•FODs were used to process and analyze soil Vis-NIR spectra.•The correlation coefficient with SOMC was best at 1.05- to 1.45-order.•MNDI was used to emphasize the SOMC information.•FOD-MNDI showed ...the best estimation accuracy, with an RPIQ reaching 3.40.
Visible-near-infrared (Vis-NIR) spectroscopy makes it possible to estimate soil organic matter content (SOMC). Spectral pretreatment techniques have important significance in the quantitative analysis of SOMC. A total of 150 soil samples collected in northwestern China were used as data sets for calibration and validation. The SOMC values and reflectance spectra were measured in the laboratory. Fractional-order derivatives (FODs) (intervals of 0.05, range of 0–2) were used for soil spectral pretreatment, and a new three-band index (modified normalized difference index, MNDI) was constructed based on the band-optimization algorithm and the existing two-band exponential form (normalized difference index, NDI). Partial least square-support vector machine (PLS-SVM) models were calibrated using spectral parameters selected based on a single dimension (FOD), two-dimensional index (NDI) and three-dimensional index (MNDI) and subsequently applied to estimate SOMC. Three model evaluation parameters, namely, the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile range (RPIQ), were used to evaluate the estimation accuracy of the models. The results showed that with increased derivative order, the spectral strength gradually decreased, but the spectral detail increased. Furthermore, the correlation between FOD spectra and SOMC was significantly enhanced in the visible region, with the most obvious effect in the 1.05- to 1.45-order range. The PLS-SVM modeling results showed that the sensitivity and estimation accuracy of SOMC increased with increasing spectral synergy (i.e., 1D (FOD) < 2D (NDI) < 3D (MNDI)). Among the models, MNDI exhibited the best model performance, yielding a validation R2 and RPIQ of 0.846 and 3.396, respectively. The combination of FOD and MNDI could weaken the soil noise information and improve the prediction accuracy of SOMC. Furthermore, the three-dimensional index has strong application potential for estimating other biochemical parameters of soil using Vis-NIR spectroscopy.
In this paper, we use the elementary methods and the estimates for character sums to study a problem related to primitive roots and the Pythagorean triples and prove the following result: let p be an ...odd prime large enough. Then, there must exist three primitive roots x, y, and z modulo p such that x2+y2=z2.
The main purpose of this article is using the elementary methods and the properties of the quadratic residue modulo an odd prime p to study the calculating problem of the fourth power mean of one ...kind two-term exponential sums and give an interesting calculating formula for it.
Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid ...and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS-NIR) spectroscopy. The soil samples (
= 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0-2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of
(0.93), RMSE (4.57 dS m
), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.
Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable ...development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) (B12 − B7) / (B12 + B7) and three-band index (TBI4) (B12 − B3) / (B3 − B11) show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2V, RMSEV, and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area.
•The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity.•Three-band index (B12 − B3) / (B3 − B11) shows a best correlation (r = 0.544) with measured EC.•RF-PLSR model was proved a suitable method for soil salinity estimating and mapping.•The study shows a large variability in soil salinity in dry and wet seasons.
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AMD3100 (plerixafor), a CXCR4 antagonist, has opened a variety of avenues for potential therapeutic approaches in different refractory diseases. The CXCL12/CXCR4 axis and its ...signaling pathways are involved in diverse disorders including HIV-1 infection, tumor development, non-Hodgkin lymphoma, multiple myeloma, WHIM Syndrome, and so on. The mechanisms of action of AMD3100 may relate to mobilizing hematopoietic stem cells, blocking infection of X4 HIV-1, increasing circulating neutrophils, lymphocytes and monocytes, reducing myeloid-derived suppressor cells, and enhancing cytotoxic T-cell infiltration in tumors. Here, we first revisit the pharmacological discovery of AMD3100. We then review monotherapy of AMD3100 and combination use of AMD3100 with other agents in various diseases. Among those, we highlight the perspective of AMD3100 as an immunomodulator to regulate immune responses particularly in the tumor microenvironment and synergize with other therapeutics. All the pre-clinical studies support the clinical testing of the monotherapy and combination therapies with AMD3100 and further development for use in humans.
•Proximal soil sensing is still attractive in Cr estimation.•Fractional order derivative and three-band index are useful for feature extraction.•The correlation and mechanism analysis between ...auxiliary attributes and chromium.•Concatenation of Proximal soil sensing and auxiliary attributes is promising.•Kriging and optimal semi-variogram function are essential in soil science.
