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Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.

ABSTRACTAccurately estimating gross primary productivity (GPP), the largest carbon flux in terrestrial ecosystems, is crucial for advancing our understanding of global carbon cycle and predicting climate feedbacks. The advancements in remote sensing (RS) have facilitated the development of GPP estimation models at regional and global scales in recent decades. This article systemically reviews the development of RS-based GPP estimation in three main aspects: theoretical foundation, key parameters and methods. Regarding the theoretical foundation, RS generally excels in representing key characteristics during the light transmission process of photosynthesis. However, it exhibits a relatively weaker ability to describe the carbon reaction process, severely limiting the in-depth understanding of the mechanisms of RS-based GPP estimation. Concerning key parameters, the definition of traditional parameters, such as leaf area index (LAI), photosynthetically active radiation (PAR), and fraction of absorbing PAR, has been detailed in the development of RS (e.g. LAI is divided into sunlit LAI and shaded LAI). However, their accuracy still needs improvement. Additionally, researchers have developed effective parameters (e.g. photochemical reflectance index, sun-induced chlorophyll fluorescence, and the maximum carboxylation rate) that possess increased capability to represent and interpret the carbon reaction process of photosynthesis. Regarding estimation methods, although the four main categories of RS-based GPP estimation models (statistical model, light use efficiency model, RS-based process model and machine learning-based model) have made significant progress in parameter optimization, the estimation accuracy and mechanism innovation remain less than satisfactory. Finally, we summarize the current issues of RS-based GPP estimation related to parameters performance and accuracy, model mechanisms and capabilities, as well as scale and connotation mismatch. Integrating more adequate in situ and comprehensive observations would enhance the interpretability of GPP estimation models, providing more reliable insights into the mechanisms in future studies. This article contributes to understanding of the photosynthetic process and RS-based GPP estimation, potentially aiding in the development of parameter optimization (improving the estimation accuracy of existing parameters and developing new ones) and model design (introducing new parameters and exploring new mechanistic models).

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex and difficult to implement. In this study, we proposed a new statistical feature extraction method, called distance coding diversity (DCD), which can measure the spatial arrangement of texture primitives with a rotation invariant characteristic. The texture features extracted by DCD were widely tested on a large dataset at three different scales: 50 digital matrix samples at the pixel neighborhood scale, 2508 pairs of small block images of pre- and post-damaged buildings at the image object scale, and five large-scale images covering different main land cover types (cropland, buildings, plantations, natural vegetation and abandoned dumps) at the regional landscape scale, and the test results were compared with Entropy of Grey-level Co-occurrence Matrix (GLCM) and Short Run Emphasis (SRE) of Grey Level Run Length Matrix (GLRLM). DCD can effectively measure the spatial arrangement of texture primitives, and its sensitivity to spatial arrangement is 98%, which is more than 5 times that of SRE (with a sensitivity value of 18%). The identification accuracy for different land cover types based on DCD improved by 12.31% over Entropy, and 14.40% over SRE. Due to the simple algorithm and superior performance, DCD can be widely used for object recognition and land cover/use classification and has wide application prospects.

Abstract Temperature sensitivity (TS) of the green‐up date (GUD) of plants is crucial for the prediction of grassland phenology that is important for animal husbandry and pasture management. Spatial variations in the TS are known to reflect interannual temperature variability and/or accumulated precipitation preceding the GUD (pre‐GUD). However, whether spatial TS variations are related to the interaction between pre‐GUD temperature variability and precipitation, which is a potential indicator of frost risk, remains unclear. Furthermore, because the interaction between interannual temperature variability and accumulated precipitation following the GUD (post‐GUD) can exert selection pressure on the plant life cycle, it may also be involved in shaping the spatial TS pattern. Using long‐term ground observations of GUD on the Tibetan Plateau, we show that TS is more negative (greater GUD advance per unit temperature increase) in areas with more pre‐GUD precipitation and low pre‐GUD interannual temperature variability, but less negative in areas with more pre‐GUD precipitation and high pre‐GUD interannual temperature variability. This result is likely because more pre‐GUD precipitation facilitates sprout and leaf development under stable temperature conditions, whereas it increases frost risk when the temperature variability is high. In contrast, TS magnitude decreases with increases in post‐GUD precipitation in areas where post‐GUD interannual temperature variability is low, but increases with post‐GUD precipitation in areas where post‐GUD interannual temperature variability is high. We speculate that because hydrothermal demands for leaf growth from the onset of green‐up to maturity are more easily fulfilled when interannual temperature variability is lower and precipitation is higher, green‐up need not be sensitive to pre‐GUD temperature. In contrast, high post‐GUD precipitation likely aggravates low‐temperature constraints on leaf growth when temperature variability is high, resulting in greater TS to maximize growing season length. These results suggest that spatial TS variations on the Tibetan Plateau are likely associated with adaptations of leaf‐out phenology to background pre‐GUD climatic conditions together with selection pressure from post‐GUD conditions.

