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Abstract While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan’s deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model—Expectation (EX), Entropy (EN), and Hyper-entropy (HE)—we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan’s forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82–39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79–7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan’s vegetation vertical differentiation.

Pine wilt disease is a major biological disaster caused by Bursaphelenchus xylophilus. This study establishes a high-quality and well-annotated genome sequence of B. xylophilus strain TS-1 from Mountain Tai in Shandong province, China. The 75-Mbp assembly containing six chromosomes was established. These genome data represent a valuable new resource for future studies on B. xylophilus and the management of pine wilt disease. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Peanut root rot, commonly referred to as rat tail or root rot, is caused by a range of Fusarium species. A strain of bacteria (named TG5) was isolated from crop rhizosphere soil in Mount Taishan, Shandong Province, China, through whole genome sequencing that TG5 was identified as Bacillus thuringiensis, which can specifically produce chloramphenicol, bacitracin, clarithromycin, lichen VK21A1 and bacitracin, with good biological control potential. Based on liquid chromatography tandem mass spectrometry metabonomics analysis and transcriptome conjoint analysis, the mechanism of TG5 and carbendazim inducing peanut plants to resist F. oxysporum stress was studied. In general, for peanut root rot caused by F. oxysporum, B. thuringiensis TG5 has greater advantages than carbendazim and is environmentally friendly. These findings provide new insights for peanut crop genetics and breeding, and for microbial pesticides to replace traditional highly toxic and highly polluting chemical pesticides. Based on the current background of agricultural green cycle and sustainable development, it has significant practical significance and broad application prospects.

Abstract Forest soils are important components of forest ecosystems, and soil quality assessment as a decision-making tool to understand forest soil quality and maintain soil productivity is essential. Various methods of soil quality assessment have been developed, which have occasionally generated inconsistent assessment results between soil types. We assessed the soil quality of five communities (herb, shrub, Quercus acutissima, Pinus thunbergii, and Q. acutissima–P. thunbergii mixed plantation) using two common methods of dry and barren mountains in the Yimeng Mountain area, China. Sixteen soil physical, chemical and biological properties were analysed. The soil quality index was determined using the established minimum data set based on the selection results of principal component analysis and Pearson analysis. Silt, soil total phosphorus (P), soil total nitrogen (N), L-leucine aminopeptidase, acid phosphatase and vector length were identified as the most representative indicators for the minimum data set. Linear regression analysis showed that the minimum data set can adequately represent the total data set to quantify the impact of different communities on soil quality (P

Litter decomposition is a key step in global biogeochemical cycling. In forest ecosystems, litter from different tree spec1ies often decompose together. Although species diversity is widely acknowledged to accelerate decomposition through the regulation of nutrient transfer between litter and decomposer communities, the underlying mechanism remains unclear. To explore the association between the bacterial community and mixed-litter chemical transformation, we conducted a one-year litter mixing decomposition experiment using leaf litter from four dominant tree species in Mount Tai (Eastern China), Robinia pseudoacacia, Quercus acutissima, Pinus tabulaeformis, and Pinus densiflora. Our results showed that: 1) Mass loss of leaf litter mixtures was significantly faster than that of leaf litter monocultures, except for R. pseudoacacia. Litter mixtures without R. pseudoacacia showed non-additive synergistic effects, whereas litter mixtures with R. pseudoacacia exerted additive effects; 2) Litter species in the absence of R. pseudoacacia significantly decreased the nutrient retention rates of litter mixtures compared to those of monocultures; 3) Litter mixtures with or without R. pseudoacacia showing additive and non-additive effects in monocultures had a distinct bacterial community structure; 4) Bacterial community structure was also modified by initial litter traits; carbon (C), nitrogen (N), and phosphorus (P) concentrations in monocultures; N/P and C/N ratios of mixtures with R. pseudoacacia; and the lignin/N ratio of mixtures without R. pseudoacacia. Overall, these findings indicate that tree species diversity controls decomposition and nutrient cycling, implying that an appropriate species community composition is beneficial to maintaining forest ecosystems.

Abstract A number of strict lockdown measures were implemented in the areas most affected by COVID-19 in China, including Ji’nan city, from 24 January to 7 February 2020. Due to these forced restrictions, the pollution levels in cities across the country drastically decreased within just a few days. Since traffic pollution and industrial emissions are important factors affecting regional air quality, congestion has a significant impact on the environment. Therefore, using the aid of air quality data for six pollutants (PM10, PM2.5, SO2, NO2, CO and O3) from 11 monitoring stations (located in urban, suburban and urban-industrial regions) across Ji’nan, we employed the air quality index (AQI) to investigate the spatial pattern of air quality in the pre-COVID-19 (pre-COVID) and COVID-19-related lockdown (COVID lockdown) periods. The results showed that air quality significantly improved during the COVID lockdown period. Among the selected pollutants, compared to the corresponding pre-COVID levels, the greatest reduction was observed for the concentration of NO2 (54.02%), while the smallest reduction was observed for the concentration of SO2 (27.92%). The PM2.5 (38.73%), PM10 (44.92%) and CO (30.60%) levels also decreased during the COVID lockdown period; only the O3 concentration increased (37.42%) during this period. Overall, air quality improved by approximate improvements of 37.33% during the COVID lockdown period. Approximately 35.48%, 37.01% and 43.43% in the AQI were observed in urban, suburban and urban-industrial regions, respectively. Therefore, the AQI exhibited remarkable regional differences in Ji'nan. This study demonstrates the contributions of the transportation sector and local emissions to improving air quality in typical urban areas, and these research results can provide guidance for the further monitoring of air pollution in northern Chinese cities.

