of all-weather monitoring of the regional snow water equivalent. [ABSTRACT FROM AUTHOR]

Grazing is very common in the grassland ecosystem, and it has a significant impact on the C stocks and cycle. One of the most important drivers of soil C stocks is functional diversity. However, limited studies have attempted to explore the effects of functional diversity on soil C stocks associated with grazing disturbance. This study was carried out in Hulunbeier grassland, Inner Mongolia, and four grazing intensities (no grazing (NG), light grazing (LG), moderate grazing (MG), and heavy grazing (HG)) were identified. The plant functional traits and important soil properties under different grazing intensities were measured. Functional identity and diversity were calculated based on the measured functional traits. The impacts of functional identity and diversity on soil organic carbon stocks (SOCstocks) were analyzed using a multi-model inference (MMI) approach. Our study showed that the functional diversity effect on soil C stocks varies depending on grazing intensity. We identified that functional richness has a significant impact on SOCstocks in NG. The community weighted mean of leaf area became the best predictor of SOCstocks in LG. As grazing intensified, functional divergence best explained SOCstocks in moderate and heavy grazing sites, and their relationship was positive. The major outcomes of this research could shed light on the mechanics of soil carbon storage. [ABSTRACT FROM AUTHOR]

Detecting changes in vegetation, distinguishing the persistence of changes, and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics. Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) version NDVI3g dataset in the Tibetan Plateau, the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18-31 years since 1982. Dynamic trends in vegetation experienced an obvious increase at regional scale, but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended. At pixel scale, areas with significant browning significantly expanded over 14 periods for all seasons, but for significant greening significantly increased only in autumn. The changes of vegetation activity in spring were the most drastic among three seasons. Increased increments of NDVI in summer, spring, and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods. Vegetation activity was mainly regulated by a thermal factor, and the dominant climatic drivers of vegetation growth varied across different seasons and regions. We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. In this study, a non-stationary 1982–2012 AVHRR NDVI 3g time series, the newest dataset, were used to evaluate spatio-temporal patterns of seasonal vegetation changes in Xinjiang province of China at regional, biome and pixel scales over progressively longer periods from 18 to 31 years, starting in 1982, and their linkages to climatic factors and human activities were analyzed. At regional scale, the increases were statistically significant for autumn NDVI during fourteen periods, for growing season and summer NDVI during the most periods, and for spring only during the first four periods. The rates of NDVI increase in growing season and all seasons significantly decreased over fourteen periods. At pixel scale, areas with significant browning rapidly increased over fourteen periods for growing season and all seasons, and these areas were mainly concentrated in northern desert of Xinjiang. Vegetation growth in Xinjiang was regulated by both moisture and thermal conditions: the response of NDVI in spring and autumn was more sensitive to thermal factors, such as temperature and potential evapotranspiration, and correlations between NDVI and precipitation and between NDVI and humidity index were stronger in summer and growing season. Extensive use of fertilizers and expanded farmland irrigated area increased vegetation growth for cropland. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation may reduce spring NDVI in the part of farmlands. Trend analysis during the multiple nested time series may contribute to a better and deep understanding of NDVI dynamic and foreseeing changes in the future. Accordingly, NDVI in Xinjiang will continuously increase at regional scale and the areas showing significant browning will also furthermore grow. [ABSTRACT FROM AUTHOR]

With the development of cities, the relationship between cities is becoming closer, and the study of urban livability based on a single city can no longer meet the guidelines and suggestions for urban agglomerations. A scientific evaluation of livability in urban agglomerations can better help cities to recognize the advantages and disadvantages. However, most studies on urban livability focus on its connotation and history and neglect simulations and analyses of the future. Based on the Yangtze River Delta agglomeration, this paper establishes an index system using data from 2011 to 2019 to simulate urban livability from 2020 to 2025 through the ARIMA model and analyzes the historical and future data by using GIS methods. The results show the following: (1) The ARIMA model has good simulation accuracy when applied to urban livability analysis and can provide a reference for future urban livability development. (2) The urban livability of the Yangtze River Delta agglomeration has obviously changed both on the whole and in subsystems. Cities in the upper ranking of livability have developed rapidly, and the difference in urban livability has increased. (3) The spatial autocorrelation of urban livability in the Yangtze River Delta agglomeration is obvious both on the whole and in subsystems. (4) The influencing factors of urban livability development are diverse. The general public budget expenditure for social security and employment, fixed assets investment in municipal public facilities, total retail sales of consumer goods, and education and medical expenditures have positive effects on the development of urban livability, while industrial SO 2 emissions have a negative effect. The results show that cities should strengthen inter-city relationships, promote the coordinated development of inter-regional cities, and formulate relevant policies to improve the level of urban environmental governance in the region. [ABSTRACT FROM AUTHOR]

