Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China

By Huanfeng Shen, Liwen Huang, Liangpei Zhang, Penghai Wu, Chao Zeng. 2016

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey of data fusion in smart city applications (2019, methodology): “[76] 3 4 3 8,9 5 3 Landscape Rating Systems [106] 3 1 1 9 5 3 City Environment Monitoring [78] 3 1 2 2,9 1 1 City Building Map Modeling [120] 3 4 4 4 5 1 Forest Types Classification [46] 2 1 1 1 5 1 Long Term Landscape Monitoring [121] 4 1 4 4 5 1 Forest Species Classification [122] 3 4 2 4 1 1 Wast”
Spatio-temporal fusion for daily Sentinel-2 images (2018, background): “…Index (NDVI) (Gao et al., 2015; Rao et al., 2015), Normalized 468 Difference Water Index (NDWI) (Jarihani et al., 2014), LST (Weng et al., 2014; Shen et al., 2016), 469 evapotranspiration (Bhattarai et al., 2014; Ke et al., 2017), LAI (Dong et al., 2016; Houborg et al., 2016), and 470 GPP…”
Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives (2018, background): “The Landsat archive has provided LST observations for more than 30 years at a high spatial resolution, but the long revisiting cycle impedes the wide application in detecting the SUHI’s inter-annual variability of SUHI [119,213].”