Validation R2 Research Articles

Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest

Heavy metal contamination of peri-urban agricultural soil is detrimental to soil environmental quality and human health. A rapid assessment of soil pollution status is fundamental for soil remediation. Heavy metals can be monitored by visible and near-infrared spectroscopy coupled with chemometric models. First and second derivatives are two commonly used spectral preprocessing methods for resolving overlapping peaks. However, these methods may lose the detailed spectral information of heavy metals. Here, we proposed a fractional-order derivative (FOD) algorithm for preprocessing reflectance spectra. A total of 170 soil samples were collected from a typical peri-urban agricultural area in Wuhan City, Hubei Province. The reflectance spectra and lead (Pb) and zinc (Zn) concentrations of the samples were obtained in the laboratory. Two calibration methods, namely, partial least square regression and random forest (RF), were used to establish the relation between the spectral data and the two heavy metals. In addition, we aimed to explore the use of spectral estimation mechanism to predict the Pb and Zn concentrations. Three model evaluation parameters, namely, coefficient of determination (R2), root mean squared error, and ratio of performance to inter-quartile range (RPIQ), were used. Overall, the spectral reflectance decreased with the increase in Pb and Zn contents. The FOD algorithm gradually removed spectral baseline drifts and overlapping peaks. However, the spectral strength slowly decreased with the increase in fractional order. High fractional-order spectra underwent more spectral noises than low fractional-order spectra. The optimal prediction accuracies were achieved by the 0.25- and 0.5-order reflectance RF models for Pb (validation R2 = 0.82, RPIQ = 2.49) and Zn (validation R2 = 0.83, RPIQ = 2.93), respectively. A spectral detection of Pb and Zn mainly relied on their covariation with soil organic matter, followed by Fe. In summary, our results provided theoretical bases for the rapid investigation of Pb and Zn pollution areas in peri-urban agricultural soils.

AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

Estimating Concentrations of PM2.5 Species with Random Forest Algorithm across Continental United States during 2005 - 2015

PM2.5 is a complex mixture of chemical species, which are emitted from various emission sources; however, currently, PM2.5 is regulated and its health effects are mainly studied based on total mass concentrations. Identifying the differential toxicity of PM2.5 species should be able to provide scientific evidence for more effectively regulatory control of PM2.5 pollution. We develop national models based on random forest algorithm by incorporating land use parameters, meteorological variables and GEOS-Chem simulated concentrations of PM2.5 species to predict ground-levels of PM2.5 sulfate, nitrate, OC and EC across Continental United States from 2005 to 2015. Overall, the random forest models achieve high validation R2 values based on daily predictions, which are 0.80 – 0.90 for sulfate and nitrate, and 0.70 – 0.80 for OC and EC during the study period. With the help of importance rankings of random forest algorithm, our modeling method is able to figure out unique combinations of potential significant contributors among large number of predictors for predicting different PM2.5 species. The long-term temporal trends of predictions of PM2.5 sulfate, nitrate, OC and EC match well with their corresponding ground measurements. The prediction maps capture both regional patterns and hotspots of the four PM2.5 species. Therefore, our national models could provide exposure assessment with high spatial and temporal flexibility for epidemiological studies to better understand the acute and chronic toxicity of PM2.5 speciation across locations in Continental United States.

Adult energy requirements predicted from doubly labeled water.

Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.

Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine

Accurate estimation of soil organic matter (SOM) is essential in understanding the spatial distribution of SOM to identify areas that need fertilization and the required grade of those fertilizers. Visible and near-infrared spectroscopy is a promising alternative to time consuming and costly conventional soil assessment methods. However, this approach is highly dependent on selecting suitable preprocessing strategies and data mining techniques for regression analysis. In this study, 2D correlation coefficients, including ratio, difference, and normalized difference indices, were introduced to select sensitive spectral parameters. The performance of extreme learning machine (ELM) was evaluated via comparison with that of support vector machine (SVM) for SOM estimation. A total of 257 soil samples were collected from Hubei Province, Central China, with SOM contents and reflectance spectra measured in the laboratory. Five spectral pretreatments, except for the raw spectra, were applied. SVM and ELM models were calibrated on spectral parameters selected by one-dimensional and 2D correlation coefficients and subsequently applied to predict SOM. Results showed that 2D correlation coefficient can effectively highlight the detailed SOM information compared with that of one-dimensional correlation coefficient. The ELM models yielded superior predictability relative to SVM models in all eight established models. The most excellent estimation accuracy was obtained by 2D ratio index and ELM (TRI-ELM) method, with an independent validation R2 and a ratio of performance to interquartile range of 0.83 and 3.49, respectively. The SOM fertility levels of predicted SOM showed that TRI-ELM method presented the largest similarity to laboratory-measured SOM levels, and misclassified samples were all concentrated within one error level. In summary, our study indicates that the TRI-ELM model is a rapid, inexpensive, and relatively accurate method for identifying SOM fertility level.

Grassland canopy cover and aboveground biomass in Mongolia and Inner Mongolia: Spatiotemporal estimates and controlling factors

Temperate and semiarid grasslands comprise 80% of the land area on the Mongolian Plateau and environs, which includes Mongolia (MG), and the province of Inner Mongolia (IM), China. Substantial land cover/use change in the last few decades, driven by a combination of post-liberalization socioeconomic changes and extreme climatic events, has degraded these water-limited grassland's structure and function. Hence, a precise estimation of canopy cover (CC, %) and aboveground biomass (AGB, g m−2) is needed. In this study, we analyzed >1000 field observations with sampling during June, July and August (JJA) in 2006, 2007, 2010 and 2016 in IM and 2010–2012 and 2014–2016 in MG. The field sampling was stratified by the dominant vegetation types on the plateau, including the meadow steppe, the typical steppe, and the desert steppe. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance and vegetation indices optimized for low cover conditions to develop and test predictive models of CC and AGB using observed samples as training and validation data through rule-based regression tree models. We then used the predictive models to estimate spatially-explicit CC and AGB for the plateau over the last decade (2000–2016). Our study demonstrated the effectiveness of our predictive models in up-scaling ground observations to the regional scale across steppe types. Our results showed that model R2 and RMSE for CC and AGB were 0.74 (13.1%) and 0.62 (85.9 g m−2), respectively. The validation R2 and RMSE for CC and AGB were 0.67 (14.4%) and 0.68 (76.9 g m−2), respectively. The mean ± SD for CC and AGB were 24.9 ± 23.4% and 155.2 ± 115.2 g m−2, respectively. We also found that our scaled up estimates were significantly related to inter-annual climatic variability and anthropogenic drivers especially distance to urban/built-up areas and livestock density. In addition to their direct use in quantifying the spatiotemporal changes in the terrestrial carbon budget, results from these predictive models can help decision makers and rangeland managers plan sustainable livestock practices in the future.

Erratum: Predicting Field‐Apparent Nitrogen Mineralization from Anaerobically Incubated Nitrogen

Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mar del Plata; Argentina. Laboratorio de Suelo S.a.; Argentina

OPTIMIZATION OF MICROWAVE ASSISTED H2SO4 HYDROLYSIS OF COCOA POD SHELLS: COMPARISON BETWEEN RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK AND PRODUCTION OF BIOETHANOL THEREOF

To release reducing sugars from cocoa pod shell, sulphuric acid pre-treatment was adopted where the variables affecting H2SO4 pre-treatment i.e., cocoa pod shell weight, H2SO4 concentration, microwave irradiation time and power were screened using one factor at a time approach. The weight of cocoa pod shell, concentration of H2SO4 showed a significant effect on the pre-treatment process and the levels of these factors were further optimized by central composite design using response surface methodology .The optimized conditions were found to be 15.65g ofcocoa pod shell, 6% v/v H2SO4 at 8 min of irradiation, released maximum reducing sugar of 9.10 g/L. A second order model was generated and validated, which was found to be a good fit with R2value of 0.89. Artificial neural network modelling proved validation R2 of 0.94 comparatively better than Response surface methodology R2 of 0.89. The reducing sugars released after acid hydrolysis at optimized conditions were subjected to fermentation by Pichia stipitis to produce bioethanol. The bioethanol produced was 3.2g/L at 2% (v/v) inoculum concentration after 72h of fermentation.

