Relationship between negative air ion and PM2.5 in Quercus variabilis under natural conditions.

In recent years, PM2.5 pollution has become a most important source of air pollution. Prolonged exposure to high PM2.5 concentrations can give rise to severe health issues. Negative air ion (NAI) is an important indicator for measuring air quality, which is collectively known as the 'air vitamin'. However, the intricate and fluctuating meteorological conditions and vegetation types result in numerous uncertainties in the correlation between PM2.5 and NAI. In this study, we collected data on NAI, PM2.5, and meteorological elements through positioning observation during the period of June to September in 2019 and 2020 under the condition of relatively constant leaf area in Quercus variabilis forest, a typical forest in warm temperate zones. We investigated the spatiotemporal variation of PM2.5 and NAI under consistent meteorological conditions, established the correlation between PM2.5 and NAI, and explicated the impact mechanism of PM2.5 on NAI in natural conditions. The results showed that NAI decreased exponentially with the increases in natural PM2.5, with a significant negative correlation (y=1148.79x-0.123). The decrease rates of NAI in PM2.5 concentrations of 0-20, 20-40, 40-80, 80-100 and 100-120 µg·m-3 were 40.1%, 36.2%, 9.4%, 2.4%, 5.1% and 6.8%, respectively. Results of the sensitivity analysis showed that the PM2.5 concentration range of 0-40 µg·m-3 was the sensitive range that affected NAI. Our findings could provide a scientific basis for better understanding the response mechanisms of NAI to environmental factors.

Effect of light intensity on negative air ion under phytotron control.

Negative air ion (NAI) is an important index for measuring air quality and has been widely recognized to be influenced by photosynthesis processes. However, vegetation type and light intensity are also known to impact NAI, contributing to significant uncertainties in the relationship between light and NAI. In this paper, we selected Pinus bungeana, Platycladus orientalis and Buxus sinica as research subjects and obtained their NAI, light intensity, and meteorological data through synchronous observation under the relatively stable condition of the phytotron. We analyzed the change characteristics of NAI and the difference of NAI production ability in needle and broadleaf vegetation under different light intensities. Finally, we determined the relationship and underlying mechanism governing light intensity and NAI using diverse tree species. The results showed that the influence of light on NAI was significant. In the environment without vegetation, the influence of different light intensities on NAI was not significant, and the mean NAI concentration was 310 ions·cm-3. Conversely, in the presence of vegetation, NAI showed a "single-peak" trend with increasing light intensity. The NAI concentration of the three tree species was significantly higher than under different light intensities when vegetation was not present. The NAI promoting ability of P. bungeana was the highest (675 ions·cm-3), followed by P. orientalis (478 ions·cm-3) and B. sinica (430 ions·cm-3), which increased by 117.5%, 53.9% and 38.6% compared to the environment without vegetation. The NAI growth rate was significantly different between needle and broadleaf vegetation based on the specific tridimensional green biomass. Additionally, the NAI growth rates of P. bungeana and P. orientalis were 647 and 295 ions·cm-3·m-3, respectively, which were 3.06 and 1.39 times that of B. sinica (211 ions·cm-3·m-3). The piecewise equation fitting effect of NAI and light intensity was better for different tree species, the determination coefficients (R2) of P. bungeana, P. orientalis and B. sinica were 0.926, 0.916 and 0.880, and the root mean square errors (RMSE) were 7.157, 6.008 and 5.389 ion·cm-3, respectively. Altogether, our study provides a theoretical basis as well as technical support for the construction of healthy vegetation stands, the selection of preferred tree species, and the optimization of vegetation models, and promotes air quality and the provision of ecosystem functions and services.

[Applicability of multiple remotely sensed vegetation indices for extracting key phenological metrics of Tamarix chinensis shrubs based on CO2 flux observation and Sentinel-2 data]. / æŸ½æŸ³çŒä¸›å ³é”®ç‰©å€™å‚æ•°å¤šç§æ¤è¢«æŒ‡æ•°é¥æ„Ÿæå–çš„é€‚ç”¨æ€§­­åŸºäºŽCO2通量观测和Sentinel-2数据.

