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• We compared vegetation phenology from a SIF product (CSIF) and from MODIS NDVI. • We found similar spatial patterns in CSIF and NDVI derived phenology. • NDVI data indicated an earlier start and later end of the growing season than CSIF. • Models based on leaf greenness may overestimate photosynthesis active period. Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. Here, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change. [ABSTRACT FROM AUTHOR]

Highlights • The 2009/2010 drought in Southwest China has damaged vegetation growth. • Ecosystem impacts by drought vary across space and vegetation types. • Precipitation deficit is the main cause of decreased vegetation growth. • Most of the drought-impacted area could recover within six months from drought. • The carbon flux responses to this drought are poorly captured by models. Abstract The 2009/2010 drought in Southwest China was a "once in a century drought" event. This drought event had strong adverse impacts, such as water scarcity, crop failure, and economic loss, on ecosystems and the human society. Explicit representations of this drought event and associated changes in vegetation dynamics and carbon cycle, however, are still largely missed in literature. Here we used the standardized anomaly of 3-month Standard Precipitation-Evapotranspiration Index (SPEI) to characterize this 2009/2010 drought event, including its onset and end months, duration and severity. We examined the drought impacts on vegetation greenness using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and assessed the terrestrial carbon flux responses to drought using data from satellite-based datasets, atmospheric inversion, one data-based model, and three ecosystem models. Our analysis showed that this "autumn-winter-spring" drought mostly suppressed vegetation growth in Yunnan, North Guangxi, Guizhou and East Sichuan. In comparisons to the same months of the reference period (2000–2015), the drought caused a large reduction in carbon uptake (4.4 ± 5 gCm−2month-1) from the decrease in gross primary production (GPP, 5.7 ± 9.5 gCm−2month-1), although ecosystem respiration (TER) also decreased by a smaller extent (1.3 ± 5.1 gCm−2month-1). Nonetheless, more than 65% of the drought-impacted area recovered in both vegetation greenness and productivity within 3 months, while about 10% of the drought-affected area failed to recover even after 6 months post the drought event. Across different vegetation types, forests and shrubs had stronger capability of drought resistance and shorter post-drought recovery time than crops and grasses. Model comparison reveals that while data from different sources generally agreed on the sign of vegetation greenness and carbon flux responses to drought, they differ significantly in the extent of such responses. [ABSTRACT FROM AUTHOR]

Abstract: Phenological changes are closely related to the carbon cycle of terrestrial ecosystems, and satellite data have been widely used in large scale phenological research. Numerous methods have been developed to reconstruct distinct satellite derived vegetation signals from continuous vegetation index time series and to track the points corresponding to important phenological events. In this study, we perform a multiple-method investigation of the spring vegetation growth onset phenology in temperate China north of 30°N with NDVI (normalized difference vegetation index) data produced from SPOT satellites. The results indicated that the spring onset dates estimated from five different methods show similar spatial pattern along latitudinal or altitudinal gradients, but with significant variances in the dates. In most areas (76% of pixels), the standard deviation (SD) of the estimated spring onset dates from different methods is within one month, while in a few places (2% of pixels), mostly agricultural cropland, the SD is more than 60 days. Comparing the satellite derived green-up onset dates against the gross primary productivity (GPP) time series curves acquired from six eddy covariance towers further highlights the strengths and limits of different methods in estimating phenological events. In a vast region with diverse vegetation types and physical environment, it is critical to choose the “right” method for the “right” place. Generally climate factors such as temperature and precipitation play an important role in controlling the start of vegetation growth in temperate China. Yet this climate-phenology relationship varies largely across different regions and vegetation types. [Copyright &y& Elsevier]

