Estimating rainfed groundnut’s leaf area index using Sentinel-2 based on Machine Learning Regression Algorithms and Empirical Models

The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut. The study tests the performance of multiple machi...

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Vydané v:	Precision agriculture Ročník 25; číslo 3; s. 1404 - 1428
Hlavní autori:	Ekwe, Michael Chibuike, Adeluyi, Oluseun, Verrelst, Jochem, Kross, Angela, Odiji, Caleb Akoji
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2024 Springer Nature B.V
Predmet:	Accuracy Agricultural management Agriculture Algorithms Atmospheric Sciences Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Crop growth Empirical analysis Estimation Gaussian process Groundnuts Kernel functions Leaf area Leaf area index Learning algorithms Leaves Life Sciences Machine learning Normalized difference vegetative index Physics Regression Regression analysis Remote Sensing/Photogrammetry Seedlings Soil Science & Conservation Statistics for Engineering Support vector machines Vegetation Seedling stage Machine learning algorithms LAI Vegetation indices Sentinel-2
ISSN:	1385-2256, 1573-1618
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Abstract	The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut. The study tests the performance of multiple machine learning regression algorithms (MLRAs) and empirical vegetation indices (VIs) in retrieving groundnut's LAI using freely available Sentinel-2 data. The bands at 665 nm, 705 nm, 842 nm, and 2190 nm are the most sensitive for retrieving groundnut's LAI, according to an analysis of its band spectrum. Results suggest that VIs computed with wavebands centered at red (665 nm), red edge (705 nm), and near-infrared (842 nm) exhibited optimal R 2 with Sentinel-2 data. Normalized difference vegetation index (NDVI), red edge normalized difference vegetation index (NDVIre), simple ratio (SR), red edge simple ratio (SRre), and green normalized difference vegetation index (gNDVI) were utilized as predictors for LAI. Regarding the results of the validation between estimated and measured LAI, SR demonstrated the highest accuracy for groundnut LAI prediction (r 2 = 0.67, RMSE = 0.89). Ten MLRAs were tested, and results indicate from the perspective of the accuracy of models, the Gaussian processes regression, GPR (r 2 = 0.73 and RMSE = 0.81), Kernel ridge regression, KRR (r 2 = 0.72 and RMSE = 0.82) and Support vector regression, SVR (r 2 = 0.70 and RMSE = 0.85) demonstrated to be the most suitable for LAI estimation for rainfed groundnut at the seedling stage. The systematic analysis based on the regression approaches tested here revealed that the GPR outperformed other models combined, therefore, most suitable for estimating rainfed groundnut LAI at the seedling stage. These findings serve as a benchmark for obtaining crop biophysical parameters in the framework of groundnut traits monitoring in a tropical West Africa.
