Bioaccumulation of Microcystin-LR and Induced Physio-Biochemical Changes in Rice (Oryza sativa L.) at Vegetative Stage under Hydroponic Culture Conditions.

Irrigation with water containing a variety of microcystins (MCs) may pose a potential threat to the normal growth of agricultural plants. To investigate the phytotoxicity of MC-LR at environmental concentrations on rice (Oryza sativa L.), the characteristics of uptake and accumulation in plant tissues, as well as a series of key physio-biochemical process changes in leaves of rice seedlings, were measured at concentrations of 0.10, 1.0, 10.0, and 50.0 µg·L-1 in hydroponic nutrient solutions for 7, 15, 20, and 34 days. Results showed that MC-LR could be detected in rice leaves and roots in exposure groups; however, a significant accumulation trend of MC-LR in plants (BCF > 1) was only found in the 0.10 µg·L-1 group. The time-course study revealed a biphasic response of O2•- levels in rice leaves to the exposure of MC-LR, which could be attributed to the combined effects of the antioxidant system and detoxification reaction in rice. Exposure to 1.0-50.0 µg·L-1 MC-LR resulted in significant depletion of GSH and MDA contents in rice leaves at later exposure times (15-34 days). Low MC-LR concentrations promoted nitric oxide synthase (NOS) activity, whereas high concentrations inhibited NOS activity during the later exposure times. The reduced sucrose synthase (SS) activities in rice exposed to MC-LR for 34 days indicated a decrease in the carbon accumulation ability of plants, and therefore may be directly related to the inhibition of plant growth under MC exposure. These findings indicate that the normal physiological status would be disrupted in terrestrial plants, even under exposure to low concentrations of MC-LR.

Comprehensive assessment of health and ecological risk of cadmium in agricultural soils across China: A tiered framework.

Soil cadmium (Cd) contamination has been increasingly serious in agricultural land across China, posing unexpected risks to human health concerning crop safety and terrestrial ecosystems. This study collected Cd concentration data from 3388 soil sites in agricultural regions. To assess the Cd risk to crop safety, a comprehensive sampling investigation was performed to develop reliable Soil Plant Transfer (SPT) model. Eco-toxicity tests with representative soils and organism was conducted to construct the Species Sensitivity Distribution (SSD) for ecological risk assessment. Then, a tiered framework was applied based on Accumulation index, deterministic method (Hazard quotient), and probabilistic assessment (Monte Carlo and Joint Probability Curve). The results revealed the widespread Cd enrichment in agricultural soils, mainly concentrated in Central, Southern, and Southwest China. Risk assessments demonstrated the greater risks related to crop safety, while the ecological risks posed by soil Cd were manageable. Notably, agricultural soils in southern regions of China exhibited more severe risks to both crop safety and soil ecosystem, compared to other agricultural regions. Furthermore, tiered methodology proposed here, can be adapted to other trace elements with potential risks to crop safety and terrestrial ecosystem.

Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty.

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.

Identifying multiple soil pollutions of potentially contaminated sites based on multi-gate mixture-of-experts network.

With the rapid increase in the amount and sources of big data, using big data and machine learning methods to identify site soil pollution has become a research hotspot. However, previous studies that used basic information of sites as pollution identification indexes mainly have problems of low accuracy and efficiency when conducting complex model predictions for multiple soil pollution types. In this study, we collected the environmental data of 199 sites in 6 typical industries involving heavy metal and organic pollution. After feature fusion and selection, 10 indexes based on pollution sources and pathways were used to establish the soil pollution identification index system. The Multi-gate Mixture-of-Experts network (MMoE) were constructed to carry out the multi-tasks of soil heavy metals, VOCs and SVOCs pollution identification simultaneously. The SHAP framework was used to reveal the importance of pollution identification indexes on the multiple outputs of MMoE and obtain their driving factors. The results showed that the accuracies of MMoE model were 0.600, 0.783 and 0.850 for soil heavy metals, VOCs and SVOCs pollution identifications, respectively, which were 0-20 % higher than their accuracies of BP neural networks of single tasks. The indexes of raw material containing organic compounds, enterprise scale, soil pollution traces and industry types have the different significant importance on site soil pollutions. This study proposed a more efficient and accurate method to identify site soil pollutions and their driving factors, which offers a step towards realizing intelligent identification and risk control of site soil pollution globally.

