The onset date of spring phenology (SOS) is regarded as a key parameter for understanding and modeling vegetation–climate interactions. Inner Mongolia has a typical temperate grassland vegetation ecosystem, and has a rich snow cover during winter. Due to climate change, the winter snow cover has undergone significant changes that will inevitably affect the vegetation growth. Therefore, improving our ability to accurately describe the responses of spring grassland vegetation phenology to winter snow cover dynamics would enhance our understanding of changes in terrestrial ecosystems due to their responses to climate changes. In this study, we quantified the spatial-temporal change of SOS by using the Advanced Very High Resolution Radiometer (AVHRR) derived Normalized Difference Vegetation Index (NDVI) from 1982 to 2015, and explored the relationships between winter snow cover, climate, and SOS across different grassland vegetation types. The results showed that the SOS advanced significantly at a rate of 0.3 days/year. Winter snow cover dynamics presented a significant positive correlation with the SOS, except for the start date of snow cover. Moreover, the relationship with the increasing temperature and precipitation showed a significant negative correlation, except that increasing Tmax (maximum air temperature) and Tavg (average air temperature) would lead a delay in SOS for desert steppe ecosystems. Sunshine hours and relative humidity showed a weaker correlation.

The start date of vegetation growing season (SOS) is generally considered as an essential indicator to reflect vegetation growth condition. To date, relatively little research has explored the combined effects of temperature and precipitation on the responses of forest spring phenology to snow cover in detail. To investigate this, we applied the developed plant phenology index (PPI) derived from Moderate Resolution Image Spectroradiometer (MODIS) to estimate SOS over Northeast China from 2004 to 2018, and explored the relationship between SOS and climate variables, such as temperature, precipitation and snow cover. Our results indicated that winter snow cover dynamics had a significant effect on the forest spring growth in the following year. SOS showed a negative correlation with snow cover duration (SCD) and the ending date of snow cover (SCED), whereas had a positive correlation with the onset date of snow cover (SCOD). It implied that a longer SCD, later SCED and earlier SCOD would promote forest growth. Furthermore, we first revealed that SOS was more closely associated with the preseason temperature than winter temperature. Most regions exhibited a significant positive correlation with increasing preseason temperature, but the winter temperature showed an opposite pattern except for the cool-temperate needleleaf forest region. Meanwhile, SOS had a negative relationship with precipitation, especially for preseason precipitation. Furthermore, with increasing of temperature and precipitation in winter, the responses of SOS to snow cover phenology in temperate needleleaf and broadleaf mixed-forest region are contrary to that in the other three regions. During the period from SCED to SOS, the responses of SOS to snow cover phenology varied among different vegetation zones and the gradients of temperature and precipitation.INDEX TERMS Climate change, spring phenology, forest, snow cover.

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

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The optimal use of hemispheric‐scale snow depth (SD) products for various hydrometeorological applications requires a comprehensive assessment of their quality. Most previous validation studies of SD products adopted in situ observations as the ground truth, which may cause representativeness errors due to spatial scale mismatch between point‐based ground SD measurements and grid‐based SD products. The extended triple collocation (ETC) technique is a powerful tool to estimate the uncertainty of three independent data sets without assuming any one data source is an error‐free “truth” reference. This study first used the ETC to assess the uncertainty of three types of hemispheric‐scale SD products, including the ground‐based analysis Canadian Meteorological Centre (CMC), the satellite‐based Advanced Microwave Scanning Radiometer 2 (AMSR2), and the model‐based Global Land Data Assimilation System (GLDAS) SD products. Furthermore, the uncertainties of each SD product were analyzed using ETC metrics, that is, the correlation coefficient (R) and error standard deviations (STDs), with respect to several environmental and perturbing factors. Overall, the CMC outperforms the AMSR2 and GLDAS, with a higher R and a smaller STD. Considering multiple environmental and perturbing factors, the poorest performance of the three SD products is mainly found in densely vegetated regions, and they are strongly related to the forest cover fraction and surface roughness. Despite the above factors, the best performance for all three SD products is found over temperate climate regions. The results demonstrate the usefulness of the ETC approach to quantify the uncertainty of SD products particularly in remote regions with sparse in situ measurements.