Evaluation of Landsat 9 and Sentinel 2 satellites in estimation of cotton leaf area index using M5 tree model
Subject Areas : Journal of Radar and Optical Remote Sensing and GISmaryam mazidi 1 , موسی حسام 2 , khalil ghorbani 3 , چوقی بایرام کمکی 4
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Keywords: data mining, decision tree regression, Landsat, Sentinel 2, leaf area index,
Abstract :
Objective: The purpose of this study is to evaluate and compare the performance of Landsat 9 and Sentinel-2 satellite data in estimating the LAI of cotton crops using the M5 tree regression model. It aims to determine the most accurate optical satellite and regression method for LAI prediction by integrating field data and spectral indices. The research underscores the role of advanced machine learning techniques in enhancing precision agriculture through satellite-based monitoring of vegetation growth dynamics.
Methods: Estimation of LAI, as a common vegetation cover index, through the collection and field measurements of LAI, and comparison with the multispectral data of Landsat 9 operational terrain imager and Sentinel 2 multispectral instrument were done. to extract LAI. The relationship between plant indices as an independent variable and field LAI as a dependent variable was searched using linear multivariate regression and M5 tree regression methods.
Results: LAI calculated with plant indices is not very accurate and needs to be modeled and recalculated using spectral indices. Considering the non-linearity of the relationship between LAI and spectral reflectance, linear multivariate regression showed almost satisfactory results, and in the best conditions, this relationship has a correlation coefficient of 75.46 and 72.91, with an error of 0.229 and 0.308, respectively, for the Landsat 9 and Sentinel 2. M5 tree model.
Conclusion: Landsat-2 combined with the M5 tree model provides superior accuracy for estimating cotton LAI, supporting advanced agricultural monitoring practices.
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