AbstractTo improve the anti-metastasis effects of honokiol (HNK) on breast cancer, we designed cationic liposomes (Lip) in which HNK was encapsulated into Lip, and its surface was modified with negatively charged polysialic acid (PSA-Lip-HNK) for efficient treatment of breast cancer. PSA-Lip-HNK possessed a homogeneous spherical shape and high encapsulation efficiency. In vitro 4T1 cell experiments indicated that PSA-Lip-HNK increased cellular uptake and cytotoxicity via the endocytosis pathway mediated by PSA and selectin receptors. Furthermore, the significant antitumor metastasis impact of PSA-Lip-HNK was confirmed by wound healing and cell migration and invasion. Enhanced in vivo tumor accumulation of the PSA-Lip-HNK was observed in 4T1 tumor-bearing mice by living fluorescence imaging. For in vivo antitumor experiments using 4T1 tumor-bearing mice, PSA-Lip-HNK exhibited a higher tumor growth and metastasis inhibition compared with unmodified liposomes. Therefore, we believe that PSA-Lip-HNK well combined biocompatible PSA nano-delivery and chemotherapy, providing a promising drug delivery approach for metastatic breast cancer therapy.

Most of the existing remote sensing-based yield estimation methods adopt the mean or cumulative value of meteorological factors within the whole growing season, which may ignore the impact of adverse meteorological conditions on the growth of winter wheat in a certain phenological period. In this study, we distinguished the developmental progression of winter wheat as three phenological periods. In each phenological period, the vegetation indices and meteorological factors were optimized. Then the accuracy and spatiotemporal transferability of the phenological piecewise modelling was compared with that of the whole-season modelling based on four regression methods (i.e. multiple linear regression, artificial neural network, support vector regression and random forest). The results showed that the optimal combinations of variables for the whole-season modelling and the phenological piecewise modelling were different. Compared with the whole-season models, the R2 for the phenological piecewise models improved by 1.4% to 7.6%, the root mean square error (RMSE) decreased by 1.1% to 8.2% among four regression methods . In addition, compared with the whole-season models, the spatiotemporal transferability for the phenological piecewise models was generally better. The accuracies after spatiotemporal transfer for the phenological piecewise models were still higher than that for the whole-season models.

Currently, the obvious side effects of anti-tumor drugs, premature drug release, and low tumor penetration of nanoparticles have largely reduced the therapeutic effects of chemotherapy. A drug delivery vehicle (MCN-SS-GQDs) was designed innovatively. For this, the mesoporous carbon nanoparticles (MCN) with the capabilities of superior photothermal conversion efficiency and high loading efficiency were used as the skeleton structure, and graphene quantum dots (GQDs) were gated on the mesopores via disulfide bonds. The doxorubicin (DOX) was used to evaluate the pH-, GSH-, and NIR-responsive release performances of DOX/MCN-SS-GQDs. The disulfide bonds of MCN-SS-GQDs can be ruptured under high glutathione concentration in the tumor microenvironment, inducing the responsive release of DOX and the detachment of GQDs. The local temperature of a tumor increases significantly through the photothermal conversion of double carbon materials (MCN and GQDs) under near-infrared light irradiation. Local hyperthermia can promote tumor cell apoptosis, accelerate the release of drugs, and increase the sensitivity of tumor cells to chemotherapy, thus increasing treatment effect. At the same time, the detached GQDs can take advantage of their extremely small size (5–10 nm) to penetrate deeply into tumor tissues, solving the problem of low permeability of traditional nanoparticles. By utilizing the photothermal properties of GQDs, synergistic photothermal conversion between GQDs and MCN was realized for the purpose of synergistic photothermal treatment of superficial and deep tumor tissues.

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

The light use efficiency (LUE) model has been widely used in regional and global terrestrial gross primary productivity (GPP) estimation due to its simple structure, few input parameters, and particular theoretical basis. As a key input parameter of the LUE model, the maximum LUE (Ɛmax) is crucial for the accurate estimation of GPP and to the interpretability of the LUE model. Currently, most studies have assumed Ɛmax as a universal constant or constants depending on vegetation type, which means that the spatiotemporal dynamics of Ɛmax were ignored, leading to obvious uncertainties in LUE-based GPP estimation. Using quality-screened daily data from the FLUXNET 2015 dataset, this paper proposed a photosynthetically active radiation (PAR)-regulated dynamic Ɛmax (PAR-Ɛmax, corresponding model named PAR-LUE) by considering the nonlinear response of vegetation photosynthesis to solar radiation. The PAR-LUE was compared with static Ɛmax-based (MODIS and EC-LUE) and spatial dynamics Ɛmax-based (D-VPM) models at 171 flux sites. Validation results showed that (1) R2 and RMSE between PAR-LUE GPP and observed GPP were 0.65 (0.44) and 2.55 (1.82) g C m−2 MJ−1 d−1 at the 8-day (annual) scale, respectively; (2) GPP estimation accuracy of PAR-LUE was higher than that of other LUE-based models (MODIS, EC-LUE, and D-VPM), specifically, R2 increased by 29.41%, 2.33%, and 12.82%, and RMSE decreased by 0.36, 0.14, and 0.34 g C m−2 MJ−1 d−1 at the annual scale; and (3) specifically, compared to the static Ɛmax-based model (MODIS and EC-LUE), PAR-LUE effectively relieved the underestimation of high GPP. Overall, the newly developed PAR-Ɛmax provided an estimation method utilizing a spatiotemporal dynamic Ɛmax, which effectively reduced the uncertainty of GPP estimation and provided a new option for the optimization of Ɛmax in the LUE model.

