Soil pH is crucial for the quality of the farmland and crop growth. The objective of this study is to analyze the spatial and temporal variations of farmland soil pH in Xinjiang (XJ), and to provide a scientific basis for soil improvement and agricultural production optimization. Based on soil pH data from XJ farmland in two periods, 2008~2010 and 2019~2021, geostatistical methods and kriging spatial interpolation techniques were employed to analyze the spatiotemporal changes in soil pH and to investigate the relationship between relevant influencing factors and pH over more than a decade. The results indicate that the spatiotemporal distribution of soil pH in XJ farmlands is uneven. Between 2019 and 2021, the average soil pH was 8.11, generally being on the higher side, with a coefficient of variation (CV) of 3.85%, indicating minimal spatial variability. In the farmland soil of Southern Xinjiang (S-XJ), the average pH value was 8.17, significantly higher than that of Northern Xinjiang (N-XJ), which was 8.10, demonstrating a spatial pattern of “higher in the south, lower in the north”. Over the past decade, soil pH in XJ has significantly increased from 8.11 to 8.13, with a 0.05 unit increase in the north and a 0.01 unit increase in the south (p < 0.05). Regionally, Altay saw the largest increase of 0.22 units, while Bortala Mongol Autonomous Prefecture (Bortala) experienced the most significant decrease, dropping by 0.59 units. Furthermore, this study found that factors such as topography, nutrients, and irrigation methods all have certain influences on the spatial distribution of soil pH in XJ farmland, while variations in climate factors and fertilization levels may affect its long-term temporal changes. These research findings will provide new insights for adjusting and updating agricultural management measures related to soil pH regulation in XJ.

The advent of precision agriculture has highlighted the necessity for the careful determination of crop phenology at increasingly smaller scales. Although remote sensing technology is extensively employed for the monitoring of crop growth, the acquisition of high-precision phenological data continues to present a significant challenge. This study, conducted in Youyi County, Shuangyashan City, Heilongjiang Province, China, employed time-series spectral index data derived from Sentinel-2 remote sensing images to investigate methodologies for the extraction of pivotal phenological phases during the primary growth stages of maize. The data were subjected to Savitzky–Golay (S-G) filtering and cubic spline interpolation in order to denoise and smooth them. The combination of dynamic thresholding with slope characteristic node recognition enabled the successful extraction of the jointing and tasseling stages of maize. Furthermore, a comparison of the extraction of phenophases based on the time-series curves of the NDVI, EVI, GNDVI, OSAVI, and MSR was conducted. The results showed that maize exhibited different sensitivities to the spectral indices during the jointing and tasseling stages: the OSAVI demonstrated the highest accuracy for the jointing stage, with a mean absolute error of 3.91 days, representing a 24.8% improvement over the commonly used NDVI. For the tasseling stage, the MSR was the most accurate, achieving an absolute error of 4.87 days, with an 8.6% improvement compared to the NDVI. In this study, further analysis was conducted based on maize cultivation data from Youyi County (2021–2023). The results showed that the maize phenology in Youyi County in 2021 was more advanced compared to 2022 and 2023, primarily due to the higher average temperatures in 2021. This study provides valuable support for the development of precision agriculture and maize phenology monitoring and also provides a useful data reference for future agricultural management.

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts.

Spatiotemporal variability of soil salinity and the driving factors of cultivated land in Xinjiang, China