Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years. Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets (GLC2000, GlobCover, CCI-LC, MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×104 to 332.46×104 km2, with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of east-central Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products, with an overall accuracy (OA) reaching 77.51% and a kappa coefficient of 0.51; CLCD is slightly less accurate than MCD12Q1 (OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its 30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China's grasslands and call for researchers and the government to actively produce a new generation of grassland maps.
Ecosystem , Grassland , China , Tibet , Forests
Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000-2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000-2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.
Grassland , Soil , Biomass , China , Desert Climate , Support Vector Machine
Surface-enhanced Raman scattering (SERS) technology combines with chemometric method of principal component analysis (PCA) was used to calculate the composition of chemical mixtures in solution. We reported here that there exists composition discrepancy between molecules in solution and molecules adsorbed on Ag@Al2O3 nanorods substrates due to difference in adsorption kinetics of each component. We proposed here a way to calculate the adsorption kinetics factor for each component using a standard sample as the reference, with which one could correct the predictions given by PCA. We demonstrate the validity of this approach in estimating the compositions of mixtures with two, three and four components of 1, 4-Benzenedithiol, 2-Naphthalenethiol, 4-Mercaptobenzoic acid, and 4-Mercaptopyridine molecules, with acceptable errors. Furthermore, a general formula applied to more complex mixtures was proposed to calculate compositions in solution.
For the attractive plasmonic structure consisting of metal nanoparticles (NPs) on a mirror, the coexistence of near-field NP-NP and NP-mirror couplings is numerically studied at normal incidence. By mapping their 3D surface charge distributions directly, we have demonstrated two different kinds of mirror-induced bonding dipole plasmon modes and confirmed the bonding hybridizations of the mirror and the NP-dimer which may offer a much stronger near-field enhancement than that of the isolated NP dimers over a broad wavelength range. Further, it is revealed that the huge near-field enhancement of these two modes exhibit different dependence on the NP-NP and NP-mirror hot spots, while both of their near-field resonance wavelengths can be tuned to the blue exponentially by increasing the NP-NP gaps or the NP-mirror separation. Our results here benifit significantly the fundamental understanding and practical applications of metallic NPs on a mirror in plasmonics.
Usage and dosage of food antiseptics are very concerned due to their decisive influence in food safety. Surface-enhanced Raman scattering (SERS) effect was employed in this research to realize trace potassium sorbate (PS) and sodium benzoate (SB) detection. HfO2 ultrathin film-coated Ag NR array was fabricated as SERS substrate. Protected by HfO2 film, the SERS substrate possesses good acid resistance, which enables it to be applicable in acidic environment where PS and SB work. Regression relationship between SERS spectra of 0.3~10 mg/L PS solution and their concentration was calibrated by partial least squares regression (PLSR) method, and the concentration prediction performance was quite satisfactory. Furthermore, mixture solution of PS and SB was also quantitatively analyzed by PLSR method. Spectrum data of characteristic peak sections corresponding to PS and SB was used to establish the regression models of these two solutes, respectively, and their concentrations were determined accurately despite their characteristic peak sections overlapping. It is possible that the unique modeling process of PLSR method prevented the overlapped Raman signal from reducing the model accuracy.
High-temperature surface-enhanced Raman scattering (SERS) sensing is significant for practical detections, and pinhole-containing (PC) metal@oxide structures possessing both enhanced thermal stability and superior SERS sensitivity are served as promising SERS sensors at extreme sensing conditions. Through tuning the Al2O3 precursors' exposure time during atomic layer deposition (ALD), Al2O3 shells with different amount of pinholes were covered over Ag nanorods (Ag NRs). By virtue of these unique PC Ag@Al2O3 nanostructures, herein we provide an excellent platform to investigate the relationship between the pinhole rate of Al2O3 shells and the melting behavior, high-temperature SERS performances of these core-shell nanostructures. Pinhole effect on the melting procedures of PC Ag@Al2O3 substrates was characterized in situ via their reflectivity variations during heating, and the specific melting point was quantitatively estimated. It is found that the melting point of PC Ag@Al2O3 raised along with the decrement of pinhole rate, and substrates with less pinholes exhibited better thermal stability but sacrificed SERS efficiency. This work achieved highly reliable and precise control of the pinholes over Al2O3 shells, offering sensitive SERS substrates with intensified thermal stability and superior SERS performances at extreme sensing conditions.
