Time-lag effects of global vegetation responses to climate change.

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time-lag effects, which are the most important mechanism of climate-vegetation interactive effects. Extensive studies focused on large-scale vegetation-climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time-lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time-lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time-lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the time-lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time-lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time-lag effects; (iii) for the area with a significant change trend (for the period 1982-2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time-lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.

Changes in forest biomass and linkage to climate and forest disturbances over Northeastern China.

The forests of northeastern China store nearly half of the country's total biomass carbon stocks. In this study, we investigated the changes in forest biomass by using satellite observations and found that a significant increase in forest biomass took place between 2001 and 2010. To determine the possible reasons for this change, several statistical methods were used to analyze the correlations between forest biomass dynamics and forest disturbances (i.e. fires, insect damage, logging, and afforestation and reforestation), climatic factors, and forest development. Results showed that forest development was the most important contributor to the increasing trend of forest biomass from 2001 to 2010, and climate controls were the secondary important factor. Among the four types of forest disturbance considered in this study, forest recovery from fires, and afforestation and reforestation during the past few decades played an important role in short-term biomass dynamics. This study provided observational evidence and valuable information for the relationships between forest biomass and climate as well as forest disturbances.

Improved global 250 m 8-day NDVI and EVI products from 2000-2021 using the LSTM model.

Satellite vegetation index (VI) products, such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), have been widely used. However, they are severely contaminated by clouds and other factors and provide false signals of the surface vegetation conditions. In this study, the new global seamless 250 m, eight-day NDVI and EVI products from 2000-2021 were developed from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data using a long short-term memory (LSTM) neural network method. High-quality globally representative time series VI samples were constructed to train the model using a combination of the Savitzky-Golay filter (SG), Global LAnd Surface Satellite (GLASS) leaf area index (LAI) fitting and upper envelope methods. To evaluate the proposed method and the 250 m VI products, the MODIS VI product (MOD13Q1) was used for the inter-comparisons using four widely used VI reconstruction methods. Assuming that the MODIS VI data of high quality represents the true values, the root mean square error (RMSE) for NDVI and EVI generated by the LSTM model are 0.0734 and 0.0509, respectively.

Positive effects of tree diversity on tropical forest restoration in a field-scale experiment.

Experiments under controlled conditions have established that ecosystem functioning is generally positively related to levels of biodiversity, but it is unclear how widespread these effects are in real-world settings and whether they can be harnessed for ecosystem restoration. We used remote-sensing data from the first decade of a long-term, field-scale tropical restoration experiment initiated in 2002 to test how the diversity of planted trees affected recovery of a 500-ha area of selectively logged forest measured using multiple sources of satellite data. Replanting using species-rich mixtures of tree seedlings with higher phylogenetic and functional diversity accelerated restoration of remotely sensed estimates of aboveground biomass, canopy cover, and leaf area index. Our results are consistent with a positive relationship between biodiversity and ecosystem functioning in the lowland dipterocarp rainforests of SE Asia and demonstrate that using diverse mixtures of species can enhance their initial recovery after logging.

Vegetation-based climate mitigation in a warmer and greener World.

The mitigation potential of vegetation-driven biophysical effects is strongly influenced by the background climate and will therefore be influenced by global warming. Based on an ensemble of remote sensing datasets, here we first estimate the temperature sensitivities to changes in leaf area over the period 2003-2014 as a function of key environmental drivers. These sensitivities are then used to predict temperature changes induced by future leaf area dynamics under four scenarios. Results show that by 2100, under high-emission scenario, greening will likely mitigate land warming by 0.71 ± 0.40 °C, and 83% of such effect (0.59 ± 0.41 °C) is driven by the increase in plant carbon sequestration, while the remaining cooling (0.12 ± 0.05 °C) is due to biophysical land-atmosphere interactions. In addition, our results show a large potential of vegetation to reduce future land warming in the very-stringent scenario (35 ± 20% of the overall warming signal), whereas this effect is limited to 11 ± 6% under the high-emission scenario.

Factors contributing to spatial-temporal variations of observed oxygen concentration over the Qinghai-Tibetan Plateau.

