气候变化领域集成服务门户(CCPortal): Generating high-temporal and spatial resolution TIR image data

CCPortal

DOI	10.1016/j.jag.2019.01.016
	Generating high-temporal and spatial resolution TIR image data
	Herrero-Huerta M.; Lagüela S.; Alfieri S.M.; Menenti M.
发表日期	2019
ISSN	15698432
起始页码	149
结束页码	162
卷号	78
英文摘要	Thermal InfraRed (TIR) image data at high temporal and spatial resolution are required to monitor the rapid development of crops during the growing season, taking into account the fragmentation of most agricultural landscapes. Moreover, integrating high-resolution satellite TIR data to calibrate hydrological models is a powerful information to efficiently monitor crop water use. Conversely, no single sensor meets these combined requirements in the TIR spectral region. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing image data to meet the combined requirements on spatial and temporal resolution. A novel spatio-temporal data fusion workflow based on a multi-sensor multi-resolution algorithm was developed and applied to generate TIR synthetic image data at high temporal and spatial resolution. The workflow includes two steps: in the first step, synthetic daily radiance images at Top of Atmosphere (TOA) and 30-m spatial resolution (at the ground) are generated using TIR radiometric data at TOA collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) daily 1-km and Landsat 8/TIRS 16-day 30-m. This procedure is applied to two image pairs on different dates. The workflow yields an estimator to generate TIR TOA radiance data on any given date, provided a MODIS radiance image is available. The next step applies constrained unmixing of the 30 m (now considered as low-resolution) TIR images using the information about sub-pixel land-cover obtained from co-registered images at higher spatial resolution in the VNIR (Visible Near InfraRed) spectrum. In our case study, the L8/TIRS synthetic image data were unmixed to the Sentinel 2/MSI with 10 m × 10 m spatial resolution. Two geographically diverse experiments were carried out using the same procedure: one in The Netherlands to evaluate the procedure and other in Puglia (Italy) to generate a time series of the 10-m × 10-m TIR image data product. The validation experiment, where an actual TIRS image was applied as a reference, gave a RMSE value of 35.3 W/(m 2 μm sr), which corresponds to a relative value of 8.5% against the TIRS reference values. The results confirm the feasibility of the proposed methodology, which yields a synthetic thermal band to integrate with the multi-spectral data provided by the S2/MSI at 10 m resolution. © 2019 Elsevier B.V.
英文关键词	Data fusion; Image processing; Multi-sensor system; Spectral unmixing; Thermal infrared data
语种	英语
scopus关键词	algorithm; data processing; image processing; infrared imagery; MODIS; sensor; spatial resolution; top of atmosphere
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156481
作者单位	Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, CN 2628, Netherlands; Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Avila, Hornos Caleros 50, Avila, 05003, Spain; Department of Agronomy, Purdue UniversityIN 47906, United States; Remote Sensing and Digital Earth Institute, Chinese Academy of Sciences, Beijing, 100101, China; Applied Geotechnologies Research Group, University of Vigo, Mining and Energy School, Campus Universitario Lagoas-Marcosende, Vigo, 36310, Spain
推荐引用方式 GB/T 7714	Herrero-Huerta M.,Lagüela S.,Alfieri S.M.,et al. Generating high-temporal and spatial resolution TIR image data[J],2019,78.
APA	Herrero-Huerta M.,Lagüela S.,Alfieri S.M.,&Menenti M..(2019).Generating high-temporal and spatial resolution TIR image data.International Journal of Applied Earth Observation and Geoinformation,78.
MLA	Herrero-Huerta M.,et al."Generating high-temporal and spatial resolution TIR image data".International Journal of Applied Earth Observation and Geoinformation 78(2019).