Spatial analysis of thermal anomalies from airborne multi-spectral data

Based on field thermal measurements, thermal anomalies caused by coal fires can be grouped into three categories, low- (up to 20°C above the background), medium- (20–120°C) and the high-amplitude (above 120?°C) ones. Night-time airborne thermal scanner data acquired in the 8–12.5?µm wavelength region of the electromagnetic spectrum clearly show the coal fire areas and the background areas. However, one of the disadvantages of the night-time data is the saturation of the medium- and high-amplitude thermal anomalies. In the daytime 8–12.5?µm image, the medium-amplitude thermal anomalies can be detected. These thermal anomalies only partly represent underground coal fires and some of them represent the solar-heated coal seams and black shale with higher emissivity. Daytime thermal infrared data acquired in the 3–5?µm wavelength region provide information from both the spectrally reflected solar radiation and radiation from high-intensity surface thermal anomalies of the underground coal fires. To reduce the effects of the spectrally reflected solar radiation, the data acquired in the 0.61–0.69?µm wavelength region were used to adjust the 3–5?µm data and the new image shows the enhanced high-amplitude thermal anomalies of the underground coal fires. The three kinds of data have been fused to integrate the background, low-, medium- and high-amplitude thermal anomalies, which are highly correlated to the field thermal measurements. On the basis of the spatial patterns of the thermal anomalies and the underground coal fire spreading models set up through field observation, the spreading direction of underground coal fires is inferred. Comparing the daytime and night-time 8–12.5?µm data, the solar-heated coal seams were detected as areas of high-risk for coal fires occurring in the future, because the temperatures in these areas measured in the field were close to the critical point of the spontaneous combustion of coal.     

Developed split window method to retrieve land surface temperature from HYTES thermal hyperspectral images using genetic algorithm

This article aims to establish a new method to retrieve land surface temperature (LST) from hyperspectral thermal emission spectrometer (HYTES) data with split window (SW) algorithm. First, the optimal bands of HYTES sensor were selected with the genetic algorithm and then were used in the SW algorithm. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapours (CWVs) and view zenith angle (VZA) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the root-mean-square error (RMSE) varies for different CWV and VZA, and with the increasing CWV and VZA, the RMSE value also increases. The emissivity, CWV, and VZA were also obtained for pixels. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapour demonstrated the good performance of the proposed algorithm. Finally, the new algorithm was applied to HYTES sensor data, and the LST was validated using LST product of HYTES sensor obtained by NASA. The results showed that the RMSE of the LST retrieval with the proposed algorithm and the LST product of sensor for data 1 and data 2 is 1.3 K and 1.6 K, respectively.     

Effect of surface wind speed and sensor view zenith angle dependence of emissivity on SST retrieval from thermal infrared data: ATSR

The Along Track Scanning Radiometer (ATSR) data are currently being processed at various places within the European community including the Rutherford Appleton Laboratory (RAL) in the U.K. In generating an atmospherically corrected sea-surface temperature (SST) field, the emissivity of the sea surface is assumed to be independent of the sensor view zenith angle, sea state and wavelength (Ian Barton, RAL, personal communication). The sensor view zenith angle dependence of the emissivity is generally not known because of the complications introduced by the surface wind speed. This paper attempts to evaluate the uncertainties introduced in the SST due to the variation of emissivity with the sensor view zenith angle and surface roughness generated by the wind speed.

Using the Cox and Munk formulation, Takashima and Takayama have simulated the sea water emissivities as a function of wind speed of up to 15ms^-1 and a range of the sensor view zenith angles. Their emissivity data for 11 and 12μm channels corresponding to the viewing geometry of the ATSR have been used in this work. It is shown that, depending on the value of the SST, there can be significant errors due to the sensor view zenith angle and sea surface roughness dependence of the emissivity. For example, if the SST is 10°C and the wind speed is 0ms^-1, then the errors due to the sensor view zenith angle dependence of the emissivity are shown to be 0·77°C and 0·55°C in 11 and 12μm channels, respectively, and at 15 ms^-l the respective errors reach about 1·ldeg K and 0·86 deg K. The errors due to the deviations of the emissivities from unity for nadir view in calm conditions are about 2·1°C and 3·5°C, respectively, in the 11 and 12μm channels. All of these errors are additive, indicating the importance of the calibration and validation.     

