Higher Root Mean Square Error Research Articles

Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

ABSTRACTFractional green vegetation cover (FVC) is a useful indicator for monitoring grassland status. Satellite imagery with coarse spatial but high temporal resolutions has been preferred to monitor seasonal and inter-annual FVC dynamics in wide geographic area such as Mongolian steppe. However, the coarse spatial resolution can cause a certain uncertainty in the satellite-based FVC estimation, which calls attention to develop a robust statistical test for the relationship between field FVC and satellite-derived vegetation indices. In the arid and semi-arid Mongolian steppe, nadir pointing digital camera images (DCI) were collected and used to produce a FVC dataset to support the evaluation of satellite-based FVC retrievals. An optimal DCI processing method was determined with respect to three color spaces (RGB, HIS, L*a*b*) and six green pixel classification algorithms, from which a country-wide dataset of DCI-FVC was produced and used for evaluating the accuracy of satellite-based FVC estimates from MODIS vegetation indices. We applied three empirical and three semi-empirical MODIS-FVC retrieval models. DCI data were collected from 96 sites across the Mongolian steppe from 2012 to 2014. The histogram algorithm using the hue (H) value of the HIS color space was the optimal DCI method (r2 = 0.94, percent root-mean-square-error (RMSE) = 7.1%). For MODIS-FVC retrievals, semi-empirical Baret model was the best-performing model with the highest r2 (0.69) and the lowest RMSE (49.7%), while the lowest MB (+1.1%) was found for the regression model with normalized difference vegetation index (NDVI). The high RMSE (>50% or so) is an issue requiring further enhancement of satellite-based FVC retrievals accounting for key plant and soil parameters relevant to the Mongolian steppe and for scale mismatch between sampling and MODIS data.

Validation of a simple equation for glomerular filtration rate measurement based on plasma iohexol disappearance

BackgroundA simple equation for glomerular filtration rate (GFR) measurement based on only plasma samples during the slow compartment after injection of iohexol was previously developed among children with chronic kidney disease and adult men with or at risk of HIV infection [Chronic Kidney Disease in Children (CKiD)-Multicenter AIDS Cohort Study (MACS) equation], but has not been externally validated. We aimed to evaluate the performance of the CKiD-MACS equation among elderly participants in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort who underwent directly measured iohexol GFR. MethodsUsing data from 287 participants of the MESA-Kidney study who underwent a five-sample measured iohexol GFR (two and three samples in the fast and slow compartments, respectively), we assessed the performance of the CKiD-MACS equation using only plasma samples in the slow compartment by sex, race and age. Agreement was assessed by bias, correlation, proportion within 5 and 10%, and the root mean square error (RMSE).ResultsThe average age and GFR of the participants were 71 years and 70.8 mL/min/1.73 m2, respectively, and 46% were black. The equation yielded excellent agreement within stratified groups with high correlation (>0.96), low bias (≤1.2 mL/min/1.73 m2) and low RMSE (<4.2 mL/min/1.73 m2).ConclusionsThe CKiD-MACS equation demonstrated valid GFR measurement using only samples in the slow compartment in this racially diverse, elderly population. While the equation yielded practically the same results as the original Brochner-Mortensen equation, the CKiD-MACS equation conforms to theoretical principles embedded in the two-compartment model of direct GFR measurement.

Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation

Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.

Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands

Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band.