Accurate estimation of the water conservation is of great significance for ecological red line planning. The water conservation of the Yellow River Basin has a vital influence on the development of the environment and the supply of ecological services in China. However, the existing methods used to estimate water conservation have many disadvantages, such as requiring numerous parameters, a complex calculation model, and using data that is often difficult acquire. It is often hard to provide sufficiently precise parameters and data, resulting in a large amount of calculation time and the difficulties in the study of large scale and long time series. In this study, a time series of the Normalized Difference Vegetation Index (NDVI) was applied to estimate water conservation in two aspects using the idea of wholeness and stratification, respectively. The overall fitting results can explain nearly 30% of the water conservation by partial least squares regression and nearly 50% of it by a support vector machine. However, the results of a stratified simulation showed that water conservation and the NDVI have a certain stratified heterogeneity among different ecosystem types. The optimal fitting result was achieved in a water/wetland ecosystem with the highest coefficient of determination (R2P) of 0.768 by the stratified support vector machine (SVM) model, followed by the forest and grassland ecosystem (both R2P of 0.698). The spatial mapping results showed that this method was most suitable for grassland ecosystem, followed by forest ecosystem. According to the results generated using the NDVI time series data, it is feasible to complete a spatial simulation of water conservation. This research can provide a reference for calculating regional or large-scale water conservation and in ecological red line planning. [ABSTRACT FROM AUTHOR]

Airborne eddy covariance (EC) measurement is one of the most effective methods to directly measure the surface mass and energy fluxes at the regional scale. It offers the possibility to bridge the scale gap between local- and global-scale measurements by ground-based sites and remote-sensing instrumentations, and to validate the surface fluxes estimated by satellite products or process-based models. In this study, we developed an unmanned aerial vehicle (UAV)-based EC system that can be operated to measure the turbulent fluxes in carbon dioxides, momentum, latent and sensible heat, as well as net radiation and photosynthetically active radiation. Flight tests of the developed UAV-based EC system over land were conducted in October 2020 in Inner Mongolia, China. The in-flight calibration was firstly conducted to correct the mounting error. Then, three flight comparison tests were performed, and we compared the measurement with those from a ground tower. The results, along with power spectral comparison and consideration of the differing measurement strategies indicate that the system can resolve the turbulent fluxes in the encountered measurement condition. Lastly, the challenges of the UAV-based EC method were discussed, and potential improvements with further development were explored. The results of this paper reveal the considerable potential of the UAV-based EC method for land surface process studies. [ABSTRACT FROM AUTHOR]

• The changes of NDVI in urbanized areas of most cities in China exhibited 'U' shapes. • More vegetation degradation was found in urbanized areas than in outer buffers. • Overall, urbanization has negative effects on vegetation for most cities of China. • The adverse effects of urbanization on vegetation gradually diminished over time. • The valid influence distance of urbanization on vegetation may be about 30–40 km. A better understanding the effects of urbanization on vegetation is vital to promoting the sustainable development of cities in China, which has also experienced the world's most remarkable urbanization during the past decades. This paper used satellite-derived night light, vegetation index, and socio-economic data to analyze spatio-temporal patterns of vegetation changes and evaluate the influence of urbanization on vegetation cover in 46 Chinese metropolises over the last four decades. To examine the effects of urbanization on vegetation and its valid distance, we proposed a new method of comparing three indicators, namely, annual average normalized difference vegetation index (NDVI), long-term trend of NDVI change and the proportion of pixels characterized by significant change in NDVI between built-up areas and its multiple buffer zones. The results showed that the effects of urbanization on vegetation have obvious spatial differences. Urbanization generally resulted in the decrease of vegetation coverage in built-up areas for most of China's central and eastern metropolises, while in Western China, usually restricted by natural conditions, the urbanization usually improved vegetation coverage. The rates of increase in NDVI were significant lower than that of the buffer zones for most cities in central and eastern China. Urbanized areas of most cities located in central and eastern China had a higher percent of vegetation pixels, representing degradation than that in the buffer zones. The opposite was true for pixels representing vegetation restoration. The changes of NDVI in the cities in Western China were basically contrary to those in central and eastern China. The impacts of urbanization on vegetation are stage-specific. With the continuous progress of urbanization level, the adverse effect of urbanization on vegetation were gradually diminished or even disappeared. Cities with higher nighttime light levels appeared to have less vegetation coverage in the early years, and negative correlations between mean NDVI and the mean digital number value have become weak in more recent years. The changes in NDVI in urbanized areas of most cities significantly followed a 'U' shape. This phenomenon may further suggest that positive effects of urbanization become more obvious over time during advanced urbanization. The spatial scope of the impacts of urbanization on vegetation was beyond the urban built-up areas, and the effect intensity appeared to attenuate as the buffer distance became further away from the urbanized area. The valid distance of impacts of urbanization on vegetation is generally about 30–40 km. [ABSTRACT FROM AUTHOR]