Abstract An enzyme-responsive colon-specific delivery system was developed based on hollow mesoporous silica spheres (HMSS) to which biodegradable chitosan (CS) was attached via cleavable azo bonds (HMSS–N=N–CS). Doxorubicin (DOX) was encapsulated in a noncrystalline state in the hollow cavity and mesopores of HMSS with the high loading amount of 35.2%. In vitro drug release proved that HMSS–N=N–CS/DOX performed enzyme-responsive drug release. The grafted CS could increase the biocompatibility and stability and reduce the protein adsorption on HMSS. Gastrointestinal mucosa irritation and cell cytotoxicity results indicated the good biocompatibility of HMSS and HMSS–N=N–CS. Cellular uptake results indicated that the uptake of DOX was obviously increased after HMSS–N=N–CS/DOX was preincubated with a colonic enzyme mixture. HMSS–N=N–CS/DOX incubated with colon enzymes showed increased cytotoxicity, and its IC50 value was three times lower than that of HMSS–N=N–CS/DOX group without colon enzymes. The present work lays the foundation for subsequent research on mesoporous carriers for oral colon-specific drug delivery.

Global warming has led to earlier spring green-up dates (GUDs) in recent decades with significant consequences for global carbon and hydrologic cycles. In addition to changes in climate, land cover change (LCC), including interchanges between vegetation and non-vegetation, and among plants with different functional traits, may also affect GUD. Here, we analyzed how satellite-derived GUD from 1992 to 2020 was impacted by changes in temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), solar radiation, and LCC for the Northern Hemisphere (>30° N). While the climate variables had larger impact overall, variability in GUD was controlled by LCC for 6% of the Northern Hemisphere, with systematically earlier or later changes among transitions between different land cover types. These changes were found mainly along the southeastern coast of the United States, in Central-north Europe, and across northeastern China. We further showed that climate change attribution of earlier GUD during 1992–2020 was overestimated by three days when the impact of LCC was ignored. Our results deepen the understanding of how LCC impacts GUD variability and enables scientists to more accurately evaluate the impact of climate change on land surface phenology.

Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas.

Pollen allergies have a serious impact on people’s physical and mental health. Accurate and efficient prediction of the outbreak date of pollen allergies plays an important role in the conservation of people sensitive to allergenic pollen. It is a frontier research to combine new social media data and satellite data to develop a model to forecast the outbreak date of pollen allergies. This study extracted the real outbreak dates of spring pollen allergies from Sina Weibo records from 2011 to 2021 in Beijing and calculated five vegetation indices of three vegetation types as phenological characteristics within the 30 days before the average outbreak date. The sensitivity coefficients and correlation coefficients were used to screen the phenological characteristics that best reflected the outbreak date of spring pollen allergy. Based on the best characteristic, two kinds of prediction models for the outbreak date of spring pollen allergy in Beijing were established (the linear fit prediction model and the cumulative linear fit prediction model), and the root mean square error (RMSE) was calculated as the prediction accuracy. The results showed that (1) the date of EVI2 (2-band enhanced vegetation index) in evergreen forest first reaching 0.138 can best reflect the outbreak date of pollen allergies in spring, and (2) the cumulative linear fit prediction model based on EVI2 in evergreen forests can obtain a high accuracy with an average RMSE of 3.6 days, which can predict the outbreak date of spring pollen allergies 30 days in advance. Compared with the existing indirect prediction models (which predict pollen concentrations rather than pollen allergies), this model provides a new direct way to predict pollen allergy outbreaks by using only remote sensing time-series data before pollen allergy outbreaks. The new prediction model also has better representativeness and operability and is capable of assisting public health management.