Impact of Soil Reflectance Variation Correction on Woody Cover Estimation in Kruger National Park Using MODIS Data

Time-series of imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) has previously been used to estimate woody and herbaceous vegetation cover in savannas. However, this is challenging due to the mixture of woody and herbaceous plant functional types with specific contributions to the phenological signal and variations in soil background reflectance signatures observed from satellite. These factors cause variations in the accuracy and precision of woody cover estimates from different modelling approaches and datasets. Here, woody cover is estimated over Kruger National Park (KNP) from the MODIS 16-day composite time-series data using dry season NDVI/SAVI images and applying NDVIsoil determination methods. The woody cover estimates when NDVIsoil was ignored had R² = 0.40, p &lt; 0.01, slope = 1.01, RMSE (root mean square error) = 15.26% and R² = 0.32, p &lt; 0.03, slope = 0.79, RMSE = 16.39% for NDVIpixel and SAVIpixel, respectively, when compared to field plot data of plant functional type fractional cover. The woody cover estimated from the soil determination methods had a slope closer to 1 for both NDVI and SAVI but also a slightly higher RMSE. For a soil-invariant method, RMSE = 19.04% and RMSE = 17.34% were observed for NDVI and SAVI respectively, while for a soil-variant method, RMSE = 18.28% and RMSE = 19.17% were found for NDVI and SAVI. The woody cover estimated from all models had a high correlation and significant relationship with LiDAR/SAR based estimates and a woody cover map produced by Bucini. Woody cover maps are required for vegetation succession monitoring, grazing impact assessment, climate change mitigation and adaptation research and dynamic vegetation model validation.

Comparison of two ultrasonic backscatter coefficient methods under nonlinear distortion

The backscatter coefficient (BSC) is a fundamental property of tissues and can be parameterized for tissue characterization. The BSC requires a reference signal, which is estimated through either the planar reflector method (PRM) or the reference phantom method (RPM). In both methods, linear acoustic propagation is assumed. In this work, the BSC estimation methods are evaluated when nonlinear distortion is present. RF data were acquired from two physical phantoms, labeled A and B, with a 5 MHz single-element transducer using low power (1 excitation level) and high power (6 increasing excitation levels) excitation signals. Phantom A contained glass beads with diameters ranging from 75 to 90 μm and phantom B had glass beads with diameters ranging from 9 to 43 μm. The BSCs estimated using the low power setting and high power settings were compared for the both the PRM and RPM through a root mean square error (RMSE). Estimates of the effective scatterer diameter (ESD) were obtained using each method for each high power setting and compared to the low power setting. The RMSE increased as the power setting increased with much higher RMSEs using the PRM compared to RPM, i.e., a maximum of 7.3 times larger for phantom A and 8.6 times larger for phantom B. Estimates of ESD matched the ranges of glass beads sizes for both phantoms except when using the PRM at higher power settings. These findings suggest that the RPM is more robust to nonlinear distortion effects compare to the PRM.

Modeling of NF/RO membrane fouling and flux decline using real-time observations

Many numerical models for membrane filtration have been developed to explain and predict fouling mechanisms. The models can simulate flux decline, transmembrane pressure increase, and fouling thickness based on theoretical equations. However, the simulated fouling layer thicknesses have not been validated by in-situ observations on membrane surfaces because the membrane system is operated under a sealed and pressurized condition. In this study, humic acid fouling layers on nanofiltration and reverse osmosis membranes were monitored in-situ and in real-time using optical coherence tomography (OCT). The OCT system detected fouling layer growth over time, and showed that the compact and thick fouling layer had an estimated thickness of 80 µm. When comparing the thickness of the fouling layer between OCT and scanning electron microscopy (SEM) images, the OCT images showed values that were approximately 8 times higher than those of the SEM images. By comparing the obtained fouling thickness values with the estimated results, two existing models (Faridirad model and pore blockage-cake filtration model) were validated in terms of root mean square error (RMSE), Akaike Information Criteria (AIC), and coefficient of determination (R2). Both models showed similar high R2 (≥0.97) and low RMSE (<10-5) values, but the pore blockage-cake filtration model had lower AIC values than the Faridirad model. This study highlighted that the in-situ and real-time monitoring of fouling thickness can provide significant information for developing an accurate and precise membrane model.

