Generating high-temporal and spatial resolution tir image data

Generating high-temporal and spatial resolution tir image data

Herrero Huerta, M. (TU Delft Optical and Laser Remote Sensing)
Lagüela, S. (Polytechnic School of Avila)
Alfieri, S.M. (TU Delft Optical and Laser Remote Sensing)
Menenti, M. (TU Delft Optical and Laser Remote Sensing; Chinese Academy of Sciences)

Lichti, D. (editor)
Weng, Q (editor)

2017-09-12

Remote sensing imagery to monitor global biophysical dynamics requires the availability of thermal infrared data at high temporal and spatial resolution because of the rapid development of crops during the growing season and the fragmentation of most agricultural landscapes. Conversely, no single sensor meets these combined requirements. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing data sets with better properties. A novel spatio-temporal data fusion model based on constrained algorithms denoted as multisensor multiresolution technique (MMT) was developed and applied to generate TIR synthetic image data at both temporal and spatial high resolution. Firstly, an adaptive radiance model is applied based on spectral unmixing analysis of. TIR radiance data at TOA (top of atmosphere) collected by MODIS daily 1-km and Landsat - TIRS 16-day sampled at 30-m resolution are used to generate synthetic daily radiance images at TOA at 30-m spatial resolution. The next step consists of unmixing the 30 m (now lower resolution) images using the information about their pixel land-cover composition from co-registered images at higher spatial resolution. In our case study, TIR synthesized data were unmixed to the Sentinel 2 MSI with 10 m resolution. The constrained unmixing preserves all the available radiometric information of the 30 m images and involves the optimization of the number of landcover classes and the size of the moving window for spatial unmixing. Results are still being evaluated, with particular attention for the quality of the data streams required to apply our approach.

Data fusion
Image processing
Multisensor system
Remote sensing
Spectral unmixing
Thermal infrared data

http://resolver.tudelft.nl/uuid:22328f6c-564d-4400-b18c-61472866c7ae

https://doi.org/10.5194/isprs-archives-XLII-2-W7-737-2017

ISPRS Geospatial Week 2017 Wuhan, China, 42

ISPRS Geospatial Week 2017, 2017-09-18 → 2017-09-22, Wuhan, China

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7

Institutional Repository

conference paper

© 2017 M. Herrero Huerta, S. Lagüela, S.M. Alfieri, M. Menenti