IntroductionTo clarify the effects of microtopography on plant growth and soil water, salt and nutrient characteristics of saline soils in mudflats within muddy coastal zones and explore suitable microtopographic modifications.MethodsSix microtopographic modification patterns, namely, S-shaped, stripe-shaped, pin-shaped, stepshaped, dense stripe-shaped and crescent-shaped patterns, were established in the coastal mudflats of the Yellow River Delta. The soil water, salt, ion, total carbon, total nitrogen, and total phosphorus contents and their ecological stoichiometric characteristics were measured and analyzed after theimplementation of different microtopographic modification patterns, with bare mudflats as the control.ResultsThe results showed that microtopographic modification significantly changed the soil water and salt contents and the soil total carbon, total nitrogen and total phosphorus contents. Compared with the bare ground, microtopographic transformation significantly promoted the growth of the pioneer plant Suaeda salsa, significantly increased the soil water and nutrient contents, and significantly decreased the soil salinity. The soil salinity was mainly reduced by Na+ and Cl- ions. The soil salinity and nutrient contents gradually decreased with increasing soil depth, indicating the occurrence of surface aggregation. Compared to that of the bare ground, the soil C/N was significantly lower and the N/P was significantly higher in the microtopographic treatments, and the overall performance suggested soil N limitation. The ions contained in the saline soil were dominated by Na+ and Cl-, followed by Mg2+ and SO42-, with lower contents of K+, Ca2+ and HCO3-. Among the six microtopography modification patterns, the crescent-shaped pattern best promoted vegetation restoration. This pattern was the most effective in reducing soil salinity, with a 98.53% reduction in soil salinity compared with that of bare ground, followed by the pin-shaped pattern. Compared with that in the bare ground samples, the nutrient content in the samples from the step-shaped modification increased by 23.27%; finally, the S-shaped, step-shaped and dense stripe-shaped patterns performed poorly in terms of plant restoration and soil improvement.DiscussionIt is suggested that a crescent-shaped pattern should be considered first when carrying out microtopographic transformation on the beaches of the Yellow River Delta, followed by stripe-shaped and pin-shaped patterns. The dense strip-shaped should not be adopted.

The irrational land use patterns in the Yellow River Delta (YRD) have resulted in an imbalance in ecological stoichiometry, leading to secondary salinization and soil degradation. However, there is limited knowledge about the long-term response of soil and enzyme stoichiometry to land use. This hampers our ability to optimize land use in the YRD to alleviate nutrient limitation and thus promote ecological stoichiometric balance. We investigated the stoichiometry of soil and enzyme carbon (C), nitrogen (N), and phosphorus (P) in three land use patterns (Alfalfa artificial grassland, AG; wheat–maize rotation field, WM; native grassland, PC) established for 19 years in the YRD. The results showed that the soil stoichiometry of the three land uses in the YRD was lower than the world and Chinese averages, indicating lower C and N levels. Nutrient limitations of soil microorganisms were C and P due to an enzyme C:N ratio greater than 1:1 and vector angle greater than 45°. The three land use patterns have different advantages in alleviating nutrient limitations in the YRD. AG promotes soil macroaggregate formation, reduces soil salt content, improves nutrient availability, and mitigates N limitation. This makes AG more conducive to improving the poor soil structure, high soil salinity, and stoichiometric imbalance in the YRD to mitigate local soil degradation and be suitable for long-term continuous cultivation. WM is beneficial for increasing soil total C content due to straw return. However, WM does not reduce soil salinity. WM is more suitable for intercropping or crop rotation to improve soil C content in the YRD. Although PC can alleviate soil microbial C limitation due to its significantly lower vector length than AG and WM, the low nutrient levels hindered its ability to alleviate local soil nutrient limitation. In conclusion, our study provides a theoretical basis for rational land use in the YRD to mitigate soil degradation.

Ulmus parvifolia (U. parvifolia) is a Chinese medicine plant whose bark and leaves are used in the treatment of some diseases such as inflammation, diarrhea and fever. However, metabolic signatures of seeds have not been studied. The seeds and bark of U. parvifolia collected at the seed ripening stage were used for metabolite profiling analysis through the untargeted metabolomics approach. A total of 2,578 and 2,207 metabolites, while 503 and 132 unique metabolites were identified in seeds and bark, respectively. Additionally, 574 differential metabolites (DEMs) were detected in the two different organs of U. parvifolia, which were grouped into 52 classes. Most kinds of metabolites classed into prenol lipids class. The relative content of flavonoids class was the highest. DEMs contained some bioactive compounds (e.g., flavonoids, terpene glycosides, triterpenoids, sesquiterpenoids) with antioxidant, anti-inflammatory, and anti-cancer activities. Most kinds of flavonoids and sesquiterpenes were up-regulated in seeds. There were more varieties of terpene glycosides and triterpenoids showing up-regulated in bark. The pathway enrichment was performed, while flavonoid biosynthesis, flavone and flavonol biosynthesis were worthy of attention. This study identified DEMs with pharmaceutical value between seeds and bark during seed maturation and offered a molecular basis for alternative or complementary use of seeds and bark of U. parvifolia as a Chinese medicinal material.