Asbestos-Environment Pollution Characteristics and Health-Risk Assessment in Typical Asbestos-Mining Area.

Asbestos has been confirmed as a major pollutant in asbestos-mining areas that are located in western China. In general, asbestos-fibre dust will is released into the environment due to the effect of intensive industrial activities and improper environmental management, such that the health of residents in and around mining areas is jeopardised. A typical asbestos mining area served as an example in this study to analyse the content and fibre morphology of asbestos in soil and air samples in the mining area. The effects of asbestos pollution in and around the mining areas on human health were also assessed based on the U.S. Superfund Risk Assessment Framework in this study. As indicated by the results, different degrees of asbestos pollutions were present in the soil and air, and they were mainly concentrated in the mining area, the ore-dressing area, and the waste pile. The concentration of asbestos in the soil ranged from 0.3% to 91.92%, and the concentration of asbestos fibres in the air reached 0.008-0.145 f·cc-1. The results of the scanning-electron microscope (SEM) energy suggested that the asbestos was primarily strip-shaped, short columnar, and granular, and the asbestos morphology of the soils with higher degrees of pollution exhibited irregular strip-shaped fibre agglomeration. The excess lifetime cancer risk (ELCR) associated with the asbestos fibres in the air of the mining area was at an acceptable level (10-4-10-6), and 40.6% of the monitoring sites were subjected to unacceptable non-carcinogenic risks (HQ > 1). Moreover, the waste pile was the area with the highest non-carcinogenic risk, followed by the ore dressing area, a residential area, and a bare-land area in descending order. In the three scenarios of adult offices or residences in the mining area, adults' outdoor activities in the peripheral residence areas, and children's outdoor activities, the carcinogenic-and non-carcinogenic-risk-control values in the air reached 0.1438, 0.2225 and 0.1540 f·cc-1, and 0.0084, 0.0090 and 0.0090 f·cc-1, respectively. The results of this study will lay a scientific basis for the environmental management and governance of asbestos polluted sites in China.

Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites.

Owing to the rapid development of big data technology, use of machine learning methods to identify soil pollution of potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due to the difficulty in obtaining key indexes of site pollution sources and pathways, current methods have problems such as low accuracy of model predictions and insufficient scientific basis. In this study, we collected the environmental data of 199 PCS in 6 typical industries involving heavy metal and organic pollution. Then, 21 indexes based on basic information, potential for pollution from product and raw material, pollution control level, and migration capacity of soil pollutants were used to established the soil pollution identification index system. We fused the original indexes into the new feature subset with 11 indexes through the method of consolidation calculation. The new feature subset was then used to train machine learning models of random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), and tested to determine whether it improved the accuracy and precision of soil pollination identification models. The results of correlation analysis showed that the four new indexes created by feature fusion have the correlation with soil pollution is similar to the original indexes. The accuracies and precisions of three machine learning models trained on the new feature subset were 67.4%- 72.9% and 72.0%- 74.7%, which were 2.1%- 2.5% and 0.3%- 5.7% higher than these of the models trained on original indexes, respectively. When the PCS were divided into typical heavy metal and organic pollution sites according to the enterprise industries, the accuracy of the model trained on the two datasets for identifying soil heavy metal and organic pollution were significantly improve to approximately 80%. Owing to the imbalance in positive and negative samples in the prediction of soil organic pollution, the precisions of soil organic pollution identification models were 58%- 72.5%, which were significantly lower than their accuracies. According to the factors analysis based on the model interpretability of SHAP, most of the indexes of basic information, potential for pollution from product and raw material, and pollution control level had different degrees of impact on soil pollution. However, the indexes of migration capacity of soil pollutants had the least effect in the classification task of soil pollution identification of PCS. Among the indexes, traces of soil pollution, industrial utilization years/start-up time, pollution control risk scores and enterprise scale having the greatest effects on soil pollution with the mean SHAP values of 0.17-0.36, which reflected their contribution rate on soil pollution and could help to optimize the current index scoring of the technical regulation for identifying site soil pollution. This study provides a new technical method to identify soil pollution based on big data and machine learning methods, in addition to providing a reference and scientific basis for environmental management and soil pollution control of PCS.

