Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.

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Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues

Remote Sensing ◽

10.3390/rs12121913 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1913 ◽

Cited By ~ 1

Author(s):

Klara Dvorakova ◽

Pu Shi ◽

Quentin Limbourg ◽

Bas van Wesemael

Keyword(s):

Remote Sensing ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Cross Validation ◽

Crop Residues ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Local Soil ◽

Sentinel 2

Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2) = 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues.

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Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Sensors ◽

10.3390/s21062195 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2195

Author(s):

Lucas de Paula Corrêdo ◽

Leonardo Felipe Maldaner ◽

Helizani Couto Bazame ◽

José Paulo Molin

Keyword(s):

Sample Preparation ◽

Prediction Models ◽

Spectral Response ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Partial Least Square ◽

Least Square ◽

Sample Type ◽

Sampling Strategies ◽

Significant Difference

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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Towards Managing Organizational Change: An Empirical Study

Annals of Contemporary Developments in Management & HR ◽

10.33166/acdmhr.2021.02.004 ◽

2021 ◽

Vol 3 (2) ◽

pp. 41-50

Author(s):

Sulaiman Abdullahi Bambale ◽

Saheed Ademola Lateef ◽

Ibrahim Abdulmalik

Keyword(s):

Organizational Change ◽

Structural Equation ◽

Employee Commitment ◽

Employee Motivation ◽

Partial Least Square ◽

Least Square ◽

Trust Building ◽

Observation Data ◽

Study Results ◽

The Relationship

This study examines the relationship between trust buildings, motivating employees, and employee commitment toward organizational change. A self-administrated questionnaire was used to gather data. The study provides a basic understanding of organizational change. Through systemic, theoretical, and conceptual understanding, the arguments of the study are built on the importance of communication in the organization and how in bringing organizational change. The current study proposed that trust-building, employee motivation, and employee commitment will be related to organizational change. A total of 292 copies of completed questionnaires were returned, representing 90.7% of the total questionnaire distribution to both managers and owners of manufacturing firms. Out of which, only 275 questionnaires were usable for the analysis after removing incomplete data and outliers. Partial Least Square-Structural Equation Modelling (PLS-SEM) was used to analyze as a popularly accepted model to justify the theory with the observation data. The study results revealed that trust-building, employee motivation and employee commitment have significant effects on organizational change. The current study also claims the importance of collaboration within employees of any organization at the level of transition. The current study will help professionals and academics and enhancing their leadership abilities, it will benefit and inspire trust members to show better outcomes. However, it is recommended that further research is needed in this direction to confirm the result of this study. Finally, this study concludes that trust-building, employee commitment and employee motivation play a significant role in organizational change.

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PENGARUH DIMENSI DIMENSI IKLIM ORGANISASI TERHADAP KINERJA PEGAWAI DI BADAN KEPEGAWAIAN DAERAH PROVINSI NUSA TENGGARA BARAT

JMM UNRAM - MASTER OF MANAGEMENT JOURNAL ◽

10.29303/jmm.v9i1.475 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Muhammad Amri Fuadi ◽

Hermanto Hermanto ◽

Lalu Suparman

Keyword(s):

Organizational Climate ◽

Social Environment ◽

Employee Performance ◽

Partial Least Square ◽

Least Square ◽

Work Behavior ◽

Positive Sign ◽

Path Coefficient ◽

Observation Data ◽

Pls Analysis

This research is directed to prove the significance of the influence of organizational climate dimensions in the form of physical environment (X1), social environment (X2) and management system (X3) on the dimensions of employee performance in the form of employee work goals (Y1) and employee work behavior (PKP). There are six hypotheses that are proven through a partial least square (PLS) analysis process. The population of this study (observation data) was 82 BKD NTB employees. Data was collected through questionnaires and all questionnaires returned in accordance with data input needs. Through the outer model stage there are two indicators of the social environment that are issued, namely the relationship of superiors with subordinates (LS1) and colleague relations (LS2) as well as two indicators of employee work behavior, namely commitment (PKP3) and leadership (PKP6). Indicators that are classified as valid get a reinforcement of criteria through the parameter AVE values above 0.50 and include reliable indicators through Cronbach's alpha parameters and composite reliability above 0.70. PLS analysis through the inner model stage found that all dimensions of the organization's climate have a positive influence (positive sign of the path coefficient) on the dimensions of employee performance.Keywords : Organizational Climate Dimensions and Employee Performance Dimensions.

