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    Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances
    Remote Sensing of Environment (2020)
    Jie XueMartha C. AndersonFeng GaoChristopher HainLiang SunYun YangKyle KnipperWilliam P. KustasAlfonso F. Torres‐RuaMitch Schull
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    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 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.
    Keywords:
    Sharpening
    Orthophoto
    Topics:
    Urban Heat Island Mitigation
    Remote Sensing in Agriculture
    Plant Water Relations and Carbon Dynamics
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    Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients
    Remote Sensing of Environment (2004)
    Koen Mertens
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    Improved monitoring of bio-optical processes in coastal and inland waters using high spatial resolution channels on SNPP-VIIRS sensor
    Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2013)
    Ryan VandermeulenRobert ArnoneSherwin LadnerPaul Martinolich
    The dynamic and small-scale spatial variability of bio-optical processes that occurs in coastal regions and inland waters requires high resolution satellite ocean color feature detection. The Visual Infrared Imaging Radiometer Suite (VIIRS) currently utilizes five ocean color M-bands (410,443,486,551,671 nm) and two atmospheric correction M-bands in the near infrared (NIR; 745,862 nm) to produce ocean color products at a resolution of 750-m. VIIRS also has several high resolution (375-m) Imaging (I)-bands, including two bands centered at 640 nm and 865 nm. In this study, a spatially improved ocean color product is demonstrated by combining the 750-meter (M- channels) with the 375-m (I1-channel) to produce an image at a pseudo-resolution of 375-m. The new approach applies a dynamic wavelength-specific spatial ratio that is weighted as a function of the relationship between proximate I- and M-band variance at each pixel. This technique reduces sharpening artifacts by incorporating the native variability of the M-bands. In addition, this work examines the viability of replacing the M7-band (862 nm) with the I2-band (865 nm) to determine the atmospheric correction and aerosol optical depth at a higher resolution. These true (I-band) and pseudo (M-band) high resolution radiance values can subsequently be utilized as input parameters into various algorithms to yield high resolution optical products. The results show new capability for the VIIRS sensor for monitoring bio-optical processes in coastal waters.
    Ocean color
    SeaWiFS
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