We analyzed the relationship between gross primary productivity (GPP) and environmental factors at Sidaoqiao Superstation of the Ejina Oasis in China's Gobi Desert, by combining eddy flux and meteorological data from 2018 to 2019 and Sentinel-2 remote sensing images from 2017 to 2020. We evaluated the applicability of 12 remote sensing vegetation indices to simulate the growth of Tamarix chinensis and extract key phenological metrics. A seven-parameter double-logistic function (DL-7) + global model function (GMF) was used to fit the growth curves of GPP and vegetation indices. Three key phenological metrics, i.e., the start of the growing season (SOS), the peak of the growing season (POS), and the end of the growing season (EOS), were extracted for each year. Growing season degree days (GDD) and soil water content were the main environmental factors affecting the phenological dynamics of T. chinensis. Compared with 2018, the lower temperatures in 2019 resulted in slower accumulation rate of accumulated temperature before the SOS. T. chinensis required longer heat accumulation to enter growing season, which might cause later SOS in 2019. The hydrothermal conditions between SOS and POS were similar for 2018 and 2019. Howe-ver, the POS in 2019 was 8 days later than that in 2018, because of the late SOS in 2019. Following the POS in 2019, high GDD and low soil water content caused the T. chinensis to suffer from water stress, resulting in a shortened late growing season. The linear regression between the standardized Sentinel-2 vegetation index and the average value of GPP between 10:00 and 14:00 indicated that the enhanced vegetation index of the broadband vegetation index and the chlorophyll red edge index, inverted red edge chlorophyll index, and red-edge normalized difference vegetation index (NDVI705) of the narrowband vegetation index were highly consistent with the GPP of T. chinensis. Remote sensing extraction of SOS and POS of T. chinensis suggested that the Sentinel-2 narrowband vegetation index was more accurate than the broadband vegetation index. The modified chlorophyll absorption in reflectance index provided the most accurate extraction of SOS, while the MERIS terrestrial chlorophyll index provided the most accurate extraction of EOS. Conversely, the Sentinel-2 broadband vegetation index was the most accurate for extracting POS, especially the 2-band enhanced vegetation index and the near-infrared reflectance of vegetation. Overall, NDVI705 was the best index to estimate phenological metrics.

[Impact of Vegetation Structure on Drought Indices Based on MODIS Spectrum].

The drought indices based on MODIS spectral reflectance data are widely used for drought characterization and monitoring in agricultural context. Based on the PROSAIL model and MODIS observational data in Shandong in 2010, the present paper studied the impact of vegetation structure of leaf area index and physiological growth cycle on MODIS spectral drought index. The results showed that the reflectance of three MODIS bands in spectrum of near-infrared and shortwave infrared changes significantly with leaf water content of vegetation. Therefore, the five kinds of MODIS spectral drought index constructed by those MODIS bands can be used to monitor the leaf water content of vegetation. However, all drought indices are affected by leaf area index. In general, the impact is serious in the case of low LAI values and is weakened with the increase in LAI value. The study found that physiological vegetation growth cycle also affects the magnitude of MODIS spectral drought indices. In conclusion, the impact of vegetation structure must be carefully considered when using MODIS spectral drought indices to monitor drought. The conclusion of this study provides a theoretical basis for remote sensing of drought monitoring.

[Modeling continuous scaling of NDVI based on fractal theory].

Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals' relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters' variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI (computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.

[Dynamic monitoring of summer maize planting information for spatial and temporal variations in Huanghuaihai plain during 2000-2010].

In order to give consideration to the change information of summer maize planting area and spatial distribution in Huanghuaihai plain, the present paper combined statistical analysis and remotely sensed classification technology to extract summer maize based on MODIS EVI images. The results showed high accuracy (> 67.35%) with the TM-derived in spatial distribution and high correlation coefficient with the agriculture statistics in planting areas at city level (R2 > 0.497 7). On this basis, change imagery-was computed using image overlying algorithm based on binaryzated images derived from classification results in Huanghuaihai plain during 2000-2010. The change detection feature was analysed according to the plates in the region. The results show that the summer maize planting area increased significantly in huanghuaihai plain from 2000 to 2010. The planting area increased steadily in southern part during study year. In the northern part, it discreased from 2000 to 2003 and increased from 2003 to 2010. The most huge change occurred in the northern part in the period of 11 years. The planting proportion increased in the north of plain but decreased in the north. The new method can be widely used in regional dynamic detection and has a good applicability and accuracy.