Search

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes. [ABSTRACT FROM AUTHOR]

With the advancement in computational approaches and experimental, simulation, and modeling tools in recent decades, a trial-and-validation method is attracting more attention in the materials community. The development of powder metallurgy Ni-based superalloys is a vivid example that relies on simulation and experiments to produce desired microstructure and properties in a tightly controlled manner. In this research, we show an integrated approach to predicting the grain size of industrial forgings starting from lab-scale cylindrical compression by employing modeling and experimental validation. (a) Cylindrical compression tests to obtain accurate flow stress data and the hot working processing window; (b) double-cone tests of laboratory scale validation; (c) sub-scale forgings for further validation under production conditions; and (d) application and validation on full-scale industrial forgings. The procedure uses modeling and simulation to predict metal flow, strain, strain rate, temperature, and the resulting grain size as a function of thermo-mechanical processing conditions. The models are calibrated with experimental data until the accuracy of the modeling predictions is at an acceptable level, which is defined as the accuracy at which the results can be used to design and evaluate industrial forgings. [ABSTRACT FROM AUTHOR]

More specifically, remote sensing of watersheds encompasses a wide range of applications that involve acquiring and utilizing hydrological, ecological, and environmental information in watersheds [[15], [17]], including watershed mapping [[18]], monitoring of underlying surface elements in the watershed [[19]], inversion of water cycle parameters [[20]], water resource assessment [[21]], acquisition of watershed ecological environment parameters [[22]], monitoring natural disasters [[23]], analyzing upstream and downstream [[24]], supporting water governance priorities [[25]], water resource development [[26]], and irrigation water management [[27]]. Data Barriers and Data Sharing Addressing data barriers and promoting data sharing is a critical perspective in remote sensing for watershed management. The advent of remote sensing has revolutionized watershed research, providing unprecedented insights into watershed dynamics and spatiotemporal patterns [[10]]. Similarly, merging remote sensing data with in situ measurements, socio-economic data, and other geospatial information can enhance the accuracy and reliability of watershed analyses. [Extracted from the article]

The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 μ g · cm − 2 ). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40   μ g · cm − 2 ), root mean square error (RMSE = 4.23   μ g · cm − 2 ), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite. [ABSTRACT FROM AUTHOR]

Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) > autumn (80.8 %) > spring (79.9 %) > summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at 10.5281/zenodo.5727119 (Chen et al., 2021b). [ABSTRACT FROM AUTHOR]

Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters and evacuation and disaster relief capacity. At present, evaluation of the spatial distribution of urban emergency shelters is mainly carried out on three aspects: effectiveness, accessibility, and safety. However, there are problems, such as individual evaluation scales and incomplete indicator systems, unreasonable allocation of indicator weights, and ignoring the influence of fuzzy incompatibility between different indicator attributes on the evaluation results. In this paper, we start from two scales, the individual emergency shelter and the regional groups of emergency shelters. Based on the five criteria of effectiveness, accessibility, safety, suitability, and fairness, the evaluation indicator system of the spatial distribution of urban emergency shelters was constructed. It was combined with AHP, CRITIC, the optimal weight coefficient solution method based on the maximum deviation sum of squares theory, and fuzzy optimization theory to construct a multi-indicator evaluation model. Further, the spatial distribution condition of the existing emergency shelter in Shanghai was evaluated. The results show that: among the existing ninety-one emergency shelters in Shanghai, there are nine places with unreasonable spatial distribution; nineteen places are comparatively unreasonable. From the scale of regional groups, there is one district (Pudong New District) with unreasonable spatial distribution: its relative superiority value is far lower than other districts, and there are three districts that are comparatively unreasonable. Further, the evaluation scores of the spatial distribution reasonableness of emergency shelters in each region of Shanghai show a high–low–middle distribution from the downtown area of Shanghai outward. The evaluation indicator system and evaluation method used in this paper can effectively reflect the deficiencies in the spatial distribution of urban emergency shelters, thus providing a reference for the relevant departments to improve and plan emergency shelters. [ABSTRACT FROM AUTHOR]

Objective: To investigate the hemodynamic changes using ultrasound according to digital subtraction angiography (DSA) findings and explore the association between ultrasound parameters and clinical symptoms of moyamoya disease (MMD). Methods: Hemodynamic parameters of extracranial internal carotid artery (EICA) and posterior cerebral artery (PCA) were evaluated by ultrasound. According to DSA findings, EICA parameters among Suzuki stages (stage I-II, III-IV, and V-VI), and PCA parameters among leptomeningeal system scores (score 0–2, 3–4, and 5–6) were compared, respectively. ROC analysis was performed based on the ultrasound parameters to distinguish stroke from non-stroke patients. Results: Forty patients with MMD were included in our study (16 men; median age, 37 years). The diameter (D), peak systolic velocity (PSV), end diastolic velocity (EDV) and flow volume (FV) of EICA decreased as the Suzuki stage advanced (D: P < 0.001; PSV: P < 0.001; EDV: P < 0.001; FV: P < 0.001). The PSV and EDV of PCA increased as the leptomeningeal system scores advanced (PSV: P < 0.001; EDV: P < 0.001). ROC analysis showed that the area under the curves (AUCs) based on the D and FV of EICA, the PSV and EDV of PCA and their combination were 0.688, 0.670, 0.727, 0.684, and 0.772, respectively, to distinguish stroke from non-stroke patients. Conclusions: Ultrasound parameters were related to Suzuki stages and leptomeningeal system scores. Ultrasound may be useful in predicting the occurrence of stroke in patients with MMD. Future prospective studies with large sample sizes and long-term follow-up are needed to confirm our preliminary findings. [ABSTRACT FROM AUTHOR]

