Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003-2022: A bibliometric analysis based on the web of science core database.

Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.

What is the relationship between land use and surface water quality? A review and prospects from remote sensing perspective.

Good surface water quality is critical to human health and ecology. Land use determines the surface water heat and material balance, which cause climate change and affect water quality. There are many factors affecting water quality degradation, and the process of influence is complex. As rivers, lakes, and other water bodies are used as environmental receiving carriers, evaluating and quantifying how impacts occur between land use types and surface water quality is extremely important. Based on the summary of published studies, we can see that (1) land use for agricultural and construction has a negative impact on surface water quality, while woodland use has a certain degree of improvement on surface water quality; (2) statistical methods used in relevant research mainly include correlation analysis, regression analysis, redundancy analysis, etc. Different methods have their own advantages and limitations; (3) in recent years, remote sensing monitoring technology has developed rapidly, and has developed into an effective tool for comprehensive water quality assessment and management. However, the increase in spatial resolution of remote sensing data has been accompanied by a surge in data volume, which has caused difficulties in information interpretation and other aspects.

Assessment of spatial and temporal ecological environment quality under land use change of urban agglomeration in the North Slope of Tianshan, China.

In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline-rise-decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type's RSEI values behave differently at different times. As the UANST is the core area of Xinjiang's urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.

Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China.

This study aims to analyze the pollution characteristics and sources of heavy metal elements for the first time in the Zhundong mining area in Xinjiang using the linear regression model. Additionaly, the health risks with their probability and infleuencing factors on different groups of people's were also evaluated using Monte Carlo (MC) simulation approach. The results shows that 89.28% of Hg was from coal combustion, 40.28% of Pb was from transportation, and 19.54% of As was from atmospheric dust. The main source of Cu and Cr was coal dust, Hg has the greatest impact on potential ecological risks. which accounted for 60.2% and 81.46% of the Cu and Cr content in soil, respectively. The all samples taken from Pb have been Extremely polluted (100%). 93.3% samples taken from As have been Extremely polluted. The overall potential ecological risk was moderate. Adults experienced higher non-carcinogenic risks of heavy metals from their diets than children. Interestingly, body weight was the main factor affecting the adult's health risks. This research provides more comprehensive information for better soil management, soil remediation, and soil pollution control in the Xinjiang mining areas.

Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index.

Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0-10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning.

Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm.

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R²) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water.

Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices.

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid regions. Producers and decision-makers thus require updated and accurate maps of salinity in agronomical and environmentally relevant regions. The goals of this study were to test various regression models for estimating soil salt content based on hyperspectral data, HJ-CCD images, and Landsat OLI data using; develop optimal band Difference Index (DI), Ratio Index (RI), and Normalization Index (NDI) algorithms for monitoring soil salt content using image and spectral data; and to compare the performances of the proposed models using a Bootstrap-BP neural network model (Bootstrap-BPNN) from different data sources. The results showed that previously published optimal remote sensing parameters can be applied to estimate the soil salt content in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). Optimal band combination indices based on DI, RI, and NDI were developed for different data sources. Then, the Bootstrap-BP neural network model was built using 1000 groups of Bootstrap samples of remote sensing indices (DI, RI and NDI) and soil salt content. When verifying the accuracy of hyperspectral data, the model yields an R2 value of 0.95, a root mean square error (RMSE) of 4.38g/kg, and a residual predictive deviation (RPD) of 3.36. The optimal model for remote sensing images was the first derivative model of Landsat OLI, which yielded R2 value of 0.91, RMSE of 4.82g/kg, and RPD of 3.32; these data indicated that this model has a high predictive ability. When comparing the salinization monitoring accuracy of satellite images to that of ground hyperspectral data, the accuracy of the first derivative of the Landsat OLI model was close to that of the hyperspectral parameter model. Soil salt content was inverted using the first derivative of the Landsat OLI model in the study area.

The influence of natural and human factors in the shrinking of the Ebinur Lake, Xinjiang, China, during the 1972-2013 period.

