Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

Consumption of wheat is widespread and increasing in South Africa. However, global wheat production is projected to decline. Wheat yield forecasting is therefore crucial for ensuring food security for the country. The objective of this study was to investigate whether the anthesis wheat growth stage is suitable for forecasting dryland wheat yields in the Central Free State region using satellite imagery and linear predictive modelling. A period of 10 years of Normalized Difference Vegetation Index data smoothed with a Savitzky–Golay filter and 10 years of wheat yield data were used for model calibration. Diagnostic plots and statistical procedures were used for model validation and assessment of model adequacy. The period 30 days before harvest during the anthesis stage was established to be the best period during which to use the linear regression model. The calibrated model had a coefficient of determination of 0.73, a p-value of 0.00161 and a root mean squared error of 0.41 tons/ha. Residual plots confirmed that a linear model had a good fit for the data. The quantile-quantile plot provided evidence that the residuals were normally distributed, which means that assumptions of linear regression were fulfilled and the model can be used as a forecasting tool. Model validation showed high levels of accuracy. The evidence indicates that use of Moderate Resolution Imaging Spectroradiometer data during the anthesis growth stage is a reliable, cost-effective and potentially time-saving alternative to ground-based surveys when forecasting dryland wheat yields in the Central Free State.

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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Remote Sensing ◽

10.3390/rs13030438 ◽

2021 ◽

Vol 13 (3) ◽

pp. 438

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Vegetation Index ◽

Coastal Wetland ◽

Thermal Emission ◽

Mean Squared Error ◽

Wetland Vegetation ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

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Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽

10.3390/land10111221 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1221

Author(s):

Yuki Hamada ◽

Colleen R. Zumpf ◽

Jules F. Cacho ◽

DoKyoung Lee ◽

Cheng-Hsien Lin ◽

...

Keyword(s):

Remote Sensing ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Environmental Sustainability ◽

Vegetation Index ◽

Mean Squared Error ◽

Spectral Band ◽

Great Promise ◽

Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

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Soybean Yield Estimation and Its Components: A Linear Regression Approach

Agriculture ◽

10.3390/agriculture10080348 ◽

2020 ◽

Vol 10 (8) ◽

pp. 348

Author(s):

Marcelo Chan Fu Wei ◽

José Paulo Molin

Keyword(s):

Linear Regression ◽

Mean Squared Error ◽

Limiting Factors ◽

Training Dataset ◽

Coefficient Of Determination ◽

Validation Dataset ◽

Learning Approaches ◽

Linear Regression Models ◽

Yield Estimation ◽

Soybean Yield

Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1.

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Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽

10.3390/agronomy10030327 ◽

2020 ◽

Vol 10 (3) ◽

pp. 327 ◽

Cited By ~ 4

Author(s):

Remy Fieuzal ◽

Vincent Bustillo ◽

David Collado ◽

Gerard Dedieu

Keyword(s):

Relative Error ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wheat Yield ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Optical Images ◽

Multi Temporal ◽

Input Variables ◽

Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

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An Insight of Linear Regression Analysis

Scientific Research Journal ◽

10.24191/srj.v15i2.9347 ◽

2018 ◽

Vol 15 (2) ◽

pp. 1

Author(s):

Set Foong Ng ◽

Yee Ming Chew ◽

Pei Eng Chng ◽

Kok Shien Ng

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Regression Model ◽

Hypothesis Testing ◽

Linear Regression Analysis ◽

Testing Procedure ◽

Coefficient Of Determination ◽

P Value ◽

Hypothesis Testing Procedure ◽

Independent Variable

Regression models are developed in various field of applications to help researchers to predict certain variables based on other predictor variables. The dependent variables in the regression model are estimated by a number of independent variables. Model utility test is a hypothesis testing procedure in regression to verify if there is a useful relationship between the dependent variable and the independent variable. The hypothesis testing procedure that involves p-value is commonly used in model utility test. A new technique that involves coefficient of determination R2 in model utility test is developed in this paper. The effectiveness of the model utility test in testing the significance of regression model is evaluated using simple linear regression model with the significance level α = 0.01, 0.025 and 0.05. The study in this paper shows that a regression model that is declared to be a significant model by using model utility test, however it fails to guarantee a strong linear relationship between the independent variable and dependent variable. Based on the evaluation presented in this paper, it is shown that the p-value approach in model utility test is not a good technique in evaluating the significance of a regression model. The results of this study could serve as a reference for other researchers applying regression analysis in their studies.

