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Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1‐m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being.65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m−2 in 2000 and 26.57 ± 3.73 kg C m−2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m−2 year−1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1‐2.6 (low‐carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5‐8.5 (high‐carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage. [ABSTRACT FROM AUTHOR]

The increasing global demands for land resource with increasing population have resulted in occurrence of soil degradation in many regions of the world. Assessment of soil quality has become the basic work for agricultural sustainable development and selecting regional indicators effectively has become very important since there are no standard evaluation methods and universal indicators. In this study, taking the Corn Belt of Northeast China as the study area, seven indicators--obstacle horizon thickness, cation exchange capacity, pH, soil organic matter, total nitrogen, total potassium, and available Fe--were selected to constitute the minimum data set from sixteen indictors of the total data set to assess the soil quality. The soil quality of the study area was dominated by moderate grade, increasing from west to east. The soil quality of Yushu, Changchun and Shuangyang had higher values, and that of Nongan was the lowest. We found that the distribution of cation exchange capacity has a good consistency with the assessment result of the soil quality. Black soils were distributed in the middle part of the study region from north to south and accounted for a higher quality, exactly where the areas of rapid urbanization are located. An ANOVA analysis showed that soil quality in the Corn Belt of Northeast China was greatly affected by topographic factors and agricultural management and climate was not the principal factor affecting soil quality. Though the minimum data set slightly reduced the evaluation accuracy, a large sampling density in our study was able to improve the precision loss that resulted from reducing the number of indicators to a certain extent. [ABSTRACT FROM AUTHOR]

Investigating the spatial and vertical patterns of wetland soil organic carbon concentration (SOCc) is important for understanding the regional carbon cycle and managing the wetland ecosystem. By integrating 160 wetland soil profile samples and environmental variables from climatic, topographical, and remote sensing data, we spatially predicted the SOCc of wetlands in China's Western Songnen Plain by comparing four algorithms: random forest (RF), support vector machine (SVM) for regression, inverse distance weighted (IDW), and ordinary kriging (OK). The predicted results of the SOCc from the different algorithms were validated against independent testing samples according to the mean error, root mean squared error, and correlation coefficient. The results show that the measured SOCc values at depths of 0–30, 30–60, and 60–100 cm were 15.28, 7.57, and 5.22 g·kg−1, respectively. An assessment revealed that the RF algorithm was the most accurate for predicting SOCc; its correlation coefficients at the different depths were 0.82, 0.59, and 0.51, respectively. The attribute importance from the RF indicates that environmental variables have various effects on the SOCc at different depths. The land surface temperature and land surface water index had a stronger influence on the spatial distribution of SOCc at the depths of 0–30 and 30–60 cm, whereas topographic factors, such as altitude, had a stronger influence within 60–100 cm. The predicted SOCc of each vertical depth increased gradually from south to north in the study area. This research provides an important case study for predicting SOCc, including selecting factors and algorithms, and helps understanding the carbon cycles of regional wetlands. [ABSTRACT FROM AUTHOR]

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations. [ABSTRACT FROM AUTHOR]

• IQI-NLS index performed better than other soil quality model in BSZJLC. • The determined MDS for BSZJLC included BD, CEC, SOM, AN, TK, ACu, and AFe. • The soil quality in study area was dominated by moderate grades. • Effects of environmental factors on soil quality has local characteristic. Soil quality assessment is an effective way to improve understanding of the heterogeneity of soil quality and to encourage the adoption of proper agricultural practices. The object of this paper is to select the most effective soil quality indices and the minimum data set (MDS) for the black soil zone of Jilin Province, China, and analyze the association between soil quality and the environmental control. Taking the croplands of the black soil zone of Jilin Province, China, as the study area, different combinations of scoring methods, indices, and data set methods were compared. A minimum data set, based on the integrated quality index derived from non-linear scoring methods (MDS IQI-NLS index), was then selected via principal components analysis to assess the soil quality of the study area. The determined minimum data set for the study area included bulk density, cation exchange capacity, soil organic matter, alkali-dissolvable N, total K, extractable Cu, and extractable Fe. The results showed that the soil quality in the study area was of dominantly moderate grades (grades II, III and IV), accounting for approximately 90% of the total as well as the minimum data set, and the soil quality index increased from the northwest to the southeast. Group analysis was applied because different combinations of environmental factors play different roles in different sub-regions. The analysis results showed that the most important factors affecting soil quality in group 1 (in decreasing order of importance) were: mean annual temperature, DEM, amount of straw return, parent material, and topographic wetness index. For group 2 they were: parent material, annual precipitation, mean annual temperature, chemical fertilizer, DEM, amount of straw return. This analysis proves to be an effective way to determine the factors that affect the spatial distribution of soil quality. [ABSTRACT FROM AUTHOR]

The Changchun⁻Jilin Economic Zone (CJEZ) is one of the most rapidly developing areas in Northeast China, as well as one of the famous golden maize belts in the world. This is a case study to assess the impacts of urban sprawl on soil resources using remote sensing imagery and geographic spatial analysis methods. The common urbanization intensity index (CUII), soil quality index, and soil landscape metrics were calculated to reflect urbanization and the response of soil resource. Results showed that the area of soil sealing changed from 112,460 ha in 2000 to 139,233 ha in 2015, and in the rural region, the area occupied by urbanization nearly kept balance with the area of rural residential expansion. Urban land increased by 26,767 ha at an annual rate of 3.23% from 2000 to 2015. All seven soil types were occupied during the urbanization process, among which black soil ranked the highest (18,560 ha) and accounted for 69.34% of the total occupied area. Soils of Grades I (3927 ha) and II (15,016 ha) were 64.75% of the total occupied soil areas. Urban land expanded in an irregular shape and a disordered way, which led to an increasing large patch index (LPI) and aggregation index (AI), and a decreasing edge density (ED) and Shannon’s diversity index (SHDI) of the soil landscape in the study area during 2000⁻2015. According to the geographically weighted regression (GWR) model analysis, the R ² between the CUII and soil landscape metrics decreased from the LPI and ED to SHDI and in turn to AI. The local R ² between SHDI, ED, and CUII showed a gradient structure from the inner city to peri-urban areas, in which larger values appeared with strongly intensive urbanization in urban fringes. Soil sealing induced by urbanization has become a significant factor threatening soil, the environment, and food security. How to coordinate regional development and ensure the sustainability of the multiple functions of soil is a problem that needs to be taken into account in the future development of the region.