This study aimed to explore the long-term vegetation cover change and its driving factors in the typical watershed of the Yellow River Basin. This research was based on the Google Earth Engine (GEE), a remote sensing cloud platform, and used the Landsat surface reflectance datasets and the Pearson correlation method to analyze the vegetation conditions in the areas above Xianyang on the Wei River and above Zhangjiashan on the Jing River. Random forest and decision tree models were used to analyze the effects of various climatic factors (precipitation, temperature, soil moisture, evapotranspiration, and drought index) on NDVI (normalized difference vegetation index). Then, based on the residual analysis method, the effects of human activities on NDVI were explored. The results showed that: (1) From 1987 to 2018, the NDVI of the two watersheds showed an increasing trend; in particular, after 2008, the average increase rate of NDVI in the growing season (April to September) increased from 0.0032/a and 0.003/a in the base period (1987–2008) to 0.0172/a and 0.01/a in the measurement period (2008–2018), for the Wei and Jing basins, respectively. In addition, the NDVI significantly increased from 21.78% and 31.32% in the baseline period (1987–2008) to 83.76% and 92.40% in the measurement period (2008–2018), respectively. (2) The random forest and classification and regression tree model (CART) can assess the contribution and sensitivity of various climate factors to NDVI. Precipitation, soil moisture, and temperature were found to be the three main factors that affect the NDVI of the study area, and their contributions were 37.05%, 26.42%, and 15.72%, respectively. The changes in precipitation and soil moisture in the entire Jing River Basin and the upper and middle reaches of the Wei River above Xianyang caused significant changes in NDVI. Furthermore, changes in precipitation and temperature led to significant changes in NDVI in the lower reaches of the Wei River. (3) The impact of human activities in the Wei and Jing basins on NDVI has gradually changed from negative to positive, which is mainly due to the implementation of soil and water conservation measures. The proportions of areas with positive effects of human activities were 80.88% and 81.95%, of which the proportions of areas with significant positive effects were 11.63% and 7.76%, respectively. These are mainly distributed in the upper reaches of the Wei River and the western and eastern regions of the Jing River. These areas are the key areas where soil and water conservation measures have been implemented in recent years, and the corresponding land use has transformed from cultivated land to forest and grassland. The negative effects accounted for 1.66% and 0.10% of the area, respectively, and were mainly caused by urban expansion and coal mining.

Soil erosion is a serious environmental problem in the Loess Plateau, China. Therefore, it is important to understand and evaluate soil erosion process in a watershed. In this study, the Chinese Soil Loss Equation (CSLE) is developed to evaluate the soil loss and analyze the impact of land use and slope on soil erosion in Jiuyuangou (JYG) watershed located in the hilly-gullied loess region of China 1970–2015. The results show that the quantities of soil erosion decreased clearly from 1977 to 2015 in the study area, which from 2011 (t/km²·a) in 1977 to 164 (t/km²·a) in 2004 and increased slowly to 320 (t/km²·a) in 2015. No significant soil erosion (<300 t/km²·a) changed in JYG watershed, which increased dramatically from 8.93% to 69.34% during 1977–2015. The area of farmland in this study area has been reduced drastically. Noting that the annual average soil erosion modulus of grassland was also showing a dropped trend from 1977 to 2015. In addition, the study shows that the annual average soil erosion modulus varied with slope gradient and the severe soil erosion often existed in the slope zone above 25°, which accounted for 4657 (t/km²·a) in 1977 and 382.27 (t/km²·a) in 2015. Meanwhile, soil erosion of different land-use types presented the similar changing trend (declined noticeably and then increased slowly) with the change of slope gradient from 1977 to 2015. Combined the investigations of extreme rainfall on 26 July 2015 for JYG watershed, the study provides the scientific support for the implementation of soil and water conservation measures to reduce the soil erosion and simplify Yellow River management procedures.

Precipitation and human activities are two essential forcing dynamics that influence hydrological processes. Previous research has paid more attention to either climate and streamflow or vegetation cover and streamflow, but rarely do studies focus on the impact of climate and human activities on streamflow and sediment. To investigate those impacts, the Zuli River Basin (ZRB), a typical tributary basin of the Yellow River in China, was chosen to identify the impact of precipitation and human activities on runoff and sediment discharge. A double mass curve (DMC) analysis and test methods, including accumulated variance analysis, sequential cluster, Lee-Heghnian, and moving t-test methods, were utilized to determine the abrupt change points based on data from 1956 to 2015. Correlation formulas and multiple regression methods were used to calculate the runoff and sediment discharge reduction effects of soil conservation measures and to estimate the contribution rate of precipitation and soil conservation measures to runoff and sediment discharge. Our results show that the runoff reduction effect of soil conservation measures (45%) is greater than the sediment discharge reduction effect (32%). Soil conservation measures were the main factor controlling the 74.5% and 75.0% decrease in runoff and sediment discharge, respectively. Additionally, the contribution rate of vegetation measures was higher than that of engineering measures. This study provides scientific strategies for water resource management and soil conservation planning at catchment scale to face future hydrological variability.

Qinling Mountains is the north–south boundary of China’s geography; the vegetation changes are of great significance to the survival of wildlife and the protection of species habitats. Based on Landsat products in the Google Earth Engine (GEE) platform, Pearson’s correlation coefficient method, and classification and regression models, this study analyzed the changes in NDVI (Normalized Difference Vegetation Index) in the Qinling Mountains in the past 38 years and the sensitivity of its driving factors. Finally, residual analysis method and accumulate slope change rate are used to identify the impact of human activities and climate change on NDVI. The research results show the following: (1) The NDVI value in most areas of Qinling Mountains is at a medium-to-high level, and 99.76% of the areas correspond to an increasing trend of NDVI, and the significantly increased area accounts for more than 20%. (2) From 1981 to 2019, the NDVI of the Qinling Mountains increased from 0.63 to 0.78, showing an overall upward trend, and it increased significantly after 2006. (3) Sensitivity analysis results show that the western high-altitude area of Qinling Mountain area dominated by grassland is mainly affected by precipitation. The central and southeastern parts of the Qinling Mountains are significantly affected by temperature, and they are mainly distributed in areas dominated by forest. (4) The contribution rates of climate change and human activities to NDVI are 36.04% and 63.96%, respectively. Among them, the positive impact of human activities on the NDVI of the Qinling Mountains accounted for 99.85% of the area. The area with significant positive effect accounted for 36.49%. The significant negative effect area accounts for only 0.006%, mainly distributed in urban areas and coal mining areas.

Soil erosion is serious in China—the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.