AbstractList	The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut. The study tests the performance of multiple machine learning regression algorithms (MLRAs) and empirical vegetation indices (VIs) in retrieving groundnut's LAI using freely available Sentinel-2 data. The bands at 665 nm, 705 nm, 842 nm, and 2190 nm are the most sensitive for retrieving groundnut's LAI, according to an analysis of its band spectrum. Results suggest that VIs computed with wavebands centered at red (665 nm), red edge (705 nm), and near-infrared (842 nm) exhibited optimal R2 with Sentinel-2 data. Normalized difference vegetation index (NDVI), red edge normalized difference vegetation index (NDVIre), simple ratio (SR), red edge simple ratio (SRre), and green normalized difference vegetation index (gNDVI) were utilized as predictors for LAI. Regarding the results of the validation between estimated and measured LAI, SR demonstrated the highest accuracy for groundnut LAI prediction (r2 = 0.67, RMSE = 0.89). Ten MLRAs were tested, and results indicate from the perspective of the accuracy of models, the Gaussian processes regression, GPR (r2 = 0.73 and RMSE = 0.81), Kernel ridge regression, KRR (r2 = 0.72 and RMSE = 0.82) and Support vector regression, SVR (r2 = 0.70 and RMSE = 0.85) demonstrated to be the most suitable for LAI estimation for rainfed groundnut at the seedling stage. The systematic analysis based on the regression approaches tested here revealed that the GPR outperformed other models combined, therefore, most suitable for estimating rainfed groundnut LAI at the seedling stage. These findings serve as a benchmark for obtaining crop biophysical parameters in the framework of groundnut traits monitoring in a tropical West Africa. The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut. The study tests the performance of multiple machine learning regression algorithms (MLRAs) and empirical vegetation indices (VIs) in retrieving groundnut's LAI using freely available Sentinel-2 data. The bands at 665 nm, 705 nm, 842 nm, and 2190 nm are the most sensitive for retrieving groundnut's LAI, according to an analysis of its band spectrum. Results suggest that VIs computed with wavebands centered at red (665 nm), red edge (705 nm), and near-infrared (842 nm) exhibited optimal R 2 with Sentinel-2 data. Normalized difference vegetation index (NDVI), red edge normalized difference vegetation index (NDVIre), simple ratio (SR), red edge simple ratio (SRre), and green normalized difference vegetation index (gNDVI) were utilized as predictors for LAI. Regarding the results of the validation between estimated and measured LAI, SR demonstrated the highest accuracy for groundnut LAI prediction (r 2 = 0.67, RMSE = 0.89). Ten MLRAs were tested, and results indicate from the perspective of the accuracy of models, the Gaussian processes regression, GPR (r 2 = 0.73 and RMSE = 0.81), Kernel ridge regression, KRR (r 2 = 0.72 and RMSE = 0.82) and Support vector regression, SVR (r 2 = 0.70 and RMSE = 0.85) demonstrated to be the most suitable for LAI estimation for rainfed groundnut at the seedling stage. The systematic analysis based on the regression approaches tested here revealed that the GPR outperformed other models combined, therefore, most suitable for estimating rainfed groundnut LAI at the seedling stage. These findings serve as a benchmark for obtaining crop biophysical parameters in the framework of groundnut traits monitoring in a tropical West Africa.
Author	Ekwe, Michael Chibuike Adeluyi, Oluseun Verrelst, Jochem Kross, Angela Odiji, Caleb Akoji
Author_xml	– sequence: 1 givenname: Michael Chibuike surname: Ekwe fullname: Ekwe, Michael Chibuike organization: Department of Geography, Planning and Environment, Concordia University, Department of Strategic Space Applications, National Space Research and Development Agency (NASRDA) – sequence: 2 givenname: Oluseun surname: Adeluyi fullname: Adeluyi, Oluseun organization: Department of Strategic Space Applications, National Space Research and Development Agency (NASRDA) – sequence: 3 givenname: Jochem surname: Verrelst fullname: Verrelst, Jochem organization: Image Processing Laboratory (IPL), Parc Científic, Universitat de València – sequence: 4 givenname: Angela surname: Kross fullname: Kross, Angela organization: Department of Geography, Planning and Environment, Concordia University – sequence: 5 givenname: Caleb Akoji surname: Odiji fullname: Odiji, Caleb Akoji organization: Department of Strategic Space Applications, National Space Research and Development Agency (NASRDA)
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Snippet	The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study...
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SubjectTerms	Accuracy Agricultural management Agriculture Algorithms Atmospheric Sciences Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Crop growth Empirical analysis Estimation Gaussian process Groundnuts Kernel functions Leaf area Leaf area index Learning algorithms Leaves Life Sciences Machine learning Normalized difference vegetative index Physics Regression Regression analysis Remote Sensing/Photogrammetry Seedlings Soil Science & Conservation Statistics for Engineering Support vector machines Vegetation
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Title	Estimating rainfed groundnut’s leaf area index using Sentinel-2 based on Machine Learning Regression Algorithms and Empirical Models
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