Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R² = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.

The summer precipitation in Northeast China (NEC) exerts vital effects on the local crop yield and food security of our country, however, the prediction skill of it is still limited as yet. This study investigated the impact of snow cover anomaly over the Russian Far East during spring (March–April) on the succeeding early summer (May–June) precipitation variability in NEC as well as the underlying physical mechanisms during the years 1961–2020 based on diagnostic analyses and model simulation. The results showed that, on the interannual timescale, the decreased (increased) spring snow cover over the Russian Far East is conducive to the increased (decreased) NEC early summer precipitation. Further analyses indicated that the negative anomaly of spring snow cover can persist into early summer, which then enhances the diabatic heating and thereby warms the overlying atmosphere via the snow‐albedo effect. The warming atmosphere favors the intensified Okhotsk High and an associated prominent anticyclonic circulation over northeast Asia. The anomalous southeast winds on the southwest flank of the anticyclone could bring abundant water vapor to the NEC, induce moisture convergence and ascending motion, and eventually contribute to the above‐than‐normal precipitation. These physical processes operate in the opposite manner in the case of the positive anomaly of spring snow cover. Our findings are beneficial to further improve the understanding and prediction ability of the summer precipitation variability over NEC.

Quantifying forest biomass is an important part of determining the regional carbon balance, but currently there is little knowledge regarding forest carbon storage at a high spatial resolution. Here, we combined a deep learning (DL) algorithm with field measurements, light detection and ranging (LiDAR) observations, Landsat and ALOS/PALSAR images to develop a spatially explicit estimate of forest aboveground carbon density at a 30 m spatial resolution for northeast China, home to nearly one-third of China’s forested area. We also conducted an uncertainty analysis using the bootstrap method. The DL method performed well, with a high coefficient of determination (R2) of 0.84 and a relatively low root mean squared error of 6.28 MgC ha-1, and is superior to traditional machine learning methods such as random forest, support vector machine and artificial neural network. The forest carbon storage is estimated to be 2.43 ± 0.10 PgC, and increases along the latitude gradient. Among climatic factors, the wettest month precipitation and annual mean temperature stand out in explaining the spatial variation of forest carbon density, with contributions reaching 15.8% and 10.8%, respectively. A model-data comparison shows that current ecosystem models generally capture the spatial pattern of forest carbon density but underestimate the forest carbon storage by 22.2%, partially due to the overestimation of high-temperature inhibition, highlighting the need to re-parameterize such temperature effects in forest carbon simulations.