Abstract
Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.
Highlights
Worldwide urban expansions are developing at various spatiotemporal scales under the rapid population and economy growth [1,2,3,4]
There is different research focusing on the new data fusion algorithms to generate higher spatial and temporal resolution land surface factors, there are still few documents to indicate simultaneously the land surface temperature (LST) dynamics and the driving forces by combining the Landsat-like data Normalized Difference Vegetation Index (NDVI) and Normalized Difference Vegetation Index (NDBI)
The implementation process was divided into six steps: (1) classify Landsat LST at time T1; (2) estimate the temporal changes taking place for each class of coarse-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) LST from T1 to T2; (3) predict the fine-resolution LST at T2 based on predicted temporal changes and calculate pixel residuals of MODIS LST; (4) use the thin plate spline (TPS) interpolation function to predict the high-spatial-resolution LST based on the MODIS LST at T2; (5) allocate the residuals to predicted high-spatial-resolution LST with the TPS interpolation function; and (6) generate final fine resolution “Landsat-like” LST at T2 based on the weights of pixels in the moving windows, which are assigned by nearest-neighborhood information
Summary
Worldwide urban expansions are developing at various spatiotemporal scales under the rapid population and economy growth [1,2,3,4]. The low spatial resolution of MODIS scans can only be used to coarse-scale research It is insufficient for producing detailed descriptions of LST variation or for analyzing thermal driving forces [39]. By utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Shen et al [45] obtained “Landsat-like” LST datasets from 1988 to 2013 and analyzed the thermal mechanism of the Wuhan city urban heat island. There is different research focusing on the new data fusion algorithms to generate higher spatial and temporal resolution land surface factors, there are still few documents to indicate simultaneously the LST dynamics and the driving forces by combining the Landsat-like data Normalized Difference Vegetation Index (NDVI) and Normalized Difference Vegetation Index (NDBI). LST: land surface temperature; MODIS: Moderate Resolution Imaging Spectroradiometer; TM: Thematic Mapper; ETM+: Enhanced Thematic Mapper Plus; OLI-TRIS: Operational Land Imager/Thermal Infrared
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