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IntroductionRapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.MethodsIn this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI.ResultsThe RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. DiscussionAppropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

Abstract Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84–0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas.

Cotton is the main economic crop in China and is important owing to its use as an industrial raw material and a cash crop. This experiment was conducted in the main cotton-producing area of Xinjiang, China. A hyperspectrometer was used to monitor the canopy spectral reflectance of cotton at different stages of growth. The results showed that the leaf area index (LAI) increased with the increase in the amount of nitrogen fertilizer added during the early full boll stage and decreased with the increase in nitrogen fertilization in the full and late boll stages. Insufficient or excessive fertilization led to a decrease in the LAI. The visible light band indicated that the canopy spectral reflectance decreased, and the amount of fertilizer increased in all the growth stages. The near-infrared band revealed that the canopy spectral reflectance increased with the amount of nitrogen applied during the bud stage, early boll stage, and the most vigorous period of boll growth. During the flowering period, the spectral reflectance followed the order N3 > N4 > N2 > N1 > N0. During the entire growth period of cotton, the values of the cotton LAI predicted using the ratio vegetation index (RVI) model were found to best fit the measured values. The LAI monitoring models of cotton in each growth stage were different. The TVI model is the best in the bud and early boll stages. The NDVI model is the best in the flowering stage, and the DVI model is the best in the full boll stage. This study provides a basis to accurately monitor the LAI in each growth period of cotton.

Abstract The cyanide content of gold tailings exceeds the standard seriously due to the cyanide extraction process. In order to improve the resource utilization efficiency of gold tailings, a medium-temperature roasting experiment was carried out on the stock tailings of Paishanlou gold mine after washing and pressing filtration treatment. The thermal decomposition rule of cyanide in gold tailings was analyzed, and the effects of different roasting temperatures and roasting durations on cyanide removal efficiency were compared. The results show that when the roasting temperature reaches 150 °C, the weak cyanide compound and free cyanide in the tailings begin to decompose. When the calcination temperature reached 300 °C, the complex cyanide compound began to decompose. When the roasting temperature reaches the initial temperature of cyanide decomposition, the cyanide removal efficiency can be improved by prolonging the roasting time. After roasting at 250–300 °C for 30–40 min, the total cyanide content in the toxic leachate decreased from 3.27 to 0.01 mg/L, which met the water quality standard of III class in China. The research results provide a low-cost and efficient way for cyanide treatment, which is of great significance for promoting the resource utilization of gold tailings and other cyanide-containing wastes.

The microstructure of shale reservoirs refers to the distribution of mineral–organic matter, pore–fracture features, diagenetic processes, and their interrelations. The comprehensive and accurate analysis of the shale microstructure plays a critical role in formulating a reasonable development plan and optimizing measures to enhance oil or gas recovery. To explore the microstructure characterization, the mineral and organic matter compositions as well as the pore types and distributions of organic-rich shale reservoirs were investigated using a series of advanced techniques, including focused ion beam–scanning electron microscopy and atomic force microscopy. This review establishes a model of pore distribution of the layered structure of shale reservoirs based on ideal shale laminae model. Among them, quartz and carbonate laminae can be classified as grain laminae clay minerals and organic matter and pyrite can be combined into organic matter aggregate due to the symbiotic relationship between pyrite, organic matter and clay minerals. Microcracks of diverse diagenetic origins can be classified together. This review also systematically summarizes the microcharacterization techniques and different characteristics of organic-rich shale reservoirs, thereby paving the way for the establishment of shale cross-scale characterization techniques.

Mercury intrusion porosimetry (MIP) has been utilized for decades to obtain the pore size, pore volume and pore structure of variable porous media including inorganic rocks and organic rock (e.g., shales and coals). Diffusivity and permeability are the two crucial parameters that control gas transport in coals. The main purpose of this work is to derive the CH4 effective gas diffusivity and permeability in different rank coals with vitrinite reflectance of 0.46–2.79% Ro,m by MIP. Furthermore, regular CH4 diffusivity and permeability measurements are conducted to compare with the results of the derived CH4 diffusivity and permeability with MIP data. In this work, CH4 diffusivity and permeability of different rank coals are acquired with established equations, which are basically in accordance with the experimental values. However, the coal rank (maximum vitrinitere flectance, Ro,m) exhibits no significant relation to the effective diffusion coefficient (De) and gas diffusivity (D′). The cementation factor (m values) varies from 2.03 to 2.46, which tends to exhibit a semi-consolidated structure for coals compared with other rocks (e.g., dolomite, limestone, sandstone and red brick). The results show that the cementation factor could be an important factor for gas flow in coals. The correlation of CH4 diffusivity to porosity and permeability of 12 coal samples were explored, and it appears that CH4 diffusivity exhibits an increasing trend with an increase of permeability, and two different exponential relationships respectively exist in diffusivity versus porosity and permeability versus porosity. Therefore, this study could be conducive to gas sequestration or gas production during enhanced coalbed methane (CBM) recovery.