A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression

Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Remote sensing (Basel, Switzerland) Switzerland), 2023-04, Vol.15 (7), p.1740
Hauptverfasser:	Liang, Minggao, Zhang, Laifu, Wu, Sensen, Zhu, Yilin, Dai, Zhen, Wang, Yuanyuan, Qi, Jin, Chen, Yijun, Du, Zhenhong
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Climate change complex nonlinearity Digital Elevation Models geographical neural network weighted regression Global temperature changes Heterogeneity High resolution Land surface temperature Landforms Machine learning Methods Model accuracy Neural networks Nonlinear systems Normalized difference vegetative index Remote sensing Sensors Soil moisture spatial downscaling Spatial heterogeneity Special effects Temperature Temperature inversions Thermal environments Urban climatology Urban heat islands Vegetation
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (
ISSN:	2072-4292 2072-4292
DOI:	10.3390/rs15071740