Effect of Macroporosity on Pedotransfer Function Estimates at the Field Scale

Core Ideas Estimation of Ks using established PTFs for one specific field was evaluated. Selected PTFs exhibited unsatisfactory prediction of Ks at the field scale. Taking macropore volume effect into account improved the prediction of Ks with PTFs. Saturated hydraulic conductivity (Ks) is one of the crucial hydraulic properties for assessing water and solute transport in soils. However, direct measurement of Ks is time consuming and arduous. Alternatively, pedotransfer functions (PTFs) have been developed to estimate Ks indirectly through more easily measurable soil properties that are part of regional, national, or international databases. These PTFs are usually based on datasets collected from large regions. However, their validity for a specific site remains unclear. The objectives of this study were to evaluate the performance of established PTFs in estimating Ks in a specific field and improve PTFs to arrive at a locally adapted estimation result for Ks. Forty‐one soil samples were collected from 10 locations at five depths at a farmland in western Kentucky for hydraulic conductivity and physical property measurements. The performance of seven PTFs in estimating Ks was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). At this scale, all the selected PTFs exhibited unsatisfactory prediction of Ks (high RMSE, low NSE and R2). In the field studied, approximately 60% of variance in Ks could be explained by soil texture and macropore components based on factor analysis. Clay content and macroporosity were identified as the most representative variables for each component. The performance of a PTF in estimating Ks for the field site investigated was significantly improved by including macroporosity (pores with diameter &gt;75 μm) as a predictor. The results confirmed that soil structure was crucial in characterizing soil hydraulic conductivity.

Can stretch sensors measure knee range of motion in healthy adults?

Abstract Study aim: There are currently limited methods available to access dynamic knee range of motion (ROM) during free-living activities. This type of method would be valuable for monitoring and progressing knee rehabilitation. Therefore, the aim of this study was to evaluate the functioning of stretch sensors for the measurement of knee ROM and to assess the level of the measurement error. Material and methods: Nine healthy participants were included in the study. Three stretch sensors (StretchSense™, Auckland, NZ) were attached on the participants’ right knees by Kinesiotape®. A Cybex dynamometer was used to standardise movement speed of the knee joint. Data was recorded through the StretchSense™ BLE application. Knee angles were obtained from the video clips recorded during the testing and were analysed by MaxTraq® 2D motion analysis software. The knee angles were then synchronised with the sensor capacitance through R programme. Results: Seven out of the nine participants presented with high coefficient of determination (R2) (&gt;0.98) and low root mean square error (RMSE) (&lt;5°) between the sensor capacitance and knee angle. Two participants did not confirm good relationship between capacitance and knee angle as they presented high RMSE (&gt;5°). The equations generated from these 7 participants’ data were used individually to predict knee angles. Conclusions: The stretch sensors can be used to measure knee ROM in healthy adults during a passive, non-weight-bearing movement with a clinically acceptable level of error. Further research is needed to establish the validity and reliability of the methodology under different conditions before considered within a clinical setting.

Detection of 3-D objects with a 2-D laser scanning sensor for greenhouse spray applications

Precise measurement of plant dimensions can provide essential information for variable-rate spray application technologies to discharge the amounts of chemicals actually needed by the plants. The accuracy of a 270° radial laser scanning sensor combined with a specially-designed plant surface mapping algorithm was evaluated in detection of complex-shaped object surfaces and sizes. Data acquisition and three-dimensional (3-D) image construction were supported with the specially-designed algorithm. The objects in the test were toy balls with a pink smooth surface, light brown rectangular cardboard boxes, black and red texture surfaced basketballs, white smooth cylinders, and two different sized artificial plants. Test variables included four different detection heights (0.25, 0.5, 0.75 and 1.0 m) above each object, five sensor travel speeds (1.6, 2.4, 3.2, 4.0 and 4.8 km h−1), and 8–15 horizontal distances to the sensor ranging from 0 to 3.5 m. Horizontal distances of the objects to the laser sensor influenced the accuracy significantly, and the influence became weaker when the objects were closer to each other. The color and finish of object surfaces did not apparently affect the sensor detection accuracy. The average root mean square error (RMSE) and coefficient of variation (CV) in the laser sensor travel direction and object height direction varied slightly with detection heights, travel speeds, and object positions. The highest RMSE and CV were 83 mm and 50.9% in the horizontal direction, 41 mm and 15.2% in the travel direction, and 16 mm and 14.0% in the height direction, respectively. These tests demonstrated the potential adaptation of the laser scanning sensor and the specially-designed algorithm to measure complex-shaped targets for future development of automatic spray systems to achieve variable-rate functions in greenhouse applications.