1D and 3D co-simulation and self-adaptive position control of electrostatic levitation in China's Space Station.

The greatest challenge of electrostatic levitation for containerless material processing is the stable control of charged material during heating. Recently, high-precision self-adaptive control of electrostatic levitation has been achieved in China's Space Station. Based on the 1D and 3D co-simulation analysis, an optimal scheduling of control strategies of sample release and retrieval in space is developed. Both simulation results and on-orbit experiments demonstrated that the inversion of surface charge is responsible for the heating induced material instability. On-orbit experiments indicated that under laser illuminations, the net surface charge of metal Zr changed from positive to negative at 900 K and from negative to positive at 1300 K. The possible physical mechanism of the charge inversion of heated material is discussed.

Derivation of Soil Criteria of Cadmium for Safe Rice Production Applying Soil-Plant Transfer Model and Species Sensitivity Distribution.

Widespread soil contamination is hazardous to agricultural products, posing harmful effects on human health through the food chain. In China, Cadmium (Cd) is the primary contaminant in soils and easily accumulates in rice, the main food for the Chinese population. Therefore, it is essential to derive soil criteria to safeguard rice products by assessing Cd intake risk through the soil-grain-human pathway. Based on a 2-year field investigation, a total of 328 soil-rice grain paired samples were collected in China, covering a wide variation in soil Cd concentrations and physicochemical properties. Two probabilistic methods used to derive soil criteria are soil-plant transfer models (SPT), with predictive intervals, and species sensitivity distribution (SSD), composed of soil type-specific bioconcentration factor (BCF, Cd concentration ratio in rice grain to soil). The soil criteria were back-calculated from the Chinese food quality standard. The results suggested that field data with a proper Cd concentration gradient could increase the model accuracy in the soil-plant transfer system. The derived soil criteria based on soil pH were 0.06-0.11, 0.33-0.59, and 1.51-2.82 mg kg-1 for protecting 95%, 50% and 5% of the rice safety, respectively. The soil criteria with soil pH further validated the soil as being safe for rice grains.

Safety Impact Analysis Considering Physical Failures and Cyber-Attacks for Mechanically Pumped Loop Systems (MPLs).

As complex systems composed of physical and cyber components, mechanically pumped loop systems (MPLs) are vulnerable to both passive threats (e.g., physical failures) and active threats such as cyber-attacks launched on the network control systems. The impact of the aforementioned two threats on MPL operations is yet unknown, and there is no practical way to evaluate their severity. To assess the severity of the impact of physical failures and cyber-attacks on MPLs, a safety impact analysis framework based on Elman Neural Network (ENN) observers and the Gaussian Mixture Model (GMM) algorithm is suggested. The framework discusses three common attack and failure modes: sensor hard failure that occurs suddenly, sensor soft failure that occurs gradually over time, and denial-of-service (DoS) attacks that prevent communication between the controller and valve. Both sensor failures and DoS attacks render the system unsafe, according to simulation data. In comparison to DoS attacks, however, sensor failures, particularly soft failures, inflict the greatest harm to the MPLs. Furthermore, sensors engaged in global control, rather than those involved in local control, need additional protection.

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study.

Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work conditions. However, industrial process may refer to complex control and operation, the extraction of relevant information from abundant sensors data may be challenging, and description of complicated process data patterns is also becoming a hot topic in soft-sensor development. In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. Firstly, mechanism model of ventilation is established via mass and energy conservation law, and object-relevant features are identified as the inputs of data-driven method. Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive RBFNN (SA-RBFNN) is proposed to improve the soft sensor performance in energy production process. Finally, effectiveness of the proposed method is verified on a real-world power plant dataset, taking coal mill ventilation soft sensing as a case study.

[A Comparative Study of Soil Environmental Standards for Agricultural Land Among Different Countries and Its Implication for China].