Abstract: Research in phenology change has been one heated topic of current ecological and climate change study. In this study, we use satellite derived NDVI (Normalized Difference Vegetation Index) data to explore the spatio-temporal changes in the timing of spring vegetation green-up in the Qinghai-Xizang (Tibetan) Plateau from 1982 to 2006 and to characterize their relationship with elevation and temperature using concurrent satellite and climate data sets. At the regional scale, no statistically significant trend of the vegetation green-up date is observed during the whole study period (R 2 =0.00, P =0.95). Two distinct periods of green-up changes are identified. From 1982 to 1999, the vegetation green-up significantly advanced by 0.88daysyear−1 (R 2 =0.56, P <0.001). In contrast, from 1999 to 2006, a marginal delaying trend is evidenced (R 2 =0.44, P =0.07), suggesting that the persistent trend towards earlier vegetation green-up in spring between 1980s and 1990s was stalled during the first decade of this century. This shift in the tendency of the vegetation green-up seems to be related to differing temperature trends between these two periods. Statistical analysis shows that the average onset of vegetation green-up over the Qinghai-Xizang Plateau would advance by about 4.1 days in response to 1°C increase of spring temperature. In addition, results from our analysis indicate that the spatial patterns of the vegetation green-up date and its change since 1982 are altitude dependent. The magnitude of the vegetation green-up advancement during 1982–1999, and of its postponement from 1999 to 2006 significantly increases along an increasing elevation gradient. [Copyright &y& Elsevier]

Increasing anthropogenic nitrogen (N) deposition will affect the global carbon (C) cycle, although the mechanisms and magnitudes of its effects are not yet fully understood. Soil respiration (Rs) and its components (i.e., heterotrophic (Rh) and autotrophic (Ra) respiration) represent the largest flux of carbon dioxide (CO 2 ) from terrestrial ecosystems to the atmosphere. However, the potentially interactive effect of N addition and stand age on Rs in forests remains unclear. We conducted a 7-year (2010–2016) field experiment in three differently-aged larch plantations (11-, 20-, and 45-year-old, representing sapling, intermediate, and mature stands) to investigate the effects of different N addition rates (control: no N addition; low-N addition, N20: 20 kg N ha −1  year −1 ; and high-N addition, N50: 50 kg N ha −1  year −1 ) on Rs. We found significant seasonal variation in Rs, Rh, and Ra, with levels highest in summer and lowest in autumn. Rs correlated strongly with soil temperature and weakly with soil moisture, and Rs, Rh, and Ra increased exponentially with soil temperature. The temperature sensitivity (Q 10 ) of Rs was lowest in the mature stand, which suggests that it will sequester relatively more C in a warmer climate. The Q 10 of Ra was higher than that of Rh, indicating that it contributed proportionally more to the total soil CO 2 efflux. N addition had no significant effect on Q 10 . Rs in the intermediate stand was significantly lower than Rs in the sapling and mature stands, which may be due to its lower soil temperature and poorer substrate quality. N addition decreased Rs by an average of 9%, but significant effects were only observed in the sapling and intermediate stands. The negative influence of N addition on Rs was largely dependent on soil temperature, with more negative effects observed in the summer when temperatures were higher, and attributable to a reduction in either Ra in the intermediate stand or Rh in the sapling stand. We hypothesize that N limitation in the young trees was alleviated by fertilization, which decreased C allocation to roots, whereas N addition may have exerted a more negative influence on soil microbial activity in the sapling stand. In summary, our results highlight the importance of stand age in regulating the negative effects of N addition on Rs and its components, which has implications for projecting the global C–climate feedback in the future. [ABSTRACT FROM AUTHOR]

Carbon use efficiency (CUE), defined as the ratio of net primary production (NPP) to gross primary production (GPP), represents the capacity of plants in converting assimilated atmospheric carbon dioxide to ecosystem carbon storage. Process-based models are important tools for simulating NPP and GPP; yet the model performance in simulating vegetation CUE has not been fully explored. The goal of this paper is thus to investigate the spatial variations in CUE from different process-based carbon cycle models in comparison with that from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and to analyze their linkage with climate factors. The global average CUE derived from the five process-based models is 0.45 ± 0.05 (range from 0.38 to 0.52), slightly lower than the value of 0.48 obtained from MODIS data. A strong latitudinal gradient of CUE, with greater CUE at high latitudes, is well agreed by these different datasets. However, there also exist considerable discrepancies in CUE estimations among those products, especially in temperate Northern Hemisphere. Furthermore, for both the satellite-based dataset and results from process-based models, vegetation CUE declines non-linearly with increase in temperature, but remains relatively stable with enhanced precipitation. Our results also indicate that the differences in global patterns of CUE estimated by different approaches could be primarily resulted from their systematic differences in autotrophic respiration (R a ) rather than in GPP. Understanding mechanisms behind spatio-temporal changes in R a is therefore a critical step towards better quantifying global CUE. [ABSTRACT FROM AUTHOR]

