Principal Component Regression Methods Research Articles

Comparison of principal component regression (PCR) and partial least square regression (PLSR) modeling methods for quantifying polyethylene (PE) in recycled polypropylene (rPP) with near-infrared spectrometry (NIR)

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE). Since polypropylene (PP) and PE are incompatible, the presence of PE compromises the performance of rPP materials and needs to be closely monitored. In our previous work, Raman and near-infrared (NIR) spectrometries were evaluated to monitor PE content in rPP with partial least square regression (PLSR) modeling. The NIR spectrometry exhibited a wider application range, but the accuracy of the prediction models might be further improved. In the current work, a different modeling method, principal component regression (PCR) was employed to analyze PE content in rPP with NIR spectrometry. Spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the NIR spectrum quality. Forward and backward interval methods were used to optimize spectral range selection. The outcomes were compared with our previous PLSR modeling results. The highest accuracy in independent validation was achieved by a PCR model with an R 2 of 0.9991 and a root-mean-square error of prediction (RMSEP) of 0.1596 PE%. On the other hand, a PLSR model achieved the lowest RMSEP of 0.9712 PE% for a non-colored post-consumer rPP sample. The PCR models might be sensitive to interference and more suitable for post-industrial materials, which have a simpler chemical composition. The PLSR models might have better stability and be more suitable for complicated post-consumer samples. Both the PCR and PLSR models were successfully applied to a gray commercial rPP sample.

Analysis of the Status Quo and Influencing Factors of Total Health Expenditure in Xinjiang Based on Principal Component Regression Method

Analysis of the Status Quo and Influencing Factors of Total Health Expenditure in Xinjiang Based on Principal Component Regression Method

In Situ Raman Methodology for Online Analysis of CO2 and H2O Loadings in a Water-Lean Solvent for CO2 Capture.

Carbon capture represents a key pathway to meeting climate change mitigation goals. Powerful next-generation solvent-based capture processes are under development by many researchers, but optimization and testing would be significantly aided by integrating in situ monitoring capability. Further, real-time water analysis in water-lean solvents offers the potential to maintain their water balance in operation. To explore data acquisition techniques in depth for this purpose, Raman spectra of CO2, H2O, and a single-component water-lean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA) were collected at different CO2 and H2O concentrations using an in situ Raman cell. The quantification of CO2 and H2O loadings in 2-EEMPA was done by principal component regression and partial least squares methods with analysis of uncertainties. We conclude with discussions on how this simultaneous online analysis method to quantify CO2 and H2O loadings can be an important tool to enable the optimal efficiency of water-lean CO2 solvents while also maintaining the critical water balance under operating conditions relevant to post-combustion CO2 capture.

CHEMOMETRIC ASSISTED UV SPECTROPHOTOMETRIC METHOD FOR QUANTIFICATION OF EMTRICITABINE AND TENOFOVIR DISOPROXIL FUMARATE

The aim of this study was to verify the ability of the UV spectrophotometric method for the simultaneous determination of emtricitabine and tenofovir disoproxil fumarate in tablet dosage form using the principal Component Regression (pCR) and partial Least Squares (pLS) multivariate calibration methods. Double beam UV-Vis spectrophotometer (Jasco V-730) with 1 cm quartz cell with 1 nm data interval and scanning speed of 400 nm min -1 was used in the study. the optimized wavelength range selected was 225-275 nm. the data obtained was processed using Unscrambler X (10.5) (64bit) software. The developed models showed good results over the concentration range of 6-36 μg mL -1 for tenofovir disoproxil fumarate and 4-24 μg mL-1 for emtricitabine with co-relation coefficient greater than 0.995 and % RSD less than 2%. the accuracy studies show % recovery within limits. The method was validated as per ICH Q2(R1) guideline.