A Flexible Height–Diameter Model for Tree Height Imputation on Forest Inventory Sample Plots Using Repeated Measures from the Past

In this study, height–diameter relations were modeled using two different mixed model types for imputation of missing heights from longitudinal data. Model Type A had a hierarchical structure of sample plot-specific and measurement occasion-specific random effects. In Model Type B, a possible temporal variance was modeled by a sample plot-specific linear time trend. Furthermore, various calibration strategies of random effects were performed on past and current data, and a combination of both. The performance of the mixed models was compared on independent data using bias and root mean square error (RMSE). The results showed that Model Type A achieved the highest precision (lowest RMSE), if sample plot-specific random effects were predicted from old data and measurement occasion-specific ones were predicted from new data. In comparison, however, Model Type B had a higher RMSE, and lower bias. Model performance was almost unaffected from the usage of past or current data for the prediction of random effects. Results revealed that a certain calibration strategy should be simultaneously applied to all random effects from the same hierarchy level. Otherwise, predictions would become imprecise and a serious bias may result. In comparison with traditional uniform height curves, the novel mixed model approach performed slightly better.

Comprehensive evaluation of empirical algorithms for estimating land surface evapotranspiration

Many empirical algorithms for obtaining evapotranspiration (ET) from vegetation indices (VIs) have been developed, but there has been little work comparing these algorithms to each other or deriving coefficients for them using large data sets for training and validation. Twelve different vegetation index-based regression algorithms for retrieval of ET on a daily basis are reviewed and evaluated here. New coefficients have been derived for four of these algorithms using data from 181 Ameriflux and Fluxnet2015 sites and 1 km MODIS subsets centered at each site location. Algorithm validation with previously published and new coefficients was performed using one year of data from each Ameriflux and Fluxnet2015 site. There was a wide range of performance of these algorithms, with the median R2 by site in the 0.6 to 0.7 range, median root mean square error (RMSE) about 25 W/m2 and median bias within 10 W/m2. When algorithm coefficients were re-derived, the RMSE and bias of the worst-performing algorithms were largely reduced, but R2 was little changed. Agricultural and wetland sites had a low bias across most of the algorithms, and wetland sites had a higher RMSE. When several of the algorithms were re-tuned to obtain coefficients specific to each surface type, the biases of the agricultural and wetland sites were reduced to those more typical of other site types, and RMSE for agricultural and wetland sites was also reduced. The effects of linear interpolation of VIs to obtain daily LE and interpolation over periods of rapid VI change at agricultural sites were examined. No significant algorithm performance degradation was found in either case. It is recommended to use more detailed algorithms when possible, with inclusion of net radiation as a parameter along with VI at a minimum.

Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

Sloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (GOST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image endmembers and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the GOST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the GOST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs.

