Abstract
Land surface temperature (LST) plays a fundamental role in various geophysical processes at varying spatial and temporal scales. Satellite-based observations of LST provide a viable option for monitoring the spatial-temporal evolution of these processes. Downscaling is a widely adopted approach for solving the spatial-temporal trade-off associated with satellite-based observations of LST. However, despite the advances made in the field of LST downscaling, issues related to spatial averaging in the downscaling methodologies greatly hamper the utility of coarse-resolution thermal data for downscaling applications in complex environments. In this study, an improved LST downscaling approach based on random forest (RF) regression is presented. The proposed approach addresses issues related to spatial averaging biases associated with the downscaling model developed at the coarse resolution. The approach was applied to downscale the coarse-resolution Satellite Application Facility on Land Surface Analysis (LSA-SAF) LST product derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor aboard the Meteosat Second Generation (MSG) weather satellite. The LSA-SAF product was downscaled to a spatial resolution of ~30 m, based on predictor variables derived from Sentinel 2, and the Advanced Land Observing Satellite (ALOS) digital elevation model (DEM). Quantitatively and qualitatively, better downscaling results were obtained using the proposed approach in comparison to the conventional approach of downscaling LST using RF widely adopted in LST downscaling studies. The enhanced performance indicates that the proposed approach has the ability to reduce the spatial averaging biases inherent in the LST downscaling methodology and thus is more suitable for downscaling applications in complex environments.
Highlights
Land surface temperature (LST) plays a critical role in surface energy balance and partitioning and drives water and biogeochemical cycles [1,2,3]
Downscaling can be defined as the synergistic utilization of the complementary nature of the high-resolution visible near-infrared (VNIR) imagery and the coarse resolution thermal infra-red (TIR) imagery, sometimes supported by ancillary information to discern the spatial distribution of thermal elements at the resolution of the VNIR imagery
As highlighted in Hutengs and Vohland, [22], Yang et al [24] and Zhao et al [25], additional predictor variables, especially those that have an influence on albedo, surface emissivity, and solar insolation are vital for downscaling LST in complex environments
Summary
Land surface temperature (LST) plays a critical role in surface energy balance and partitioning and drives water and biogeochemical cycles [1,2,3]. Cognizant to the fundamental role of LST in the understanding of various geophysical processes, and the need to capture their spatial-temporal evolution, space-borne missions often include thermal infra-red (TIR) sensors dedicated to LST mapping. The applicability of LST products derived from these sensors for operational purposes is hampered by the inherent spatial-temporal trade-off in TIR imagery. Downscaling, referred to as disaggregation, has come to the fore as a cheaper alternative solution to the spatial-temporal trade-off problem associated with TIR imagery [14,15]. Statistical downscaling techniques based on the well-established relationship between LST and vegetation indices (VI) [16], such as the disaggregation procedure for radiometric surface temperature (DisTrad) [17], and the algorithm for sharpening thermal imagery (TsHARP) [18] have shown excellent performance in relatively homogeneous vegetation canopies. The assumptions made in the LST-VI feature space-based approach are rarely satisfied in fragmented landscapes undermining their performance [14,15,19]
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