[A Forward Kernel Function for Fitting in situ Measured Snow Bidirectional Reflectance Factor].

Modelling and fitting the reflectance anisotropy of land surfaces is one of the most important issues in remote sensing studies. In the traditional linear kernel-driven model, the most widely used kernel functions are derived from radiative transfer model of vegetation canopy. Therefore, it is not validate to represent the forward scattering effect of snow/ice surfaces. We proposed a method by adding a forward kernel function to the traditional linear kernel-driven model, and validate it with in situ measured bidirectional reflectance factor (BRF) data. The validation results show that this method is efficient for fitting the BRF of snow/ice surfaces (R2=0.997 5, RMSE=0.022 6). We also compared it with empirical functions and the traditional linear kernel-driven model. The results show that: (1) The fitting results of linear kernel-driven model are better than those of empirical functions; (2) The fitting results can be significantly improved by adding the forward kernel function; (3) The fitting results of the improved linear kernel-driven model are stable at different wavelengths.

[Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution level, using stepwise multiple linear regression and cross validation on the dataset which is consisting of 44 groups of copper ion content information in the polluted vegetation leaves from Dexing Copper Mine in Jiangxi Province to build up a statistical model by also incorporating the HJ-1 satellite images. This model was then used to estimate the copper content distribution over the whole research area at Dexing Copper Mine. The result has shown that there is strong statistical significance of the model which revealed the most sensitive waveband to copper ion is located at 516 nm. The distribution map illustrated that the copper ion content is generally in the range of 0-130 mg · kg⁻¹ in the vegetation covering area at Dexing Copper Mine and the most seriously polluted area is located at the South-east corner of Dexing City as well as the mining spots with a higher value between 80 and 100 mg · kg⁻¹. This result is consistent with the ground observation experiment data. The distribution map can certainly provide some important basic data on the copper pollution monitoring and treatment.

[Temporal variation analysis for spectral reflectance of maize leaves using a fitting method].

The present study is to investigate the temporal variation patterns through the dataset of spectral reflectance of maize leaf using a fitting method. In the field experiment, 1 261 pieces of spectral reflectance of maize leaves at different leaf positions were measured every day during its life cycle. After signal/noise analysis, the visible and near infrared (VNIR) band (400-960 nm) was selected in this study. The spectral reflectance was fitted using a spectral scatter diagram (SSD) method. Seven fitting coefficients were employed to denote the temporal variation patterns of maize leaf, which can also be fitted by bi-variate quadratic functions. The comparison of the fitted results with the original measurement data shows that the fitting results are reasonably good, where for 98.7% leaves r is larger than 0.99, and for 80.9% leaves RMSE is less than 0.001 5. All the fitted spectral reflectance is compared with the original measurement data, where r is 0.997 8, and RMSE is 0.0105. The results show that this method is particularly suitable for presenting the temporal variation patterns of the spectral reflectance of maize leaves.

[A novel method for extracting leaf-level solar-induced fluorescence of typical crops under Cu stress].

The leaf-level solar-induced fluorescence changes when the typical crops are under Cu stress, which can be considered as a sensitive indicator to estimate the stress level. In the present study, wheat (Triticum aestivum L.), pea (Pisum sativum L.) and Chinese cabbage (Brassica campestris L.) were selected and cultured with copper solutions or copper polluted soil with different Cu stress. The apparent reflectance of leaves was measured by an ASD Fieldspec spectrometer and an integrating sphere. As the apparent reflectance was seldom affected by the fluorescence emission at 580-650 and 800-1000 nm, so the apparent solar-induced fluorescence can be separated from the apparent reflectance based on PROSPECT model. The re-absorption effect of chlorophyll was corrected by three methods, called GM (Gitelson et al.'s model), AM (Agati et al.'s model) and LM (Lagorio et al.'s model). After the re-absorption correction, the solar-induced fluorescence under different Cu stress was obtained, and a positive relationship was found between the height of far RED fluorescence (FRF) and the copper contents in leaves.

MicroRNA-140-5p aggravates hypertension and oxidative stress of atherosclerosis via targeting Nrf2 and Sirt2.

In the present study, the function of microRNA (miR)­140­5p on oxidative stress in mice with atherosclerosis was investigated. A reverse transcription­quantitative polymerase chain reaction assay was used to determine the expression of miR­140­5p. Oxidative stress kits and reactive oxygen species (ROS) kits were used to analyze alterations in oxidative stress and ROS levels. The alterations in protein expression were determined using western blot analysis and an immunofluorescence assay. miR­140­5p expression was increased in mice with atherosclerosis with hypertension. Consistently, miR­140­5p expression was also increased in mice with atherosclerosis. Upregulation of miR­140­5p increased oxidative stress and ROS levels by suppressing the protein expression of nuclear factor erythroid 2­related factor 2 (Nrf2), sirtuin 2 (Sirt2), Kelch­like enoyl­CoA hydratase­associated protein 1 (Keap1) and heme oxygenase 1 (HO­1) in vitro. By contrast, downregulation of miR­140­5p decreased oxidative stress and ROS levels by activating the protein expression of Nrf2, Sirt2, Keap1 and HO­1 in vitro. Sirt2 agonist or Nrf2 agonist inhibited the effects of miR­140­5p on oxidative stress in vitro. Collectively, these results suggested that miR­140­5p aggravated hypertension and oxidative stress of mice with atherosclerosis by targeting Nrf2 and Sirt2.

[Study on the spectrum response of Brassica Campestris L leaf to the zinc pollution].

In the present paper, the spectrum response of Brassica Campestris L leaf to the stress of heavy metal zinc pollution was studied in three spectral rangess of the red edge position (680-740 nm), the visible spectrum (460-680 nm) and the near infrared spectrum (750-1000 nm). The results indicate that the Zn content in cabbage leaves increases and the chlorophyll level reduces with the increase in Zn concentration in soil. With the Zn content of Brassica Campestris L leaves increasing, the leaf spectral reflectivity in visible light (A1) and the range of red edge shift (S) ascends, the the leaf spectral reflectivity in the near infrared light (A2) decreases. The three indices of A1, A2 and S are fitted much linearly with the logarithm of zinc content in Brassica Campestris L leaves with the high squared regression coefficients of 0.942, 0.981 and 0.969 respectively. The regression models are reliable to estimate the zinc content in Brassica Campestris L leaves.

[Studies of relationships between Cu pollution and spectral characteristics of TritiZnm aestivum L].

The effect of Cu pollution on the Cu uptake by wheat, the characteristics of chlorophyll concentration, and the visible-near infrared spectra were studied under the condition of solution culture, and the relationships among the three indices were discussed. The results indicate that the content of Cu in TritiZnm aestivum L. increases and the concentration of chlorophyll reduces with the increase of Cu in solution. The spectral characteristics also take on the disciplinary diversification (the spectral reflectivity ascended in the visible light band and decreased in the near infrared band; the range of red edge shift decreased) with the increase of Cu in solution.