Evaluation of Crop to Crop Water Demand Forecasting: Tomatoes and Bell Peppers Grown in a Commercial Greenhouse

Forecasting crop water demand is a critical part of any greenhouse’s day-to-day operations. This study focuses on a region located in Essex County, Ontario Canada where water demand is dominated by commercial greenhouse operations (78% of capacity). Development of complex and elaborate forecasting methods such as artificial neural networks (ANN) can be costly to develop and implement, especially with the limited resources available to greenhouses. This study proposes simplified forecasting methods that would be used in conjunction with a more complex base model architecture. These simplified methods use one crop water usage as an indicator of another’s, and is titled crop-to-crop forecasting (C2C). In this study, tomatoes and peppers were evaluated, and three C2C models were developed along with an ANN base model to provide a basis for evaluation. The models were created using a dataset containing hourly watering data along with climatic and temporal data for the period between June 2015 and August 2016. The three C2C architectures used were linear regression (LR), quotient method (QM), and feed-forward neural network (FFNN), compared with the (ANN) model, which is a feed-forward neural network with extra inputs (FFNN-EI). Each model was evaluated using the root mean squared error (RMSE) and the normalized root mean squared error (NRMSE). The results show that all C2C methods have higher RMSE and NRMSE than that of the base model, with an average RMSE increase of 12% for peppers and 29% for tomatoes.

Comparison of measured (XRPD) and modeled (A2M) soil mineralogies: A study of some Swedish forest soils in the context of weathering rate predictions

Quantitative soil mineralogy has been identified as a key factor influencing PROFILE weathering estimates, and is often calculated with normative methods, such as the “Analysis to Mineralogy” (‘A2M’) model. In Sweden and other countries, there is a large request for accurate base cation weathering estimates in order to establish how sustainable harvest of biomass should be performed in the future. However, there is a lack of knowledge around the accuracy of the arithmetic mean output of A2M estimates, the most common A2M solution used in weathering studies. To our knowledge, a thorough investigation of how A2M input data affect the arithmetic mean output (center of gravity of the A2M solution space) is missing.In this study, the indirect geochemical normative method (A2M) was compared with a direct x-ray powder diffraction method (XRPD) to quantify soil mineralogy at two sites and 8 soil profiles, at a 10cm depth interval. We explored the hypothesis that normative calculations performed with A2M produce an output in closer agreement with the mineralogy obtained from XRPD, if site specific mineralogical input data are used rather than regional data. Site-specific mineralogical input data consisted of mineral stoichiometry data measured by electron microprobe analysis (EMPA) and mineral identity determined by XRPD, whereas regional mineral input data were based on previously reported data on mineral stoichiometry and mineral identity, derived from three geological regions in Sweden.The results from this comparison showed that the site-specific approach yielded relatively low average biases and root mean square errors (RMSE) for most minerals, with the exception of quartz (Average bias of −4.8wt%, RMSE of 5.3wt%) at the Asa site. The regional approach yielded deviating results for K-feldspar and dioctahedral mica, with high average biases and RMSE for dioctahedral mica (Asa: 7.8wt%, 9.0wt%; Flakaliden: 12.8wt%, 15.5wt%) and for K-feldspar (Asa: −5.2wt%, 6.1wt%; Flakaliden: −5.6wt%, 6.7wt%). The results from this study were supported by a close agreement between measured geochemistry and normalized geochemistry derived from a back calculation of the XRPD mineralogy (i.e. mineral budgeting).In conclusion, our findings suggest that A2M results in combination with site-specific mineralogical input data are improved independent of study site and soil profile. However, for future weathering studies it might be beneficial to find constraints of how to select a solution from the entire A2M solution space which is in better agreement with the XRPD mineralogy.

Abstract MP016: Glycated Albumin and Fructosamine May Not Overcome Limitations of HbA1c in the Setting of Chronic Kidney Disease

