Soil salinity is one of the major factors causing land degradation and desertification on earth, especially its important damage to farming activities and land-use management in arid and semiarid regions. The salt-affected land is predominant in the Keriya River area of Northwestern China. Then, there is an urgent need for rapid, accurate, and economical monitoring in the salt-affected land. In this study, we used the electrical conductivity (EC) of 353 ground-truth measurements and predictive capability parameters of WorldView-2 (WV-2), such as satellite band reflectance and newly optimum spectral indices (OSI) based on two dimensional and three-dimensional data. The features of spectral bands were extracted and tested, and different new OSI and soil salinity indices using reflectance of wavebands were built, in which spectral data was pre-processed (based on First Derivative (R-FD), Second Derivative (R-SD), Square data (R-SQ), Reciprocal inverse (1/R), and Reciprocal First Derivative (1/R-FD)), utilizing the partial least-squares regression (PLSR) method to construct estimation models and mapping the regional soil-affected land. The results of this study are the following: (a) the new OSI had a higher relevance to EC than one-dimensional data, and (b) the cross-validation of established PLSR models indicated that the β-PLSR model based on the optimal three-band index with different process algorithm performed the best result with R²_V = 0.79, Root Mean Square Errors (RMSE_V) = 1.51 dS·m⁻¹, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions.

Hyperspectral remote sensing technology can provide a rapid and nondestructive method for soil nickel (Ni) content detection. In order to select a high-effective method for estimating the soil Ni content using a hyperspectral remote sensing technique, 88 soil samples were collected in Urumqi, northwest China, to obtain Ni contents and related hyperspectral data. At first, 12 spectral transformations were used for the original spectral data. Then, Pearson’s correlation coefficient analysis (PCC) and the CARS method were used for selecting important wavelengths. Finally, partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) models were used to establish the hyperspectral inversion models of the Ni content in the soil using the important wavelengths. The coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were selected to evaluate the inversion effects of the models. The results indicated that using the PCC and CARS method for the original and transformed wavebands can effectively improve the correlations between the spectral data and Ni content of the soil in the study area. The random forest regression model, based on the first-order differentiation of the reciprocal (RTFD–RFR), was more stable and had the best inversion effects, with the highest predictive ability (R² = 0.866, RMSE = 1.321, MAE = 0.986, RPD = 2.210) for determining the Ni content in the soil. The RTFD–RFR methods can be used as a means of the inversion of the Ni content in urban soil. The results of the study can provide a technical support for the hyperspectral estimation of the Ni content of urban soil.

With the rapid development of industry, heavy metal pollution in farmland soil environments has become a common concern globally. To explore the environmental risks in farmlands, three typical heavy metals (Hg, Pb and Cd) in 232 surface soil samples (0-20 cm) from representative farmland areas in Midong District, Urumqi city, Xinjiang, were comprehensively investigated. The results revealed the highest soil heavy metal content for Pb (32.7 mg/kg), followed by Cd (0.74 mg/kg) and Hg (0.41 mg/kg). The average Hg and Cd concentrations in the rice and sweet potato cultivation areas were 0.071 and 0.21 mg/kg and 0.065 and 0.21 mg/kg, respectively, which exceeded the background values in Xinjiang (0.017 and 0.12 mg/kg, respectively). The average concentrations of the three heavy metals in the cotton and corn cultivation areas all exceeded the Xinjiang background values. The soil pollution level was evaluated via the pollution load index and Nemerow composite index. The results indicated varying soil Pb (slight: 65.52 %), Cd (moderate: 31.03 %) and Hg (severe: 1.72 %) pollution levels. The risk of heavy metals in soil was quantified via the human health risk assessment and Monte Carlo simulation methods, thereby calculating the risk of soil heavy metals to human health. In human health risk assessment, the overall noncarcinogenic risks of adults and children were low, whereas the overall carcinogenic risk of children was greater than that of adults. The Monte Carlo simulation results revealed that Pb yielded the lowest overall cancer risk in children and adults, whereas Cd mots notably influenced the carcinogenic risk in children. Among the four crops, the noncarcinogenic and carcinogenic risks at the corn planting sites were greater than those at the other planting sites. The average total carcinogenic risk (TCR) values at the sweet potato, cotton and rice planting sites all exceeded 1E-06. Combining the random forest and XGBoost models, the following three heavy metal sources were determined through principal component analysis: F1 (anthropogenic activities), F2 (industrial activities), and F3 (long-term phosphorus fertilizer use). Overall, this study emphasized the importance of monitoring heavy metal pollution in farmland soil, providing a sound basis for effectively managing heavy metal pollution in farmland soil and ensuring farmland soil quality maintenance.

