•Satellite-based product suites were evaluated over global mountainous areas.•GLASS and MODIS well tracked the dynamics of mountain vegetation photosynthesis.•GLASS presented a better consistency ...than MODIS over mountainous areas.•Product suites performed much better over flat areas than over mountainous areas.•Product suites performed better in vegetation types with obvious leaf phenology.
Monitoring the mountain vegetation photosynthesis is essential to understanding global climate change. Currently, several long-term satellite-based product suites have generated various variables associated with photosynthesis, while little is known about the consistency and accuracy of multiple variables in the same product suite. Here, the performances of three typical product suites during 2000–2018, namely Moderate-resolution Imaging Spectroradiometer (MODIS), Global LAnd Surface Satellite (GLASS), and Global Inventory Modeling and Mapping Studies (GIMMS), were assessed over global mountainous areas. Considering the limited in situ measurements over mountainous areas, high-quality solar-induced chlorophyll fluorescence (SIF) retrievals were adopted innovatively to evaluate multiple variables from different suites, and the consistencies of multiple variables in the same product suite were also assessed by investigating their concurrent extremes. Results illustrated that the combination of multiple variables from GLASS and MODIS tracked the dynamics of mountain vegetation photosynthesis well, with the relative root mean square error (rRMSE) of 41% and 44%, respectively. The concurrent extremes of GLASS matched better with the existing conclusions than those of MODIS, suggesting a better consistency of GLASS over mountainous areas. GLASS and MODIS presented a better ability (1) over flat areas than over mountainous areas (with a lower rRMSE of ∼5%) and (2) in vegetation types with obvious leaf phenology. Results also showed that GLASS leaf area index (LAI) had a better ability to track the dynamics of photosynthesis than GIMMS LAI. This work can provide essential references in modeling mountain vegetation photosynthesis based on satellite-based product suites.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The WUSCHEL-related homeobox (WOX) gene family plays a crucial role in regulating embryonic development, organ formation, and stress resistance. Yellowhorn (Xanthoceras sorbifolia Bunge), a ...drought-resistant tree known for its oil production, lacks sufficient information regarding the WOX gene family. To understand the evolutionary mechanisms and potential functions of this gene family in yellowhorn, we conducted a comprehensive investigation on its expression patterns and evolutionary characteristics. Our analysis revealed the presence of nine XsWOX genes in the yellowhorn genome, which could be categorized into three distinct clades through a phylogenetic analysis. A chromosomal localization analysis indicated that these nine XsWOX genes were situated on six out of the fifteen chromosomes. An intra-species collinear analysis revealed only one pair of tandem duplicated genes within the XsWOX family. The promoter regions of the XsWOX family were found to contain responsive cis-acting elements associated with plant growth and development, stress responses, and hormone signaling. Moreover, an analysis of the gene expression profiles in different developmental stages of callus revealed significant expressions of XsWOX1, XsWOX4, and XsWOX5 in embryogenic callus and somatic embryo formation, suggesting that they have special roles in regulating yellowhorn’s somatic embryogenesis. Furthermore, the expression level of XsWOX5 indicated its potential involvement not only in organ formation but also in responding to low temperature, salt, and saline-alkali stresses. Overall, our findings lay a solid foundation for future in-depth studies on the functionality and evolution of XsWOX genes in yellowhorn.
Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is difficult to obtain accurate diagnosis results. ...In this study, a long short-term memory (LSTM) approach is used for plasma probe diagnosis to derive electron density (
) and temperature (
) more accurately and quickly. The LSTM network uses the data collected by Langmuir probes as input to eliminate the influence of the discharge device on the diagnosis that can be applied to a variety of discharge environments and even space ionospheric diagnosis. In the high-vacuum gas discharge environment, the Langmuir probe is used to obtain current-voltage (I-V) characteristic curves under different
and
. A part of the data input network is selected for training, the other part of the data is used as the test set to test the network, and the parameters are adjusted to make the network obtain better prediction results. Two indexes, namely, mean squared error (MSE) and mean absolute percentage error (MAPE), are evaluated to calculate the prediction accuracy. The results show that using LSTM to diagnose plasma can reduce the impact of probe surface contamination on the traditional diagnosis methods and can accurately diagnose the underdense plasma. In addition, compared with
, the
diagnosis result output by LSTM is more accurate.
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•A topographic correction method was proposed to improve mountainous MODIS GPP.•Radiation, temperature, and water heterogeneity are useful for GPP correction.•Improvement of MODIS GPP after ...correction was proved at 11 mountainous sites.•It is essential to incorporate topography into coarse resolution GPP products.
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Sea buckthorn polysaccharides are acid polysaccharides with pyranose rings, with a Mw of 6.26 × 103 kDa.•Sea buckthorn polysaccharides can increase the abundance of beneficial ...bacteria and SCFAs content in the gut.•Sea buckthorn polysaccharides can help to restore the cefixime-induced gut microbiota disorder in mice.
Sea buckthorn pomace is a by-product of sea buckthorn products that is not effectively utilized. This study obtained sea buckthorn polysaccharides (SPs) from pomace, analyzed its structure, and investigated its regulatory effect on the gut microbiota imbalance induced by cefixime. The results showed that SPs had an average molecular weight of 6.26 × 103 kDa and mainly consisted of galacturonic acid, galactose, and rhamnose. Biochemical analysis showed that SPs increased the concentration of short-chain fatty acids (SCFAs) and the abundance of Proteobacteria, Verrucomicrobia, and Akkermanis in the gut of cefixime-treated mice. Correlation analysis suggested that various microorganisms had a significant (p < 0.05) relationship with SCFAs. Consequently, the underlying mechanism of SPs in restoring cefixime-induced gut microbiota disorder may be due to the promotion of SCFAs and SCFA-producing bacteria in the gut and gut microbiota regulation, benefiting gut health. This study is significant for developing and utilizing sea buckthorn pomace.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current ...GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.