Supervised Principal Component Regression for Functional Responses with High Dimensional Predictors

We propose a supervised principal component regression method for relating functional responses with high-dimensional predictors. Unlike the conventional principal component analysis, the proposed method builds on a newly defined expected integrated residual sum of squares, which directly makes use of the association between the functional response and the predictors. Minimizing the integrated residual sum of squares gives the supervised principal components, which is equivalent to solving a sequence of nonconvex generalized Rayleigh quotient optimization problems. We reformulate the nonconvex optimization problems into a simultaneous linear regression with a sparse penalty to deal with high dimensional predictors. Theoretically, we show that the reformulated regression problem can recover the same supervised principal subspace under certain conditions. Statistically, we establish nonasymptotic error bounds for the proposed estimators when the covariate covariance is bandable. We demonstrate the advantages of the proposed method through numerical experiments and an application to the Human Connectome Project fMRI data. Supplementary materials for this article are available online.

Development of statistical regression and artificial neural network models for estimating nitrogen, phosphorus, COD, and suspended solid concentrations in eutrophic rivers using UV–Vis spectroscopy

River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet–visible (UV–Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions. To address this, a framework integrating desktop and in situ UV–Vis spectrometers was developed to establish reliable conversion models. The absorption spectra obtained from a desktop spectrometer were utilized to create models for estimating nitrate-nitrogen (NO3-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). We validated these models using the absorption spectra obtained from an in situ spectrometer. Partial least squares regression (PLSR) employing selected wavelengths and principal component regression (PCR) employing all wavelengths demonstrated high accuracy in estimating NO3-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH4-N concentration using all wavelengths. Due to the dominance of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and PCR methods utilizing all wavelengths effectively estimated TP and SS, respectively. The determination coefficients (R2) of all the calibrated models exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were within 0.4. Our approach shows excellent efficiency and potential in establishing reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This approach eliminates the need for time-consuming and uncertain in situ absorption spectrum measurements during model setup, which may be affected by fluctuating natural and anthropogenic environmental conditions.

Parameterization of Entrainment Rate for Cumulus Clouds with WRF Simulation

By using Weather Research and Forecasting Model (WRF) to simulate a southwest vortex precipitation process, this work studies the correlations between entrainment rate (λ) and dynamical parameters in the cloud and further fit λ. We relate the probability density distribution (PDF) to the parameterization of λ and find that the greater the probability, the larger the slope of the logarithmic liner function. The slope of the log-linear fitting function in fitting decreases for developing and enhancing cumulus clouds, which is related to the increase in updraft motion and the decrease in λ. Then, we group clouds according to cloud top heights and calculate average λ and dynamic parameters, and the results indicate that when only one dynamic parameter is used, vertical wind velocity (w) is more suitable than buoyancy (B) to be used to fit λ. The fitting functions combing one single parameter and more parameters by principal components regression are compared with two traditional schemes, and we found that λ obtained by our fitting schemes are between the two traditional schemes. Because the principal component regression method takes into account the interaction between more dynamic factors and entrainment, the fitting function, including w and B, is suitable to be applied to fit λ in the parameterization scheme for cumulus clouds.

PERBANDINGAN GULUD REGRESSION DAN PRINCIPAL COMPONENT REGRESSION (PCR) TERHADAP PEMODELAN INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR

Human resources is valuable asset in a country. Human Development Index (HDI) becomes important indicator of quality of human resources in an area. HDI value is affected by a variety of factors that are strongly related to each other so they cause multicollinearity. This observation aims to deal with multicollinearity optimally by comparing Gulud Regression to Component Regression in modeling factors that affect East Java HDI in 2020. Data that are used in this observation are East Java HDI in 2020 (Y), Life Expectancy (X1), Infant Mortality Rate (X2), Mean Years of Schooling (X3), Expected Years of Schooling (X4), Open-Unemployment Rate (X5), Average Household Expenditure per Capita (X6), and Labor Force Participation Rate (X7). Based on MSE value, the Gulud Regression method is better than Principal Component Regression (PCR) method in dealing with multicollinearity problem. Based on adjusted score that is 0,954, feasibility test of the best model of Gulud Regression method is a strong model.