Oxygen (O2) is the most abundant molecule in the atmosphere after nitrogen. Previous studies have documented that oxygen concentration remains nearly constant (20.946%) at all altitudes. Here we show for the first time that oxygen concentration varies significantly from earlier consensus and shows strong spatial and seasonal differences. Field observations on the Qinghai-Tibetan Plateau (QTP) indicate oxygen concentration of 19.94-20.66% (2018, n = 80), 19.98-20.78% (2019, n = 166) and 19.97-20.73% (2020, n = 176), all statistically different from earlier reports (p < 0.001) and are lower than the nearly constant. The mean oxygen concentration in summer (20.47%) is 0.31% higher than that of winter (20.16%) (n = 53) at identical locations in 2019, sampled in the Qilian Mountains, northwest QTP. We used LMG (The Lindeman, Merenda and Gold) method to estimate the relative contributions of altitude, air temperature and vegetation index (Fractional Vegetation Cover, FVC and Leaf Area Index, LAI) to oxygen concentration, which are 47%, 32% and 3% (FVC, R2 = 82%); 45%, 30% and 7% (LAI, R2 = 82%), respectively. These findings provide a new perspective for in-depth understanding on population risk in high altitude regions in the context of global climate change, to ensure the health and safety of residents and tourists in high altitude regions and promoting the stability, prosperity and sustainable development of high-altitude regions worldwide.

Quantifying the response of potential flooding risk to urban growth in Beijing.

Global urban growth leads to a great increase in the impervious surface area (ISA) such as roads, plazas, airports, and parking lots, and consequently reshapes hydrological regimes in urban basins. Beijing, the capital of China, has experienced rapid urban growth since the 1980s. However, the spatial-temporal variability of the ISA and its impact on flooding risk are unclear. This study monitored urban growth (i.e., the evolution of the ISA) in Beijing for the period of 1980-2015 based on Landsat data, and identified the response of surface runoff yield using a land-surface hydrological model. The modeling at a relatively high spatial resolution (~6 km) was driven with retrieved long-term ISA dynamics, Global LAnd Surface Satellite (GLASS) product, and climate forcings. The results show that the impervious surface fraction (ISF) in Beijing increased from 8.73% (1448.16 km2) in 1980 to 22.22% (3685.92 km2) in 2015. With a demarcation at around the year 2000, the ISA growth presents a new pattern with a northeast-southwest direction from the Core Functional Zone (Core-Zone). Due to the ISA expansion, the simulated runoff coefficient in 2010 is approximately doubled compared to that of 1980. We identified an ISF threshold of approximately 6%, beyond which every 1% increase in the ISF may increase the surface runoff by approximately 5.51 mm/year, and thereby poses a high potential flooding risk even under a moderate rainfall event. In four typical historical storms, the sensitivity coefficients of surface runoff to precipitation and ISF were 0.97 and 0.63, respectively, indicating impervious surfaces dramatically enhanced the potential flooding risk. Our findings have implications for urban planning and the construction of sponge city in Beijing.

[Estimating leaf area index by fusing MODIS and MISR data].

Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.

Strong cooling induced by stand-replacing fires through albedo in Siberian larch forests.

The Siberian larch forests, taking up about a fifth of the global boreal biome, are different from the North American boreal forests in that they generally do not undergo a secondary succession. While wildfires in the boreal forests in North America have been shown to exert a cooling effect on the climate system through a sharp increase in surface albedo associated with canopy removal and species composition change during succession, the magnitude of the surface forcing resulting from fire-induced albedo change and its longevity in Siberia have not been previously quantified. Here we show that in contrast to previous expectations, stand-replacing fires exert a strong cooling effect similar in magnitude to that in North America. This cooling effect is attributable to the increase in surface albedo during snow-on periods. However, the observed earlier snowmelt in the region, and subsequently a longer snow-free season, has resulted in a warming effect which has the potential to offset the fire-induced cooling. The net albedo-induced forcing of the Siberian larch forests in the future would hinge on the interaction between the fire-induced cooling effect and the climate-induced warming effect, both of which will be impacted by the expected further warming in the region.

Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting.