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Visible and near-infrared (VIS–NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS–NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS–NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans

The aim of the current work was to use hyperspectral imaging (HSI) in the spectral range 1000–2500 nm to quantitatively predict fermentation index (FI), total polyphenols (TP) and antioxidant activity (AA) of individual dry fermented cocoa beans scanned on a single seed basis, in a non-destructive manner. Seventeen cocoa bean batches were obtained and 10 cocoa beans were used from each batch. PLS regression models were built on 170 samples. The developed HSI predictive models were able to quantify three quality-related parameters with sufficient performance for screening purposes, with external validation R2 of 0.50 (RMSEP = 0.27, RPD = 1.40), 0.70 (RMSEP = 34.1 mg ferulic acid g−1, RPD = 1.77) and 0.74 (60.0 mmol Trolog kg−1, RPD = 1.91) for FI, TP and AA, respectively. The calibrations were subsequently applied at a single bean and pixel level, so that the distribution was visualised within and between single seeds (chemical images). HSI is thus suggested as a promising approach to estimate cocoa bean composition rapidly and non-destructively, thus offering a valid tool for food inspection and quality control.

Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean

Pre-visual detection of crop disease is critical for food security. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were spectrally measured throughout a growing season to assess the capacity of leaf and canopy level spectral measurements to detect non-visual foliage symptoms induced by Fusarium virguliforme (Fv, which causes sudden death syndrome). Canopy reflectance measurements were made using the Piccolo Doppio dual field-of-view, two-spectrometer (400 to 1630 nm) system on a tractor. Leaf level measurements were obtained, in different plots, using a handheld spectrometer (400 to 2500 nm). Partial least squares discriminant analysis (PLSDA) was applied to the spectroscopic data to discriminate between Fv-inoculated and control plants. Canopy and leaf spectral data allowed identification of Fv infection, prior to visual symptoms, with classification accuracy of 88% and 91% for calibration, 79% and 87% for cross-validation, and 82% and 92% for validation, respectively. Differences in wavelengths important to prediction by canopy vs. leaf data confirm that there are different bases for accurate predictions among methods. Partial least square regression (PLSR) was used on a late-stage canopy level data to predict soybean seed yield, with calibration, cross-validation and validation R2 values 0.71, 0.59 and 0.62 (p < 0.01), respectively, and validation root mean square error of 0.31 t·ha−1. Spectral data from the tractor mounted system are thus sensitive to the expression of Fv root infection at canopy scale prior to canopy symptoms, suggesting such systems may be effective for precision agricultural research and management.

Predicting Field‐Apparent Nitrogen Mineralization from Anaerobically Incubated Nitrogen