To explore the adaptation of the fine root morphology and chemical characteristics of Tamarix chinensis to water–salt heterogeneity in the groundwater–soil system of a coastal wetland zone, T. chinensis forests at different groundwater levels (high: GW1 0.54 m and GW2 0.83 m; medium: GW3 1.18 m; low: GW4 1.62 m and GW5 2.04 m) in the coastal wetland of the Yellow River Delta were researched, and the fine roots of T. chinensis standard trees were excavated. The fine roots were classified by the Pregitzer method, and the morphology, nutrients, and nonstructural carbohydrate characteristics of each order were determined. The results showed that the groundwater level had a significant indigenous effect on the soil water and salt conditions and affected the fine roots of T. chinensis. At high groundwater levels, the specific root length and specific surface area of fine roots were small, the root tissue density was high, the fine root growth rate was slow, the nutrient use efficiency was higher than at low groundwater levels, and the absorption of water increased with increasing specific surface area. With decreasing groundwater level, the N content and C/N ratio of fine roots first decreased and then increased, and the soluble sugar, starch, and nonstructural carbohydrate content of fine roots first increased and then decreased. At high and low groundwater levels, the metabolism of fine roots of T. chinensis was enhanced, and their adaptability to high salt content and low water content soil environments improved. The first- and second-order fine roots of T. chinensis were mainly responsible for water and nutrient absorption, while the higher-order (from the third to fifth orders) fine roots were primarily responsible for the transportation and storage of carbohydrates. The fine root morphology, nutrients, nonstructural carbohydrate characteristics, and other aspects of the water and salt environment heterogeneity cooperated in a synergistic response and trade-off adjustment.

Root rot caused by Fusarium oxysporum is a serious root disease that affects the growth and yield of peanut crops worldwide. Identifying resistance genes is of great significance for both conventional and molecular breeding. At present, there are a few identified disease resistance genes, and the defense response mechanism against this pathogen remains unclear. Using transcriptome analysis techniques, 21 differentially expressed genes (DEGs) were identified in plants treated with Bacillus thuringiensis TG5 and carbendazim. Among these DEGs, the TIFY10A protein gene, pyruvate decarboxylase, and UDP glycosyltransferase 74B1 genes play an important role in early resistance to root rot in peanut plants, leading to regulatory factors that trigger immunity. Our study provides important information on the response mechanism of peanut plants to F. oxysporum-induced root rot and the protective benefits of colonization by the microbial pesticide, B. thuringiensis.

To explore the spatial distribution pattern of the Tamarix chinensis population and the influencing factors in the muddy coastal zone of Bohai Bay, we studied the T. chinensis forest on the beach of the muddy area of Beihai New District, Binzhou City, Shandong Province. A combination of field investigations and laboratory-based sample analyses were used to measure and analyze the forest density, height, cover, distance from the sea, groundwater depth, soil salinity, basic soil physical properties and soil nutrients. The results showed that the coefficients of variation for the 18 soil and forest indices for T. chinensis were between 0.03 and 0.96; the coefficients of variation for forest density, soil Ca2+ and Na+ content were the greatest, followed by those for soil Mg2+, soil salt content, distance from sea, and available phosphorus, while those for soil bulk density, total phosphorus, pH and other indicators changed less. The population of T. chinensis on the beach of the coastal zone of the Yellow River Delta showed an aggregated distribution, and its aggregation intensity decreased with increasing spatial scale, and it tended to evolve into a random distribution on a larger scale. The spatial distribution of the T. chinensis population showed a certain density dependence; that is, the greater the density was, the greater the aggregation intensity. The reason for the aggregated distribution of the T. chinensis population was mainly related to the biological characteristics of the organism itself or to the interactions between its biological characteristics and environmental factors. T. chinensis forest density was significantly and negatively correlated with organic carbon, total phosphorus, Ca2+, and Na+ (P

