Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters

Abstract

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472.

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

“…However, RF succeeded in separating these classes with a high user accuracy score. Similar results have been reported [28,54,56], claiming that RF possesses a strong generalization technique for LULC analysis, making it less sensitive to the small variations in input vector data.…”

“…On the other hand, NB and ANN showed bidirectional misclassification between the built-up and mining classes. These findings are in line with [46,56]. However, RF succeeded in separating these classes with a high user accuracy score.…”

“…Then, the total neuron values are subjected to the activation function, which determines the neurons that are going to contribute to the inputs of the next phase [55]; all the activated neurons transmit the inputs to the next phase in a process called forward propagation. The inputs are propagated through the network to the output phase, where the inputs are assigned to a corresponding class [56]. In addition, the misclassified data are backpropagated through the network model iteratively for revision by adjusting the weights.…”

Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

“…Then, the total neuron values are subjected to the activation function, which determines the neurons that are going to contribute to the inputs of the next phase [56]; all the activated neurons transmit the inputs to the next phase; the process is called forward propagation. The inputs propagated through the network to the output phase, where the inputs are assigned to a corresponding class [57]. In addition, the misclassified data is backpropagated through the network model iteratively for revision by adjusting the weights.…”