Located at the junction between the continental climate region and marine climate region, the Qilian Mountains have experienced significant climate change. Vegetation phenology in the Qilian Mountains is sensitive to climate change. However, the response of vegetation phenology to temperature and precipitation change is still unclear, and the same is true for their interactions. First, we extracted grassland phenological parameters such as SOS (the start of the growing season), EOS (the end of the growing season), and LOS (the length of the growing season) from revised MODIS-NDVI data in the Qilian Mountains during the period from 2000 to 2019. Second, we analyzed change trends of the phenological parameters, temperature, and precipitation. Furthermore, the effects of each meteorological element changes and their interaction on multiple phenological parameters were detected using the GeoDetector method. The result implied that (1) the SOS in most areas except the northwestern mountain region showed an advanced trend (10 d/10a); the EOS showed a delayed trend in the southeast (5 d/10a), and an advanced trend (5 d/10a) in the northwest; the LOS showed an extended trend (10 d/10a) in the southeast, and a shortened trend (5 d/10a) in the northwest. (2) Compared with a single meteorological element in a single period, the interaction of temperature and precipitation in different periods had a higher impact on grassland phenology, with the maximum q-value increasing by about 0.4 for each phenological parameter. (3) The change in the grassland phenology in the Qilian Mountains was inconsistently complete with climate change in the spatial distribution. Our research reveals the response of grassland phenology to the interaction of different meteorological elements in different periods. Compared with a single element, this can reflect the response of vegetation phenology to climate change more comprehensively.

The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both frequently used method to construct estimation model based on remote sensing imaging. The aim of this study was to find out which estimation model of apple tree canopy chlorophyll content based on the vegetation indices constructed with visible, red edge and near-infrared bands of the sensor of Sentinel-2 was more accurate and stabler. The results were as follows: The calibration set coefficient of determination (R²) value of 0.729 and validation set R² value of 0.667 of the model using the SVMR method based on the vegetation indices (NDVI_green + NDVI_red + NDVI_re) were higher than those of the model using the BPNN method by 8.2% and 11.0%, respectively. The calibration set root mean square error (RMSE) of 0.159 and validation set RMSE of 0.178 of the model using the SVMR method based on the vegetation indices (NDVI_green + NDVI_red + NDVI_re) were lower than those of the model using the BPNN method by 5.9% and 3.8%, respectively.

The influence of the equidistant sampling method was explored in a hyperspectral model for the accurate prediction of the water content of apple tree canopy. The relationship between spectral reflectance and water content was explored using the sample partition methods of equidistant sampling and random sampling, and a stepwise regression model of the apple canopy water content was established. The results showed that the random sampling model was Y = 0.4797 − 721787.3883 × Z₃ − 766567.1103 × Z₅ − 771392.9030 × Z₆; the equidistant sampling model was Y = 0.4613 − 480610.4213 × Z₂ − 552189.0450 × Z₅ − 1006181.8358 × Z₆. After verification, the equidistant sampling method was verified to offer a superior prediction ability. The calibration set coefficient of determination of 0.6599 and validation set coefficient of determination of 0.8221 were higher than that of the random sampling model by 9.20% and 10.90%, respectively. The root mean square error (RMSE) of 0.0365 and relative error (RE) of 0.0626 were lower than that of the random sampling model by 17.23% and 17.09%, respectively. Dividing the calibration set and validation set by the equidistant sampling method can improve the prediction accuracy of the hyperspectral model of apple canopy water content.

It is necessary to assess and analyze the factors that influence surface water since they are crucial to human activities such as agriculture, raising livestock, and industry. Previous research has mostly focused on how land use and landscape patterns affect the quality of surface waters; it has seldom addressed the industrial and agricultural production activities that are directly connected to human society. Therefore, the research area’s surface water quality was assessed by single factor index (SFI) and composite water quality index (WQI), divided into flood and non-flood periods, and water quality indicators with severe pollution and significant seasonal variations were selected; A total of 28 indicators were selected from three main factors-topography, socio-economic, and land use type-and analyzed using the Spearman correlation coefficient model. (1) SFI data reveal substantial seasonal changes in pH, DO, NH₃-N, TN, and TP water quality indicators. The well-developed agricultural and aquaculture in the studied region is the primary cause of the excess TN and NH3-N concentrations; (2) The sample points’ water quality index (WQI) scores range from 50 to 80, with 62% of them having “medium” water quality; (3) The study area’s seasonal variation in water quality is primarily caused by human socio-economic activities (GDP, industrial effluent discharge, COD discharge, aquatic product quality, and the proportion of primary, secondary, and tertiary industries), as well as land use type (forest, shrubland, and cropland). Topography has little effect on the study area’s surface water quality. This study offers a fresh viewpoint on surface water quality management and driver analysis, and a new framework for managing and safeguarding aquatic ecosystems.