Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000-2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

In this study, we critically evaluated the performance of an emerging technology, hyperspectral imaging (HSI), for detecting microplastics (MPs) in soil. We examined the technology\'s robustness against varying environmental conditions in five groups of experiments. Our findings show that near-infrared (NIR) hyperspectral imaging (HSI) effectively detects microplastics (MPs) in soil, though detection efficacy is influenced by factors such as MP concentration, color, and soil moisture. We found a generally linear relationship between the levels of MPs in various soils and their spectral responses in the NIR HSI imaging spectrum. However, effectiveness is reduced for certain MPs, like polyethylene, in kaolinite clay. Furthermore, we showed that soil moisture considerably influenced the detection of MPs, leading to nonlinearities in quantification and adding complexities to spectral analysis. The varied responses of MPs of different sizes and colors to NIR HSI present further challenges in detection and quantification. The research suggests pre-grouping of MPs based on size before analysis and proposes further investigation into the interaction between soil moisture and MP detectability to enhance HSI\'s application in MP monitoring and quantification. To our knowledge, this study is the first to comprehensively evaluate this technology for detecting and quantifying microplastics.

Soil metaproteomics could explore the proteins involved in life activities and their abundance in the soils to overcome the difficulty in pure cultures of soil microorganisms and the limitations of proteomics of pure cultures. However, the complexity and heterogeneity of soil composition, the low abundance of soil proteins, and the presence of massive interfering substances (including humic compounds) generally lead to an extremely low extraction efficiency of soil proteins. Therefore, the efficient extraction of soil proteins is a prerequisite and bottleneck problem in soil metaproteomics. In this chapter, a soil protein extraction method suitable for most types of soils with low cost and enabling simple operation (about 150 μg protein can be extracted from 5.0 g soil) is described. The quantity and purity of the extracted soil proteins could meet the requirements for further analysis using routine mass spectrometry-based proteomics.

Forest management influences the occurrence of tree species, the organic matter input to the soil decomposer system, and hence, it can alter soil microbial community and key ecosystem functions it performs. In this study, we compared the potential effect of different forest management, coppice and high forest, on soil microbial functional diversity, enzyme activities and chemical-physical soil properties in two forests, turkey oak and beech, during summer and autumn. We hypothesized that coppicing influences soil microbial functional diversity with an overall decrease. Contrary to our hypothesis, in summer, the functional diversity of soil microbial community was higher in both coppice forests, suggesting a resilience response of the microbial communities in the soil after tree cutting, which occurred 15-20 years ago. In beech forest under coppice management, a higher content of soil organic matter (but also of soil recalcitrant and stable organic carbon) compared to high forest can explain the higher soil microbial functional diversity and metabolic activity. In turkey oak forest, although differences in functional diversity of soil microbial community between management were observed, for the other investigated parameters, the differences were mainly linked to seasonality. The findings highlight that the soil organic matter preservation depends on the type of forest, but the soil microbial community was able to recover after about 15 years from coppice intervention in both forest ecosystems. Thus, the type of management implemented in these forest ecosystems, not negatively affecting soil organic matter pool, preserving microbial community and potentially soil ecological functions, is sustainable in a scenario of climate change.

A novel adsorbent, calcium alginate-modified HAP (Hydroxyapatite)-wood ear mushroom sticks biochar (CA-HAPMB), was synthesized to enhance the immobilization of Cd and Pb in soil. Over 150 days, applying CA-HAPMB at concentrations of 0%-3% in contaminated soils from Chenzhou City in Hunan Province (CZ) and Shenyang City in Liaoning Province (SY) resulted in decreased effective concentrations of Cd and Pb. Specifically, in CZ soil, Cd and Pb decreased by 30.9%-69.3% and 31.9%-78.6%, respectively, while in SY soil, they decreased by 27.5%-53.7% and 26.4%-62.3%, respectively. Characterization results, obtained after separating CA-HAPMB from the soil, indicate that complexation, co-precipitation, and ion exchange play crucial roles in the efficient immobilization of Cd and Pb by CA-HAPMB. Additionally, adjusting the amount of CA-HAPMB added allows modulation of soil pH, leading to increased soil organic matter and nutrient content. Following treatment with CA-HAPMB for immobilizing Cd and Pb, soil bacteria abundance and diversity increased, further promoting heavy-metal immobilization.

