A Hybrid Approach for Extracting Large-Scale and Accurate Built-Up Areas Using SAR and Multispectral Data

Abstract

This article examines the use of multisensor data fusion for land classification in three Moroccan cities. The method employs a Random Forest classification algorithm based on multispectral, synthetic aperture radar (SAR), and derived land surface temperature (LST) data. The study compares the proposed approach to existing datasets on impervious surfaces (Global Artificial Impervious Area—GAIA, Global Human Settlement Layer—GHSL, and Global 30 m Impervious Surfaces Dynamic Dataset—GIS30D) using traditional evaluation metrics and a common training and validation dataset. The results indicate that the proposed approach has a higher precision (as measured by the F-score) than the existing datasets. The results of this study could be used to improve current databases and establish an urban data hub for impervious surfaces in Africa. The dynamic information of impervious surfaces is useful in urban planning as an indication of the intensity of human activities and economic development.

Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project

“…Currently, synthetic aperture radar is the only technology capable of penetrating cloud cover to acquire surface data. 24,25 However, challenges pertaining to cloud occlusion in MODIS LST products remain unresolved. Therefore, optimizing the use of remote sensing imagery to enhance the accuracy of LST data has emerged as a research priority in thermal infrared remote sensing.…”

“…In these areas, visible-light remote sensing can only identify cloud pixels without providing direct surface information. Currently, synthetic aperture radar is the only technology capable of penetrating cloud cover to acquire surface data 24 , 25 . However, challenges pertaining to cloud occlusion in MODIS LST products remain unresolved.…”