Determination of Chemical Content of Vanilla Pods (Vanilla planifolia) Non-destructively Using NIR Spectroscopy

This study aims to assess Near infrared (NIR) spectroscopy to predict vanillin and moisture content non-destructively. Twenty-four samples of vanilla pods (@55 grams/sample) from Sumatra and Sulawesi Island were tested. The reflectance spectra of vanilla pod were measured using a NIRFlex N-500 spectrometer at the wavelength of 1000-2500 nm. After that, the vanillin and moisture content were measured using the reference method. The reflectance spectra were transformed to absorbance spectra and several pre-treatment methods of NIR spectra were applied, and the results were calibrated with chemical data using principal component regression (PCR) and partial least square (PLS) methods. The best estimation for vanillin content using absorbance spectra with OSC pre-treatment at 3 PLS factors with the accuracy parameters of r = 0.91, SEC = 1.31%, SEP = 1.34%, CV = 45.60%, RPD = 2.30 with a consistency of 97.96%. The best prediction of vanillin content using PCR method was obtained with SNV pre-treated spectra at 6 PCR factors with the accuracy parameters of r = 0.89, SEC = 1.47%, SEP = 1.49%, CV = 43.21%, RPD = 2.09 and 101.40% consistency. The best estimation for water content using the PLS method is using the SNV pre-treatment spectra with a PLS factor of 5 with the accuracy parameters of r = 0.99, SEC = 2.11%, SEP = 2.13%, CV = 6.12%, RPD = 7.98 with consistency = 98.86%. While using the PCR method, the best estimation for moisture content was also obtained by SNV pre-treated spectra at 4 PCR factors with the accuracy values of r = 0.99, SEC = 2.33%, SEP = 2.25%, CV = 6.25%, RPD = 7.58 and consistency = 103.90%. The PLS calibration provide a better accuracy than the PCR. The NIR spectroscopy associated with the selected pre-treatments and factor numbers of PLS and PCR can be used for determination of chemical content of vanilla pods.

Predict Farmer Exchange Rate in the Food Crop Sector Using Principal Component Regression

Farmer Exchange Rate (FER) in Indonesia is very concerning. According to BPS data, there are various regions that experience increases and decreases every year. The goal of this paper is to predict Farmer Exchange Rate in the food crop sector using Principal Component Regression (PCR) since there is multicollinearity in the data. Therefore, with the PCR method using data based on 33 different provinces in Indonesia can determine the Farmer Exchange Rate with supporting factors. The model used can help farmers to be able to improve the welfare and economic growth of Indonesia as it depends on farmers. Further analysis found that the Harvest Area, production, and Human Development Index had an effect on farmer exchange rate. By using this model, it is expected that farmers in Indonesia have an increasing level of welfare and solve multicollinearity problem.

A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement

Appear in Many Application Areas for Regression Analysis and Presence the case of More Than One Variable Dependent Affected by A variety of Explanatory Variable and at The Same Time The Number of Observation is Relatively Small Compared to The Number of Variables, and Show Here The Problem of not Provide Offers Several Hypotheses Multiple Regression Analysis, More Over Prominence Problem of Multicollinearity between the Explanatory Variables Beside The Correlation between The Explanatory Variables and The Dependent Variables and Reflexive that on the Regression Estimates. and in This Research was Dealing with Problems from this Type Related to Variables Kirkuk Cement Factory, used the Methods, Principal Component PC and Partial Least Squares PLS to Solve the Problems Above, The First Method is Considered as one of the Commonest Methods used in Solving the Problem of Multicollinearity between the Explanatory Variables and the Second Method is Considered as one of the Methods Which Dally Methodically Different in Deduction the Components Dependent on Curing The Correlation the Presence between The Explanatory Variables and The Dependent Variables. Through the Statistical Analysis, Orphan Conduction to The PLS Method it has Succeeded in Establishing the Optimal Regression Model for all Depended Variables, Besides Superiority This Method Whence Ability on the Prediction for Futuristic Values Apiece Dependent Variables and Also Whence Dimension Reduction. The (Minitab, Version, 16.1) is used in the Statistical Analysis for the Data of this Research .