The Ebinur Lake is a closed inland lake located within the arid region of the Xinjiang Autonomous Region in the northwestern part of China, near the Kazakhstan border. The shrinkage of the lake area is believed to be caused by ecological environmental deterioration and has become an important restraining factor for the social development of the local population. Of all the lakes in the Xinjiang Autonomous Region, the Ebinur Lake is the most severely impacted water body. The lake has undergone change in size naturally for over thousands of years due to natural causes. However, the authors observed the dramatic changes in the freshwater resources of this region from the aerial images from 1972 to 2013. Thus, this paper traces and analyzes the change in the Ebinur Lake surface area in the past 41 years. A set of six satellite images acquired between 1972 and 2013 was employed to map the change in the surface area of the Ebinur Lake using the water index approach. The authors applied the traditional normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) to quantify the change in the water body area of the Ebinur Lake during the study period. The results indicate that the lake area has experienced a dramatic decrease of 31.4% from 1972 to 2013. The paper also examines the natural processes and human activities that may have contributed to the decrease in the lake area. The results show that the decrease in total lake area appears to coincide with periods of rapid land reclamation in the study area. Moreover, the uncontrolled land reclamation activities, such as irrigation, can increase the sedimentation in the Ebinur Lake thereby reducing the lake size. Reduction of the lake area has a negative ecological impact on the environment and on human life and property. The lake area is the most important factor to ensure the environment of the watershed and the key index to measure the environment balance.

Spectral reflectance properties of major objects in desert oasis: a case study of the Weigan-Kuqa river delta oasis in Xinjiang, China.

Aiming at the remote sensing application has been increasingly relying on ground object spectral characteristics. In order to further research the spectral reflectance characteristics in arid area, this study was performed in the typical delta oasis of Weigan and Kuqa rivers located north of Tarim Basin. Data were collected from geo-targets at multiple sites in various field conditions. The spectra data were collected for different soil types including saline-alkaline soil, silt sandy soil, cotton field, and others; vegetations of Alhagi sparsifolia, Phragmites australis, Tamarix, Halostachys caspica, etc., and water bodies. Next, the data were processed to remove high-frequency noise, and the spectral curves were smoothed with the moving average method. The derivative spectrum was generated after eliminating environmental background noise so that to distinguish the original overlap spectra. After continuum removal of the undesirable absorbance, the spectrum curves were able to highlight features for both optical absorbance and reflectance. The spectrum information of each ground object is essential for fully utilizing the multispectrum data generated by remote sensing, which will need a representative spectral library. In this study using ENVI 4.5 software, a preliminary spectral library of surface features was constructed using the data surveyed in the study area. This library can support remote sensing activities such as feature investigation, vegetation classification, and environmental monitoring in the delta oasis region. Future plan will focus on sharing and standardizing the criteria of professional spectral library and to expand and promote the utilization of the spectral databases.

Environmental biogeochemical behaviors of rare earth elements in soil-plant systems.

With the continual increase in the utilization of rare earth elements (REEs) for industrial and agricultural purposes in China, the research into the environmental biogeochemical behavior of REEs has become a pressing issue. The REEs' content in soil and various parts of wheat under different conditions in soil-plant systems were measured by INAA and ICP-MS. The results showed four aspects. (1) The mean value of total REEs in soil of China was 176.8 mg kg(-1). The mean ratio of SigmaLREE/SigmaHREE in soils was 8.0 and cerium accounts for 42% of the total REEs. The content of REEs in wheat seed ranged between 10(-11) and 10(-8) g g(-1), 3-4 orders of magnitude lower than that in soil. (2) The REEs contents in ryegrass, especially in roots, were significantly related to that of soil. The bioavailability of REEs in soil mainly depended on the exchangeable fraction of REEs, which was strongly affected by the physico-chemical properties of the soil. (3) Long-term foliage-dressing with Changle microfertilizer of REEs did not affect the contents and distribution patterns of REEs in soil. At the maturing stage of spring wheat, the REEs content was in the order of root > leaf >stem and crust. Compared with the control, foliage-dressing has a higher accumulation of REEs in root and leaf. However, no significant difference was found in stem and crust between the two treatments. (4) There was no significant accumulation with the soil-dressing method. When comparing controls in both foliage- and soil-dressing methods, no distinct residue of REEs in grains was found.