The speeds of both Arctic surface warming and sea-ice shrinking have accelerated over recent decades. However, the causes of this unprecedented phenomenon remain unclear and are subjects of considerable debate. In this study, we report strong observational evidence, for the first time from long-term (1984-2014) spatially complete satellite records, that increased cloudiness and atmospheric water vapor in winter and spring have caused an extraordinary downward longwave radiative flux to the ice surface, which may then amplify the Arctic wintertime ice-surface warming. In addition, we also provide observed evidence that it is quite likely the enhancement of the wintertime greenhouse effect caused by water vapor and cloudiness has advanced the time of onset of ice melting in mid-May through inhibiting sea-ice refreezing in the winter and accelerating the pre-melting process in the spring, and in turn triggered the positive sea-ice albedo feedback process and accelerated the sea ice melting in the summer.

An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations.

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.

Observed contrast changes in snow cover phenology in northern middle and high latitudes from 2001-2014.

Quantifying and attributing the phenological changes in snow cover are essential for meteorological, hydrological, ecological, and societal implications. However, snow cover phenology changes have not been well documented. Evidence from multiple satellite and reanalysis data from 2001 to 2014 points out that the snow end date (De) advanced by 5.11 (±2.20) days in northern high latitudes (52-75°N) and was delayed by 3.28 (±2.59) days in northern mid-latitudes (32-52°N) at the 90% confidence level. Dominated by changes in De, snow duration days (Dd) was shorter in duration by 5.57 (±2.55) days in high latitudes and longer by 9.74 (±2.58) days in mid-latitudes. Changes in De during the spring season were consistent with the spatiotemporal pattern of land surface albedo change. Decreased land surface temperature combined with increased precipitation in mid-latitudes and significantly increased land surface temperature in high latitudes, impacted by recent Pacific surface cooling, Arctic amplification and strengthening westerlies, result in contrasting changes in the Northern Hemisphere snow cover phenology. Changes in the snow cover phenology led to contrasting anomalies of snow radiative forcing, which is dominated by De and accounts for 51% of the total shortwave flux anomalies at the top of the atmosphere.

Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome.

The satellite-derived normalized difference vegetation index (NDVI), which is used for estimating gross primary production (GPP), often includes contributions from both mosses and vascular plants in boreal ecosystems. For the same NDVI, moss can generate only about one-third of the GPP that vascular plants can because of its much lower photosynthetic capacity. Here, based on eddy covariance measurements, we show that the difference in photosynthetic capacity between these two plant functional types has never been explicitly included when estimating regional GPP in the boreal region, resulting in a substantial overestimation. The magnitude of this overestimation could have important implications regarding a change from a current carbon sink to a carbon source in the boreal region. Moss abundance, associated with ecosystem disturbances, needs to be mapped and incorporated into GPP estimates in order to adequately assess the role of the boreal region in the global carbon cycle.

Satellite-based analysis of evapotranspiration and water balance in the grassland ecosystems of Dryland East Asia.

The regression tree method is used to upscale evapotranspiration (ET) measurements at eddy-covariance (EC) towers to the grassland ecosystems over the Dryland East Asia (DEA). The regression tree model was driven by satellite and meteorology datasets, and explained 82% and 76% of the variations of ET observations in the calibration and validation datasets, respectively. The annual ET estimates ranged from 222.6 to 269.1 mm yr(-1) over the DEA region with an average of 245.8 mm yr(-1) from 1982 through 2009. Ecosystem ET showed decreased trends over 61% of the DEA region during this period, especially in most regions of Mongolia and eastern Inner Mongolia due to decreased precipitation. The increased ET occurred primarily in the western and southern DEA region. Over the entire study area, water balance (the difference between precipitation and ecosystem ET) decreased substantially during the summer and growing season. Precipitation reduction was an important cause for the severe water deficits. The drying trend occurring in the grassland ecosystems of the DEA region can exert profound impacts on a variety of terrestrial ecosystem processes and functions.

Clear sky visibility has decreased over land globally from 1973 to 2007.

Visibility in the clear sky is reduced by the presence of aerosols, whose types and concentrations have a large impact on the amount of solar radiation that reaches Earth's surface. Here we establish a global climatology of inverse visibilities over land from 1973 to 2007 and interpret it in terms of changes in aerosol optical depth and the consequent impacts on incident solar radiation. The aerosol contribution to "global dimming," first reported in terms of strong decreases in measured incident solar radiation up to the mid-1980s, has monotonically increased over the period analyzed. Since that time, visibility has increased over Europe, consistent with reported European "brightening," but has decreased substantially over south and east Asia, South America, Australia, and Africa, resulting in net global dimming over land.