气候变化领域集成服务门户(CCPortal): Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances

CCPortal

DOI	10.1016/j.rse.2020.112055
	Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances
	Xue J.; Anderson M.C.; Gao F.; Hain C.; Sun L.; Yang Y.; Knipper K.R.; Kustas W.P.; Torres-Rua A.; Schull M.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	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 co-collected 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. © 2020 The Authors
英文关键词	Data Mining Sharpener; Evapotranspiration; Land surface temperature; Remote sensing; Thermal sharpening
语种	英语
scopus关键词	Agricultural robots; Agriculture; Alignment; Antennas; Atmospheric temperature; Data visualization; Electric substations; Land surface temperature; Radiometers; Reflection; Remote sensing; Space stations; Surface measurement; Surface properties; Thermography (imaging); Unmanned aerial vehicles (UAV); Agricultural monitoring; Agricultural water use; Diagnostic indicators; Ortho-rectification; Spatio-temporal resolution; Surface reflectance; Thermal infrared bands; Visible infrared imaging radiometer suites; Data mining; detection method; land surface; Landsat; satellite data; spatial resolution; spatiotemporal analysis; surface reflectance; surface temperature; VIIRS; California; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179128
作者单位	USDA, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, United States; NASA, Marshall Space Flight Center, Earth Science Office, Huntsville, AL 35805, United States; Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, United States; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, United States
推荐引用方式 GB/T 7714	Xue J.,Anderson M.C.,Gao F.,et al. Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances[J],2020,251.
APA	Xue J..,Anderson M.C..,Gao F..,Hain C..,Sun L..,...&Schull M..(2020).Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances.Remote Sensing of Environment,251.
MLA	Xue J.,et al."Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances".Remote Sensing of Environment 251(2020).