Integrating Corine Land Cover data and Landsat TM for surface emissivity definition: application to the urban area of Athens,Greece

This study examines the potential of the combined use of the land cover/land use information provided by the Corine Land Cover (CLC) database with Landsat satellite data for the definition and quantitative correlation of emissivity with various land covers and land uses that describe a certain territory. Surface emissivity in the 10.5–12.5 µm wavelength range is derived using Landsat data and the Normalized Difference Vegetation Index Thresholds method (NDVI^THM), whereas mean emissivity values for selected urban/non‐urban land cover types are estimated by integrating the emissivity image with the land cover vector data. The method is applied to the greater Athens area, Greece, in order to estimate the emissivity of various land cover types found within the urban setting. Analysis of variance (ANOVA) indicates statistically significant differences in emissivity associated with different land cover types. Furthermore, statistical results demonstrate that the method is very effective and can provide emissivity values of different land cover types with good accuracy and therefore can quantitatively link emissivity with surface type.     

Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data

In this paper, a theoretical study complementary to others given in the literature about the errors committed on the land surface temperature retrieved from the radiative transfer equation in the thermal infrared region by remote sensing techniques has been analysed. For this purpose, the MODTRAN 3.5 code has been used in order to simulate different conditions and evaluate the influence of several parameters on the land surface temperature accuracy: atmospheric correction, noise of the sensor, land surface emissivity, aerosols and other gaseous absorbers, angular effects, wavelength uncertainty, full‐width half‐maximum of the sensor and band‐pass effects. The results show that the most important error source is due to atmospheric effects, which leads to an error on surface temperature between 0.2 K and 0.7 K, and land surface emissivity uncertainty, which leads to an error on surface temperature between 0.2 and 0.4 K. Hence, assuming typical uncertainties for remote sensing measurements, a total error for land surface temperature between 0.3 K and 0.8 K has been found, so it is difficult to achieve an accuracy lower than these values unless more accurate in situ values for emissivity and atmospheric parameters are available.     

Simultaneous retrieval of aerosol and ocean properties by optimal estimation: SeaWiFS case studies for the Santa Barbara Channel

In this work, some sea viewing wide field‐of‐view sensor (SeaWiFS) images over the Santa Barbara Channel (SBC) are analysed. Pixel‐by‐pixel measurements of radiances at eight SeaWiFS channels and analytic Jacobians are simulated using a coupled atmosphere–ocean radiative transfer model. The inverse algorithm is based on optimal estimation with loosely constrained a priori data. The five‐element state vector has two aerosol (optical depth at 865 nm, bimodal fraction of particles) and three marine (chlorophyll concentration, detrital/dissolved‐matter absorption at 443 nm, and backscattering coefficient at 443 nm) parameters. The retrieval is stable and well posed; the results are smoother and show less spread than those derived from the standard SeaDAS v4.8 algorithm. For a 28 February 2003 SeaWiFS image, the average radiance residual is less than 1% for seven SeaWiFS channels, and less than 2% for the 765 nm channel. For a series of SBC SeaWiFS match‐up cases over a 4‐year period, estimated water‐leaving radiances agree well with field measurements.     

Thermal–infrared emissivities of natural surfaces: improvements on the experimental set-up and new measurements

Ground measurements of thermal infrared emissivities of terrestrial surfaces are required to derive accurate temperatures from radiometric measurements, and also to apply and validate emissivity models using satellite sensor observations. This paper focuses on the demanding aspects that are involved in the field measurement of emissivity using the box method and a hand-held radiometer. Measuring emissivities in field conditions can be hampered by external factors such as wind and solar irradiance. This can increase the time spent on the field campaign but, most importantly, it can cause no-sense fluctuations between consecutive observations. Here we propose original developments for the experimental instrumentation to ensure consistency of measurements. Moreover, we present a dataset of emissivity values for different soils, rocks and vegetation samples measured in the 8–14, 8.2–9.2, 10.5–1 1.5 and 11.5–12.5 µm wavebands.     

Liquid film thickness measurement underneath a gas slug with miniaturized sensor matrix in a microchannel

Measurement of liquid film thickness is essential for understanding the dynamics of two-phase flow in microchannels. In this work, a miniaturized sensor matrix with impedance measurement and MEMS technology to measure the thin liquid film underneath a bubble in the air–water flow in a horizontal microchannel has been developed. This miniaturized sensor matrix consists of 5 × 5 sensors where each sensor is comprised of a transmitter and a receiver electrode concentrically. The dimension and performance of the sensor electrodes were optimized with simulation results. The maximum diameter of the sensor ring is 310 µm, allowing a measurable range of liquid film thickness up to 83 µm. These sensors were distributed on the surface of a wafer with photolithography technology, covering a total length of 8 mm and a width of 2 mm. A spatial resolution of 0.5 × 2.0 mm² and a temporal resolution of 5 kHz were achieved for this sensor matrix with a measurement accuracy of 0.5 µm. A series of microchannels with different heights were used in the calibration in order to achieve the signal-to-thickness characteristics of each sensor. This delicate sensor matrix can provide detailed information on the variation of film thickness underneath gas–water slug directly, accurately and dynamically.     

Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons

Previous studies have shown that spectral signatures of hydrocarbon-bearing materials are characterized by prominent absorption features at 1.73 and 2.31?µm. Many other materials also show absorption features at wavelengths in the interval from 2.0 to 2.5?µm, yielding a mixed response in spectral signatures. In contrast to this wavelength range, most materials show similar spectral characteristics in the 1.73?µm range. Mainly hydrocarbon-bearing materials produce an absorption feature which is unique and prominent at 1.73?µm. A Hydrocarbon Index (HI) was developed and tested for the direct detection of hydrocarbons. The HI transforms multispectral data into a single image band that shows the distribution of hydrocarbons on the ground surface. The HI takes advantage of reflection differences around the 1.73?µm feature in hydrocarbon spectra. The HI indicates the presence of the 1.73?µm hydrocarbon absorption feature in a pixel spectrum. HI values can be easily calculated from radiance and reflectance data recorded by high signal-to-noise ratio hyperspectral scanners.     

Near-surface air temperature retrieval from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) data

Spatially distributed near-surface air temperature data are a very important input parameter for several land-surface models. Such data are often lacking because there are few traditional meteorological stations. It is of great significance in both theoretical research and practical applications to retrieve air temperature data from remote-sensing observations. Based on the radiative transfer theory, this article addresses the estimate of near-surface air temperature from data from the first Chinese operational geostationary meteorological satellite, FengYun-2C (FY-2C), in two thermal infrared channels (IR1, 10.3–11.3 μm and IR2, 11.5–12.5 μm) and the MODIS atmospheric profile (MOD07) product, which provide profiles of water vapour and air temperature in different atmospheric layers. The algorithm involves only two essential parameters (transmittance and emissivity). Sensitivity analysis of the algorithm has been performed for evaluation of probable near-surface air temperature estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of near-surface air temperature from FY-2C data. Results from the sensitivity analysis indicate that the average air temperature estimation error is less than 1.2 K for a possible transmittance error of 0.05 in both channels under an emissivity range 0.95–0.98. Assuming an error of 0.005 in ground emissivity for the two thermal channels, the average near-surface air temperature error is 0.6 K. Measured air temperature datasets have been used to validate the algorithm. All the validated data indicate that the estimate error is less than 3 K in more than 80% of the samples. The high accuracy for this dataset confirms the applicability of the proposed algorithm as an alternative method for accurate near-surface air temperature retrieval from FY-2C data.     

Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX field campaign

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land‐surface temperature (LST) and emissivity (LSE) from remotely‐sensed data. The proposed method is based on a synergistic usage of the split‐window (SW) algorithm and the two‐temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel‐2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied.     

Airborne FTIR measurements over the Mt Etna volcano: preliminary results from the 2003 FASA Campaign

From 16 to 26 July 2003 an extensive field campaign was carried out around Mt Etna, Sicily, Italy. During the campaign a new airborne system, Fire Airborne Spectral Analyzer (FASA), was tested. The main instrument of the payload is a high resolution Michelson Interferometer with Rotating Retroreflector (MIROR), which operates in the nominal infrared 600–3000 cm^?1 spectral range with a high resolution of 0.12 cm^?1. This work investigates the feasibility of using MIROR data to retrieve the surface temperature, the hyper‐spectral emissivity and the SO₂ volcanic plume abundance. The results for the surface parameters show a good agreement with those obtained by satellite data and on ground measurements. In particular, the spectral emissivity shows a meaningful minimum, around 1040 cm^?1, consistent with the basaltic properties of Mt Etna's surface. A sensitivity study has also been carried out to show the difficulties of SO₂ columnar abundance retrieval due to the too low SO₂ volcanic emission and the too high instrumental noise.     

SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping

This study focuses on mapping surface minerals using a new hyperspectral thermal infrared (TIR) sensor: the spatially enhanced broadband array spectrograph system (SEBASS). SEBASS measures radiance in 128 contiguous spectral channels in the 7.5- to 13.5-μm region with a ground spatial resolution of 2 m. In September 1999, three SEBASS flight lines were acquired over Virginia City and Steamboat Springs, Nevada. At-sensor data were corrected for atmospheric effects using an empirical method that derives the atmospheric characteristics from the scene itself, rather than relying on a predicted model. The apparent surface radiance data were reduced to surface emissivity using an emissivity normalization technique to remove the effects of temperature. Mineral maps were created with a pixel classification routine based on matching instrument- and laboratory-measured emissivity spectra, similar to methods used for other hyperspectral data sets (e.g. AVIRIS). Linear mixtures of library spectra match SEBASS spectra reasonably well, and silicate and sulfate minerals mapped remotely, agree with the dominant minerals identified with laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Though improvements in instrument calibration, atmospheric correction, and information extraction would improve the ability to map more pixels, these hyperspectral TIR data nevertheless show significant advancement over multispectral thermal imaging by mapping surface materials and lithologic units with subtle spectral differences in mineralogy.     

A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between ?80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively.     

Intercalibration of NOAA and Meteosat window channel brightness temperatures

This study presents an intercalibration of Meteosat‐5 11 µm channel and NOAA‐14 10.8 µm and 12.0 µm channels, and their comparison for sea and land pixels. The intercalibration empirical relation is derived for clear‐sky sea measurements, with similar zenith viewing angles. The root mean square difference between NOAA‐14 and Meteosat‐5 intercalibrated brightness temperatures is about 1.4 K (4.7 K) for all clear‐sky sea (land) pixels. The discrepancies over land are analysed in terms of viewing angle, surface type, terrain elevation and exposure to sunlight. The satellite viewing geometry is responsible for two major impacts, namely: the obstruction by neighbouring clouds towards one of the satellites; and differences in surface solar illumination viewed by each sensor. It is also shown that the higher discrepancies between intercalibrated temperatures occur for the most heterogeneous surfaces (e.g. Open Shrublands). The effect of terrain variability is not linear and depends strongly on the surface type.     

Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site

In this paper, a methodology using a single-channel and a two-channel method is presented to estimate the land surface temperature from the DAIS (Digital Airborne Imaging Spectrometer) thermal channels 74 (8.747?µm), 75 (9.648?µm), 76 (10.482?µm), 77 (11.266?µm), 78 (11.997?µm) and 79 (12.668?µm). The land surface temperature retrieved with both methods has been validated over the Barrax site (Albacete, Spain) in the framework of the DAISEX (Digital Airborne Imaging Spectrometer Experiment) field campaigns. Prior to the validation an analysis of the DAIS data quality has been performed in order to check the agreement between in situ data and the values extracted from the DAIS images supplied by the DLR (German Optoelectronic Institute). Suitable differences between in situ and DAIS data have been found. To solve this problem a linear re-calibration of the DAIS thermal channels has been applied using two ground calibration points (bare soil and water). For the land surface temperature retrieved, rms deviations of 0.96?K using a single-channel method and 1.46?K using a two-channel method with the DAIS thermal channels 77 and 78 have been obtained considering re-calibrated data.     

Analysis of a small agricultural watershed using remote sensing techniques

Salinization of land and sweet water is an increasing problem worldwide. In the Carpathian Basin, particularly in arid and semi‐arid regions, irrigation is a contributing factor to the secondary salinization problems, one of the major problems affecting soils in Hungary. Conventional broadband sensors such as SPOT, Landsat MSS, and Landsat ETM+ are not suitable for mapping soil properties, because their bandwidth of 100–200 mm cannot resolve diagnostic spectral features of terrestrial materials. Analytical techniques, developed for analysis of broadband spectral data, are incapable of taking advantage of the full range of information present in hyperspectral remote sensing imagery. In our pilot project in Tedej farm in the Great Plain Region, Hungary, the DAIS sensor was used to assess salinity risk, covering the spectral range from the visible to the thermal infrared wavelengths at 5 m spatial resolution, and other major indicators of soil salinization (NDVI, SAVI, canopy cover) were quantified with advanced remote sensing techniques using the TETRACAM ADC agricultural multispectral camera which offers red/green and NIR imaging at megapixel resolution. As a result, prominent absorption bands around 1450 nm and 1950 nm wavelength in most soil spectra are attributed to water and hydroxyl ions. Occasional weaker absorption bands caused by water also occur at 970, 1200, and 1700 nm. Absorption features near the 400 nm wavelength for all samples are also noticeable. Absorption bands at 1800 and 2300 nm are attributed to gypsum, while strong absorption features near 2350 nm are assigned to calcite (CaCo₃). Saline soils exhibited significantly higher reflectance values all throughout the 325–2500 nm wavelengths of the spectrum. Soils with a high amount of soluble salts gave a higher average reflectance than soils with a low salt content. In the project, an ADC camera‐based real‐time integrated system was developed to take advantage of more specialized spectral information and to provide even more accurate and useful data directly from the field. The results revealed that the NDVI and SAVI index and the canopy cover mapping taken with multispectral cameras can be useful as an indirect marker and help for detecting salinization. However, we did not find a strong correlation between NDVI and soil salinity. This is probably because the detection and assessment of lower levels of salinity are difficult, mainly owing to the nature of the remotely sensed images; with such images, it is not possible to obtain information on the third dimension of the 3‐D soil body. Also, the impact of salinity on electromagnetic properties needs to be explored further to understand how it can be derived indirectly from remotely sensed information. With the rapid validation of remotely sensed hyperspectral data, the decision in the future, with the best trade‐off between irrigation and sustainable land use made by agricultural specialists in this region, can be more environmentally sound and more accurate using the results from the pilot.     