The existing of avidity cancer stem cells (CSCs) made it an optical strategy to kill cancer cells and CSCs at the same time. Here, we constructed a CSCs specific nanocarrier naming T-S-NLC using the CD133+ targeting peptide TISWPPR (TR) as the targeting moiety attached to the distal end of PEG on salinomycin (Sal) loaded nanostructured lipid carriers (NLC), its pharmaceutical characteristics proved it 128.73 ± 2.09 nm, anionic spheroid with sustained release profile. It's in vitro targeting effect in CD133+ CSCs indicated that it exhibited superior CSCs internalization over non-modified NLC or free drug. Afterwards, it was used in combination with previously designed EGFR specific A-P-NLC (AEYLR peptide-PEG-modified paclitaxel loaded NLC) to achieve the goal to kill the cancer cells and CSCs, simultaneously. The in vitro tumor targeting effect of T-S-NLC + A-P-NLC was affirmed by cellular uptake and proliferation inhibition effect in NCI-H1299 and S180 cell lines showing advanced results over single preparation groups. In vivo tumor targeting effect in S180 tumor-bearing mice also validated the better tumor accumulative effect of the combined group. Last but not least, the in vivo antitumor effect strongly identified the greater tumor suppression effect of T-S-NLC + A-P-NLC than single preparation groups or combined use of free drugs while maintaining a good living state of the mice. To sum up, the combined usage of PTX and Sal active targeting NLC naming A-P-NLC + T-S-NLC which killed cancer cells and CSCs at the same time was a promising drug delivery system.

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications.

Indirect influences of phenology on productivity (e.g., phenology-functional traits-productivity) could exist due to close associations between phenology and plant functional traits, which may further result in the divergent responses of vegetation biomass to phenology among plant functional groups (PFGs). Here, we introduced functional traits (i.e., plant height and growth rate) to analyse the influences of phenology on aboveground biomass (AGB) of two symbiotic PFGs based on 19-year in-situ observational data for plant phenology at 7 sites across the Tibetan Plateau alpine grasslands. The vegetative growth phase (VGP) and reproductive growth phase (RGP) were calculated according to the observed green-up date, first flowering date and first fruit-ripening date. The commonality analysis and structural equation modelling were applied to explore the paths and degrees of phenological influence on AGB. The results showed that the phenological influences on herbaceous AGB included both direct (“phenology-AGB”) and indirect paths (“phenology-functional traits-AGB”) and differed between two PFGs. Phenological metrics, especially VGP, were the key factors driving the adjustment (path coefficients |w| = 0.19 – 0.67), although maximum plant height (Hmax) contributed to the most to the AGB (|w| = 0.78). Specifically, the grasses had two indirect phenological influence paths on AGB, while the sedges had multiple direct and indirect influence paths. Comparing the differences in phenology and functional traits between two PFGs, our study implied that grasses may tend to preferentially adjust vegetative (stem) growth via phenology, while sedges may tend to preferentially adjust root and reproductive growth via phenology, which could be relevant to the differences in the synthesis, accumulation, allocation, and decomposition of organic matter in diverse organs and the growth and reproduction strategies, competition and symbiosis of different PFGs.

A spherical iron oxide precursor was prepared using a solvothermal method, and then treated thermally at 400°C to obtain α-Fe2O3 nanoparticles. The structures and morphology of the as-obtained products were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). The results showed that the diameter of the α-Fe2O3 nanoparticles was approximately 500 nm. In addition, we formed the α-Fe2O3 nanoparticles into a thick film as a gas sensor and performed a gas sensing test. When the working temperature was set at 250°C, the α-Fe2O3 nanoparticle displayed very good selectivity and high sensitivity for trimethylamine (TMA). The minimum detection was as low as 1 ppm, and the response value for 100 ppm TMA gas was 27.8. Taken together, our findings illustrated that the α-Fe2O3 nanoparticles could be used as a gas-sensitive material to test the freshness of fish.

Vegetation net primary productivity (NPP) is a core parameter regulating carbon cycles of terrestrial ecosystem, which also has close relations with climates. The alpine ecosystems on the Tibetan Plateau (TP) are highly sensitive to climate changes. However, systematic analyses on the seasonal and annual responses of NPP to climatic factors in different grassland types on the TP are still lacking. In this study, the spatial and temporal patterns of NPP and their responses to temperature, precipitation and solar radiation during 2001–2015 at seasonal and annual temporal scales were investigated based on outputs of an improved Carnegie–Ames–Stanford Approach (CASA) model. The improved CASA model showed solid performances in simulating NPP in reference to field observations (R2 = 0.79, P

Abstract In this study, glycyrrhetinic acid (GA) liposomes were successfully prepared using lyophilization monophase solution method. Preformulation studies comprised evaluation of solubility of soybean phosphatidylcholine (SPC), cholesterol, and GA in tert-butyl alcohol (TBA)/water co-solvent. The influences of TBA volume percentage on sublimation rate were investigated. GA after lyophilization using TBA/water co-solvent with different volume percentage was physicochemically characterized by DSC, XRD, and FTIR. The XRD patterns of GA show apparent amorphous nature. FTIR spectroscopy results show that no chemical structural changes occurred. Solubility studies show aqueous solubility of GA is enhanced. The optimum formulation and processing variables of 508 mg SPC, 151 mg cholesterol, 55% volume percentage of TBA, 4:1 trehalose/SPC weight ratio were obtained after investigating by means of Box-Benhnken design and selection experiment of lyoprotectant. Under the optimum conditions, satisfactory encapsulation efficiency (74.87%) and mean diameter (191 nm) of reconstituted liposomes were obtained. In vitro drug release study showed that reconstituted liposomes have sustained-release properties in two kinds of release medium. Furthermore, in vitro cell uptake study revealed that uptake process of drug-loaded liposomes by Hep G2 cells is time-dependent.