Considering the nanogap and lattice effects, there is an attractive structure in plasmonics: closely spaced metallic nanoarrays. In this work, we demonstrate experimentally and theoretically the lattice coupling of multipole plasmon modes for closely spaced gold nanorod arrays, offering a new insight into the higher order cavity modes coupled with each other in the lattice. The resonances can be greatly tuned by changes in inter-rod gaps and nanorod heights while the influence of the nanorod diameter is relatively insignificant. Experimentally, pronounced suppressions of the reflectance are observed. Meanwhile, the near-field enhancement can be further enhanced, as demonstrated through surface enhanced Raman scattering (SERS). We then confirm the correlation between the near-field and far-field plasmonic responses, which is significantly important for maximizing the near-field enhancement at a specific excitation wavelength. This lattice coupling of multipole plasmon modes is of broad interest not only for SERS but also for other plasmonic applications, such as subwavelength imaging or metamaterials.
By three-dimensional (3D) finite element method (FEM) plasmon mapping, gradual plasmon evolutions of both bonding dipole plasmon (BDP) and charge transfer plasmon (CTP) modes are visualized. In particular, the evolved BDP mode provides a physical insight into the rapid degeneration of electromagnetic hot spots in practical applications, while the rising CTP mode enables a huge near-field enhancement for potential plasmonic devices at infrared wavelengths.
Surface-enhanced Raman scattering has been proven a powerful means in the fast detection and recognition of chemicals at trace levels, while quantitative analysis especially the compositional analysis of trace chemical mixtures remains a challenge. We report here a "triangle-rule" based on the principal component analysis (PCA) of surface-enhanced Raman scattering spectra, to calculate the composition of individual component of ternary chemical mixtures at trace levels, which can be simplified into the "balance-rule" for binary mixtures. We demonstrated the validity of the triangle-rule and balance-rule in estimating the composition of ternary and binary mixtures of methyl orange, methylene blue, and crystal violet with different molecular structures, and the validity for ternary and binary mixtures of three isomers of monochlorobiphenyl with very similar molecular structures. This idea might be also applicable to mixtures of more components at the trace levels.
Surface-enhanced Raman scattering (SERS) effect is quite preferred to detect trace pollutants, and reusable SERS substrate is of important practical value. In this research, a kind of effective SiO2 nanorods (NRs)@ Au nanoparticles (NPs) substrate was fabricated completely with physical methods, and it was quite sensitive so that 1 × 10(-6) M monochlorobiphenyl (CB) could be detected. Furthermore, congeners of CB could be detected by reusing this kind of SERS substrate, and the cleaning treatment between every two detections was very simple. The excellent performance of the reusable SERS substrate indicated its great application potential.
In the field of plasmonics, the nanogap effect is often related to one aspect like the near-field enhancement at a single excitation wavelength or the far-field resonance shift. In this study, taking full advantage of finite element method (FEM) calculations, we present a comprehensive and quantitative analysis of the nanogap effect on the plasmonic behaviors of metallic nanoparticle dimers. Firstly, near-field spectroscopy is proposed in order to extract the near-field resonance wavelengths. Focusing on the bonding dipole mode, it is found that the near-field enhancement factors exhibit a weak power-law dependence on the gap size, while the near-field resonance shift decays nearly exponentially as the gap size increases, with a lower decay length than that for the far-field resonance shift. The spectral deviation between these two shifts is suggested to be taken into account for spectroscopy applications of plasmonic devices, although it may be negligible for dimer structures with rather small gaps.
TiO2-coated Ag nanorods (Ag@TiO2 NRs) have been fabricated as multifunctional surface-enhanced Raman scattering (SERS) substrates. Uniform TiO2 shells could sufficiently protect the internal Ag NRs against oxidation and sulfuration, thus the temporal stability of SERS substrates was markedly improved. Meanwhile, due to the synergetic effect between crystalline TiO2 and Ag, the nanocomposites could clean themselves via photocatalytic degradation of the adsorbed molecules under ultraviolet irradiation and water dilution, making the SERS substrates renewable. Such Ag@TiO2 NRs were shown to serve as outstanding SERS sensors featuring high sensitivity, superior stability and recyclability.
Silver nanostructures have been considered as promising substrates for surface-enhanced Raman scattering (SERS) with extremely high sensitivity. The applications, however, are hindered by the facts that their morphology can be easily destroyed due to the low melting points (~100 °C) and their surfaces are readily oxidized/sulfured in air, thus losing the SERS activity. It was found that wrapping Ag nanorods with an ultrathin (~1.5 nm) but dense and amorphous Al2O3 layer by low-temperature atomic layer deposition (ALD) could make the nanorods robust in morphology up to 400 °C, and passivate completely their surfaces to stabilize the SERS activity in air, without decreasing much the SERS sensitivity. This simple strategy holds great potentials to generate highly robust and stable SERS substrates for real applications.
