Vegetation phenology has been used in studies as an indicator of an ecosystem’s responses to climate change. Satellite remote sensing techniques can capture changes in vegetation greenness, which can be used to estimate vegetation phenology. In this study, a long-term vegetation phenology study of the Greater Khingan Mountain area in Northeastern China was performed by using the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) dataset from the years 1982–2012. After reconstructing the NDVI time series, the start date of the growing season (SOS), the end date of the growing season (EOS) and the length of the growing season (LOS) were extracted using a dynamic threshold method. The response of the variation in phenology with climatic factors was also analyzed. The results showed that the phenology in the study area changed significantly in the three decades between 1982 and 2012, including a 12.1-day increase in the entire region’s average LOS, a 3.3-day advance in the SOS and an 8.8-day delay in the EOS. However, differences existed between the steppe, forest and agricultural regions, with the LOSs of the steppe region, forest region and agricultural region increasing by 4.40 days, 10.42 days and 1.71 days, respectively, and a later EOS seemed to more strongly affect the extension of the growing season. Additionally, temperature and precipitation were closely correlated with the phenology variations. This study provides a useful understanding of the recent change in phenology and its variability in this high-latitude study area, and this study also details the responses of several ecosystems to climate change.

Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m²/m² and 0.23 m²/m² for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m²/m²), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m²/m² and 0.35 m²/m² for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m²/m² to 0.28 m²/m² for PLSR and from 0.33 m²/m² to 0.28 m²/m² for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m²/m² to 0.32 m²/m²).