A comprehensive analysis of impacts of socio-economic development and land use on river water quality in a megacity-region: a case study

A comprehensive analysis of impacts of socio-economic development and land use on water quality is very useful for better planning and management of river basins by government. In this study, the North Canal River through a megacity-region of Beijing-Tianjin-Hebei Province in China was chosen to quantify impact of 10 socio-economic factors and 6 land use types on water quality in the watershed. The principal component regression (PCR) method was thus applied in this study to quantify effects of socio-economic development and land use types on river water quality through overcoming multicollinearity problems among influencing factors. Results showed that the water quality in the North Canal River improved significantly from serious pollution. Compared with 2010, the annual average pollution index (PI) of COD, NH3–N, TN, and TP decreased significantly in 2018, especially the PI of NH3–N decreased the most, from 8.62 in 2010 to <1 in 2018, implying that the concentration of NH3–N basically met the water quality assessment standard in 2018. The major water pollutant in the basin had shifted from NH3–N in 2010 to TN in 2018. The absolute impact coefficients of industrial restructuring, population density, upgraded municipal sewage treatment requirements (including sewage treatment rate and discharge standards of water pollutants for municipal WWTPs), and urbanization rate with COD, NH3–N and TP were higher than those of other factors, indicating that the impact of socio-economic factors on water quality was more significant than that of land use types, and the socio-economic factors may offset or alter the impact of land use on river water quality in areas disturbed by human activities.

Simultaneous Quantitative Analysis of Salmeterol and Fluticasone in Inhalation Spray Using HPLC and a Fast Spectrophotometric Technique Combined with a Time Series Neural Network and Multivariate Calibration Methods.

Chromatographic methods have been used for the simultaneous determination of salmeterol (SMT) and fluticasone (FLU). The methods take a lot of analysis time, need sample pre-treatment and large amounts of solvents, and are costly. The aim of this paper was to propose a simple, quick, and low-cost method for the determination of SMT and FLU using a time series neural network and multivariate calibration methods, including partial least-squares (PLS) and principal component regression (PCR). The simultaneous spectrophotometric determination of SMT and FLU in binary mixtures and anti-asthma spray was performed by applying multivariate calibration methods and an intelligent approach. The coefficient of determination (R2) of the time series neural network was obtained: 1 and 0.9997 for SMT and FLU, respectively. The mean recovery of PLS was found to be 99.29% and 99.84% for SMT and FLU, respectively. Also, the mean recovery related to the PCR method was 102.05% and 103.72% for SMT and FLU, respectively. The inhalation spray was assessed using HPLC and the results were compared with chemometrics methods via an analysis of variance (ANOVA) test. Intelligent and multivariate calibration methods were proposed. Simultaneous spectrophotometric determination of salmeterol and fluticasone was studied in an anti-asthma spray. HPLC as a reference method was performed and compared with chemometrics methods. Rapid, simple, low-cost, and accurate are the benefits of the proposed approaches.

BIOENERGETIC CULTURES: ESTIMATE OF OIL CONTENT IN MACAW PALM VIA COMPUTER VISION

Macaw palm (Acrocomia aculeata) is a potential raw material for biodiesel production owing to its high oil content. In this study, a model is created that can estimate the oil percentage in Macaw fruit using spectral indices obtained via computer vision. Ninety Macaw fruits are used to perform an experiment. They are categorized into three stages of maturation and counted in terms of weeks after flowering. Images of the fruits are captured using a multispectral camera in the visible spectral region (RGB) and infra-red regions (NIR), which allows spectral indices to be obtained. A model is established using the principal component regression method and used to estimate the oil content based on the colorimetric characteristics of Macaw palm fruits. The selected model is composed of the first two principal components, which indicate an explanatory potency of 91.57% of the variance. Applying this information to the validation data, the model exhibits a determination coefficient of 0.91, mean square error of 10.61%, and standard error of 5.46% in terms of the oil content, which indicates that computer vision is a promising alternative method for estimating oil content.