Soil environmental quality of agricultural land plays a determinate role in the quality of agricultural products, human health, and the safety of the ecosystem. In 2018, China issued the "soil environment quality risk control standard for soil contamination of agriculture land" (GB 15618-2018), which has been essential to soil pollution prevention and the control of agricultural land. In this study, a systematic and comparative analysis of soil environmental standards for the agricultural land of 17 countries or regions was conducted, including the framework, protection objective, derivation method, contaminant elements, analyses methods, and standard values, as well as the impact factors. The results showed that the number of contaminants of GB 15618-2018 was insufficient with the simple consideration of total concentrations. Meanwhile, there was a lack of the standardized derivation method. On such a basis, we put forward some suggestions to improve GB 15618-2018 in light of the aforementioned problems, including strengthening the research of soil environmental benchmark and background values; establishing the scientific and standardized derivation method; and improving the number, form, and availability of indicators for risk control. In the meantime, the regional and local background environmental concentration of soil was highly proposed as a supplement and optimization to soil screening values.

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With the rapid urbanization and industrial structure adjustment in China, many contaminated sites have been left for remediation. It is essential to develop and implement ecological risk assessment (ERA) before remediating contaminated sites at a large scale as well as sequential management. In this review, we discussed the key problems in ecological risk assessment of soils in contaminated sites focusing on scientific principles, frameworks, techniques, and approaches, including 1) the site-specific framework, 2) uncertainty of conceptual model, 3) toxic mechanisms of combined contamination in soil, 4) screening of assessment endpoints, and 5) development of assessing approaches and frameworks. Then, two perspectives were addressed: the toxicological mechanism of soil combined pollution including bioavailability of contaminants in soil and their joint effect is the scientific problem in ecological risk assessment of soil in contaminated site; and weight of evidence approach based on USEPA four-step approach and EU Tier approach is applicable for ecological risk assessment in field conditions. Future studies should focus on: 1) the coordination of ecological risk assessment (ERA) framework and risk management framework, 2) conceptual mo-del, 3) process-based reactive transport models for exposure evaluation, 4) ecotoxicological mechanism of combined contamination in site soil, and 5) high ecological level endpoints. The aim of this review was to provide theoretical base and framework for the establishment of local guideline of ecological risk assessment in China.

Evaluation of joint toxicity of heavy metals and herbicide mixtures in soils to earthworms (Eisenia fetida).

It is widely acknowledged that a simplified and robust approach to evaluating thecombined effects of chemical mixtures is critical for ecological risk assessment (ERA) of contaminated soil. The earthworm (Eisenia fetida) was used as a model to study the combined effects of polymetallic contamination and the herbicide siduron in field soil using a microcosm experiment. The responses of multiple biomarkers, including the activities of catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GR) and acetylcholine esterase (AChE), the concentrations of glycogen, soluble protein (SP), malonaldehyde (MDA), and metallothionein (MT), and the neutral red uptake test (NRU), were investigated. Multivariate analysis, Principal Component Analysis (PCA) and Spearman's Rank Correlations analysis (BVSTEP) revealed that the activities of AChE and CAT and the NRU content were the prognostic biomarkers capturing the minimum data set of all the variables. Internal Cd (tissue Cd) in earthworms was closely related to the health status of worms under combined contamination of heavy metals and siduron. The integrated effect (Emix) calculated based on the activities of AChE and CAT and NRU content using the stress index method had significantly linear regression with internal Cd (p<0.01). Emix(10), Emix(20), and Emix(50) were then calculated, at 1.27, 1.63 and 2.71 mg/kg dry weight, respectively. It could be concluded that a bioassay-based approach incorporating multivariate analysis and internal dose was pragmatic and applicable to evaluating combined effects of chemical mixtures in soils under the guidance of the top-down evaluation concept of combined toxicity.

Ecological risk of combined pollution on soil ecosystem functions: Insight from the functional sensitivity and stability.