Introduction: Alterations in red blood cell turnover in the setting of chronic kidney disease (CKD) may limit the interpretability of hemoglobin A1c (HbA1c) as a measure of diabetic glucose control. Where HbA1c is problematic, glycated albumin and fructosamine have been proposed as better markers of hyperglycemia. We investigated whether the associations of HbA1c, glycated albumin, and fructosamine with fasting glucose and HbA1c varied by CKD stage. Methods: We performed a cross-sectional analysis of 1,678 persons with diagnosed diabetes aged 65 years or older from the Atherosclerosis Risk in Communities Studies (list 5, 2011-2013). We compared Spearman’s rank correlation coefficients and root mean square error (RMSEs) for HbA1c, glycated albumin, and fructosamine with fasting glucose and HbA1c across CKD stages. CKD stages were defined using estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (ACR). Results: Mean age was 76 years, 54% (910 of 1,678) were female, and 29% (479 of 1,678) were black. The associations of HbA1c, glycated albumin, and fructosamine with fasting glucose were weaker (lower correlations and higher RMSEs) in persons with worse kidney function compared to those with preserved kidney function (Table). Both glycated albumin and fructosamine had stronger associations with HbA1c compared to fasting glucose and their associations with HbA1c were relatively similar across CKD stages. Conclusions: In persons with worse kidney function, HbA1c, glycated albumin, and fructosamine were all poorly correlated with fasting glucose. Yet the associations between glycated albumin and fructosamine with HbA1c were similar across CKD stages. Our data suggest that glycated albumin and fructosamine might not overcome limitations of HbA1c as a marker of hyperglycemia in older adults with CKD.

An improved spectral optimization algorithm for atmospheric correction over turbid coastal waters: A case study from the Changjiang (Yangtze) estuary and the adjacent coast

Remote sensing-based retrieval of the concentrations of water components relies largely on the accuracy of the atmospheric correction. Although a variety of atmospheric correction algorithms have been developed for turbid waters, the water-leaving reflectance is still underestimated in extremely turbid waters, such as in the Changjiang (Yangtze) estuary and the adjacent coast. To address this issue, this paper proposes an improved algorithm that is based on a spectral optimization algorithm (ESOA) with a coupled water-atmosphere model. The model combines an aerosol model that is constructed from Aerosol Robotic Network (AERONET) observation data and a simple semi-empirical radiative transfer (SERT) model (Shen et al. 2010) for water component retrieval. Four unknown parameters are involved in the coupled model: the relative humidity (RH), fine-mode fraction (FMF), aerosol optical thickness in the near-infrared (NIR) wavelength τa(λ0) and suspended particulate matter (SPM) concentration (Cspm). These parameters are estimated by a global optimization approach that is based on a genetic algorithm (GA) without any initial inputs. Validation results of the atmospherically corrected remote sensing reflectance Rrs(λ) from matchups between Geostationary Ocean Color Imager (GOCI) data and in situ data show that the algorithm has satisfactory accuracy. The root mean square error (RMSE) and the absolute percentage difference (APD) are 0.0089 and 35.12, respectively. By contrast, the Rrs(λ) values retrieved from the same matchups using the GOCI data processing system (GDPS) have higher RMSE and APD of 0.0104 and 69.15, respectively. The ESOA method can be implemented conveniently within the open source code of SeaDAS (v7.1) as an alternative and operational tool for atmospheric correction of ocean color data, including GOCI, MERIS and MODIS, over highly turbid estuarine and coastal regions, such as the Yangtze estuary, the Hangzhou Bay and most of the coastal ocean in Eastern China.

Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects.

Diverse studies have demonstrated the importance of monitoring breathing rate (BR). Commonly, changes in BR are one of the earliest and major markers of serious complications/illness. However, it is frequently neglected due to limitations of clinically established measurement techniques, which require attachment of sensors. The employment of adhesive pads or thoracic belts in preterm infants as well as in traumatized or burned patients is an additional paramount issue. The present paper proposes a new robust approach, based on data fusion, to remotely monitor BR using infrared thermography (IRT). The algorithm considers not only temperature modulation around mouth and nostrils but also the movements of both shoulders. The data of these four sensors/regions of interest need to be further fused to reach improved accuracy. To investigate the performance of our approach, two different experiments (phase A: normal breathing, phase B: simulation of breathing disorders) on twelve healthy volunteers were performed. Thoracic effort (piezoplethysmography) was simultaneously acquired to validate our results. Excellent agreements between BR estimated with IRT and gold standard were achieved. While in phase A a mean correlation of 0.98 and a root-mean-square error (RMSE) of 0.28bpm was reached, in phase B the mean correlation and the RMSE hovered around 0.95 and 3.45bpm, respectively. The higher RMSE in phase B results predominantly from delays between IRT and gold standard in BR transitions: eupnea/apnea, apnea/tachypnea etc. Moreover, this study also demonstrates the capability of IRT to capture varied breathing disorders, and consecutively, to assess respiratory function. In summary, IRT might be a promising monitoring alternative to the conventional contact-based techniques regarding its performance and remarkable capabilities.