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An Insight of Linear Regression Analysis

Scientific Research Journal ◽

10.24191/srj.v15i2.5477 ◽

2018 ◽

Vol 15 (2) ◽

pp. 1

Author(s):

Set Foong Ng ◽

Yee Ming Chew ◽

Pei Eng Chng ◽

Kok Shien Ng

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Regression Model ◽

Hypothesis Testing ◽

Linear Regression Analysis ◽

Testing Procedure ◽

Coefficient Of Determination ◽

P Value ◽

Hypothesis Testing Procedure ◽

Independent Variable

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Wavelet discrete transform, ANFIS and linear regression for short-term time series prediction of air temperature

International Journal of Advances in Intelligent Informatics ◽

10.26555/ijain.v3i2.101 ◽

2017 ◽

Vol 3 (2) ◽

pp. 68 ◽

Cited By ~ 1

Author(s):

Devi Munandar

Keyword(s):

Time Series ◽

Linear Regression ◽

Time Series Data ◽

Mean Squared Error ◽

Pearson Correlation ◽

Time Series Prediction ◽

Coefficient Of Determination ◽

Series Data ◽

Discrete Wavelet ◽

Inference System

This paper investigates the ability of Discrete Wavelet Transform and Adaptive Network-Based Fuzzy Inference System in time-series data modeling of weather parameters. Plotting predicted data results on Linear Regression is used as the baseline of the statistical model. Data were tested in every 10 minutes interval on weather station of Bungus port in Padang, Indonesia. Mean absolute errors (MAE), the coefficient of determination (R2), Pearson correlation coefficient (r) and root mean squared error (RMSE) are used as performance indicators. The result of Plotting ANFIS data against linear regression using 1-input data is the optimal values combination of output predictions.

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Using SPOT-7 for Nitrogen Fertilizer Management in Oil Palm

Agriculture ◽

10.3390/agriculture10040133 ◽

2020 ◽

Vol 10 (4) ◽

pp. 133

Author(s):

Mohammad Yadegari ◽

Redmond R. Shamshiri ◽

Abdul Rashid Mohamed Shariff ◽

Siva K. Balasundram ◽

Benjamin Mahns

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Accuracy Assessment ◽

Vegetation Indices ◽

Coefficient Of Determination ◽

P Value ◽

Statistical Parameters ◽

N Content ◽

Regression Formula ◽

Non Destructive

Environmental concerns are growing about excessive applying nitrogen (N) fertilizers, especially in oil palm. Some conventional methods which are used to assess the amount of nutrient in oil palm are time-consuming, expensive, and involve frond destruction. Remote sensing as a non-destructive, affordable, and efficient method is widely used to detect the concentration of chlorophyll (Chl) from canopy plants using several vegetation indices (VIs) because there is an influential relation between the concentration of N in the leaves and canopy Chl content. The objectives of this research are to (i) evaluate and compare the performance of various vegetation indices (VIs) for measuring N status in oil palm canopy using SPOT-7 imagery (AIRBUS Defence & Space, Ottobrunn, Germany) to (ii) develop a regression formula that can predict the N content using satellite data to (iii) assess the regression formula performance on testing datasets by testing the coefficient of determination between the predicted and measured N contents. SPOT-7 was acquired in a 6-ha oil palm planted area in Pahang, Malaysia. To predict N content, 28 VIs based on the spectral range of SPOT-7 satellite images were evaluated. Several regression models were applied to determine the highest coefficient of determination between VIs and actual N content from leaf sampling. The modified soil-adjusted vegetation index (MSAVI) generated the highest coefficient of determination (R2 = 0.93). MTVI1 and triangular VI had the highest second and third coefficient of determination with N content (R2 = 0.926 and 0.923, respectively). The classification accuracy assessment of the developed model was evaluated using several statistical parameters such as the independent t-test, and p-value. The accuracy assessment of the developed model was more than 77%.

