Location: Hydrology and Remote Sensing LaboratoryTitle: Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances
|XUE, J. - US Department Of Agriculture (USDA)|
|HAIN, C. - Nasa Marshall Space Flight Center|
|SUN, L. - Chinese Academy Of Agricultural Sciences|
|YANG, Y. - US Department Of Agriculture (USDA)|
|Kustas, William - Bill|
|TORRES, A. - Utah State University|
|SCHULL, M.A. - University Of Maryland|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/23/2020
Publication Date: 9/8/2020
Citation: Xue, J., Anderson, M.C., Gao, F.N., Hain, C., Sun, L., Yang, Y., Knipper, K.R., Kustas, W.P., Torres, A., Schull, M. 2020. Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances. Remote Sensing of Environment. 251:112055. https://doi.org/10.1016/j.rse.2020.112055.
Interpretive Summary: Thermal infrared imagery collected from satellite platforms has proven to be a valuable input to remote sensing models estimating crop water use and stress. Landsat provides thermal imaging at 100 m spatial resolution, allowing us to map crop water use at sub-field scales – a useful input to agricultural water management decision support systems. Landsat does not provide imagery frequently enough for this purpose (one image every 8-16 days or longer) so thermal imaging from other sensors is used to improve the frequency of crop water use mapping for operational applications in agriculture. This paper describes a technique for bringing thermal images from sensors on multiple satellite platforms to a common spatial resolution such that water use can be mapped more continuously in time. The technique is applied to data collected by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In combination, ECOSTRESS and VIIRS can significantly augment the temporal coverage provided by Landsat.
Technical Abstract: Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180-270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data.