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

Abstract Global warming is dramatically altering the plant phenology of terrestrial ecosystem and thus has caused significant effects on the terrestrial carbon cycle. Many studies have investigated the relationships between the spring phenological changes and the dynamics of carbon cycle, but the seasonal differences in these relationships are still unclear. This study used eddy covariance measurements of grassland net ecosystem productivity (NEP) from nine FLUXNET sites (73 site‐years of data) to investigate the relationships between the spring phenological changes (i.e., early green‐up, EGU; middle green‐up, MGU; and late green‐up, LGU) and the dynamics of carbon cycle (including NEP; ecosystem respiration, ER; and gross ecosystem production, GEP) at a ten‐day scale. Weak relationships were found between the spring phenological changes and the dynamics of ER at all sites. The advance of spring phenology mainly caused significant increases in GEP (or NEP) during the initial 20–50 d after the green‐up date at most sites. With an advancement of 1 d in EGU (MGU and LGU), GEP and NEP were increased by 1.5–6.0 (3.5–7.1 and 2.4–4.7) and 0.7–3.6 (1.5–3.9 and 1.9–4.7) g C/m2 during the early growing season (EGS), respectively. The advance of spring phenology did not have significant impacts on GEP (or NEP) during the middle and late growing season (MGS and LGS) at most sites, but yielded a significant decrease in GEP (or NEP) during MGS at a few sites. As the grasslands turning green, the duration of the impact of the changes in EGU, MGU, and LGU on GEP (or NEP) was reduced, whereas the magnitude of the impact of the changes on the ten‐day cumulative GEP (or NEP) was increased at most sites. Thus, there were two possible impact models for the spring phenological changes on the dynamics of carbon cycle for grasslands: The advance of spring phenology only increases the GEP (or NEP) during EGS, and the advance of spring phenology only decreases the GEP (or NEP) during MGS. These results could be helpful in further understanding the influences of spring phenological changes on the dynamics of carbon cycle for grasslands.

Heading and flowering are two key phenological stages in the growth process of winter wheat. It is of great significance for agricultural management and scientific research to accurately monitor and forecast the heading and flowering dates of winter wheat. However, the monitoring accuracy of existing methods based on remote sensing needs to be improved, and these methods cannot realize forecasting in advance. This study proposed an accumulated temperature method (ATM) for monitoring and forecasting the heading and flowering dates of winter wheat from the perspective of thermal requirements for crop growth. The ATM method consists of three key procedures: (1) extracting the green-up date of winter wheat as the starting point of temperature accumulation with the dynamic threshold method from remotely sensed vegetation index (VI) time-series data, (2) calculating the accumulated temperature and determining the thermal requirements from the green-up date to the heading date or the flowering date based on phenology observation samples, and (3) combining the satellite-derived green-up date, daily temperature data, and thermal requirements to monitor and forecast the heading date and flowering date of winter wheat. When applying the ATM method to winter wheat in the North China Plain during 2017–2019, the root mean square error (RMSE) for the estimated heading date was between 4.76 and 6.13 d and the RMSE for the estimated flowering date was between 5.30 and 6.41 d. By contrast, the RMSE for the heading and flowering dates estimated by the widely used maximum vegetation index method was approximately 10 d. Furthermore, the forecasting accuracy of the ATM method was also high, and the RMSE was approximately 6 d. In summary, the proposed ATM method can be used to accurately monitor and forecast the heading and flowering dates of winter wheat in large spatial scales and it performs better than the existing maximum vegetation index method.

As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p < 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p > 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies.

Crop phenology is an important parameter for crop growth monitoring, yield prediction, and growth simulation. The dynamic threshold method is widely used to retrieve vegetation phenology from remotely sensed vegetation index time series. However, crop growth is not only driven by natural conditions, but also modified through field management activities. Complicated planting patterns, such as multiple cropping, makes the vegetation index dynamics less symmetrical. These impacts are not considered in current approaches for crop phenology retrieval based on the dynamic threshold method. Thus, this paper aimed to (1) investigate the optimal thresholds for retrieving the start of the season (SOS) and the end of the season (EOS) of different crops, and (2) compare the performances of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in retrieving crop phenology with a modified version of the dynamic threshold method. The reference data included SOS and EOS ground observations of three major crop types in 2015 and 2016, which includes rice, wheat, and maize. Results show that (1) the modification of the original method ensures a 100% retrieval rate, which was not guaranteed using the original method. The modified dynamic threshold method is more suitable to retrieve crop SOS/EOS because it considers the asymmetry of crop vegetation index time series. (2) It is inappropriate to retrieve SOS and EOS with the same threshold for all crops, and the commonly used 20% or 50% thresholds are not the optimal thresholds for all crops. (3) For single and late rice, the accuracies of the SOS estimations based on EVI are generally higher compared to those based on NDVI. However, for spring maize and summer maize, results based on NDVI give higher accuracies. In terms of EOS, for early rice and summer maize, estimates based on EVI result in higher accuracies, but, for late rice and winter wheat, results based on NDVI are closer to the ground records.

