Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), extracted from the Moderate Resolution Imaging Spectrometer (MODIS), are widely used to monitor phenology by calculating land surface reflectance. However, the applicability of the vegetation index based on 'greenness' to monitor photosynthetic activity is hindered by poor observation conditions (e.g., ground shadows, snow, and clouds). Recently, satellite measurements of solar-induced chlorophyll fluorescence (SIF) from OCO-2 sensors have shown great potential for studying vegetation phenology. Here, we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China, exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI. The results show that NDVI has obvious SOS advance and EOS lag, and EVI is closer to SIF. The growing season length based on SIF is often the shortest, while it can represent the true phenology of vegetation because it is closely related to photosynthesis. SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices. We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions. The results show that the length of growing season of vegetation in predominantly continuous permafrost (zone I) is longer than in permafrost with isolated taliks (zone II). Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

The estimation of post-fire vegetation recovery is essential for forest management and wildfire policy-making. In the last few decades, vegetation indices have been widely used to monitor post-fire vegetation recovery by comparison with the pre-fire state. In this study, vegetation recovery is estimated using Solar-Induced chlorophyll Fluorescence (SIF), which is a by-product of photosynthesis and can reflect the physiological characteristics of a plant. We found that 20 years is insufficient for vegetation recovery, as the SIF within burned areas exhibited a significant increasing trend, which was most notable within the first 6 to 10 years after a wildfire. When comparing the SIF within and outside burned areas, we found that, during the first 3 to 6 years, SIF values outside burned areas were larger than that within burned areas; however, after ~6 years, the SIF within the burned areas exceeded that outside burned areas owing to the different carbon sequestration intensities of different vegetation recovery stages. Field photos of recovering vegetation were then compared with the Enhanced Vegetation Index (EVI) trend within the burned area, and it was found that, although the EVI reached pre-fire levels or stabilised, vegetation recovery was continuing.

The decomposition of coarse woody debris (CWD) affects the energy flow and nutrient cycling in forest ecosystems. Previous studies on CWD have focused on the input, decomposition, reserve dynamics, and CWD functions, but coarse woody debris decomposition is complex and the results from different regions vary considerably. It is not clear which factors affect decay rate (k), especially at different decomposition stages. In this study, a single-exponential decay model was used to analyze the characteristics of CWD decomposition in Larix gmelinii forests over the 33 years following a fire in the Greater Khingan Mountains. The results show that the decay rate of coarse woody debris was positively correlated to decay class. The average decomposition rate was 0.019, and 41 years and 176 years are needed for a 50% and 95% mass loss, respectively. CWD nutrient content, density, and water content could explain the variance in the decay rate (~ 42%) of the decay factors such as amount of leaching, degree of fragmentation, respiration of the debris, and biotransformation, and varied significantly between different decay classes. Using the space–time substitution method, this study arranged the coarse woody debris of different mortality times to form a 33 year chronosequence which revealed the decomposition process. It was concluded that the decay rate was mainly explained by structural component of the debris and its nitrogen and water contents. This paper quantifies the indicators affecting CWD decay to explain the decomposition process.