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Modeling yield and backscatter using satellite derived biophysical variables of rice crop based on Artificial Neural Networks

Journal of Agrometeorology ◽

10.54386/jam.v22i1.120 ◽

2021 ◽

Vol 22 (1) ◽

pp. 41-47

Author(s):

MAHESH PALAKURU ◽

SIRISHA ADAMALA ◽

HARISH BABU BACHINA

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Mean Squared Error ◽

Rice Yield ◽

Absolute Error ◽

Coefficient Of Determination ◽

Yield Model ◽

Area Index ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this study, ‘observed rice yield (ton acre-1)’ and ‘remotely sensed backscatter’are modelled using artificial neural network (ANN) and multiple linear regression (MLR) methods for East and West Godavari districts of Andhra Pradesh in India. The biophysical variables viz. backscatter (bs), normalized difference vegetation index (NDVI), Chlorophyll (chfl), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), canopy water content (CWC), and fraction of vegetation cover (Fcover) were derived from Scatterometer Satellite-1 (SCATSAT-1), Moderate Imaging Spectrometer (MODIS) and Sentinel-2 satellite data.Inputs selected are bs, NDVI, chfl, FAPAR, LAI, CWC, and Fcover for rice yield model, whereas NDVI, chfl, FAPAR, LAI, CWC, and Fcover are inputs for backscatter models. The performance of ANN and MLR models was evaluated using three indices such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results concluded that the ANN models achieved R2 of 0.908 and 0.884 which are 42.73% and 28.85% higher than that of the MLR method for rice yield and backscatter, respectively.

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ANALISIS PENGARUH CUSTOMER EXPERIENCE TERHADAP PEMBELIAN ULANG (Studi Kasus pada Konsumen Restoran Gudeg Yu Djum Jln. Kaliurang Km. 5 Koncoran Gg. Sri Katon 2 Yogyakarta)

Jurnal Fokus Manajemen Bisnis ◽

10.12928/fokus.v5i1.1618 ◽

2020 ◽

Vol 5 (1) ◽

pp. 84

Author(s):

Muhamad Zaenuri ◽

Aftoni Sutanto

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Sampling Method ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Coefficient Of Determination ◽

P Value ◽

Analysis Method ◽

Validity And Reliability ◽

Regression Methods

This study aims to determine the effect of the repurchase of Gudeg Yu Djum restaurant. The sample used in this study amounted to 100 respondents, while the sampling method used was purposive sampling method. Data analysis method used is quantitative analysis using validity and reliability tests, F tests, coefficient of determination, t tests and multiple linear regression analysis. Analysis tools using multiple linear regression methods can be concluded that the sense variable has no significant effect on repurchases with a significance value (P value) of 0.840> 0.05, the feel variable has a significant effect on repeat purchases with a significance value (P value) of 0.007 <0 , 05, think variable has a significant effect on repurchase with a significance value (P value) of 0.005 <0.05, Act variable has no significant effect on repurchase with a significance value (P value) 0.945> 0.05, the relate variable has a significant effect on repurchase with a significance value (P value) 0.042 <0.05. Simultaneously sense, feel, think, act, and relate, significantly influence the repurchase with an F count of 22,010 with a significant number (P value) 0,000 <0.005. The coefficient of determination generated is 0.515, which means 51.5% change in the repurchase variable is explained by the variables sense, feel, think, act, and relate together, while the remaining 48.5% is explained by other variables not included in this research.

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