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A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show ...spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Land surface models intended for large‐scale applications are often executed at coarse resolutions, and the sub‐grid heterogeneity is usually ignored. Here, a spatial scaling algorithm that ...integrates the information of vegetation heterogeneity (land cover type and leaf area index) and surface topography (elevation, slope, relative azimuth (Raz) between the sun and the slope background, sky‐view factor, and topographic wetness index), was proposed to correct errors in gross primary productivity (GPP) estimates at a coarse spatial resolution. An eco‐hydrological model named BEPS‐TerrainLab was used to simulate GPP at 30 and 480 m resolutions for 16 mountainous watersheds selected globally. Results showed that an obvious improvement on GPP estimates at 480 m resolution was achieved after the correction in comparison with GPP modeled at 30 m resolution, with the determination coefficient increased by 0.38 and mean bias error reduced by 203gCm−2 yr−1. The combination of all the seven factors made the largest improvement for GPP estimation at 480 m resolution, suggesting that a larger improvement would be achieved when more factors of surface heterogeneity are considered. More specifically, our results indicated that five factors, including land cover type and leaf area index regarded as integrated outcomes of all the environmental conditions, Raz and sky‐view factor associated with radiation redistribution, and slope related to soil water redistribution, were especially important in the spatial scaling procedure. This study suggests that incorporating the information of surface heterogeneity into the spatial scaling algorithm is useful for improving coarse resolution GPP estimates over mountainous areas.
Key Points
A spatial scaling algorithm that integrates surface heterogeneity information is proposed to improve gross primary productivity (GPP) estimates at coarse resolutions
A larger improvement would be achieved when more factors of surface heterogeneity are considered in the spatial scaling process
LC, LAI, Raz, SVF, and slope factors are especially useful for reducing errors in coarse resolution GPP estimates over mountainous areas
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The Gross Primary Productivity (GPP) is an important component in regional and global carbon budgets. Southeastern China has experienced vegetation greening and climate change. Yet, it remains ...unclear how these changes have impacted GPP in this region. As one of six special national parks in China with complex topography, high biodiversity, and little destructive human activities to the ecosystems, the Wuyi Mountain region is selected to study these impacts. In this study, we use a hydroecological model (BEPS-TerrainLab V2.0) to simulate the spatial and temporal variations of GPP in the Wuyi Mountain region over 2001–2018. We quantitatively separate the effects of vegetation greening and climate change on the trend and interannual variation in GPP through sensitivity experiments. The results show a significant increasing trend in Leaf Area Index (LAI) in the region over 2001–2018 (0.06 m2 m−2 yr−1, p < 0.01). For climate, a significant warming trend (0.03°C yr−1, p = 0.06) and an insignificant wetting trend are found, companied with large interannual variations. The sensitivity experiments suggest that the combined effect of vegetation greening and climate change makes the annual GPP increase significantly over 2001–2018 (14.41 g C m−2 yr−2, p < 0.01). Vegetation greening plays a dominant role in the GPP increasing trend with a positive contribution of 15.76 g C m−2 yr−2. Climate change only makes an insignificant negative contribution (−0.43 g C m−2 yr−2), mainly due to warming. However, the climate modulates the interannual variation of GPP dominantly, with temperature being the most influential climate factor. Our results underscore the critical impact of vegetation greening on the GPP trend and the impact of climate on the GPP interannual variation in this subtropical forest region over east coast of China.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Due to the spatial heterogeneity of land surfaces, downscaling is an important issue in the development of carbon cycle models when evaluating the role of ecosystems in the global carbon cycle. In ...this study, a downscaling algorithm was developed to model gross primary productivity (GPP) at 500 m in a time series over rugged terrain, which considered the effects of spatial heterogeneity on carbon flux simulations. This work was carried out for a mountainous area with an altitude ranging from 2606 to 4744 m over the Gongga Mountain (Sichuan Province, China). In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product at 1 km served as the primary dataset for the downscaling algorithm, and the 500 m MODIS GPP product was used as the reference dataset to evaluate the downscaled GPP results. Moreover, in order to illustrate the advantages and benefits of the proposed downscaling method, the downscaled results in this work, along with ordinary kriging downscaled results, spline downscaled results and inverse distance weighted (IDW) downscaled results, were compared to the MODIS GPP at 500 m. The results showed that (1) the GPP difference between the 500 m MODIS GPP and the proposed downscaled GPP results was primarily in the range of −1, 1, showing that both vegetation heterogeneity factors (i.e., LAI) and topographic factors (i.e., altitude, slope and aspect) were useful for GPP downscaling; (2) the proposed downscaled results (R2 = 0.89, RMSE = 1.03) had a stronger consistency with the 500 m MODIS GPP than those of the ordinary kriging downscaled results (R2 = 0.43, RMSE = 1.36), the spline downscaled results (R2 = 0.40, RMSE = 1.50) and the IDW downscaled results (R2 = 0.42, RMSE = 1.10) for all Julian days; and (3) the inconsistency between MODIS GPP at 500 m and 1 km increased with the increase in altitude and slope. The proposed downscaling algorithm could provide a reference when considering the effects of spatial heterogeneity on carbon flux simulations and retrieving other fine resolution ecological-physiology parameters (e.g., net primary productivity and evaporation) over topographically complex terrains.
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