Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai–Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai–Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R²) of 0.76 and a root mean square error (RMSE) of 70 g/m². The average AGB for alpine meadows was determined to be 285 g/m², while for alpine steppes, it was 204 g/m², both surpassing the regional averages in the Qinghai–Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

Plant phenology provides an integrative biometeorological indicator of how agroecosystems respond to climate variability and long-term change. The Lanzhou lily ( Lilium davidii var. unicolor ), a key edible–medicinal bulb crop in Northwest China, is cultivated in a temperate semi-arid region that has experienced pronounced warming and a concurrent decline in sunshine duration over recent decades. Using continuous field phenological observations (1995–2024) and daily meteorological records, we quantified long-term trends in four phenological metrics (emergence, full bloom, shoot senescence, and growing season length) and identified phase-specific climatic sensitivity windows via a sliding-window correlation approach. We then parameterized multiple regression models using the optimal windows to quantify the relative roles of temperature, sunshine duration, and precipitation. Over the 30-year period, emergence, full bloom, and shoot senescence advanced significantly (1.06, 1.23, and 1.69 days per decade, respectively), while growing season length shortened (0.67 days per decade). Temperature exerted a consistent advancing effect across all phases, whereas sunshine duration acted in the opposite direction by delaying full bloom and senescence. Precipitation contributed a minor, late-season influence on growing season length. The identified response windows (from early spring to late summer) provide a practical basis for biometeorological monitoring and phenology forecasting of this perennial bulb crop under ongoing climate change.