Spartina alterniflora is one of the most hazardous invasive plant species in China. Monitoring the changes in dominant plant species can help identify the invasion mechanisms of S. alterniflora, thereby providing scientific guidelines on managing or controlling the spreading of this invasive species at Jiuduansha Wetland in Shanghai, China. However, because of the complex terrain and the inaccessibility of tidal wetlands, it is very difficult to conduct field experiments on a large scale in this wetland. Hence, remote sensing plays an important role in monitoring the dynamics of plant species and its distribution on both spatial and temporal scales. In this study, based on multi-spectral and high resolution (

Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and characterized their driving forces using climatic data sets. A significant advancement trend was observed throughout the entire study area from 1999 to 2013 with a linear tendency of 6.3 days/decade (p < 0.01); the largest advancement trend was over the Yellow River source region (8.6 days/decade, p < 0.01). Spatially, the green-up date increased from the southeast to the northwest, and the green-up date of 87.4% of pixels fell between the 130th and 150th Julian day. Additionally, about 91.5% of the study area experienced advancement in the green-up date, of which 80.2%, mainly distributed in areas of vegetation coverage increase, experienced a significant advance. Moreover, it was found that the green-up date and its trend were significantly correlated with altitude. Statistical analyses showed that a 1-°C increase in spring temperature would induce an advancement in the green-up date of 4.2 days. We suggest that the advancement of the green-up date in the TRHR might be attributable principally to warmer and wetter springs.

In the current study, mesoporous carbon (MC) with pore volume (1.53 cm3/g) and pore size (9.74 nm) was successfully prepared as a carrier for celecoxib (CEL). Celecoxib was loaded into the pore channels of MC using three different methods: solvent evaporation method, absorption method and physical mixing method. Solid-state characterization methods, such as SEM, TEM, BET, DSC and XRD were used to systematically investigate the process of the drug loading system. Dissolution tests were performed to examine the effects of MC on the release of CEL. Furthermore, the cytotoxicity, wound healing, migration and invasion experiments were carried out to measure the contribution of MC to the anti-tumor metastasis ability of celecoxib on MDA-MB-231 cells. The results showed that CEL could be kept in a non-crystalline state when they were incorporated into the MC using the solvent evaporation method or absorption method. The dissolution rate of CEL released from MCS (Mesoporous carbon – Celecoxib – Solvent evaporation method) and MCA (Mesoporous carbon – Celecoxib – Absorption evaporation method) was all significantly higher than that of pure CEL. The cumulative release for MCS within the 5 min was up to 51.86%. MCS enhanced the inhibitory effect of CEL on the migration and invasion of MDA-MB-231 cells.

Mesoporous carriers have been extensively applied to improve the dissolution velocity and bioavailability of insoluble drugs. The goal of this work was to compare the drug-loading efficiency (LE) and drug-dissolution properties of mesoporous silica nanoparticles (MSN) and mesoporous carbon nanoparticles (MCN) as drug vectors oral delivery of water-insoluble drugs. For this purpose, MSN and MCN with similar particle size, surface area, and mesoporous diameter were prepared to precisely evaluate the effects of different textures on the drug-loading and dissolution behavior of insoluble drugs. Carvedilol (CAR), a Bio-pharmaceutic Classification System (BCS) class II drug, was loaded in the MSN and MCN by the solvent adsorption method and solvent evaporation method with different carrier−drug ratios. The carboxylated MCN (MCN−COOH) had a higher LE for CAR than MSN for both the two loading methods due to the strong adsorption effect and π−π stacking force with CAR. In vitro drug dissolution study showed that both MSN and MCN-COOH could improve the dissolution rate of CAR compared with the micronized CAR. In comparison to MSN, MCN-COOH displayed a slightly slower dissolution profile, which may be ascribed to the strong interaction between MCN-COOH and CAR. Observation of cell cytotoxicity and gastrointestinal mucosa irritation demonstrated the good biocompatibility of both MSN and MCN−COOH. The present study encourages further research of different carriers to determine their potential application in oral administration.

