A Scale-Separating Framework for Fusing Satellite Land Surface Temperature Products

Yichen Yang,Xuhui Lee

doi:10.3390/rs14040983

Abstract

The trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as the urban heat island (UHI). Here, we developed a fusion framework, characterized by a scale-separating process, to generate LST data with high spatiotemporal resolution. The scale-separating framework breaks the fusion task into three steps to address errors at multiple spatial scales, with a specific focus on intra-scene variations of LST. The framework was experimented with MODIS and Landsat LST data. It first removed inter-sensor biases, which depend on season and on land use type (urban versus rural), and then produced a Landsat-like sharpened LST map for days when MOIDS observations are available. The sharpened images achieved a high accuracy, with a RMSE of 0.91 K for a challenging heterogeneous landscape (urban area). A comparison between the sharpened LST and the air temperature measured with bicycle-mounted mobile sensors revealed the roles of impervious surface fraction and wind speed in controlling the surface-to-air temperature gradient in an urban landscape.

Highlights

Land surface temperature (LST) is a key driver of surface-air energy exchanges [1].Satellite LST data are used in a large array of studies, ranging from climate change [2,3,4,5], to agriculture [6,7], forestry [8,9], hydrology [10,11], and ecology [12,13]
Being continuous in space and repetitive in time, satellite-based LST data are especially useful for studies of the urban heat island (UHI) [14,15,16].satellite thermal imagery always involves a tradeoff between spatial resolution and temporal coverage [17]
Sharpened LST data are affected by errors at three separate scales

Summary

Introduction

Land surface temperature (LST) is a key driver of surface-air energy exchanges [1].Satellite LST data are used in a large array of studies, ranging from climate change [2,3,4,5], to agriculture [6,7], forestry [8,9], hydrology [10,11], and ecology [12,13]. Being continuous in space and repetitive in time, satellite-based LST data are especially useful for studies of the urban heat island (UHI) [14,15,16].satellite thermal imagery always involves a tradeoff between spatial resolution and temporal coverage [17]. Landsat satellites acquire thermal imageries in fine spatial resolution (better than 120 m) every 16 days. MODIS LST data are provided daily but the spatial resolution is coarser (1 km). This trade-off is a technological bottleneck [18] that limits the utility of satellite LST data in characterizing intra-city variations of the UHI intensity. One solution to overcome the bottleneck is to develop scaling methods and produce LST data at a finer spatial resolution (e.g., the resolution of Landsat) from daily images acquired at a coarser spatial resolution (e.g., MODIS LST data products)

Results

Discussion

Conclusion