Assessing the ecological risk of combined pollution, especially from a holistic perspective with the consideration of the overarching functions of soil ecosystem, is crucial and beneficial to the improvement of ecological risk assessment (ERA) framework. In this study, four soils with similar physicochemical properties but contrasting heavy metals contamination levels were selected to explore changes in the integrated functional sensitivity (MSI), resistance (MRS) and resilience (MRL) of soil microbial communities subjected to herbicide siduron, based on which the ecological risk of the accumulation of siduron in the four studied soils were evaluated. The results suggested that the microbial biomass carbon, activity of denitrification enzyme and nitrogenase were indicative of MSI and MRS, and the same three parameters plus soil basal respiration were indicative of MRL. Significant dose-effect relationships between siduron residues in soils and MSI, MRS and MRL under combined pollution were observed. Heavy metal polluted soils showed higher sensitivity and lower resistance to the additional disturbance of herbicide siduron due to the lower microbial biomass, while the resilience of heavy metal polluted soils was much higher due to the pre-adaption to the chemical stresses. The quantifiable indicator microbial functional stability was incorporated in the framework of ERA and the results showed that the accumulation of siduron in the studied soils could exhibit potential harm to the integrated functional stability of soil microbial community. Thus, this work provides insights into the application of integrated function of soil microbial community into the framework of ERA.

Mechanisms and uncertainties of Zn supply on regulating rice Cd uptake.

Application of Zinc (Zn) is considered an effective measure to reduce Cadmium (Cd) uptake and toxicity in Cd-contaminated soils for many plant species. However, interaction between Zn and Cd in rice plant is complex and uncertain. In this study, four indica rice cultivars were selected to evaluate the effect of Zn exposure in an EGTA-buffered nutrient solution under varying Zn activities and a field level of Cd activity to characterize the interaction between Zn and Cd in rice. Severe depression in shoots' biomass, tiller number, and SPAD (Soil and Plant Analyzer Development) value were found at both Zn deficiency and Zn phytotoxicity levels among four tested rice cultivars. There existed a strong antagonism interaction between Zn and Cd in both shoot and root from Zn deficiency to Zn phytotoxicity. The reduction of Cd accumulation in roots and shoots could be explained by the competition between Zn and Cd as well as the dilution effect of increasing biomass. The conflicting effect of Zn supply on Cd uptake may be attributed to the increasing transfer ratio of Cd from root to shoot with the increasing Zn2+ activities and the strong depression of Fe and Mn in shoots with the increasing Zn2+ activities as well as the variation of genotypes. Balance between Zn and Cd should be considered in field application.

Evaluation of combined toxicity of Siduron and cadmium on earthworm (Eisenia fetida) using Biomarker Response Index.

Agrochemicals and heavy metals are widespread contaminants in urban soil and could co-exist as mixture, which could cause unexpected risk to terrestrial organism. To assess the joint effect of herbicide Siduron and Cd, a battery of sub-lethal biomarkers was studied using earthworm ecotoxicological assay. Most selected biomarkers appeared significant but complicated responses with the increasing concentration of contaminants after 28-day exposure. In order to quantify the overall effect of the mixture contaminants, Biomarker Response Index (BRI) was used to integrate the multiple responses. Concentration Addition Index (CAI) and Effect Addition Index (EAI) were introduced to assess types of joint effect. Results showed significantly dose-effect responses between BRI and contaminant exposure concentrations. Integrated toxicity increased obviously under joint treatments of Siduron and Cd compared to their individual treatments. According to CAI, a clear antagonism was observed at relatively lower effects and gradually transformed to slight synergism with an increase of effects, while EAI showed the joint effect of addition at the whole range of effect levels. Thus, compared to the simple analysis of those complicated responses, BRI is an effective method to determine the integrated toxicity of mixture and its combination with joint effect indices (CAI and EAI) provides more worthy risk assessment on toxicity interaction among compounds.

Ecological risk evaluation of combined pollution of herbicide siduron and heavy metals in soils.

