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High weathered soils in arid and semi-arid area are characterized by low soil fertility and high erosion potential. This paper evaluated the influences of nano-carbon on soil erosion and water loss on the sloping land (15°) of the Chinese Loess Plateau. Simulated rainfall experiments were performed on a natural, fallow loessial slope in the Shenmu Erosion and Environment Research Station, Institute of Soil and Water Conservation, Chinese Academy of Sciences, which locates in the Liudaogou watershed, Shenmu County, Shaanxi Province. Five application rates (i.e., 0, 0.1%, 0.5%, 0.7%, and 1.0%) of nano-carbon were selected during the simulated rainfall experiments (rainfall intensity of 60 mm/h); and 4 vegetation species (i.e., caragana, alfafa, bean, and maize) were planted on the slope to evaluate the vegetation effect on soil and water conservation with the bare as the control. A constant-intensity rainfall method was adopted under natural conditions. Field plots were established on the slope that had been fallow for 5 years. The rainfall simulator came from the independent design of Xi'an University of Technology. Under the 60 mm/h rainfall intensity, the simulated rainfall experiments were carried out for 50 times in the 25 different pretreated plots in order to estimate soil erosion and water loss for all the treatments. The main results were as follows: the initial runoff time was delayed with the increase of nano-carbon contents. Compared to the control treatment without nano-carbon application in bare land, the initial runoff time in alfalfa plot was delayed by 287.1%, which was the most obvious. Furthermore, with the increase of nano-carbon contents, the runoff in each treatment decreased obviously. The nano-carbon alfalfa coverage also had the most obvious effect in the reducing runoff, and the runoff was reduced by 66.47%; for other vegetation species, the reduction was between 31.5% and 33.6%. Moreover, the nano-carbon had a significant effect on soil loss controlling. The nano-carbon in alfalfa land showed the best effect for soil loss controlling, which was followed by caragana. Soybean land showed the highest soil loss. For the bare land (control) and alfalfa coverage, when the nano-carbon content was 1%, the lowest sediment yield was obtained; while for caragana, soybean and maize coverage, when the nano-carbon content was 0.5%, the sediment yield was the lowest. Based on the correlation analysis between nano-carbon and runoff and sediment yield, it could be concluded that nano-carbon has a significant impact on the experimental results. In the evaluation of soil and water loss control effect, the 0.5% nano-carbon had the obvious effect, which was similar with the 0.7% and 1.0% nano-carbon content, so considering the price, 0.5% nano-carbon content was appropriate on soil and water loss control in loess area. The results provide a guide for controlling soil water and nutrient loss on the sloping land. [ABSTRACT FROM AUTHOR]

Rapidly inverting soil salinity is crucial to the soil water and salt migration for the prevention of the secondary salinization. However, the accuracy and efficiency of soil salinity inversion models are hindered to the coupling relationship between water and salt in soil, particularly for time-consuming and labor-intensive soil collection. This study aims to reduce the interference of soil moisture for obtaining better sample diversity and further improving the robustness of soil salinitu inversion models by using spectral technology. A total of 113 normal and 115 saline soil samples were collected in the Xinjiang cotton fields. These samples were further subjected to different levels of wetting treatment. and subsequently 467 soil samples with varing salt and moisture contents were obtained. Soil salt content was calibrated using the conductivity of soil leaching solution. Spectral data of samples was captured using an ASD ground object spectrometer (400-1000 nm) and a near-infrared spectrometer (960-1693 nm). The soil moisture was also corrected using the external parameter orthogonalization (EPO). Additionally, deep convolutional generative adversarial networks (DCGAN) with different transposed convolution stride strategies were designed to evaluate the sample set using Fréchet Inception Distance (FID) scores. Machine learning models were employed to invert the soil salinity, including partial least squares regression (PLSR), random forest (RF), and onedimensional convolutional neural network (1D-CNN) models using VGG (VNet), EfficientNet (ENet), and ResNet (RNet) architectures. The results demonstrated that the EPO can effectively reduce the interference of moisture on the salinity, indicating the better prediction performance of different models. RNet out performed PLSR, RF, VNet, Enet, RNet, and exhibited the best performance to predict the soil salinity in cotton fields. The lightweight residual neural network without attention mechanism was more suitable for one-dimensional hyperspectral data. There was an increase in the convolution stride and kernel length of the deep convolutional adversarial generative network. The better samples were obtained for the hyperspectral data with long sequences. The superior FID scores were achieved in the generated augmented sample set using Generator B (designed with the larger convolution stride and kernel size), compared with the rest. Specifically, the FID scores were reduced by 7.9% and 13.4%, respectively, compared with GA and GC. The weight distribution of attention was optimized after expanding the training set by DCGAN. The stability and accuracy of the model were further enhanced to predict the soil salinity under certain constraints on training samples. The EPO-DCGAN(GB)-RNet (called EPO-DCGAN-RNet) model was achieved in the superior RMSE and R² values of 136.472 μS/cm and 0.910, respectively, on the validation set, compared with the EPO-SG-RNet (using SG filtering denoising) and EPO-RNet (without sample augmentation). Furthermore, 1D-CNN with Grad CAM was employed to identify the characteristic bands of soil conductivity in the soil leaching solution of cotton field. In summary, an accurate inversion model EPO-DCGAN-RNet was constructed for inversing soil salinity in the cotton fields using spectral technology. Water correction and sample augmentation were incorporated for the soil salt composition. The improved model has the promising potential to the salt-tolerant cotton varieties and irrigation strategies using slightly salty water in cotton fields [ABSTRACT FROM AUTHOR]