Abstract Background Robinia pseudoacacia is a widely planted pioneer tree species in reforestations on barren mountains in northern China. Because of its nitrogen-fixing ability, it can play a positive role in soil and forest restoration. After clear-cutting of planted stands, R. pseudoacacia stands become coppice plantations. The impacts of shifting from seedling to coppice stands on soil bacterial community and soil properties have not been well described. This study aims to quantify how soil properties and bacterial community composition vary between planted seedling versus coppice stands. Methods Nine 20 m × 20 m plots were randomly selected in seedling and coppice stands. The bulk soil and rhizosphere soil were sampled in summer 2017. Bulk soil was sampled at 10 cm from the soil surface using a soil auger. Rhizosphere soil samples were collected using a brush. The soil samples were transported to the laboratory for chemical analysis, and bacterial community composition and diversity was obtained through DNA extraction, 16S rRNA gene amplification and high-throughput sequencing. Results The results showed that, compared to seedling plantations, soil quality decreased significantly in coppice stands, but without affecting soil exchangeable Mg2+ and K+. Total carbon (C) and nitrogen (N) were lower in the rhizosphere than in bulk soil, whereas nutrient availability showed an opposite trend. The conversion from seedling to coppice plantations was also related to significant differences in soil bacterial community structure and to the reduction of soil bacterial α-diversity. Principal component analysis (PCA) showed that bacterial community composition was similar in both bulk and rhizosphere soils in second-generation coppice plantations. Specially, the conversion from seedling to coppice stands increased the relative abundance of Proteobacteria and Rhizobium, but reduced that of Actinobacteria, which may result in a decline of soil nutrient availability. Mantel tests revealed that C, N, soil organic matter (SOM), nitrate nitrogen (NO3 −-N) and available phosphorus positively correlated with bacterial community composition, while a variation partition analysis (VPA) showed that NO3 −-N explained a relatively greater proportion of bacterial distribution (15.12%), compared with C and SOM. Surprisingly, N showed no relationship with bacterial community composition, which may be related to nitrogen transportation. Conclusions The conversion from seedling to coppice stands reduced soil quality and led to spatial-temporal homogenization of the soil bacterial community structure in both the rhizosphere and bulk soils. Such imbalance in microbial structure can accelerate the decline of R. pseudoacacia. This may affect the role of R. pseudoacacia coppice stands in soil and forest restoration of barren lands in mountain areas.

The Sentinel-2B offers high spatial resolution optical imagery from 13 bands in the visible, near infrared, and short-wave infrared range of the electromagnetic spectrum, providing enhanced continuity to monitoring global terrestrial surfaces and coastal waters. In this article, its radiometry calibration is evaluated using 37 clear-sky observations in 2018 from the four sites of radiometric calibration network for assuring data quality. However, since the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, it is difficult to determine which calibration sample is much more trustable. In view of this, by assuming that the calibration samples are independent of each other, a state-of-the-art reference value is derived by combining 37 calibration samples using a weighted average method, which has much lower uncertainty and approaches the “true” value. The reference value also could be used to compare each calibration result. The result shows that the reference value of the relative difference between the simulated and observed top-of-atmosphere reflectance is 4.29%, 4.95%, 4.54%, 5.34%, respectively, for bands 2, 3, 4, and 8, and the corresponding uncertainty is 1.09%, 1.10%, 1.10%, and 1.12%, respectively; the degree of equivalence for each sample is calculated by comparing each calibration result with the reference value. It is worth noting that the degrees of equivalence are lower than 5%, and the four samples on July 9, July 13, October 4, and October 11 perform worse than the other sample.

Canopy chlorophyll content (CCC) indicates the photosynthetic functioning of a crop, which is essential for the growth and development and yield increasing. Accurate estimation of CCC from remote-sensing data benefits from including information on leaf chlorophyll and canopy structures. However, conventional nadir reflectance is usually subject to the lack of an adequate expression on the geometric structures and shaded parts of vegetation canopy, and the derived vegetation indices (VIs) are prone to be saturated at high CCC level. Using 3-year field experiments with different wheat cultivars, leaf colors, structural types, and growth stages, and integrated with PROSPECT+SAILh model simulation, we studied the potential of multi-angle reflectance data for the improved estimation of CCC. The characteristics of angular anisotropy in spectral reflectance were investigated. Analyses based on both simulated and experimental multi-angle hyperspectral data were carried out to compare performances of 20 existing VIs at different viewing angles, and to propose an algorithm to develop novel biangular-combined vegetation indices (BCVIs) for tracking CCC dynamics in wheat. The results indicated that spectral reflectance values, as well as the coefficient of determination (R2) between mono-angular VIs and CCC, at back-scattering directions, were mostly higher than those at forward-scattering directions. Mono-angular VIs at +30° angle, were closest to the hot-spot position in our case, achieved the highest R2 among 13 viewing angles including the nadir observation. The general formulation for the newly developed BCVIs was BCVIVI = f × VI(θ1) − (1 − f) × VI(θ2), in which the VI was used to characterize chlorophyll status, while the subtraction of VI at θ1 and θ2 viewing angles in a proportion was used to highlight the canopy structural information. From our result, the values of the θ1 and θ2 around hot-spot and dark-spot positions, and the f of 0.6 or 0.7 were found as the optimized values. Through comparisons revealed that large improvements on CCC modeling could be obtained by the BCVIs, especially for the experimental data, indicated by the increase in R2 by 25.1–51.4%, as compared to the corresponding mono-angular VIs at +30° angle. The BCVIMCARI[705,750] was proved to greatly undermine the saturation effect of mono-angular MCARI[705,750], expressing the best linearity and the most sensitive to CCC, with R2 of 0.98 and 0.72 for simulated and experimental data, respectively. Our study will eventually have extensive prospects in monitoring crop phenotype dynamics in for example large breeding trials.