Combined pollution of agrichemicals and heavy metals in urban lawn soils were commonly observed throughout the world, and the co-existed two chemicals could interact with each other both in environment behavior and toxic effect. However, little has been reported on the ecological risk of their combined pollution, especially in field due to lack of systematic methodology. In this study, four soils (C, N1, N2, N3) from two public parks in Beijing, China, with similar properties but contrasting heavy metal contaminated level were chosen to assess the ecological risks of co-existed herbicide siduron and heavy metals. Environmental behaviors of siduron in studied soils were investigated with batch experiments in lab, based on which the environmental exposure level of siduron was simulated with HYDRUS-1D. Results suggested that soil organic matter (SOM) rather than the co-existed heavy metals was the dominant factor affecting the fate and the accumulation of siduron in soils. Soil N2 with the highest SOM, showed the strongest tendency to retain siduron among the studied soils. Significant joint effect of siduron and heavy metals on cucumber root elongation was observed through lab experiments. Thus, the joint toxicity of siduron and heavy metals were calculated based on single toxicology data of them using independent action (IA) and concentration addition (CA) model. Then, the predicted no effect concentration (PNECsoil) of siduron was calculated with equilibrium partitioning method and extrapolation techniques. The PNECsoil of siduron was the lowest in heaviest heavy metal contaminated soil N3. The risk characterization ratios (RCR) of siduron in four soils were all >1. The highest RCR of siduron in soil N3 suggested that it was the joint toxicity of siduron and heavy metals to organisms determining the ecological risks of siduron in combined polluted soils.

Ecological risk assessment of polymetallic sites using weight of evidence approach.

Ecological risk assessment (ERA) of polymetallic contamination in soils has caused extensive solicitude. However, objective and feasible methods suitable for soil ERA are limited. Therefore, in this study, a multidisciplinary and quantitative weight of evidence approach (WOE) specific to soil ecosystems was developed based on the previous WOE for aquatic ecosystems. The framework consisted of four lines of evidence (LOEs): DTPA-extractable heavy metal in soils, bioaccumulation in earthworms, integration of biomarker responses and expected community effect (multi-substance Potentially Affected Fraction, msPAF). These four LOEs were initially evaluated by each hazard quotient (HQ) of them based on the ratio to the reference (RTR) of each parameter. Then, Environmental risk index (EnvRI) integrated by HQs with different weights was calculated. At last, three sites, one for reference (N1) and two for contaminated soils (N2 and N3) were chosen to apply the modified WOE approach. Results showed that heavily contaminated site, N3 had higher HQ classification for each LOE and its EnvRI was classified as Major levels, while the EnvRI of N2 was assigned into Moderate. What's more, HQ of biomarker response (HQbiomarker) integrated by RTRs of biomarkers increased gradiently with the increase of heavy metal levels in soils though irregular changes were observed for most of those biomarkers. Overall, our results indicated that the quantitative WOE framework specific to soil ERA had the advantage of obtaining a comprehensive and objective risk assessment.

Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.

In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

[Analysis of sensitive spectral bands for burning status detection using hyper-spectral images of Tiangong-01].

To obtain the sensitive spectral bands for detection of information on 4 kinds of burning status, i. e. flaming, smoldering, smoke, and fire scar, with satellite data, analysis was conducted to identify suitable satellite spectral bands for detection of information on these 4 kinds of burning status by using hyper-spectrum images of Tiangong-01 (TG-01) and employing a method combining statistics and spectral analysis. The results show that: in the hyper-spectral images of TG-01, the spectral bands differ obviously for detection of these 4 kinds of burning status; in all hyper-spectral short-wave infrared channels, the reflectance of flaming is higher than that of all other 3 kinds of burning status, and the reflectance of smoke is the lowest; the reflectance of smoke is higher than that of all other 3 kinds of burning status in the channels corresponding to hyper-spectral visible near-infrared and panchromatic sensors. For spectral band selection, more suitable spectral bands for flaming detection are 1 000.0-1 956.0 and 2 020.0-2 400.0 nm; the suitable spectral bands for identifying smoldering are 930.0-1 000.0 and 1 084.0-2 400.0 nm; the suitable spectral bands for smoke detection is in 400.0-920.0 nm; for fire scar detection, it is suitable to select bands with central wavelengths of 900.0-930.0 and 1 300.0-2 400.0 nm, and then to combine them to construct a detection model.