Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m⁻¹), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R² = 0.88, root mean square error (RMSE) = 4.89 dS m⁻¹, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

The spatial pattern of landscape has great influence on the biodiversity provided by ecosystem. Understanding the impact of landscape pattern dynamics on habitat quality is significant in regional biodiversity conservation, ensuring ecological security guarantee, and maintaining the ecological environmental sustainability. Here, combining CA-Markov and InVEST model, we investigated the evolution of landscape pattern and habitat quality, and presented an explanation for variability of biodiversity linked to landscape pattern in Hubei section of Three Gorges Reservoir Area (TGRA). The spatial-temporal evolution characteristic of landscape pattern from 1990 to 2010 were analyzed by Markov chain. Then, the spatial pattern of habitat quality and its variation in three phases were computed by InVEST model. The driving force for landscape variation was explored by using Logistic regression analysis. Next, the CA-Markov model was used to simulate the future landscape pattern in 2020. Finally, future habitat quality maps were obtained by InVEST model predicted landscape maps. The results concluded that, the overall landscape pattern has changed slightly from 1990 to 2010. Woodland, waters and construction land had the greatest variations in proportion among the landscape types. The area of woodland has been decreasing gradually below the average elevation of 140 m, and the area of waters and construction land increased sharply. Logistics regression results indicated that terrain and climate were the most influencing natural factors compared with human factors. The Kappa coefficient reached 0.92, indicating that CA-Markov model had a good performance in future landscape prediction by adding nighttime light data as restriction factor. The biodiversity has been declining over the past 20 years due to the habitat degradation and landscape pattern variation. Overall, the maximum values of habitat degradation index were 0.1188, 0.1194 and 0.1195 respectively, showing a continuously increasing trend from 1990 to 2010. Main urban areas of Yichang city and its surrounding areas has higher habitat degradation index. The average values of habitat quality index of the whole region were 0.8563, 0.8529 and 0.8515 respectively, showing a continuously decreasing trend. The lower habitat quality index mainly located in the urban land as well as the main and tributary banks of the Yangtze River. Under the business as usual scenario, habitat quality continued to maintain the variation trend of the previous decade, showing a reducing habitat quality index and an increasing area of artificial surface. Under the ecological protection scenario, the variation of habitat quality in this scenario represented reverse trend to the previous decade, exhibiting an increase of habitat quality index and an increasing area of woodland and grassland. Construction of Three Gorges Dam, impoundment of Three Gorges Reservoir (TGR), resettlement of Three Gorges Project and urbanization were the most explanatory driving forces for landscape variation and degradation of habitat quality. The research may be useful for understanding the impact of landscape pattern dynamics on biodiversity, and provide scientific basis for optimizing regional natural environment, as well as effective decision-making support to local government for landscape planning and biodiversity conservation.

Collapsing gullies are one of the most serious soil erosion problems in the tropical and subtropical areas of southern China. However, few studies have been performed on the relationship of soil Atterberg limits with soil profiles of the collapsing gullies. Soil Atterberg limits, which include plastic limit and liquid limit, have been proposed as indicators for soil vulnerability to degradation. Here, the soil Atterberg limits within different weathering profiles and their relationships with soil physicochemical properties were investigated by characterizing four collapsing gullies in four counties in the hilly granitic region of southern China. The results showed that with the fall of weathering degree, there was a sharp decrease in plastic limit, liquid limit, plasticity index, soil organic matter, cation exchange capacity and free iron oxide. Additionally, there was a gradual increase in liquidity index, a sharp increase in particle density and bulk density followed by a slight decline, a decrease in the finer soil particles, a noticeable decline in the clay contents, and a considerable increase in the gravel and sand contents. The plastic limit varied from 19.43 to 35.93 % in TC, 19.51 to 33.82 % in GX, 19.32 to 35.58 % in AX and 18.91 to 36.56 % in WH, while the liquid limit varied from 30.91 to 62.68 % in TC, 30.89 to 57.70 % in GX, 32.48 to 65.71 % in AX and 30.77 to 62.70 % in WH, respectively. The soil Atterberg limits in the sandy soil layers and detritus layers were lower than those in the surface layers and red soil layers, which results in higher vulnerability of the sandy soil layers and detritus layers to erosion and finally the formation of the collapsing gully. The regression analyses showed that soil Atterberg limits had significant and positive correlation with SOM, clay content, cationic exchange capacity and Fe_d, significant and negative correlation with sand content and no obvious correlation with other properties. The results of this study revealed that soil Atterberg limits are an informative indicator to reflect the weathering degree of different weathering profiles of the collapsing gullies in the hilly granitic region.