[Display omitted] • Constructing a new ecological stability index based on optimal equilibrium state. • Developing a systematic evaluation system for the impact of tidal flat reclamation. • Scientifically screened the current ecological stability of Jiangsu coastal wetlands. • Analyzing the impact and cumulative effect of reclamation on ecological stability. • Nature reserve has a positive effect on strengthening regional ecosystem stability. Tidal flats have great potential to expand human living space and provide abundant resources. However, large-scale reclamation and development activities have had profound impacts on regional ecosystems, and it is important to grasp the comprehensive characteristics of the effects from a macro perspective. We selected the coastal area Jiangsu, China, as a typical area, and investigated the impact of reclamation on the coastal wetland system from the perspective of ecosystem services stability. The optimal equilibrium state was used as a benchmark and a stability deviation index (SDI) of ecosystem services was constructed by clarifying the connotation of stability. The stable state and evolution characteristics of ecosystems in different areas (coastal administrative areas, tidal flat areas, and Yancheng National Nature Reserve (YNNR)) were screened and evaluated. Based on the SDI , an overall ecological impact assessment system was developed, and the impact of tidal flat reclamation on ecological stability and its cumulative effects were quantified. The results showed that in the past 40 years, the overall ecological stability of Jiangsu coastal area was "Basically Stable (II)", while that of the YNNR was "Weakly Stable (III)". The impact of reclamation on the ecosystem stability of the coastal tidal flat area was significant, with a contribution rate of 73.5 %, and the threshold of stability transition in this area was reached when the reclamation amount was 1251.33 km2. The extent to which reclamation explained the stability variation in the core zone, buffer zone, and experimental zone of YNNR gradually decreased, indicating that the impact of reclamation was greater when strong protection measures were in place. The southern region will reach the maximum threshold of the cumulative effect of instability in 2072. This study provides ideas for the analysis of the comprehensive impact of reclamation on coastal wetland ecosystems, and can provide guidance for the efficient utilization and protection of tidal flats. [ABSTRACT FROM AUTHOR]

• SOC mineralization was reduced under moderate thinning treatments (MT). • Higher soil temperatures were measured under MT. • Active carbon stocks and soil temperatures affect C stock under thinning treatments. How to reduce the rising carbon (C) in the atmosphere by enhancing forest soil C-holding capacity requires further research. Forest density via thinning has long been studied for this reason. However, how changes in density affect soil C sequestration is still uncertain for many other factors also affect soil C in open forest ecosystems. To measure the effect of thinning on soil organic carbon (SOC) stock relative to other contributing factors, we selected 12 25 × 25 m plots, and applied a series of thinning treatments: CK (control plots; 2173 ± 12 trees ha−1, which was how all plots started), LT (light thinning; 1834 ± 12 trees ha−1), MT (moderate thinning; 1418 ± 7 trees ha−1) and HT (heavy thinning; 1089 ± 13 trees ha−1). We measured the SOC stocks (kg C m−2) for 11 seasons crossing three years. In addition, soils of the four treatment plots (0–50 cm soil depth, 5 layers, 10 cm per layer) were collected once per season, and then the samples were incubated for 56 days before measuring their cumulate mineralized carbon (CMC). The temperature at 10 cm underground was measured every 60 min throughout two years. The active carbon (DOC (dissolved organic C) and MBC (microbial biomass C)) stocks were also measured. We found that soil in MT plots held more C overall, with more active carbon per unit of SOC. Soil temperatures were affected by the thinning as well as the C and N (nitrogen) stock. Soil temperatures and C, N stock alike rose from CK levels and peaked in MT, before decreasing in HT. However, SOC mineralization rate (mineralized-C) was reduced in MT across seasons, and mineralized-C varied seasonally. Soil C and N stocks were enhanced in MT plots, partly due to the increased soil temperatures and the reduced mineralized-C. [ABSTRACT FROM AUTHOR]