A novel organic carbon accumulation mechanism in croplands in the Yellow River Delta, China

Different bacterial and fungal community patterns in restored habitats in coal-mining subsidence areas

Soil microbiota, which plays a fundamental role in ecosystem functioning, is sensitive to environmental changes. Studying soil microbial ecological patterns can help to understand the consequences of environmental disturbances on soil microbiota and hence ecosystem services. The different habitats with critical environmental gradients generated through the restoration of coal-mining subsidence areas provide an ideal area to explore the response of soil microbiota to environmental changes. Here, based on high-throughput sequencing, we revealed the patterns of soil bacterial and fungal communities in habitats with different land-use types (wetland, farmland, and grassland) and with different restored times which were generated during the ecological restoration of a typical coal-mining subsidence area in Jining City, China. The α-diversity of bacterial was higher in wetland than in farmland and grassland, while that of fungi had no discrepancy among the three habitats. The β-diversity of bacterial community in the grassland was lower than in the farmland, and fungal community was significant different in all three habitats, showing wetland, grassland, and farmland from high to low. The β-diversity of the bacterial community decreased with restoration time while that of the fungal community had no significant change in the longer-restoration-time area. Furthermore, soil electrical conductivity was the most important driver for both bacterial and fungal communities. Based on the taxonomic difference among different habitats, we identified a group of biomarkers for each habitat. The study contributes to understand the microbial patterns during the ecological restoration of coal-mining subsidence areas, which has implications for the efficient ecological restoration of subsidence areas.

Distribution of SOCD along different offshore distances in China's fresh-water lake-Chaohu under different habitats

Carbon storage in wetland ecosystems is an important part of the carbon cycle of terrestrial ecosystems and provides important ecosystem services. Chaohu Wetland is a typical freshwater lake wetland in China. In this study, soil and plant samples were collected every 500 m through three sample lines of different vegetation habitats (estuarine banks, woodlands and shrub beaches) and different offshore distances, revealing the spatial distribution characteristics of soil organic carbon density (SOCD) in Chaohu wetland. The overall SOCD of Chaohu wetland was low, with different habitats ranking as Woodland > Estuary and riverside > Shrub and beach. SOCD of different offshore distances had no obvious law, and the SOCD decreased significantly with soil depth. The plant biomass was significantly higher at the woodland habitat than at other habitats. Most of soil nutrient indicators were the highest at the woodland habitat, while the estuary-riverside habitat had the highest N and P contents. Soil and plant nutrients at different offshore distances had no obvious change patterns. The contents of soil K, Ca, Mg, and N were significantly positively correlated with SOCD, but soil bulk density and pH were significantly negatively correlated with SOCD, and vegetation P content was significantly negatively correlated with SOCD. The spatial pattern of SOCD changes in this lake coastal wetland was determined by the combined effects of plant nutrients, biomass, and soil physical and chemical properties. Our results indicate Chaohu wetlands may have been experiencing serious degradation. The SOCD of Chaohu wetland is lower than that of other wetlands in China, which is mainly affected by human activities. Different offshore distances and habitat heterogeneity are the main factors affecting the soil carbon cycle of the wetland.

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.