Vegetation phenology is a commonly used indicator of ecosystem responses to climate change and plays a vital role in ecosystem carbon and hydrological cycles. Previous studies have mostly focused on the response of vegetation phenology to temperature and precipitation. Soil moisture plays an important role in maintaining vegetation growth. However, our understanding of the influences of soil moisture dynamics on vegetation phenology is sparse. In this study, using a time series of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (MODIS) dataset (2001–2020), the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS) in the Qilian Mountains (QLMs) were extracted. The spatiotemporal patterns of vegetation phenology (SOS, EOS, and LOS) were explored. The partial coefficient correlations between the SOS, EOS, and seasonal climatic factors (temperature, precipitation, and soil moisture) were analyzed. The results showed that the variation trends of vegetation phenology were not significant (p > 0.05) from 2001 to 2020, the SOS was advanced by 0.510 d/year, the EOS was delayed by 0.066 d/year, and the LOS was prolonged by 0.580 d/year. The EOS was significantly advanced and the LOS significantly shortened with increasing altitude. The seasonal temperature, precipitation, and soil moisture had spatiotemporal heterogeneous effects on the vegetation phenology. Overall, compared with temperature and soil moisture, precipitation had a weaker influence on the vegetation phenology in the QLMs. For different elevation zones, the temperature and soil moisture influenced the vegetation phenology in most areas of the QLMs, and spring temperature was the key driving factor influencing SOS; the autumn soil moisture and autumn temperature made the largest contributions to the variations in EOS at lower (3500 m a.s.l.), respectively. For different vegetation types, the spring temperature was the main factor influencing the SOS for broadleaf forests, needleleaf forests, shrublands, and meadows because of the relative lower soil moisture stress. The autumn soil moisture was the main factor influencing EOS for deserts because of the strong soil moisture stress. Our results demonstrate that the soil moisture strongly influences vegetation phenology, especially at lower elevations and water-limited areas. This study provides a scientific basis for better understanding the response of vegetation phenology to climate change in arid mountainous areas and suggests that the variation in soil moisture should be considered in future studies on the influence of climate warming and environmental effects on the phenology of water-limited areas.

Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that (1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R2 0.04~0.39, and RMSE 859.68 kg/ha ~ 1075.09 kg/ha, respectively; (2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate non-parameter models; (3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45 % - 44.27 %. Meanwhile, RF models can explain 89.41 % variation in AGB . This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy of alpine grassland AGB estimation based on MODIS data were greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVI_MOD, NDVI_CCD and NDVI_OLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVI_OLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R² and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R² and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend.

Topographic factors are critical for influencing vegetation distribution patterns, and studying the interactions between them can enhance our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000–2019), combined with the Digital Elevation Model (DEM), and vegetation type data for trend analysis, and explored NDVI variation and its relationship with topographic factors through an integrated geographically-weighted model in the Three Parallel Rivers Region (TPRR) of southeastern Qinghai-Tibet Plateau (QTP) in the past 20 years. Our results indicated that there was no significant increase of NDVI in the entire basin between 2000–2019, except for the Lancang River basin. In the year 2004, abrupt changes in NDVI were observed across the entire basin and each sub-basin. During 2000–2019, the mean NDVI value of the whole basin increased initially and then decreased with the increasing elevation. However, it changed marginally with variations in slope and aspect. We observed a distinct spatial heterogeneity in vegetation patterns with elevation, with higher NDVI in the southern regions NDVI than those in the north as a whole. Most of the vegetation cover was concentrated in the slope range of 8~35°, with no significant difference in distribution except flat land. Furthermore, from 2000 to 2019, the vegetation cover in the TPRR showed an improving trend with the changes of various topographic factors, with the largest improvement area (36.10%) in the slightly improved category. The improved region was mainly distributed in the source area of the Jinsha River basin and the southern part of the whole basin. Geographically weighted regression (GWR) analysis showed that elevation was negatively correlated with NDVI trends in most areas, especially in the middle reaches of Nujiang River basin and Jinsha River basin, where the influence of slope and aspect on NDVI change was considerably much smaller than elevation. Our results confirmed the importance of topographic factors on vegetation growth processes and have implications for understanding the sustainable development of mountain ecosystems.