Spectrophotometric quantification of paracetamol and tramadol hydrochloride by chemometric calibration methods.

The results of UV spectrophotometric analysis were analysed using partial least squares (PLS) and principal component regression (PCR) techniques to allow simultaneous evaluation of tramadol hydrochloride (TRA) and p-acetaminophen (PAR) in tablets. A calibration set of 16 mixtures, each containing PAR and TRA in various amounts, was created using a 24-full fractional design. The absorbance data set for the calibration set were obtained between 215-280 nm (Δλ = 0.1 nm). Subsequently, the concentration and absorbance sets were used to generate PCR and PLS calibrations. The ratio spectra- first derivative method was devised as a solution to the same problem to compare the outcomes of the chemometric methods used for the same experiment. After the proposed methods were shown to be accurate in the testing of validation samples, they were used in analysing commercial tablet samples. The analytical results showed that PCR and PLS methods can be used as alternative methods to high-performance liquid chromatography (HPLC). Correlation coefficients were determined for the working concentration range of 6-36 g.mL-1 for PAR and 4-22 g.mL-1 for TRA. The limits of detection and quantification were calculated as 0.9104 μg.mL-1 and 3.0347 μg.mL-1, respectively. The test results of the chemometric analyses and ratio spectra- first derivative method of the commercial tablet form are in agreement with the results of the one-way ANOVA with a confidence interval of 95%. This study shows that the ratio spectra- first derivative method, PCR and PLS models based on spectrophotometric measurements are very useful and straightforward techniques for the quantitative resolution of a two-component pharmaceutical preparation, requiring little sample preparation and little time for analysis.

Prediction of Model Generated Patellofemoral Joint Contact Forces Using Principal Component Prediction and Reconstruction.

It is not currently possible to directly and noninvasively measure in vivo patellofemoral joint contact force during dynamic movement; therefore, indirect methods are required. Simple models may be inaccurate because patellofemoral contact forces vary for the same knee flexion angle, and the patellofemoral joint has substantial out-of-plane motion. More sophisticated models use 3-dimensional kinematics and kinetics coupled to a subject-specific anatomical model to predict contact forces; however, these models are time consuming and expensive. We applied a principal component analysis prediction and regression method to predict patellofemoral joint contact forces derived from a robust musculoskeletal model using exclusively optical motion capture kinematics (external approach), and with both patellofemoral and optical motion capture kinematics (internal approach). We tested this on a heterogeneous population of asymptomatic subjects (n = 8) during ground-level walking (n = 12). We developed equations that successfully capture subject-specific gait characteristics with the internal approach outperforming the external. These approaches were compared with a knee-flexion based model in literature (Brechter model). Both outperformed the Brechter model in interquartile range, limits of agreement, and the coefficient of determination. The equations generated by these approaches are less computationally demanding than a musculoskeletal model and may act as an effective tool in future rapid gait analysis and biofeedback applications.

Express Analysis of Cartilage Tissue Using Multivariate Analysis of IR Spectra.