Determination of saturation, heart rate, and respiratory rate at forearm using a Nellcor™ forehead SpO2-saturation sensor.

Alterations in arterial blood oxygen saturation, heart rate (HR), and respiratory rate (RR) are strongly associated with intra-hospital cardiac arrests and resuscitations. A wireless, easy-to-use, and comfortable method for monitoring these important clinical signs would be highly useful. We investigated whether the Nellcor™ OxiMask MAX-FAST forehead sensor could provide data for vital sign measurements when located at the distal forearm instead of its intended location at the forehead to provide improved comfortability and easy placement. In a prospective setting, we recruited 30 patients undergoing surgery requiring postoperative care. At the postoperative care unit, patients were monitored for two hours using a standard patient monitor and with a study device equipped with a Nellcor™ Forehead SpO2 sensor. The readings were electronically recorded and compared in post hoc analysis using Bland-Altman plots, Spearman's correlation, and root-mean-square error (RMSE). Bland-Altman plot showed that saturation (SpO2) differed by a mean of -0.2% points (SD, 4.6), with a patient-weighted Spearman's correlation (r) of 0.142, and an RMSE of 4.2 points. For HR measurements, the mean difference was 0.6bpm (SD, 2.5), r=0.997, and RMSE=1.8. For RR, the mean difference was -0.5 1/min (4.1), r=0.586, and RMSE=4.0. The SpO2 readings showed a low mean difference, but also a low correlation and high RMSE, indicating that the Nellcor™ saturation sensor cannot reliably assess oxygen saturation at the forearm when compared to finger PPG measurements.

Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation

An evaluation of convective cloud forecasts performed with the numerical weather prediction (NWP) model COSMO and extrapolation of cloud fields is presented using observed data derived from the geostationary satellite Meteosat Second Generation (MSG). The present study focuses on the nowcasting range (1–5h) for five severe convective storms in their developing stage that occurred during the warm season in the years 2012–2013. Radar reflectivity and extrapolated radar reflectivity data were assimilated for at least 6h depending on the time of occurrence of convection. Synthetic satellite imageries were calculated using radiative transfer model RTTOV v10.2, which was implemented into the COSMO model. NWP model simulations of IR10.8μm and WV06.2μm brightness temperatures (BTs) with a horizontal resolution of 2.8km were interpolated into the satellite projection and objectively verified against observations using Root Mean Square Error (RMSE), correlation coefficient (CORR) and Fractions Skill Score (FSS) values. Naturally, the extrapolation of cloud fields yielded an approximately 25% lower RMSE, 20% higher CORR and 15% higher FSS at the beginning of the second forecasted hour compared to the NWP model forecasts. On the other hand, comparable scores were observed for the third hour, whereas the NWP forecasts outperformed the extrapolation by 10% for RMSE, 15% for CORR and up to 15% for FSS during the fourth forecasted hour and 15% for RMSE, 27% for CORR and up to 15% for FSS during the fifth forecasted hour. The analysis was completed by a verification of the precipitation forecasts yielding approximately 8% higher RMSE, 15% higher CORR and up to 45% higher FSS when the NWP model simulation is used compared to the extrapolation for the first hour. Both the methods yielded unsatisfactory level of precipitation forecast accuracy from the fourth forecasted hour onward.