Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images

Abstract

Different types of methods have been developed to retrieve vegetation attributes from remote sensing data, including conventional empirical regressions (i.e., linear regression (LR)), advanced empirical regressions (e.g., multivariable linear regression (MLR), partial least square regression (PLSR)), machine learning (e.g., random forest regression (RFR), decision tree regression (DTR)), and radiative transfer modelling (RTM, e.g., PROSAIL). Given that each algorithm has its own strengths and weaknesses, it is essential to compare them and evaluate their effectiveness. Previous studies have mainly used single-date multispectral imagery or ground-based hyperspectral reflectance data for evaluating the models, while multi-seasonal hyperspectral images have been rarely used. Extensive spectral and spatial information in hyperspectral images, as well as temporal variations of landscapes, potentially influence the model performance. In this research, LR, PLSR, RFR, and PROSAIL, representing different types of methods, were evaluated for estimating vegetation chlorophyll content from bi-seasonal hyperspectral images (i.e., a middle- and a late-growing season image, respectively). Results show that the PLSR and RFR generally performed better than LR and PROSAIL. RFR achieved the highest accuracy for both images. This research provides insights on the effectiveness of different models for estimating vegetation chlorophyll content using hyperspectral images, aiming to support future vegetation monitoring research.

A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data

“…These may be broadly categorised into two: conventional regression methods and machine learning techniques [31]. A major limitation of conventional techniques, such as linear regression (MLR), is that they assume an explicit relationship between measured biophysical parameters and spectral observations, thus limiting their applicability to spatially complex datasets [32]. Recently, machine learning regression techniques such as support vector machines (SVM), random forest (RF), artificial neural network (ANN), partial least squares (PLS), and decision trees (DT) have gained popularity for their high performance in computing, quantifying, and understanding complex processes in agricultural applications [33].…”

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

“…To train the generated LUT database, a variety of ML algorithms have been introduced into hybrid methods for retrieving canopy traits. Among ML algorithms, Random Forest Regression (RF) and Gaussian Process Regression (GPR) have been well applied in several studies, due to their robustness and efficient implementation [39,[43][44][45][46][47][48][49]. RF is a regression tree-based ensemble algorithm which can handle several input variables without overfitting while also being less sensitive to outliers and noise [50,51].…”

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

“…However, it has been reported that the outputs from these instruments can be obscured depending on the leaf thickness, as it affects light transmission and scattering [20]. Hyperspectral remote sensing, which mainly concentrates on visible-near infrared (400-1000 nm) light and sometimes contains short-wave infrared ranges (1000-2500 nm), offers some alternative methods to monitor biochemical properties such as chl [21][22][23][24]. Besides the biochemical properties, some narrow wavebands possess high sensitivity to subtle changes in plants caused by stress or diseases, effectively detecting various stress or disease indicators [25][26][27][28].…”