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Pengaruh Variabel Internal Perbankan Terhadap Kinerja Bank Konvensional di Indonesia

J-MAS (Jurnal Manajemen dan Sains) ◽

10.33087/jmas.v4i1.75 ◽

2019 ◽

Vol 4 (1) ◽

pp. 90

Author(s):

Ali Akbar

Keyword(s):

Commercial Banks ◽

Time Series Data ◽

Partial Least Square ◽

Least Square ◽

Series Data ◽

Internal Variables ◽

Operating Income ◽

Performance Factors ◽

Conventional Banks ◽

Dominant Influence

This research is to know the influence of internal variables of banking (amount of credit and operational expense than operating income (BOPO)) on the performance of conventional banks. The population in this research is the whole of conventional commercial banks in Indonesia year of 2010-2017. The sampel is the conventional commercial banks by as much as 14 banks, with time series data. The method used is the analysis of partial least square (PLS). The results showed that internal variables of banking (amount of credit, BOPO) negative and no significant effect on performance of conventional banks (CAR, NPL, ROA, LDR) and amount of credit credit is an indicator of a dominant influence variation/change from a conventional banks performance factors (CAR, NPL, ROA and LDR).

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Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.467 ◽

2005 ◽

Vol 13 (3) ◽

pp. 147-154 ◽

Cited By ~ 6

Author(s):

Wolfgang Becker ◽

Norbert Eisenreich

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Process Conditions ◽

Transmission Spectra ◽

Mean Square ◽

Score Plot ◽

Least Square Regression

Near infrared spectroscopy was used as an in-line control system for the measurement of polypropylene filled with different amounts of Irganox additives. For this purpose transmission probes were installed in an extruder. The probes can withstand temperatures up to 300°C and pressures up to 60 MPa. Transmission spectra of polypropylene mixed with an Irganox additive were recorded. PCA score plot was carried out revealing the influence of varying conditions for the mixing of the sample preparation. Prediction models were generated with partial least square regression which resulted in a model which estimated Irganox with a coefficient of detremination of 0.984 and a root mean square error of prediction of 0.098%. Furthermore the possibilities for controlling process conditions by measuring transmission at a specific wavelength were shown.

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Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.667 ◽

2011 ◽

Vol 460-461 ◽

pp. 667-672

Author(s):

Yun Zhao ◽

Xing Xu ◽

Yong He

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Training Set ◽

Test Set ◽

Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

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The employment of Fourier Transform Infrared Spectroscopy (FTIR) and chemometrics for analysis of candlenut oil in binary mixture with grape seed oil

Food Research ◽

10.26656/fr.2017.4(1).279 ◽

2019 ◽

Vol 4 (1) ◽

pp. 184-190

Author(s):

A.B. Riyanta ◽

S. Riyanto ◽

E. Lukitaningsih ◽

Abdul Rohman

Keyword(s):

Ftir Spectroscopy ◽

Seed Oil ◽

Prediction Models ◽

Principal Component ◽

Analytical Techniques ◽

Grape Seed ◽

Ftir Spectra ◽

Partial Least Square ◽

Least Square ◽

Grape Seed Oil

Candlenut oil (CDO) is the target of adulteration with other plant oils to get economical profits, therefore, reliable analytical techniques should be developed. Based on the principal component analysis (PCA), grape seed oil (GSO) has the close similarity with CDO. Therefore, this study was intended to make modelling in the authentication analysis of CDO from GSO using Fourier transformed infrared (FTIR) spectroscopy in combination with chemometrics of partial least square calibration (PLSR) and discriminant analysis (DA). FTIR spectra of CDO, GSO and its binary mixtures were subjected to FTIR spectral measurement at wavenumbers of 4000-650 cm-1 , and its absorbances were used for modelling of PLSR and DA. FTIR spectra were also subjected to pre-processing including Savitzy-Golay derivatization. The optimization results showed that FTIR spectra using second derivative at the combined wavenumbers of 3000-2800 and 1600-650 cm-1 offered the optimum models. The coefficient determination (R2 ) for the relationship between actual values and FTIR predicted values was 0.9996 and 0.9975 in calibration and internal validation (prediction) models, respectively. The errors in calibration and validation were relatively low, i.e. 0.84% and 2.19 %vol/vol, respectively. Using the same FTIR spectra, DA could discriminate pure CDO and that mixed with GSO at concentration range of 1-50%vol/vol. The combination of FTIR spectroscopy and chemometrics offered effective tools for the quantification and discrimination of CDO mixed with GSO with the main advantage of its simplicity and rapidity.