This article addressed the simultaneous retrieval of land surface temperature (LST) and emissivity (LST&E) from the time-series thermal infrared data. On the basis of the assumption that the time-series LSTs can be described by a piecewise linear function, a new method has been proposed to simultaneously retrieve LST&E from atmospherically corrected time-series thermal infrared data using LST linear constraint. A detailed analysis has been performed against various errors, including error introduced by the method assumption, instrument noise, initial emissivity, atmospheric downwelling radiance error, etc. The proposed method from the simulated data is more immune to noise than the existing methods. Even with a noise equivalent delta temperature of 0.5 K, the root-mean-square error of LST is observed to be only 0.13 K, and that of the land surface emissivity (LSE) is 1.8E-3. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. To validate the proposed method, a field experiment from June to September 2017 was conducted for sand target in Baotou site, China. The results show that the samples have an accuracy of LST within 0.87 K and that the mean values of LSE are accurate to 0.01.

Optical satellite sensor generally suffers from drifts and biases relative to their prelaunch calibration, caused by launch and the space environment. Radiometric cross-calibration is valuable for calibrating sensors without coincident surface measurements through transferring radiometric values between satellite sensors via a stable target. The data quality of visible and near-infrared (VNIR) sensor onboard Gaofen-4 (GF-4) satellite seriously depends on vicarious calibration, which is performed once every year, thus, a cross-calibration approach is proposed for monitoring its radiometric performance much more frequently. In this study, the GF-4/VNIR is cross-calibrated by Landsat8/OLI, Sentinel-2/MSI, and Terra/MODIS with the aid of the tandem scenes during 2017 and 2018 after correcting the discrepancy on viewing geometries and spectrum. The results show that there is test site dependency and sensor dependency, and cross-calibration coefficients in the four bands roughly have similar variation trend as a sinusoidal function during 2017 and 2018, varying from 0.00023 to 0.00079. Furthermore, uncertainty analysis of the cross-calibration is carried out to analyze the uncertainty contributions affecting the cross-calibration accuracy. Note that the calibration accuracy of referenced sensors, the uncertainty of the MODTRAN model, BRDF uncertainty are the three main factors affecting the cross-calibration accuracy, the total uncertainty is approximately 3.9%-6.64%.

Enhancing nitrogen (N) use efficiency (NUE) and reducing urea N losses are major challenges in ensuring sustainable agriculture. The aim of this study was to investigate the effect of humic acid urea on N losses, soil microbial nutrient balance and NUE through leaching experiments, soil incubation experiments and field experiments of maize-wheat rotation. We set up four N gradients (240 kg N hm−2, 216 kg N hm−2, 192 kg N hm−2, 168 kg N hm−2) and two N fertilizer types (urea and humic acid urea) to make up five treatments, with no N application as the control. The results showed that humic acid urea reduced the fertilizer N losses by 25.51%, 23.07% and 23.08% in the three pathways of N leaching, NH3 volatilization and N2O emission, respectively, compared with urea. Humic acid urea significantly increased soil ammonium N, nitrate N and available phosphorus contents, and brought the enzyme stoichiometry ratio closer to 1:1:1, which promoted microbial nutrient balance. Application of humic acid urea significantly increased yield, NUE and annual net economic profit of maize and wheat. Among all treatments, the application of humic acid urea at 216 kg N hm−2 maximized NUE, reduced environmental pollution and increased yield.

Aniline, a synthetic compound widely used in industrial and pesticide production, is a potential environmental pollutant. The removal of aniline is extremely important to minimize threats to human health and the surrounding environment. The objectives of this study were to investigate the removal efficiency and physiological response of Salix. babylonica cuttings to aniline pollution. Photosynthesis, chlorophyll fluorescence, spectral reflectance and the concentration of aniline in leaves, stems and roots were analysed. The experiment showed that S. babylonica has a strong removal effect on aniline wastewater. Cuttings from S. babylonica stems and roots played an important role in accumulating aniline. However, this increase in aniline concentration was dose dependent and was not always linear. With increasing aniline concentration in S. babylonica was increasingly stressed, with negative impacts on photosynthesis, chlorophyll fluorescence and spectral reflectance index in S. babylonica leaves. These results indicate that non-stomatal limitations are the main reason for the reduction in Pn in S. babylonica leaves due to chlorophyll structure destruction under aniline stress. In addition, aniline concentrations result in an unbalanced distribution of excitation energy between the two light systems, thereby hindering photosynthetic electron transfer and restricting the efficient operation of photosynthesis. Salix babylonica can endure moderate concentrations of aniline and has potential for the phyto-management of aniline-polluted wastewater, although further studies are needed using polluted wastewater.