Accurate assessment of the strength of China’s terrestrial ecosystem carbon sink is key to understanding its regional carbon budget. However, large uncertainties in current carbon sink estimations still exist, which hinder the prediction of future climate change trajectories. In this study, we generated a high-resolution (1 km × 1 km) dataset of China’s net ecosystem productivity (NEP) in the last decade via a model tree ensemble approach combined with data from 46 flux sites in China and neighboring regions. The upscaling also included detailed information on nitrogen (N) deposition and forest age that have often been neglected in previous studies. The performance of MTE algorithm in simulating NEP at the site level is relatively high for both training (R 2  = 0.81, RMSE = 0.73 gC m −2  day −1 ) and validation datasets (R 2  = 0.76, RMSE = 0.81 gC m −2  day −1 ). Our data-driven estimation showed that roughly 70% of the area is a carbon sink, and the largest carbon sinks are found in the southeast and southwest monsoon regions. The total annual NEP in China in the last decade was 1.18 ± 0.05 Pg C yr −1 , which is similar to the results found by another foundational global-scale study. Yet, the two studies significantly differ in the spatial distribution of carbon sink density. The seasonality of China’s NEP is characterized by region-specific kurtosis and skewness in most areas. Furthermore, ecosystem carbon use efficiency (CUE), defined as the annual NEP/GPP ratio, also showed high spatial variation. For example, the Xiaoxing’anling and Changbai Mountains in northeastern China, the eastern edge of the Tibetan Plateau, and bordering areas of the southeast and southwest monsoon regions have a larger CUE than the rest of China. On average, China’s terrestrial ecosystem CUE is approximately 0.17. Our data-driven NEP and CUE estimates provide a new tool for assessing China’s carbon dioxide flux. Our study also highlights the necessity to incorporate more environmental variables related to vegetation growth and more data derived from flux sites into NEP upscaling to reduce uncertainties in carbon budget estimations. [ABSTRACT FROM AUTHOR]

Abstract: Over the last century, precipitation has shown significant changes, reflecting both natural variability and radiative forcing changes from greenhouse gases and aerosols. Arid and semi-arid ecosystems are particularly sensitive to changes in precipitation regimes. In this study, we investigate how variation in annual total, seasonal distribution and frequency of precipitation affect CO2 fluxes of semi-arid grassland in Inner Mongolia. To this aim, we combine eddy-covariance measurements with a process-based model (ORCHIDEE). First, the ORCHIDEE parameters were optimized using half-hourly CO2 flux data and actual precipitation forcing. Second, the response of CO2 fluxes to altered precipitation scenarios is computed using the model with optimized parameters. Our results show that modeled net primary production (NPP) responds non-linearly to increased vs. decreased rainfall. Re-allocating precipitation from other seasons to spring enhances annual NPP and net ecosystem production (NEP). By contrast, re-allocating more precipitation to autumn was found to decrease annual NPP and NEP. Increasing the frequency of heavy rainfall days (PF10mm, days with precipitation more than 10mm) induces a positive response of simulated NPP during the growing season. However, the increase of the frequency of moderately rainy days (PF5–10mm, days with precipitation between 5mm and 10mm) rather increases soil heterotrophic respiration. Taken together, our modeling results are consistent with that of grassland field manipulation experiments. This study highlights the importance of changes in precipitation seasonality and frequency for semi-arid grassland. Further investigations should focus on testing the ability of models to reproduced manipulation experiment data, and on evaluating the performances of climate models to reproduce rainfall intensity/frequency in semi-arid areas. [Copyright &y& Elsevier]