Downscaling methods are crucial for accessing high-resolution thermal data simultaneously. The DisTRAD model is commonly used for downscaling thermal images, but changes in soil moisture, such as those caused by irrigation operations, can lead to errors in the process. This study investigated the potential use of TOTRAM and OPTRAM models to reduce errors in LST downscaling in irrigated fields. Sentinel satellite imagery was utilised to enhance the resolution of MODIS Land Surface Temperature (LST) from 1000 to 20 m in the fields of Megsal and Hezarjolfa agro-industrial company in Qazvin province. Soil moisture was estimated using the OPTRAM model, and the results were compared with observational data. The findings indicated that on days with NDVI greater than 0.6, the R2 value exceeded 0.88 and the RMSE value was less than 0.06 cm3/cm3. Then, MODIS LST images were downscaled to 20 m using codes in Google Earth Engine (GEE). Evaluation was conducted using observational data from collected land surface temperature data for 36 points. Comparison of the downscaled LST data with observational data on days with irrigation revealed a decrease in MAE and RMSE error indices by approximately 0.4 and 1.2 degrees Celsius, respectively, in the OPTRAM-TPTRAM model compared to the DisTRAD model. Consequently, the OPTRAM-TOTRAM model generally outperforms the DisTRAD model in LST downscaling. Lastly, it is recommended to assess the TOTARM and OPTRAM models for downscaling MODIS sensor LST in other irrigated fields.

Soil fluoride is a critical determinant of soil fertility, human health and crop productivity. Soil fluoride can be increased by climatic conditions, irrigation water and anthropogenic activity, and it is important to control fluoride by understanding the complex relationships between atmospheric conditions and water systems. In this research, a detailed focus is on the hydrological and meteorological aspects of soil fluorides in semi-saturated and saturated soils to discuss the impact of irrigation, capillary rise and the combination of rainfall and anthropogenic activities such as fertilizer application on the soils in the dry spell and monsoon seasons of 2021 and 2022. A Sentinel-1 data can be used to estimate fluoride levels to the above soil conditions. In an effort to estimate fluoride levels in different hydro-meteorological scenarios, we have put forward a hypothesis that focuses on understanding the potential connections between hydro-meteorological factors (precipitation, groundwater levels, and temperature) and the levels of fluoride. The findings indicate that the extensive use of groundwater for irrigation leads to a rise in fluoride levels, posing a significant threat to crop health over time. Furthermore, the combined effects of irrigation and upheaval leaching on fluoride levels have shown strong statistical conformity (R2 > 0.85) with the relevant field-measured fluoride data for the year 2022. Importantly, areas affected by F upheaval are more sensitive to the sand and clay percentage in the soil because potential and dispersion behaviour enlarge the capillaries to decelerate the upward movement. A region-based discussion details the factors contributing to the increase of fluoride in soil helpful in taking remedial measures and mitigation plans.

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Grassland is one of the largest terrestrial biomes, providing critical ecosystem services such as food production, biodiversity conservation, and climate change mitigation. Global climate change and land-use intensification have been causing grassland degradation and desertification worldwide. As one of the primary medium for ecosystem energy flow and biogeochemical cycling, grassland carbon (C) cycling is the most fundamental process for maintaining ecosystem services. In this review, we first summarize recent advances in our understanding of the mechanisms underpinning spatial and temporal patterns of the grassland C cycle, discuss the importance of grasslands in regulating inter- and intra-annual variations in global C fluxes, and explore the previously unappreciated complexity in abiotic processes controlling the grassland C balance, including soil inorganic C accumulation, photochemical and thermal degradation, and wind erosion. We also discuss how climate and land-use changes could alter the grassland C balance by modifying the water budget, nutrient cycling and additional plant and soil processes. Further, we examine why and how increasing aridity and improper land use may induce significant losses in grassland C stocks. Finally, we identify several priorities for future grassland C research, including improving understanding of abiotic processes in the grassland C cycle, strengthening monitoring of grassland C dynamics by integrating ground inventory, flux monitoring, and modern remote sensing techniques, and selecting appropriate plant species combinations with suitable traits and strong resistance to climate fluctuations, which would help design sustainable grassland restoration strategies in a changing climate.

Enhancing saline water productivity in arid regions is essential for sustainable agriculture. Adding biochar can improve the quantity and quality of tomato yield under higher levels of salinity and lower levels of irrigation. The experiment aimed to evaluate the effects of biochar on enhancing tomato fruit quality and yield under salinity and drought stress. The experiment combines two treatments for irrigation water quality (0.9 and 2.3 dS m-1), four irrigation levels (40, 60, 80, and 100%) of crop evapotranspiration (ETc), and the addition of 5% of biochar to treated soil (BC5%) and untreated soil (BC0%). The results showed that the decrease in the water quality and irrigation levels negatively impacted the yield and properties of tomato fruit, while 5% of biochar application positively improved the yield. Adding biochar decreased the tomato yield by 29.33% and 42.51% under lower-saline-irrigation water than the control, negatively affecting the fruit\'s physical parameters and mineral content. In contrast, adding biochar and irrigating with saline water at 60% of ETc improved the firmness and quality characteristics of the fruit by 56.60%, 67.19, 99.75, and 73.57% for vitamin C (VC), total titratable acidity (TA), total soluble solids (TSS), and total sugars (TS), respectively, compared to the control, and also reduced the sodium content of the fruits under all irrigation levels compared to untreated plants by biochar. Generally, biochar with saline water under deficit irrigation with 80 and 60% of ETc could be an excellent strategy to enhance the qualitative characteristics of tomato fruits and save approximately 20-40% of the applied water.