The leaf area index (LAI) is not only an important parameter used to describe the geometry of vegetation canopy but also a key input variable for ecological models. One of the most commonly used methods for LAI estimation is to establish an empirical relationship between the LAI and the vegetation index (VI). However, the LAI-VI relationships had high seasonal variability, and they differed among phenophases and VIs. In this study, the LAI-VI relationships in different phenophases and for different VIs (i.e., the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv)) were investigated based on 82 site-years of LAI observed data and the Moderate Resolution Imaging Spectroradiometer (MODIS) VI products. Significant LAI-VI relationships were observed during the vegetation growing and declining periods. There were weak LAI-VI relationships (p > 0.05) during the flourishing period. The accuracies for the LAIs estimated with the piecewise LAI-VI relationships based on different phenophases were significantly higher than those estimated based on a single LAI-VI relationship for the entire vegetation active period. The average root mean square error (RMSE) ± standard deviation (SD) value for the LAIs estimated with the piecewise LAI-VI relationships was 0.38 ± 0.13 (based on the NDVI), 0.41 ± 0.13 (based on the EVI) and 0.41 ± 0.14 (based on the NIRv), respectively. In comparison, it was 0.46 ± 0.13 (based on the NDVI), 0.55 ± 0.15 (based on the EVI) and 0.55 ± 0.15 (based on the NIRv) for those estimated with a single LAI-VI relationship. The performance of the three VIs in estimating the LAI also varied among phenophases. During the growing period, the mean RMSE ± SD value for the estimated LAIs was 0.30 ± 0.11 (LAI-NDVI relationships), 0.37 ± 0.11 (LAI-EVI relationships) and 0.36 ± 0.13 (LAI-NIRv relationships), respectively, indicating the NDVI produced significantly better LAI estimations than those from the other two VIs. In contrast, the EVI produced slightly better LAI estimations than those from the other two VIs during the declining period (p > 0.05), and the mean RMSE ± SD value for the estimated LAIs was 0.45 ± 0.16 (LAI-NDVI relationships), 0.43 ± 0.23 (LAI-EVI relationships) and 0.45 ± 0.25 (LAI-NIRv relationships), respectively. Hence, the piecewise LAI-VI relationships based on different phenophases were recommended for the estimations of the LAI instead of a single LAI-VI relationship for the entire vegetation active period. Furthermore, the optimal VI in each phenophase should be selected for the estimations of the LAI according to the characteristics of vegetation growth.

The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.

Agricultural land use and cropping patterns are closely related to food production, soil degradation, water resource management, greenhouse gas emission, and regional climate alterations. Methods for reliable and cost-efficient mapping of cropping pattern, as well as their changes over space and time, are therefore urgently needed. To cope with this need, we developed a phenology-based method to map cropping patterns based on time-series of vegetation index data. The proposed method builds on the well-known ‘threshold model’ to retrieve phenological metrics. Values of four phenological parameters are used to identify crop seasons. Using a set of rules, the crop season information is translated into cropping pattern. To illustrate the method, cropping patterns were determined for three consecutive years (2008–2010) in the Henan province of China, where reliable validation data was available. Cropping patterns were derived using eight-day composite MODIS Enhanced Vegetation Index (EVI) data. Results show that the proposed method can achieve a satisfactory overall accuracy (~84%) in extracting cropping patterns. Interestingly, the accuracy obtained with our method based on MODIS EVI data was comparable with that from Landsat-5 TM image classification. We conclude that the proposed method for cropland and cropping pattern identification based on MODIS data offers a simple, yet reliable way to derive important land use information over large areas.

Accurate data about the spatial distribution and planting area of maize is important for policy making, economic development, environmental protection and food security under climate change. This paper proposes a new identification method for spring maize based on spectral and phenological features derived from the moderate resolution imaging spectroradiometer (MODIS) land surface reflectance time-series data. The method focused on the spectral differences of different land cover types in the specific phenological phases of spring maize by testing the selections and combinations of classification metrics, feature extraction methods and classifiers. Taking Liaoning province, a representative planting region of spring maize in Northeast China, as the study area, the results indicated that the combined multiple metrics, including the red reflectance, near-infrared reflectance and normalized difference vegetation index (NDVI), were conducive to the maize identification and were better than any single metric. With regard to the feature extraction and selection, maize identification based on different phenological features selected with prior knowledge was more efficient than that based on statistical features derived from the principal component analysis. Compared with the maximum likelihood classification method, the decision tree classification based on expert knowledge was more suitable for phenological features selected from some prior knowledge. In summary, discriminant rules were defined with those phenological features from multiple metrics, and the decision tree classification was used to identify maize in the study area. The producer’s accuracy of maize identification was 98.57%, and the user’s accuracy was 81.18%. This method can be potentially applied to an operational identification of maize at large scales based on remote sensing time-series data.