Using remote sensing technology, this study evaluates changes in mountain grassland ecosystem service values (ESVs) and the factors driving them. Degraded grasslands in Urumqi city, northwest China, reached 1.22×105 ha in 2000. However, the grassland degradation rate decreased by 387 ha annually from 2000 to 2015. Total grassland ESVs decreased unpredictably with an annual reduction of USD$1.64×107. Ecological losses were mainly caused by degradation of mountain desert grassland, mountain desert steppe-type rangeland, and mountain meadow and mountain steppe-type rangeland. Among individual ESVs, grasslands contributed more to regulating and supporting services, while grassland provisioning and cultural services were relatively low, comprising 0. 7 and 0.5% of the total ESV respectively. The mean Moran’s I value was 0.869 for grassland ESVs (from 1994 to 2015), and exhibited high spatial autocorrelation. A driving factor analysis indicated that grassland degradation is continuing as a consequence of meteorological factors and overgrazing. Controlling grazing intensity has a positive impact on growth of grassland ESVs.

Betalains (Bt) are a collective group of natural, edible, red-yellow plant pigments, and are an important biochemical parameter in vegetable growth. Betalains are believed to have fungicidal activity and their presence in plants is a key to explaining the physiological response and resistance of plants caused by different environmental stress factors or seasonal fluctuations. Hyperspectral remote sensing has proven potential to understand many biochemical processes widely used in investigating vegetation growth condition. To better understand vegetation containing betalain pigments, for growth monitoring and agronomic decision-making, a novel betalain source, Suaeda salsa (S. salsa) was studied by extracting betalain at different phenological periods, and registering the results with time-course measurements of canopy spectral reflectance via hyperspectral remote imaging. Partial least squares regression (PLSR) analysis was undertaken with three spectral transformation methods (the raw hyperspectral reflectance (R), first derivative reflectance (FDR) and second derivative reflectance (SDR). The variable importance in projection (VIP) score resulting from PLSR model was used to determine the key spectral wavelengths and reduce the dimensionality of the hyperspectral reflectance data matrix. The study results demonstrated that Bt content had a significant correlation with the effective wavelength (FD-R_436nm, R_847nm, R_848nm, R_860nm, R_871nm, R_971nm and SD-R_437nm, R_496nm, R_499nm, R_508nm, R_740nm, R_772nm, R_774nm, R_780nm) and optimized spectral indices (simple normalized difference spectral indices (NDSI_{(802nm,408nm)}, NDSI_{(781nm,776nm)}), normalized polarization indices (NPDI_{(781nm,775nm)}, NPDI_{(781nm,776nm)}), ratio spectral indices (RSI_{(781nm,776nm)}, RSI_{(781nm,775nm)}), chlorophyll indices(CI_{(781nm,776nm)}, CI_{(781nm, 775nm)}) derived from first derivative spectral reflectance. The PLSR-5 (constructed with the spectral indices) model resulted in an R²_val (determination coefficient) of 0.79 and root mean square error (RMSE_val) was 0.81 μg cm⁻². Compared to PLSR-3 model built by sensitive bands, the PLSR-5 model estimation accuracy increased 15%. These results suggest the possibility of estimating Bt content using hyperspectral indices, which contain sufficient information for a successful estimation. Study findings will help researchers in deciding suitable Bt indices for S. salsa growth status and stress assessment, and will facilitate detection growth condition of the vegetables with betalain pigments.

Background: Global biodiversity and ecosystem services are threatened by invasive alien plants. Ragweed (Ambrosia artemisiifolia L.), a globally problematic weed, has rapidly spread across Xinjiang's Ili Prefecture—particularly in Xinyuan County—and presents serious obstacles to local agriculture, animal husbandry, and human health. However, in the context of climate change, the fine-scale distribution dynamics and key driving mechanisms of this species remain poorly understood. Methods: Based on field survey data from Xinyuan County, a Kuenm-optimized MaxEnt model was combined with four shared socioeconomic pathway (SSP) scenarios from CMIP6 to systematically simulate the spatiotemporal evolution of the potential habitat of ragweed at present and from the 2030s to the 2090s. Results: (1) The optimized model exhibited exceptional predictive accuracy (AUC = 0.991; Partial ROC ratio = 1.959, p < 0.001; empirical omission rate = 0.0267), with precipitation seasonality (BIO15) and isothermality (BIO3) as the primary environmental factors constraining ragweed distribution. (2) Current high-risk zones predominantly exhibit distributions along river valley alluvial plains and road networks. Overlay analysis revealed that mountain steppe grasslands face the most severe stress, followed by low–middle mountain meadow grasslands, whereas mountain meadow steppe is least affected. (3) In the future, the total area of highly suitable ragweed habitats will decrease, with a significant decrease in the area of core suitable zones (mountain steppe). Under most future scenarios and periods, the distribution centroid is projected to exhibit predominantly westward and northwestward passive displacement (maximum displacement: 0.829 km under the SSP585 scenario in the 2050s), driven by the contraction of suitable habitat in low-elevation river valleys rather than by active colonization of high-elevation zones. High-elevation alpine grassland ecosystems (mountain meadow steppe and mountain desert steppe) consistently demonstrated strong resilience against ragweed invasion across all projected periods and scenarios. Discussion: This study reveals the response pattern of ragweed, characterized by "contraction in low-elevation core areas, passive westward–northwestward displacement of the distribution centroid, and maintenance of the alpine barrier." A zoned control strategy focused on implementing physical eradication and replacement restoration in river valley core areas and establishing early warning systems in the transitional zones between mountain grasslands and meadows is recommended to safeguard regional ecological security.