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.

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.

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 R² between the CUII and soil landscape metrics decreased from the LPI and ED to SHDI and in turn to AI. The local R² 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.

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 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 with understanding the carbon cycles of regional wetlands.