Remote sensing ‘ground-based automatic UV / visible spectrometer’ for the study of atmospheric trace gases

An advance remote sensing instrument, the ‘ground-based automatic UV / visible spectrometer’, has been developed indigenously at Pune (18° 31′ N, 73° 55′ E) to cover the spectra (462–498 nm) of zenith sky scattered light. A spectrometry technique is used to find out the vertical column density (VCD) of many atmospheric trace gases, such as NO₂, O₃, H₂O and O₄. The VCDs of these gases are extracted from observed spectra by comparing the magnitude of the differential optical depth (DOD) of each species in the 462–498 nm spectral range. Slant column densities (SCDs) of each species are found to increase with solar zenith angle (SZA), due to the approaching higher path length of sunlight. The VCDs of O₃ and NO₂ derived by the UV / visible spectrometer are compared with the ozone monitoring instrument (OMI) Aura satellite and ground-based Brewer spectrometer data. The compared VCD values are found to be close to satellite and ground-based measurements.     

AVHRR split window temperature differences and total precipitable water over land surfaces

Measurements from the thermal infrared split window channels of the AVHRR sensor were investigated for their relationship to the total atmospheric water vapour amount over land surfaces. The difference in brightness temperature between the AVHRR channel 4 and 5 (10·3–11·3μm and 11·4–12·3μm respectively) was found to be a linear function of total precipitable water, for several stations in differing climatic regimes. For each individual location the total precipitable water was estimated with a standard error ranging from 0·22 to 0·48 cm for the complete range of conditions from wet to dry season or summer to winter. For mid-latitude continental locations there is very little influence of atmospheric aerosols on the relationship while for the African Sahel region the effect of large airborne particulates with a silicate component introduces a significant effect at large values of aerosol optical depth due to absorption. The influence of spectral emissivity variation in the split window region was also observed for arid regions where there is a significant quartz component to the soil. It is concluded that for regional retrieval of precipitable water, this technique provides sufficient accuracy for application to correction of near-infrared satellite data such as AVHRR channel 2 (0·71 –0·98 μm), however the site specific relation between T ₄-T ₅ and PW needs to be established with independent PW measurements.     

Laser polishing and 2PP structuring of inside microfluidic channels in fused silica

This study presents the development of post-processing steps for microfluidics fabricated with selective laser etching (SLE) in fused silica. In a first step, the SLE surface—even inner walls of microfluidic channels—can be smoothed by laser polishing. In addition, two-photon polymerization (2PP) can be used to manufacture polymer microstructures and microcomponents inside the microfluidic channels. The reduction in the surface roughness by laser polishing is a remelting process. While heating the glass surface above softening temperature, laser radiation relocates material thanks to the surface tension. With laser polishing, the RMS roughness of SLE surfaces can be reduced from 12 µm down to 3 nm for spatial wavelength λ < 400 µm. Thanks to the laser polishing, fluidic processes as well as particles in microchannels can be observed with microscopy. A manufactured microfluidic demonstrates that SLE and laser polishing can be combined successfully. By developing two-photon polymerization (2PP) processing in microchannels we aim to enable new applications with sophisticated 3D structures inside the microchannel. With 2PP, lenses with a diameter of 50 µm are processed with a form accuracy rms of 70 nm. In addition, this study demonstrates that 3D structures can be fabricated inside the microchannels manufactured with SLE. Thanks to the combination of SLE, laser polishing and 2PP, research is pioneering new applications for microfluidics made of fused silica.