Disaggregation of remotely sensed land surface temperature: A new dynamic methodology. Issue 18 (23rd September 2016)

• Record Type: Journal Article
• Authors: Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Zhu, Xiaolin; Gao, Lun; Zhou, Ji; Ju, Weimin
• Is Part Of: Journal of geophysical research. Volume 121:Issue 18(2016)
• Page Start: 10, 538
• Page End: 10, 554
• Publication Date: 2016-09-23
• Subjects: land surface temperature -- dynamic disaggregation -- diurnal temperature cycle -- annual temperature cycle -- temperature cycle model -- surface energy balance; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD024891
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The trade‐off between the spatial and temporal resolutions of satellite‐derived land surface temperature (LST) gives birth to disaggregation of LST (DLST). However, the concurrent enhancement of the spatiotemporal resolutions of LST remains difficult, and many studies disregard the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs. Here we propose a new dynamic methodology to enhance concurrently the spatiotemporal resolutions of satellite‐derived LSTs. This methodology conducts DLST by the controlling parameters of the temperature cycle models, i.e., the diurnal temperature cycle (DTC) model and annual temperature cycle (ATC) model, rather than directly by the LST. To achieve the conservation of thermal radiance between predisaggregated and postdisaggregated LSTs, herein we incorporate a modulation procedure that adds temporal thermal details to coarse resolution LSTs rather than straightforwardly transforms fine‐resolution scaling factors into LSTs. Indirect validations at the same resolution show that the mean absolute error (MAE) between the predicted and reference LSTs is around 1.0 K during a DTC; the associated MAE is around 2.0 K during an ATC, but this relatively lower accuracy is due more to the uncertainty of the ATC model. The upscaling validations indicate that the MAE is around 1.0 K and the normalized mean absolute error is around 0.3. Comparisons between the DTC‐ and ATC‐based DLST illustrate that the former retains a higher accuracy, but the latter holds a higher flexibility on days when background low‐resolution LSTs are unavailable. This methodology alters the static DLST into a dynamic way, and it is able to provide temporally continuous fine‐resolution LSTs; it will also promote the design of DLST methods for the generation of high‐quality LSTs. Key Points: A dynamic methodology that disaggregates the controlling parameters rather than LSTs is proposed for DLST Diurnal and annual temperature cycle models are used to help DLST A modulation process that adds thermal details to coarse LSTs is incorporated