Cartilages of the nasal septum, knee joint, rib, and nucleus pulposus of the intervertebral disc, as well as trypsinized and defective cartilage samples, were examined as samples. The IR spectra of the cartilage samples, as well as calibration mixtures of collagen and chondroitin sulfate, were obtained. The IR spectra were collected using the attenuated total reflectance techniques, and their processing was performed using the TQ Analyst software and the principal component regression calibration technique. Based on calibration dependence, the Ksp coefficient was determined as the ratio of the mass fractions of collagen and chondroitin sulfate. Its value was compared with the value of Kchem, equal to the ratio of the mass fractions of collagen and chondroitin sulfate, obtained using the classical chemical analysis of these substances. The IR spectra of cartilage tissues are a superposition of the IR spectra of collagen and chondroitin sulfate and qualitatively reflect their composition. A change in the ratio between the relative intensities of the characteristic bands of compounds in the IR spectrum is obvious only with a significant change in the content of these compounds in cartilage. This change occurs after trypsinization, when Ksp increases from 0.88±0.05 (Kchem~0.8) to 4.55. The use of a calibration model with a complete analysis of the cartilage IR spectrum made it possible to determine the difference in the ratio of the main components in the matrix of different samples in the absence of obvious changes in the IR spectra. Thus, a statistically significant decrease in the content of chondroitin sulfate in degraded articular cartilage (Ksp=4.4±1.8; Kchem~5.5) was shown compared with intact samples (Ksp=2.8±1.1; Kchem~2.6). IR spectrometric express analysis of cartilage tissue employing the principal component regression method allows a correct determination of the ratio of the main components in the cartilage matrix, those of collagen and glycosaminoglycans. The proposed technique includes one measurement, does not require prolonged and laborious sample preparation, does not require long, multi-stage and laborious chemical manipulations to determine each of the components, and makes it possible to determine the features and changes in the composition for a large set of samples of cartilage tissue of different types. In future, this approach can be used for non-invasive diagnostics of cartilage tissue.

Modeling of monthly rainfall analysis in Aceh Besar District with Indonesia’s Sea-Surface Temperatures as the primary component

Abstract. There are various fields that crucially require information about rainfall, including agriculture. A major drop or rise in rainfall may significantly impact the agriculture in Indonesia. For this reason, research regarding on variables that effected rainfall are still being improved by applying variety of models in order to provide accurate and closely related to the original data on rainfall forecast. Precise and accurate rainfall forecast is essential to help extension workers and farmers to determine when to start planting their crops and to decide what kind of crops are suitable for a particular region. Data applied in this research are data on monthly Sea-Surface Temperature (SST) data which are limited by 10°N – 15°S and 90°E – 145°E with a 1° x 1° grid, as well as data on monthly rainfall in Aceh Besar district which were obtained by averaging the data from 10 rain post points in collaboration with the BMKG of Aceh Besar District between 2010 – 2019. Correlation analysis between Sea-Surface Temperature (SST) and rainfall resulted in correlation value that fulfill the requirement of “sufficient” to “high” in a number of regions for each month. The highest correlation value was recorded by 0.87 for the positive correlation in October, and it was recorded by -0.87 for the highest negative value of correlation in December. Under those circumstances, by utilizing the Principal Component Regression (PCR) method, a forecasting model is created for each of the region with the highest correlation which allows for the identification of the area that most impacted rainfall in district of Aceh Besar. Model validation obtained error value of 37.1 or roughly around 19.5%. The highest value difference was occurred in October 2014, measuring by the value difference of 96 mm. The results of monthly rainfall forecast in 2021 provide a pattern of rainfall that is similar with the observation data obtained in August to December. Research results indicate that sea-surface Temperature (SST) in regions of Indonesia can be utilized as the indicator in forecasting rainfall at a district level.

INCORPORATING ANN WITH PCR FOR PROGRESSIVE DEVELOPING OF AIR POLLUTION INDEX FORECAST

This study circumscribes the modelling for concentration of Air Pollutant Index (API) in Selangor, Malaysia. The five monitored environmental pollutant concentrations (O3, CO, NO2, SO2, PM10) for ten years (2006 to 2015) data are used in this study to develop the prediction of API. The selected study area is located in rapid urbanised areas and surrounded by a number of industries, and is highly influenced by congested traffic. The principal component regression (PCR) for the combination of the principal component analysis together with multiple regression analysis, and artificial neural network (ANN), are used to predict the API concentration level. An additional approach using a combination method of PCR and ANN are included into the study to improve the API accuracy of prediction. The resulting prediction models are consistent with the observed value. The prediction techniques of PCR, ANN, and a combination method of R2 values are 0.931, 0.956, and 0.991 respectively. The combination method of PCR and ANN are detected to reduce the root mean square error (RMSE) of API concentration. In conclusion, different techniques were used in the combination method of API prediction which had improved and provided better accuracy rather than being dependent on the single prediction model.

Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region

ABSTRACT The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.