The rapid and accurate determination of soil chromium (Cr) is crucial for preventing toxic element pollution in soils and ensuring ecological security. Proximal sensing technology uses visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy, which has been demonstrated to be a viable approach for monitoring soil Cr concentrations. However, at trace levels, soil Cr is not especially spectrally active, thus limiting the practical application of using corresponding spectral data for quantifying soil Cr concentrations. In this study, we hypothesized that fused proximal sensing and soil auxiliary attributes (including organic matter (OM) and pH) could improve estimation of Cr concentrations in the soil. Additionally, the introduction of best-fit variogram models was theoretically possible to improve spatial visualization. To address these hypotheses, we collected 168 soil samples from the open coal mine area in the Eastern Junggar Basin, China. Fractional-order derivative (FOD) pretreatment and optimal band combination methods were implemented for spectral data mining and the derivation of spectral parameters, respectively. Soil Cr estimation models were calibrated with a partial least squares (PLS) approach through four designed strategies with different predictors: (I) full Vis-NIR variables, (II) effective three-band spectral indices (TBIs), (III) the effective TBIs and OM, and (IV) the effective TBIs, OM, and the pH. The results suggest that FOD could identify abundant spectral variability. Compared with full Vis-NIR variables, the effective TBIs can effectively magnify the subtle spectral signals concerning soil Cr. The optimal estimation model was determined as Strategy IV, indicating that the introduction of soil auxiliary attributes (pH and OM) can improve the estimation performance of the model; notably, the coefficient of determination (R2) and ratio of performance to interquartile distance (RPIQ) were 0.87 and 2.68, respectively. Based on the optimal semivariance model, we used kriging interpolation to map regional soil Cr. In the study area, the soil Cr distribution features strong spatial dependence and strong associations. Our study might inspire further research on soil contamination mapping based on proximal Vis-NIR sensors.
Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has ...been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model's SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0-10 cm) from the farmland (2.5 × 10
m
) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (
= 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (
= 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.
•Vis-NIR spectra were used to estimate the SOM in salt-affected soils.•Seven spectral resolutions (SRs) were tested from 1 nm to 100 nm.•The optimal band combination algorithm is useful for ...extracting spectral variables.•The soil salinity had a strong negative influence on the SOM model performance.•Vis-NIR spectra with an SR of 20 nm was recommended to estimate the SOM.
Visible and near-infrared (Vis-NIR) spectroscopy is a cost-effective technique for alternative soil physical and chemical analyses for estimating soil properties. The optimal band combination algorithm is an effective method of extracting spectral variables by considering the interaction information between wavebands, but for laboratory Vis-NIR spectral data, this method is susceptible to the “dimensional curse”. Here, we hypothesized that properly degrading the spectral configuration (i.e., decreasing the number of spectral bands and coarsening the spectral resolution) can improve the computational efficiency without affecting the prediction accuracy. To test this hypothesis, we constructed six degraded spectral configurations from an initial spectral database (i.e., consisting of 2001 spectral bands acquired with a portable ASD spectroradiometer) with a reduction in the number of spectral bands from 2001 to 19, a coarsened spectral resolution from 3 to 100 nm, and a spectral sampling interval equal to the spectral resolution (i.e., uniform interval sampling). In this study, the databases consisted of 255 soil samples collected from the Ebinur Lake area in Northwest China. The relationship between the soil organic matter (SOM) and the spectra was established using a partial least-squares-support vector machine (PLS-SVM) through two strategies: one is in accordance with the different salinity levels, and the other involves applying the optimal band combination algorithm from each spectral configuration. The results indicated that the soil salinity had a strong negative influence on the performance of the SOM models (R2cv, 0.46–0.81). However, the optimal band combination algorithm can improve the sensitivity (R2pre, 0.36–0.65) of spectral information and the SOM. Overall, the prediction accuracy obtained through the optimal band combination algorithm was generally superior to that from full-spectrum data. The prediction performance of the optimal band combination algorithm was accurate (R2pre ≥ 0.85) and stable (RPIQ pre, ~3.20), with a spectral resolution between 3 and 20 nm (i.e., the number of spectral bands decreased from 2001 to 99). Considering the accuracy and time-consuming nature of this approach, the combination of a 20 nm spectral resolution and an optimal band combination algorithm was the most effective method. In summary, this research will guide future studies in transforming hyperspectral datasets into parsimonious representations and uses the optimal band combination algorithm efficiently to determine the informative variable. Furthermore, the optimal band combination algorithm has broad application prospects in soil Vis-NIR spectroscopy and other fields of spectroscopy.
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.
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