Abstract To study the fractal dimensions of the soil particle size distributions (PSDs) within different plantations (of Pinus densiflora, Quercus acutissima, Robinia pseudoacacia, and Larix kaempferi) and evaluate PSDs as an indicator of the likelihood of Robinia pseudoacacia invasion, the soil porosity of 0–20 cm soil layers was measured at different plantations in the Yaoxiang Forest Farm, Shandong Province, China. The results showed that the fractal dimension (D m) values varied from 2.59 to 2.70 among the different plantations and were significantly negatively correlated to sand content and positively correlated to silt content and clay content. Significant negative correlations were observed between D m and both soil organic matter (SOM) (P

Robust calibration and validation (Cal and Val) should guarantee the accuracy of the retrieved information, make the remote sensing data consistent and traceable, and maintain the sensor performance during the operational phase. The DRAGON program has set up many remote sensing research topics on various application domains. In order to promote the effectiveness of data modeling and interpretation, it is necessary to solve various challenges in Cal and Val for quantitative RS applications. This project in the DRAGON 4 program aims to promote the cooperation of the Cal and Val experts from European and Chinese institutes in Cal and Val activities, and several achievements have been obtained in the advanced on-orbit optical sensor calibration, as well as microwave remote sensor calibration and product generation. The outcomes of the project have benefited the related remote sensing modeling and product retrieval, and promoted the radiometric calibration network (RadCalNet) as an international operational network for calibration, intercalibration, and validation. Moreover, this project provided local governments with a more accurate OMI NO2 data in China, which were used to study the air quality control during APEC period, Parade period and G20 period. This will be of ongoing be value for monitoring atmospheric environmental quality and formulating pollution control strategies.

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

The field of view (FOV) of pushbroom hyperspectral imager is limited by the compromise of the detector scale and requirements of spatial resolution. Combining imagers along the sampling direction effectively expands its FOV and improves the imaging efficiency. Due to the small overlapping area between the adjacent imagers, stitching the images using traditional methods need a large amount of ground control points (GCPs) or additional strips, which reduce the efficiency of both image acquisition and processing. This paper proposed a new method to precisely stitch images acquired from multiple pushbroom imagers. First, the relative orientation model was built based on the homonymy points to calculate the relative relationship between the adjacent imagers. Then rigorous geometric imaging model was adopted to generate a seamless stitching image. Simulation data was used to verify the accuracy of the method and to quantitatively analyze the effect of different error sources. Results show that the stitching accuracy is better than two pixels. Overall, this method provides a novel solution for stitching airborne multiple pushbroom images, to generate the seamless stitching image with wide FOV.

To meet both landscape aesthetics and health needs, the effects of different concentrations of volatile organic compounds (VOCs) from Pistacia chinensis Bunge (P. chinensis) and Juniperus chinensis cv. Kaizuka (J. chinensis) on mouse spontaneous behavior were studied during successive six-day experiments. The results were as follows: 1) The excitability of the mice and their total moving distance increased significantly upon exposure to low volatile concentrations of P. chinensis (P

Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when in orbit. In order to overcome this problem, it has been proposed to deploy space-based radiometric reference systems which intercalibrate measurements from multiple satellite platforms. Such reference systems have been strongly recommended by international expert teams. This paper describes the Chinese Space-based Radiometric Benchmark (CSRB) project which has been under development since 2014. The goal of CSRB is to launch a reference-type satellite named LIBRA in around 2025. We present the roadmap for CSRB as well as requirements and specifications for LIBRA. Key technologies of the system include miniature phase-change cells providing fixed-temperature points, a cryogenic absolute radiometer, and a spontaneous parametric down-conversion detector. LIBRA will offer measurements with SI traceability for the outgoing radiation from the Earth and the incoming radiation from the Sun with high spectral resolution. The system will be realized with four payloads, i.e., the Infrared Spectrometer (IRS), the Earth-Moon Imaging Spectrometer (EMIS), the Total Solar Irradiance (TSI), and the Solar spectral Irradiance Traceable to Quantum benchmark (SITQ). An on-orbit mode for radiometric calibration traceability and a balloon-based demonstration system for LIBRA are introduced as well in the last part of this paper. As a complementary project to the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS), LIBRA is expected to join the Earth observation satellite constellation and intends to contribute to space-based climate studies via publicly available data.

Field calibration is a feasible way to evaluate space-borne optical sensor observations via natural or artificial sites on Earth’s surface with the aid of synchronous surface and atmospheric characteristic data. Since field calibration is affected by the coupled effects of surface and atmospheric characteristics, the single calibration results acquired under different surface and atmospheric conditions have different biases and different uncertainties, making it difficult to determine the consistency of these multiple calibration results. In view of this, by assuming that the radiometric performance is invariant during field calibration and the calibration samples are independent of each other, the surface–atmosphere invariant Key Comparison Reference Value (KCRV) is essentially derived from various calibration results. As the number of calibration samples increases, the uncertainty in the KCRV should decrease, and the KCRV should approach the “true” value. This paper addresses a novel method for estimating a weighted average value from multiple calibration results that can be used to compare each calibration result, and this value is accepted as the KCRV. Furthermore, this method is preliminarily applied to the field calibration of the Multispectral Instrument (MSI) onboard the Sentinel-2B satellite via the desert target at the Baotou site, China. After employing a chi-squared test to verify that 12 calibration samples are independent from each other, the KCRV of the 12 calibration samples at the Baotou site is derived, which exhibits much lower uncertainty than a single sample. The results show that the KCRVs of the relative differences between the simulated and observed at-sensor reflectance are 3.75%, 5.11%, 6.09%, and 5.03% for the four bands of Sentinel-2B/MSI, respectively, and the corresponding uncertainties are 1.84%, 1.87%, 1.90%, and 1.93%. It is noted that the KCRV uncertainty obtained with only 12 calibration samples is reduced significantly, and in the future, more samples in other instrumented sites will be used to validate this method thoroughly.