Objective: To explore the association between ultrasound parameters and previous ischemic or hemorrhagic stroke in patients with moyamoya disease (MMD), and develop an ultrasound-based nomogram to identify stroke in patients with MMD. Methods: We prospectively enrolled 52 consecutive patients (92 hemispheres) with MMD at the Beijing Tiantan Hospital. Thirty-six patients (65 hemispheres) were assigned to the training dataset from September 2019 to February 2020, and 16 patients (27 hemispheres) were assigned to the validation dataset from March 2020 to July 2020. Multivariate logistic regression analysis was applied to identify ultrasound parameters associated with previous history of ipsilateral stroke in patients with MMD, and a nomogram was subsequently constructed to identify stroke in patients with MMD. The performance of the nomogram was evaluated with respect to discrimination, calibration, and clinical usefulness. Results: Multivariate analysis indicated that the flow volume (FV) of the extracranial internal carotid artery (EICA) and the peak systolic velocity (PSV) of the posterior cerebral artery (PCA) were independently associated with ipsilateral stroke in patients with MMD, a nomogram incorporating these two parameters was constructed to identify stroke patients. The area under the receiver operating characteristic (AUROC) curves was 0.776 (95% CI, 0.656–0.870) in the training dataset and 0.753 (95% CI, 0.550–0.897) in the validation dataset suggested that the model had good discrimination ability. The calibration plot showed good agreement in both the two datasets. The decision curve analysis (DCA) revealed that the nomogram was clinically useful. Conclusions: Ultrasound parameters of EICA and PCA are independently associated with history of previous ipsilateral ischemic or hemorrhagic stroke in patients with MMD. The present ultrasound-based nomogram might provide information to identify MMD patients with high risk of stroke. Future long-term follow-up studies are needed to prove the predictive value in other independent cohorts. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx. Unique Identifier: ChiCTR1900026075. [ABSTRACT FROM AUTHOR]

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that maternal exposure to particles with aerodynamic diameter <2.5 μm (PM 2.5) is associated with adverse birth outcomes. However, the critical exposure windows remain inconsistent. A retrospective cohort study was conducted in Huai River Basin, Henan, China during 2013–2018. Daily PM 2.5 concentration was collected using Chinese Air Quality Reanalysis datasets. We calculated exposures for each participant based on the residential address during pregnancy. Binary logistic regression was used to examine the trimester-specific association of PM 2.5 exposure with preterm birth (PTB), low birth weight (LBW) and term LBW (tLBW), and we further estimated monthly and weekly association using distributed lag models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each 10 μg/m3 increase in PM 2.5 exposure. Stratified analyses were performed by maternal age, infant gender, parity, and socioeconomic status (SES). In total, 196,780 eligible births were identified, including 4257 (2.2%) PTBs, 3483 (1.8%) LBWs and 1770 (0.9%) tLBWs. Maternal PM 2.5 exposure during the second trimester were associated with the risk of PTB and LBW. At the monthly level, the PTB and LBW risks were associated with PM 2.5 exposure mainly in the 4th -6th month. By estimating the weekly-specific association, we observed that critical exposure windows of PM 2.5 exposure and PTB were in the 18th- 27th gestational weeks. Stronger associations were found in younger, multiparous mothers and those with a female baby and in low SES. In conclusion, the results indicate that maternal PM 2.5 exposure during the second trimester was associated with PTB and LBW. Younger, multiparous mothers and those with female babies and in low SES were susceptible. [Display omitted] • Exposure to PM 2.5 showed adverse effects on PTB and LBW. • The susceptible windows mainly occurred in the second trimester of pregnancy. • The effects were modified by maternal age, parity, infant gender and SES. [ABSTRACT FROM AUTHOR]

