Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale

Glycyrrhizic acid alleviates the meconium-induced acute lung injury in neonatal rats by inhibiting oxidative stress through mediating the Keap1/Nrf2/HO-1 signal pathway

Meconium aspiration syndrome (MAS) is a disease closely related to inflammation and oxidative stress. Glycyrrhizic acid (GA) is a triterpenoid isolated from licorice with multiple bioprotective properties. In the present study, impacts of GA against MAS rats, as well as the potential mechanism, will be investigated. MAS model was established on newborn rats, followed by the treatment of 12.5, 25, and 50 mg/kg GA. The wet/dry weight ratio of lung tissues was calculated. The production of IL-6, IL-1β, TNF-α, malonaldehyde (MDA), superoxide dismutase (SOD), glutathione (GSH) was measured using ELISA assay. HE staining was used to evaluate the pathological state of lung tissues and TUNEL assay was used to detect the apoptotic state. The protein expression of Nrf2, Keap1, HO-1, Bcl-2, Bax, and cleaved-Caspase3 was measured by Western blotting assay. The elevated W/D ratio, release of inflammatory factors, lung injury score, and apoptotic index, as well as the activated oxidative stress and suppressed Keap1/Nrf2/HO-1 pathway, in MAS rats were significantly alleviated by GA. After introducing the inhibitor of Nrf2, ML385, the protective property of GA on the pathological state, apoptotic index, and oxidative stress in MAS rats was pronouncedly abolished. Taken together, glycyrrhizin alleviated GAH in rats by suppressing Keap1/Nrf2/HO-1 signaling mediated oxidative stress

Geochemical Anomaly Characteristics of Cd in Soils around Abandoned Lime Mines: Evidence from Multiple Technical Methods

Lime mines are a potential source of pollution, and the surrounding soil environment is generally at threat, especially in abandoned lime mines. This paper focuses on the study area in eastern Anhui, attempting to analyze whether Cd enrichment is related to abandoned mines. On the basis of geological investigation, this study systematically used XRD, XRF, GTS and universal Kriging interpolation to determine the distribution law of Cd in the study area, and evaluated the potential ecological risk of Cd. The results showed that the main mineral types of soil samples of red clastic rock soil parent material (RdcPm) and soil samples of carbonate soil parent material (CPm) were not completely the same. Correlation analysis showed that CaO, MgO and Cd were positively correlated with the CPm. Human activities led to the accumulation of Cd in the study area. High Cd was mainly concentrated in the northwest of the study area, which was correlated with abandoned mines and soil parent materials. The study area was dominated by slight potential risks, although some areas had medium potential risks and high potential risks. All potential high risks were in the CPm field. This study provides a scientific basis for the comprehensive utilization and development planning of soil in the study area

One-Bath Pretreatment for Enhanced Color Yield of Ink-Jet Prints Using Reactive Inks

In order to facilely increase the color yield of ink-jet prints using reactive inks, one-bath pretreatment of cotton fabrics with pretreatment formulation containing sodium alginate, glycidyltrimethylammonium chloride (GTA), sodium hydroxide, and urea is designed for realizing sizing and cationization at the same time. The pretreatment conditions, including the concentrations of GTA and alkali, baking temperature, and time are optimized based on the result of thecolor yield on cationic cotton for magenta ink. The mechanism for color yield enhancement on GTA-modified fabrics is discussed and the stability of GTA in the print paste is investigated. Scanning electron microscopey, tear strength, and thermogravimetric analysis of the modified and unmodified cotton are studied and compared. Using the optimal pretreatment conditions, color yield on the cationic cotton for magenta, cyan, yellow, and black reactive inks are increased by 128.7%, 142.5%, 71.0%, and 38.1%, respectively, compared with the corresponding color yield on the uncationized cotton. Much less wastewater is produced using this one-bath pretreatment method. Colorfastness of the reactive dyes on the modified and unmodified cotton is compared and boundary clarity between different colors is evaluated by ink-jet printing of colorful patterns

Basic Characteristics of Coal Gangue in a Small-Scale Mining Site and Risk Assessment of Radioactive Elements for the Surrounding Soils

The accumulation/improper treatment of coal gangue will not only lead to waste of land, but also cause environmental pollution. Especially the impact of radioactive elements on the surrounding ecological environment is widely concerned by many scholars. In this study, the concentration of radioactive elements (uranium (U) and thorium (Th)) of small-scale coal gangue mining site and surrounding soil in the northern region of Xieqiao coal mine were tested, the material composition of coal gangue was analyzed via XRF and XRD, the modes of occurrence of U and Th elements were investigated, and their potential ecological risks and ecological effectiveness were evaluated. The results show that the clay minerals with high content in coal gangue are the key minerals for the adsorption of uranium and thorium in coal gangue. The specific activity of two radioactive elements (U and Th) in soil is much lower than that of coal gangue. With the increase of the distance from the soil collection point to the gangue piles and the depth of the soil profile, the specific activities of the two radioactive elements decrease gradually. On the basis of the concentration curve, the range of the radioactive contamination halo of gangue piles is limited (≤30 m), speculating qualitatively that the gangue dump has no significant influence on the radioactivity of the surrounding water. The modes of occurrence of U and Th in coal gangue and soil are altered. According to the index of geo-accumulation, Th is easier to accumulate in soil environment, but Th and U pollution in soil is not obvious. In contrast to U element, the active state of Th element in soil is generally affected by exogenous (coal gangue) export, which may have a potential environmental effects. This study provides a research idea for the investigation of radioactive element pollution to the surrounding soil in small-scale coal gangue plies

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model