Core IdeasStudy aimed to predict field N mineralization (Nmin) from anaerobically incubated N (Nan).Nandid not predict Nminin areas with contrasting edaphic‐climatic properties.Nminwas predicted by a model including Nan, temperature and rainfall.The nitrogen (N) released after a 7‐d anaerobic incubation (Nan) is a good estimator of the size of the soil N mineralizable pool. However, there is a lack of information on how soil properties and climate affect the apparent field N mineralization (Nmin) of this pool. The objective of our study was to develop and validate a simple model to estimate Nminfrom Nanin corn (Zea maysL.) and wheat (Triticum aestivumL.) fields. To this end, we performed 100 field experiments where we measured Nmin, Nan, rainfall, temperature (TC), soil texture, pH, soil organic matter (SOM), and pre‐sowing mineral N concentration (Ninitial). We performed a stepwise analysis to develop a model to predict Nminusing data from 70 sites, while the rest of the data was saved for model validation. The Nanranged from 16 to 94 mg kg–1while Nminranged from 22 to 232 kg ha–1. There was a strong association between Nanand Nminwithin regions with similar climate and edaphic properties. However, we could not fit a single significant model to estimate Nminbased solely on Nanto be used in all regions. By considering other variables besides Nan, we developed a model that allowed predicting Nminindependently from the site [Nmin= –252 + 12.3(TC) + 1.37(Nan) + 0.27(rainfall)] (R2= 0.89, model validationR2= 0.83). This model could be useful to adjust N fertilizer recommendations for corn and wheat, reducing the economic and environmental impact of fertilization.

Classification of the hot air heat treatment degree of larch wood using a multivariate analysis of near-infrared spectroscopy

In this study, hot air heat treatments of larch (Larix kaempferi) wood specimens were conducted at various temperatures (160–220 °C) and times (1–12 h) to classify the degree of hot air heat treatment using near-infrared (NIR) spectroscopy. NIR reflectance spectra were acquired from specimen cross-sections and were then preprocessed using the standard normal variate. Hierarchical clustering analysis (HCA) using the complete linkage and squared Euclidean distance was conducted to classify the three degrees of heat treatment. A principal component score plot of the NIR spectra was well grouped by the HCA grouping result, and the first component reflected the cluster analysis grouping well. A partial least squares discriminant analysis was performed to develop the discriminant regression model of the three heat treatment degrees. The R2 and root mean square error of validation were 0.959 and 0.191, respectively. NIR is considered to be a good candidate to routinely measure the degree of hot air treatment for larch wood.

Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression

Bee pollen consumption has increased in the last years, mainly due to its nutritional value and therapeutic applications. The quantification of mineral constituents is of great importance in order to evaluate both, the toxicity and the beneficial effect of essential elements. The purpose of this work was to quantify the essential elements, Ca, Mg, Zn, P and K, by diffuse reflectance spectra in the near infrared region (NIR) combined with partial least squares regression (PLS), which is a clean and fast method. Reference method used was ICP OES.The determination coefficients for calibration models (R2) were above 0.87 and the mean percent calibration error varied from 5 to 10%. For external validation R2 values were higher than 0.76. The results indicated that NIR spectroscopy can be useful for an approximate quantification of these minerals in bee pollen samples and can be used as a faster alternative to the standard methodologies.

Exploring Structural Requirement for Design and Development of compounds with Antimalarial Activity via CoMFA, CoMSIA and HQSAR

Malaria is lethal infectious diseases in the world caused by the protozoal species Plasmodium claiming more lives than any other parasitic infections. Novel therapies are needed against malaria because of emergence of multidrug resistant plasmodium falciparum to existing drugs. Molecular modeling studies were performed by using three different QSAR methods, Comparative Molecular Field Analysis (CoMFA), Comparative similarity indices analysis (CoMSIA) and Hologram QSAR (HQSAR), to determine the factors required for the activity of these compounds. The developed models on one hundred and twelve 7-substituted 4-aminoquinoline derivatives showed good statistical significance in internal cross validation (q2) and non-cross validation (r2) values of 0.509, 0.992 by the CoMFA model and 0.358 0.838 by the CoMSIA model respectively for antimalarial activity. Structural features were correlated in terms of their several properties as steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor interactions. Furthermore, the bioactive conformation was explored and explained by docking of compounds the active binding site of lactate dehydrogenase of Plasmodium falciparum.

Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015.