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Factors affecting the import of soybean in Indonesia

Jurnal Perspektif Pembiayaan dan Pembangunan Daerah ◽

10.22437/ppd.v8i5.11015 ◽

2020 ◽

Vol 8 (5) ◽

pp. 523-530

Author(s):

Adlaida Malik ◽

Saidin Nainggolan

Keyword(s):

Direct Effect ◽

Time Series Data ◽

Partial Least Square ◽

Least Square ◽

Series Data ◽

Soybean Production ◽

Macroeconomic Conditions ◽

Production And Consumption ◽

The Republic ◽

The Government

As an agricultural country, Indonesia still imports soybeans to meet domestic soybean needs. The gap between national soybean production and consumption causes the government to import. Based on this, this study aims to analyze the factors that influence soybean imports in Indonesia. The data used are time series data for the period 2003-2018. Data is sourced from the Food and Agriculture Organization (FAO), United Nations International Trade Statistics Database (UN COMTRADE), the Central Bureau of Statistics of the Republic of Indonesia, World Bank, Bank Indonesia, and the Ministry of Trade of the Republic of Indonesia. The analysis method uses SEM-Partial Least Square (PLS). The results showed that the macroeconomic conditions directly affect soybean production and consumption. On the other hand, consumption has a direct effect, but production has no direct effect on soybean imports. Macroeconomic conditions do not have a direct effect on soybean imports. Nevertheless, the total effect (combined direct and indirect effects) is significant from macroeconomic conditions on soybean imports.

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Near-Infrared Technology in Agriculture: Non-Destructive Determination of Inner Quality Parameters in Intact Cocoa Beans

Trends in Sciences ◽

10.48048/tis.2021.31 ◽

2021 ◽

Vol 18 (20) ◽

pp. 31

Author(s):

Zulfahrizal Zulfahrizal ◽

Agus Arip Munawar

Keyword(s):

Prediction Models ◽

Quality Attributes ◽

Quality Parameters ◽

Fat Content ◽

Prediction Performance ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Cocoa Beans ◽

Orthogonal Signal Correction

This present study aimed to apply the near-infrared technology based on reflectance spectroscopy or NIRS in determining 2 main quality attributes on intact cocoa beans namely fat content (FC) and moisture content (MC). Absorbance spectral data, in a wavelength range from 1000 to 2500 nm were acquired and recorded for a total of 110 bulk cocoa bean samples. Meanwhile, actual reference FC and MC were obtained using standard laboratory approaches and Soxhlet and Gravimetry methods. Samples were split onto calibration and validation datasets. The prediction models, used to determine both quality attributes were developed from the calibration dataset using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR). To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) were employed. The results showed that PLSR was better than PCR for both quality parameters prediction. Moreover, spectra corrections enhanced the prediction accuracy and robustness from which OSC was found to be the best correction method for FC and MC determination. The prediction performance using validation dataset generated a correlation coefficient (r), ratio prediction to deviation (RPD), and ratio error to range (RER) indexes for FC were 0.93, 3.16 and 7.12, while for MC prediction, the r coefficient, RPD and RER indexes were 0.96, 3.43 and 9.25, respectively. Based on obtained results, it may conclude that NIRS combined with proper spectra correction and regression approaches can be used to determine inner quality attributes of intact cocoa beans rapidly and simultaneously. HIGHLIGHTS We study and apply NIRS technology as a fast and novel method to predict inner quality parameters of intact cocoa beans in form of moisture and fat content Prediction models, used to determine both quality attributes were developed using 2 regression methods: Principal component regression (PCR) and partial least square regression (PLSR) To obtain more accurate and robust prediction performance, 4 different spectra correction methods namely baseline shift correction (BSC), mean normalization (MN), standard normal variate (SNV), and orthogonal signal correction (OSC) The best prediction performance was obtained when the models are constructed using PLSR in combination with OSC correction approach The maximum correlation coefficient (r) and ratio prediction to deviation (RPD) indexes for Fat content were 0.93 and 3.16, while for moisture content prediction, the r coefficient and RPD indexes were 0.96 and 3.43, respectively GRAPHICAL ABSTRACT

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