Vegetation phenology is considered a sensitive indicator of climate change, which controls carbon, nitrogen, and water cycles within terrestrial ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) is an important moderate resolution remote sensing data for monitoring vegetation phenology. However, Terra MODIS Collection 5 (C5) vegetation index products were identified to be affected by sensor degradation, which has been addressed in the recently released MODIS Collection 6 (C6) vegetation index products. In order to compare the difference between MODIS C5 and C6 NDVI in monitoring vegetation phenology, the start and end of growing season (SOS and EOS) of the alpine grassland on the Tibetan Plateau (TP) were extracted using four common methods. Then, the C5 and C6 NDVI-derived SOS (SOSC5 and SOSC6) and EOS (EOSC5 and EOSC6) were compared with ground-observed phenology data. Results showed that the multi-year average growing season NDVIs of C6 were lower than those of C5 in most areas, while the inter-annual variation patterns of regional average SOSC5 and SOSC6 (EOSC5 and EOSC6) were consistent. However, large spatial differences in phenological trends were found between C5 and C6 NDVI products. From C5 to C6, pixels with a SOS (EOS) trend shifting from significant to insignificant or from insignificant to significant accounted for at least 14.58% (9.07%) of the total pixels. SOSC5 was more consistent than SOSC6 with the ground-observed green-up dates. C5 NDVI may be more appropriate for monitoring SOS than C6 NDVI in the study region, but more ground-observed phenology records are needed to confirm it due to only four observational sites in this study. However, large differences and poor correlations existed between EOSC5 (EOSC6) and the ground-observed beginning of leaf coloring. To further evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods should be applied.

The currently available studies on the green-up date were mainly based on ground observations and/or satellite data, and few model simulations integrated with wide coverage satellite data have been reported at large scale over a long time period (i.e., > 30 years). In this study, we combined phenology mechanism model, long-term climate data and synoptic scale remote sensing data to investigate the change in the green-up dates for Quercus mongolica over 33 weather stations in Northeast China and its climate-driven mechanism during 1962-2012. The results indicated that the unified phenology model can be well parameterized with the satellite derived green-up dates. The optimal daily mean temperature for chilling effect was between -27°C and 1°C for Q. mongolica in Northeast China, while the optimal daily mean temperature for forcing effect was above -3°C. The green-up dates for Q. mongolica across Northeast China showed a delayed latitudinal gradient of 2.699 days degree-1, with the earliest date on the Julian day 93 (i.e., 3th April) in the south and the latest date on the Julian day 129 (i.e., 9th May) in the north. The green-up date for Q. mongolica in Northeast China has advanced 6.6 days (1.3 days decade-1) from 1962 to 2012. With the prevailing warming in autumn, winter and spring in Northeast China during the past 51 years, the chilling effect for Q. mongolica has been weakened, while the forcing effect has been enhanced. The advancing trend in the green-up dates for Q. mongolica implied that the enhanced forcing effect to accelerate green-up was stronger than the weakened chilling effect to hold back green-up while the changes of both effects were caused by the warming climate.

Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.

Open, Grass- and Forb-Dominated (OGFD) ecosystems, including tundra, tropical grasslands and savanna, provide habitat for both wild and domesticated large ungulate herbivores. These ecosystems exist across a wide temperature gradient from the Arctic regions to the Equator, but are confined to a narrow set of moisture conditions that range from arid deserts to forest-dominated systems. Primary productivity in OGFD ecosystems appears extremely sensitive to environmental change. We compared global trends in the annual maximum and mean values of the Normalized Difference Vegetation Index (NDVI) and identified the key bioclimatic indices that controlled OGFD productivity changes in various regions for the period from 1982 to 2011. We found significantly increased or decreased annual maximum NDVI values of 36.3% and 4.6% for OGFD ecosystems, respectively. Trends in the annual mean NDVI are similar for most OGFD ecosystems and show greater area decreases and smaller area increases than trends in the annual maximum NDVI in global OGFD ecosystems during the study period. Ecosystems in which the productivity significantly increased were distributed mainly in the Arctic, mid-eastern South America, central Africa, central Eurasia and Oceania, while those with decreasing trends in productivity were mainly on the Mongolian Plateau. Temperature increases tended to improve productivity in colder OGFD ecosystems; and precipitation is positively correlated with productivity changes in grassland and savannas, but negatively correlated with changes in the Arctic tundra. Simple bioclimatic indices explain 42% to 55% of productivity changes in OGFD systems worldwide, and the main climatic predictors of productivity differed significantly between regions. In light of future climate change, the findings of this study will help support management of global OGFD ecosystems.

The water solubility and side effects of lamivudine limit its application for the treatment of viral hepatitis type B and human immunodeficiency virus. In order to increase the solubility of LA and improve the in vivo membrane permeability of the drug, LA was modified with hexadecane acid to prepare the prodrug lamivudine palmitic acid (LAP) and loaded into nanoemulsome (NES). LAP-NES was prepared by the thin film dispersion method. The LAP-NES showed the sustained release performance up to 72h in pH 7.4 PBS. Moreover, the pharmacokinetics of LAP-NES after tail vein injection in rats and the biodistribution characteristics were evaluated. The tmax of LAP-NES was 2.5h. The t1/2, clearance rate and average retention time of LAP-NES obviously prolonged compared with free LAP. The tissue biodistribution behavior of NES in vivo showed the good targeting in the liver and spleen, with the maximum at 4h and then the fluorescence slowly decreased until 72h. LAP-NES could significantly delay the release of LA in vivo, effectively prolong the elimination time, and had obvious liver-targeting ability. In summary, LAP-NES shows great potential for liver-targeting delivery to increase the therapeutic effect and decrease the side effects of LA. [ABSTRACT FROM AUTHOR]