Vicarious calibration and validation techniques are important tools to ensure the long-term stability and inter-sensor consistency of satellite sensors making observations in the solar-reflective spectral domain. Automated test sites, which have continuous in situ monitoring of both ground reflectance and atmospheric conditions, can greatly increase the match-up possibilities for a wide range of space agency and commercial sensors. The Baotou calibration and validation test site in China provides operational high-accuracy and high-stability vicarious calibration and validation for high spatial resolution solar-reflective remote-sensing sensors. Two sites, given the abbreviations BTCN (an artificial site) and BSCN (a natural sandy site), have been selected as reference sites for the Committee on Earth Observation Satellites radiometric calibration network (RadCalNet). RadCalNet requires sites to provide data in a consistent format but does not specify the required operational conditions for a RadCalNet site. The two Baotou sites are the only sites to date that make spectral measurements for their continuous operation. One of the core principles of RadCalNet is that each site should have a metrologically rigorous uncertainty budget which also describes the site’s traceability to the international system of units, the SI. This paper shows a formalized metrological approach to determining and documenting the uncertainty budget and traceability of a RadCalNet site. This approach follows the Guide to the Expression of Uncertainty in Measurement. The paper describes the uncertainty analysis for bottom-of-atmosphere and top-of-atmosphere reflectance in the spectral region from 400 to 1000 nm for the Baotou sites and gives preliminary results for the uncertainty propagating this to top-of-atmosphere reflectance.

Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutional neural network (EMMCNN) for high spatial resolution (HSR) image land-cover classification. The EMMCNN first segmented the images into superpixels using the ETPS algorithm with false-color composition and enhancement and built parallel convolutional neural networks (CNNs) with dense connections for superpixel multi-scale deep feature learning. Then, the multi-resolution segmentation (MRS) object hand-delineated features were extracted and mapped to superpixels for complementary multi-segmentation and multi-type representation. Finally, a hybrid network was designed to consist of 1-dimension CNN and multi-layer perception (MLP) with channel-wise stacking and attention-based weighting for adaptive feature fusion and comprehensive classification. Experimental results on four real HSR GaoFen-2 datasets demonstrated the superiority of the proposed EMMCNN over several well-known classification methods in terms of accuracy and consistency, with overall accuracy averagely improved by 1.74% to 19.35% for testing images and 1.06% to 8.78% for validating images. It was found that the solution combining an appropriate number of larger scales and multi-type features is recommended for better performance. Efficient superpixel segmentation, networks with strong learning ability, optimized multi-scale and multi-feature solution, and adaptive attention-based feature fusion were key points for improving HSR image land-cover classification in this study.

Effects of volatiles from Pistacia chinensis Bunge and Juniperus chinensis cv. Kaizuka on human emotions were investigated. The volatiles were analyzed using a gas chromatograph-mass spectrometer (GC-MS). Human emotions were defined and measured using several physiological indices, including oxygen saturation of blood (OSB), finger temperature (FT), pulmonary ventilation volume (PVV), and electrocardiograph (ECG). These indices were measured using a multi-conductive physiology technique. The results showed that after participants had inhaled volatiles from P. chinensis Bunge, their OSB and FT decreased significantly (P＜0.05) in comparison to the control group, while the PVV increased. After inhaling the volatiles from J. chinensis cv. Kaizuka, the OSB and FT increased, while the PVV decreased. In addition, women’s FT and PVV and men’s OSB and PVV differed significantly from those of the control group (P＜0.05). Some ECG indices were also affected by the inhalation of volatiles. These results indicated that people tend to feel more excited or nervous when in the presence of P. chinensis Bunge, and more relaxed and comfortable while in the presence of J. chinensis cv. Kaizuka.

The Moon is a stable light source for the radiometric calibration of satellite sensors. It acts as a diffuse panel that reflects sunlight in all directions, however, the lunar surface is heterogeneous due to its topography and different mineral content and chemical composition at different locations, resulting in different optical properties. In order to perform radiometric calibration using the Moon, a lunar irradiance model using different observation geometry is required. Currently, two lunar irradiance models exist, namely, the Robotic Lunar Observatory (ROLO) and the Miller and Turner 2009 (MT2009). The ROLO lunar irradiance model is widely used as the radiometric standard for on-orbit sensors. The MT2009 lunar irradiance model is popular for remote sensing at night, however, the original version of the MT2009 lunar irradiance model takes less consideration of the heterogeneous lunar surface and lunar topography. Since the heterogeneity embedded in the lunar surface is the key to the improvement of the lunar irradiance model, this study analyzes the influence of the heterogeneous surface on the irradiance of moonlight based on model data at different scales. A heterogeneous correction factor is defined to describe the impact of the heterogeneous lunar surface on lunar irradiance. On the basis of the analysis, the following conclusions can be made. First, the influence of heterogeneity in the waning hemisphere is greater than that in waxing hemisphere under all 32 wavelengths of the ROLO filters. Second, the influence of heterogeneity embedded in the lunar surface exerts less impact on lunar irradiance at lower resolution. Third, the heterogeneous correction factor is scale independent. Finally, the lunar irradiance uncertainty introduced by topography is very small and decreases as the resolution of model data decreases due to the loss of topographic information.

Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system that can simultaneously collect both spatial information and extensive spectral information from targets. Compared with traditional LiDAR technology, which can only obtain range and intensity information at the selected spectral wavelengths, HSL detection technology has demonstrated its potential and adaptability for various quantitative applications from its spectrally resolved waveforms. However, with most previous HSLs, the collected spectral information is discrete, and such information might be insufficient and restrict the further applicability of the HSLs. In this paper, a tunable HSL technology using an acousto-optic tunable filter (AOTF) as a spectroscopic device was proposed, designed, and tested to address this issue. Both the general range precision and the accuracy of the spectral measurement were evaluated. By tuning the spectroscopic device in the time dimension, the proposed AOTF-HSL could achieve backscattered echo with continuous coverage of the full spectrum of 500–1000 nm, which had the unique characteristics of a continuous spectrum in the visible and near infrared (VNIR) regions with 10 nm spectral resolution. Yellow and green leaves from four plants (aloe, dracaena, balata, and radermachera) were measured using the AOTF-HSL to assess its feasibility in agriculture application. The spectral profiles measured by a standard spectrometer (SVC© HR-1024) were used as a reference for evaluating the measurements of the AOTF-HSL. The difference between the spectral measurements collected from active and passive instruments was minor. The comparison results show that the AOTF-based consecutive and high spectral resolution HSL was effective for this application.

Directional gap probability or gap fraction is a basic parameter in the optical remote sensing modeling. Although some approaches have been proposed to estimate this gap probability from remotely sensed measurements, few efforts have been made to investigate the scaling effects of this parameter. This paper analyzes the scaling effect through aggregating the high-resolution directional gap probability (pixel size of 20 meters) estimated from leaf area index (LAI) images of VALERI database by means of Beer's law and introduces an extension of clumping index, Ĉ, to compensate the scaling bias. The results show that the scaling effect depends on both the surface heterogeneity and the nonlinearity degree of the retrieved function. Analytical expressions for the scaling bias of gap probability and Ĉ are established in function of the variance of LAI and the mean value of LAI in a coarse pixel. With the VALERI dataset, the study in this paper shows that relative scaling bias of gap probability increases with decreasing spatial resolution for most of land cover types. Large relative biases are found for most of crops sites and a mixed forest site due to their relative large variance of LAI, while very small biases occur over grassland and shrubs sites. As for Ĉ, it varies slowly in the pure forest, grassland and shrubs sites, while more significantly in crops and mixed forest.

The aim of this study is to evaluate the aerosol influence on LST retrieval with two algorithms (split-window (SW) method and a four-channel based method) using simulated data under typical conditions. The results show that the root mean square error (RMSE) decreases to approximately 2.3 K for SW method and 1.5 K for four channel based method when VZA = 60° and visibility = 3 km; an RMSE would be increased by approximately 1.0 K when visibility varies from 3 km to 23 km. Moreover, a detailed sensitivity analysis under a visibility of 3 km and 23 km is performed in terms of uncertainties of land surface emissivity (LSE), water vapor content (WVC), and instrument noise, respectively. It is noted that the four-channel based method is more sensitive to LSE than SW method, especially for dry atmosphere; LST error caused by a WVC uncertainty of 20% is within 1.5 K for SW method and within 0.8 K for four-channel based method; the instrument noise would introduce LST error with a maximum standard deviation of 0.5 K and 0.04 K for the four-channel based method and SW method, respectively.

To evaluate the in-flight performance of a new hyperspectral sensor onboard an unmanned aerial vehicle (UAV-HYPER), a comprehensive field campaign was conducted over the Baotou test site in China on 3 September 2011. Several portable reference reflectance targets were deployed across the test site. The radiometric performance of the UAV-HYPER sensor was assessed in terms of signal-to-noise ratio (SNR) and the calibration accuracy. The SNR of the different bands of the UAV-HYPER sensor was estimated to be between approximately 5 and 120 over the homogeneous targets, and the linear response of the apparent reflectance ranged from approximately 0.05 to 0.45. The uniform and non-uniform Lambertian land surface reflectance was retrieved and validated using in situ measurements, with root mean square error (RMSE) of approximately 0.01-0.07 and relative RMSE of approximately 5%-12%. There were small discrepancies between the retrieved uniform and non-uniform Lambertian land surface reflectance over the homogeneous targets and under low aerosol optical depth (AOD) conditions (AOD = 0.18). However, these discrepancies must be taken into account when adjacent pixels had large land surface reflectance contrast and under high AOD conditions (e.g. AOD = 1.0).

Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral and viewing angle differences adequately; the method is applied to the FY-3C/Visible Infrared Radiometer (VIRR), taking the Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) as a reference. The results show that the relative difference between the two sets increases from January to May 2014, and becomes lower for the data on 24 July, 11 September, and 16 September, within approximately 10%. This phenomenon is caused by the updating of the calibration coefficients in the VIRR datasets with results from a vicarious method on June 2014. After performing an approximate estimation of the uncertainty, it is demonstrated that this calibration has a total uncertainty of 5.5%–6.0%, which is mainly from the uncertainty of the Bidirectional Reflectance Distribution Function model.