Compact meta-spectral image sensor for mobile applications

We have demonstrated a compact and efficient metasurface-based spectral imager for use in the near-infrared range. The spectral imager was created by fabricating dielectric multilayer filters directly on top of the CMOS image sensor. The transmission wavelength for each spectral channel was selected by embedding a Si nanopost array of appropriate dimensions within the multilayers on the corresponding pixels, and this greatly simplified the fabrication process by avoiding the variation of the multilayer-film thicknesses. The meta-spectral imager shows high efficiency and excellent spectral resolution up to 2.0 nm in the near-infrared region. Using the spectral imager, we were able to measure the broad spectra of LED emission and obtain hyperspectral images from wavelength-mixed images. This approach provides ease of fabrication, miniaturization, low crosstalk, high spectral resolution, and high transmission. Our findings can potentially be used in integrating a compact spectral imager in smartphones for diverse applications.

Global evaluation of the precipitable-water-vapor product from MERSI-II (Medium Resolution Spectral Imager) on board the Fengyun-3D satellite

Atmospheric water vapor plays a key role in Earth's radiation balance and hydrological cycle, and the precipitable-water-vapor (PWV) product under clear-sky conditions has been routinely provided by the advanced Medium Resolution Spectral Imager (MERSI-II) on board Fengyun-3D since 2018. The global evaluation of the PWV product derived from MERSI-II is performed herein by comparing it with PWV from the Integrated Global Radiosonde Archive (IGRA) based on a total of 462 sites (57 219 matchups) during 2018–2021. The monthly averaged PWV from MERSI-II presents a decreasing distribution of PWV from the tropics to the polar regions. In general, a sound consistency exists between PWV values of MERSI-II and IGRA; their correlation coefficient is 0.951, and their root mean squared error (RMSE) is 0.36 cm. The histogram of mean bias (MB) shows that the MB is concentrated around zero and mostly located within the range from −1.00 cm to 0.50 cm. For most sites, PWV is underestimated with the MB between −0.41 and 0.05 cm. However, there is also an overestimated PWV, which is mostly distributed in the area surrounding the Black Sea and the middle of South America. There is a slight underestimation of MERSI-II PWV for all seasons with the MB value below −0.18 cm, with the bias being the largest magnitude in summer. This is probably due to the presence of thin clouds, which weaken the radiation signal observed by the satellite. We also find that there is a larger bias in the Southern Hemisphere, with a large value and significant variation in PWV. The binned error analysis revealed that the MB and RMSE increased with the increasing value of PWV, but there is an overestimation for PWV smaller than 1.0 cm. In addition, there is a higher MB and RMSE with a larger spatial distance between the footprint of the satellite and the IGRA station, and the RMSE ranged from 0.33 to 0.47 cm. There is a notable dependency on solar zenith angle of the deviations between MERSI-II and IGRA PWV products.

Land Surface Temperature Retrieval from Fengyun-3D Medium Resolution Spectral Imager II (FY-3D MERSI-II) Data with the Improved Two-Factor Split-Window Algorithm

Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R2 of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy.

Aberration analysis of AOTF-based stereoscopic spectral imager using optical design software

Stereoscopic spectral imagers using acousto-optic tunable filters (AOTF) provide high-resolution narrow band images acquired from two viewpoints with different polarization in arbitrary spectral intervals, which allows obtaining three-dimensional hyperspectral models of inspected objects for many applications. We discuss modeling of acousto-optic (AO) cell for optical system design and introduce a program module for ray tracing through AO cell compatible with Zemax optical design software. A detailed study of the optical aberrations that limit the image quality in two AOTF-based stereoscopic systems implementing simultaneous AO diffraction of two differently polarized beams in single AO cell is presented. This approach may be used to design various AOTF-based imaging systems, but the limitations of ray tracing analysis should be considered.

Coffee plantation soil characterization using a multi-method approach near the Volcano Nevado del Ruiz, Colombian Central Cordillera

The presence of iron oxides may provide a sensitive indicator of the effects of cropping practices on coffee plantations. Authors characterized the mineral phases present in soil A horizons at three different farms located in the Department of Tolima within the regions of Líbano and Villahermosa. Our analysis includes X-ray diffraction, Mössbauer spectroscopy, and remote sensing to discriminate the distribution of the different magnetic mineral phases. X-ray diffraction was used to identify the mineralogical properties of iron oxide such as hematite, goethite, and ferrihydrite (Fh), as well as tectosilicate minerals such as albite and sanidine. Mössbauer spectroscopy results for samples taken at room temperature indicate the presence of Fe2+ and Fe3+ mineral phases, which possibly correspond to ilmenite or magnetite. Finally, Sentinel-2A multi-spectral imager (MSI) data was used to map the distribution of iron oxides and study the influence of their distribution throughout the study area. A high correlation between Mössbauer spectroscopy and Sentinel-2A MSI data exists throughout the study area. The results suggest that farms close to the main Nevado del Ruíz Volcano have a more significant mineralogical variability. In contrast, more distant farms are characterized by soils with more iron oxides, the product of weathering, erosion, and human activities.