Disk additions to an RAID-6 storage system can increase the I/O parallelism and expand the storage capacity simultaneously. To regain load balance among all disks including old and new, RAID-6 scaling requires moving certain data blocks onto newly added disks. Existing approaches to RAID-6 scaling, restricted by preserving a round-robin data distribution, require migrating all the data, which results in an expensive cost for RAID-6 scaling. In this paper, we propose RS6—a new approach to accelerating RDP RAID-6 scaling by reducing disk I/Os and XOR operations. First, RS6 minimizes the number of data blocks to be moved while maintaining a uniform data distribution across all data disks. Second, RS6 piggybacks parity updates during data migration to reduce the cost of maintaining consistent parities. Third, RS6 selects parameters of data migration so as to reduce disk I/Os for parity updates. Our mathematical analysis indicates that RS6 provides uniform data distribution, minimal data migration, and fast data addressing. We also conducted extensive simulation experiments to quantitatively characterize the properties of RS6. The results show that, compared with existing “moving-everything” Round-Robin approaches, RS6 reduces the number of blocks to be moved by 60.0%–88.9%, and saves the migration time by 40.27%–69.88%. [ABSTRACT FROM AUTHOR]

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images. [ABSTRACT FROM AUTHOR]

Association between ambient air pollution and congenital heart diseases (CHDs) remains inconclusive, and the critical exposure windows has not been well studied. This case-control study aimed to assess the effect of ambient air pollution exposure on the risk of CHDs and the subtypes in Henan, China, and further to explore potential susceptible windows. Daily average particulate matter with an aerodynamic diameter of ≤2.5 µm (PM 2.5) and ≤10 µm (PM 10), nitrogen dioxide (NO 2), sulfur dioxide (SO 2), carbon monoxide (CO) and ozone (O 3) were collected by Chinese Air Quality Reanalysis datasets. Binary logistic regression was used to examine trimester-specific associations between per 10 μg/m3 increase in air pollutants and CHDs as well as the major subtypes. Distributed lag models incorporating logistic regression were applied to explore weekly-specific associations. A total of 196,069 singleton live births were included during 2013–2018, 643 CHDs were identified (3.3‰). We found that first and second trimester CO exposure increased overall CHDs risk, the adjusted odds ratio (aOR) and 95% confidence interval (CI) were 1.066 (1.010–1.125) and 1.065 (1.012–1.122). For CHDs subtypes, we observed that NO 2 and CO in first trimester, PM 2.5 and PM 10 in the second trimester exposure were associated with the risk of atrial septal defect (ASD), the susceptible windows of air pollutants and ASD mainly occurred in the 1st- 6th gestational weeks. No positive association was observed for air pollution and tetralogy of Fallot. Our findings suggest that ambient air pollution exposure is associated with the risk of CHDs especially for ASD, and the susceptible windows generally occurred in first trimester. Further well-designed longitudinal studies are warranted to confirm our findings. [Display omitted] • Maternal CO exposure was associated with the occurrence of overall CHDs. • Maternal air pollution exposure increases the risk of atrial septal defect (ASD). • The susceptible windows of air pollution and ASD mainly occur in first trimester. [ABSTRACT FROM AUTHOR]

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data. [ABSTRACT FROM AUTHOR]

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data. [ABSTRACT FROM AUTHOR]

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions. • A novel method is proposed to assess TSS concentrations under data scarce conditions. • Sentintinel-2 MSI derived NDVI is an effective tool to evaluate watershed condition. • TSS concentration is negatively related with NDVI at five different spatial scales. • 300 m could be considered as the most appropriate scale for assessing TSS concentrations. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions. Image 1 • An indirect remote sensed method for retrieving total nitrogen concentration in water are proposed. • Fractional order derivative is an effective data mining technology for drone-borne hyperspectral data. • Decision-level fusion (DLF) model allows to reduce potential uncertainties without sacrificing accuracy. • Combined use of Bootstrap and DLF model is effective when dealing with small sample size. [ABSTRACT FROM AUTHOR]

For the first time, a novel NiFe2O4/paper-based magnetoelastic (ME) biosensor was developed for rapid, sensitive, and portable detection of human serum albumin (HSA). Due to the uniquely magnetoelastic effect of NiFe2O4 nanoparticles and the excellent mechanical properties of the paper, the paper-based ME biosensor transforms the surface stress signal induced by the specific binding of HSA and antibody modified on the paper into the electromagnetic signal. The accumulated binding complex generates a compressive stress on the biosensor surface, resulting in a decrease in the biosensor's static magnetic permeability, which correlates to the HSA concentrations. To improve the sensitivity of the biosensor, the concentration of NiFe2O4 nanofluid and the impregnated numbers of the NiFe2O4 nanofluid-impregnated papers were optimized. The experimental results demonstrated that the biosensor exhibited a linear response to HSA concentrations ranging from 10 μg∙mL−1 to 200 μg∙mL−1, with a detection limit of 0.43 μg∙mL−1, which is significantly lower than the minimal diagnosis limit of microalbuminuria. The NiFe2O4/paper-based ME biosensor is easy to fabricate, and allows the rapid, highly-sensitive, and selective detection of HSA, providing a valuable analytical device for early monitoring and clinical diagnosis of microalbuminuria and nephropathy. This study shows the successful integration of the paper-based biosensor and the ME sensing analytical method will be a highly-sensitive, easy-to-use, disposable, and portable alternative for point-of-care monitoring. [ABSTRACT FROM AUTHOR]

Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red–green–blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture. [ABSTRACT FROM AUTHOR]