Satellite-based aerosol optical depth (AOD) is now comprehensively applied to estimate ground-level concentrations of fine particulate matter (PM2.5). This study aimed to construct the AOD-PM2.5 estimation models over Taiwan. The AOD-PM2.5 modeling in Taiwan island is challenging owing to heterogeneous land use, complex topography, and humid tropical to subtropical climate conditions with frequent cloud cover and prolonged rainy season. The AOD retrievals from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites were combined with the meteorological variables from reanalysis data and high resolution localized land use variables to estimate PM2.5 over Taiwan island from 2005 to 2015. Ten-fold cross validation was carried out and the residuals of the estimation model at various locations and seasons are assessed. The cross validation (CV) R2 based on monitoring stations were 0.66 and 0.66, with CV root mean square errors of 14.0μg/m3 (34%) and 12.9μg/m3 (33%), respectively, for models based on Terra and Aqua AOD. The results provided PM2.5 estimations at locations without surface stations. The estimation revealed PM2.5 concentration hotspots in the central and southern part of the western plain areas, particularly in winter and spring. The annual average of estimated PM2.5 concentrations over Taiwan consistently declined during 2005-2015. The AOD-PM2.5 model is a reliable and validated method for estimating PM2.5 concentrations at locations without monitoring stations in Taiwan, which is crucial for epidemiological study and for the assessment of air quality control policy.

Spatial Simulation of Black Carbon Concentrations Based on a Land Use Regression Model and Mobile Monitoring over Shanghai, China

Black carbon (BC) is an important component of atmospheric pollution and has significant impacts on air quality and human health. Choosing Shanghai city for a case study, this paper explores the statistical characteristics and spatial patterns of BC concentrations using a mobile monitoring method, which differs from traditional fixed-site observations. Land use regression (LUR) modeling was conducted to examine the determinants for on-road BC concentrations, e.g. population, economic development, traffic, etc. These results showed that the average on-road BC concentrations were (9.86±8.68) μg·m-3, with a significant spatial variation. BC concentrations in suburban areas[(10.47±2.04) μg·m-3] were 32.03% (2.54 μg·m-3) higher than those in the city center[(7.93±2.79) μg·m-3]. Besides, meteorological factors (e.g. wind speed and relative humidity) and traffic variables (e.g. the length of roads, distance to provincial roads, distance to highway) had significant effects on on-road BC concentrations (r:0.5-0.7, P<0.01). Moreover, the LUR model, including meteorological and traffic variables performed well (adjusted R2:0.62-0.75, cross validation R2:0.54-0.69, RMSE:0.15-0.20 μg·m-3), which demonstrates that on-road BC concentrations in Shanghai are mainly affected by these factors and traffic sources to some extent. Among them, the most accurate LUR model was developed with a 100 m buffer, followed by the LUR model with a 5 km buffer. This study is of great significance for the identification of spatial distribution patterns for on-road BC concentration and exploring their influencing factors in Shanghai, which can provide a scientific basis and theoretical support for simulating and predicting the response mechanisms of BC on human health and the natural environment.

Evaluation of the nitrogen content during the new-shoot-growing stage in apple leaves using two-dimensional correlation spectroscopy.

The new-shoot-growing stage is an important period of apple tree nutrition distribution. The objective of this study is to provide technical support for apple tree nutrition diagnosis by constructing quantitative evaluation models between the apple leaf nitrogen content during the new-shoot-growing stage and characteristic spectral parameters. The correlation coefficients between the original spectral data and the nitrogen content were calculated. Then, the sensitive bands of the nitrogen content were selected using the theory of two-dimensional (2D) correlation spectroscopy. Finally, partial least squares regression (PLSR) and support vector machine (SVM) evaluation models were established using 2 parameters: Rx (maximum spectral reflectivity in the waveband) and Sx (total spectral reflectivity in the waveband). The results showed that the sensitive bands in the 2D correlation synchronous and asynchronous spectrograms were 537–560 nm and 708–719 nm. The PLSR model can be used to estimate the nitrogen content. Compared with PLSR, SVM provided better modeling and testing results, with a larger coefficient of determination (R2) and a smaller root-mean-square error (RMSE). The SVM model based on Sx was a good backup method. The calibration R2 of the model was 0.821, its RMSE was 0.710 g·kg-1, the validation R2 was 0.768, and its RMSE was 1.019 g·kg-1. The SVM model based on 2D correlation spectroscopy can be used to quantitatively estimate the nitrogen content in apple leaves.

Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China

As China is suffering from severe fine particle pollution from dense industrialization and urbanization, satellite-derived aerosol optical depth (AOD) has been widely used for estimating particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5). However, the correlation between satellite AOD and ground-level PM2.5 could be influenced by aerosol vertical distribution, as satellite AOD represents the entire column, rather than just ground-level concentration. Here, a new column-to-surface vertical correction scheme is proposed to improve separation of the near-surface and elevated aerosol layers, based on the ratio of the integrated extinction coefficient within 200–500 m above ground level (AGL), using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)) aerosol profile products. There are distinct differences in climate, meteorology, terrain, and aerosol transmission throughout China, so comparisons between vertical correction via CALIOP ratio and planetary boundary layer height (PBLH) were conducted in different regions from 2014 to 2015, combined with the original Pearson coefficient between satellite AOD and ground-level PM2.5 for reference. Furthermore, the best vertical correction scheme was suggested for different regions to achieve optimal correlation with PM2.5, based on the analysis and discussion of regional and seasonal characteristics of aerosol vertical distribution. According to our results and discussions, vertical correction via PBLH is recommended in northwestern China, where the PBLH varies dramatically, stretching or compressing the surface aerosol layer; vertical correction via the CALIOP ratio is recommended in northeastern China, southwestern China, Central China (excluding summer), North China Plain (excluding Beijing), and the spring in the southeast coast, areas that are susceptible to exogenous aerosols and exhibit the elevated aerosol layer; and original AOD without vertical correction is recommended in Beijing and the southeast coast (excluding spring), where the elevated aerosol layer rarely occurs and a large proportion of aerosol is aggregated in near-surface. Moreover, validation experiments in 2016 agreed well with our discussions and conclusions drawn from the experiments of the first two years. Furthermore, suggested vertical correction scheme was applied into linear mixed effect (LME) model, and high cross validation (CV) R2 (~85%) and relatively low root mean square errors (RMSE, ~20 μg/m3) were achieved, which demonstrated that the PM2.5 estimation agreed well with the measurements. When compared to the original situation, CV R2 values and RMSE after vertical correction both presented improvement to a certain extent, proving that the suggested vertical correction schemes could further improve the estimation accuracy of PM2.5 based on sophisticated model in China. Estimating PM2.5 with better accuracy could contribute to a more precise research of ecology and epidemiology, and provide a reliable reference for environmental policy making by governments.

Geostatistical mapping of topsoil organic carbon and uncertainty assessment in Western Paris croplands (France)

This study considers approaches for calculating and mapping statistical predictions of topsoil organic carbon (SOC) content and attendant uncertainty from data across a small region of Northern France, the Versailles Plain (221km2). Cropland topsoil SOC content was modelled through a bootstrap resampling procedure: a set of 384 models were built from cokriging up to 3 soil properties (pH, iron content, plus five earth-size fractions, being clay, coarse sand, fine sand, coarse silt, and fine silt) with an external drift (DEM or a soilscapes map). The approach provided estimation for the uncertainty globally and for each sample location, using a criterion of pointwise RMSE. The model exhibiting the lowest RMSE median of 3.17g.Kg−1 was the one including clay content and elevation covariates and had the highest validation R2 (0.52). Highest uncertainties were observed at locations marked by organic matter inputs, past landuse, calcareous soils. This knowledge gives hints for further sampling and reachable bounds for further approaches that would incorporate spatial data on agricultural practices and past landuse.