Strategies for the Efficient Estimation of Soil Moisture through Spectroscopy: Sensitive Wavelength Algorithm, Spectral Resampling and Signal-to-Noise Ratio Selection

It is found that the remote sensing parameters such as spectral range, spectral resolution and signal-to-noise ratio directly affect the estimation accuracy of soil moisture content. However, the lack of research on the relationship between the parameters and estimation accuracy restricts the prolongation of application. Therefore, this study took the demand for this application as the foothold for developing spectrometry. Firstly, a method based on sensitivity analysis of soil radiative transfer model-successive projection algorithm (SA-SPA) was proposed to select sensitive wavelengths. Then, the spectral resampling method was used to select the best spectral resolution in the corresponding sensitive wavelengths. Finally, the noise-free spectral data simulated by the soil radiative transfer model was added with Gaussian random noise to change the signal-to-noise ratio, so as to explore the influence of signal-to-noise ratio on the estimation accuracy. The research results show that the estimation accuracy obtained through the SA-SPA (RMSEP < 12.1 g kg−1) is generally superior to that from full-spectrum data (RMSEP < 14 g kg−1). At selected sensitive wavelengths, the best spectral resolution is 34 nm, and the applicable signal-to-noise ratio ranges from 150 to 350. This study provides technical support for the efficient estimation of soil moisture content and the development of spectrometry, which comprehensively considers the common influence of spectral range, spectral resolution and signal-to-noise ratio on the estimation accuracy of soil moisture content.

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.

iSHELL: a 1–5 micron R = 80,000 Immersion Grating Spectrograph for the NASA Infrared Telescope Facility

iSHELL is a 1.06–5.3 μm high spectral resolution spectrograph built for the 3.2 m NASA Infrared Telescope Facility (IRTF) on Maunakea, Hawaii. Dispersion is accomplished with a silicon immersion grating in order to keep the instrument small enough to be mounted at the Cassegrain focus of the telescope. The white pupil spectrograph produces resolving powers of up to about R ≡ λ/δλ = 80,000 (0.″375 slit). Cross-dispersing gratings mounted in a tiltable mechanism allow observers to select different wavelength ranges and, in combination with a slit wheel and Dekker mechanism, slit widths ranging from 0.″375 to 4.″0 and slit lengths ranging from 5″ to 25″. One Teledyne 2048 × 2048 HAWAII-2RG array is used in the spectrograph, and one Raytheon 512 × 512 Aladdin 2 array is used in a 1–5 μm slit viewer for object acquisition, guiding, and scientific imaging. iSHELL has been in productive regular use on IRTF since first light in 2016 September. In this paper we discuss details of the science case, design, construction and astronomical use of iSHELL.

Application of the space-based optical interferometer towards measuring cosmological distances of quasars.

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. It is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. As any of the basic observational parameters become larger, the relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

Ground Validation Experiment and Spectral Detection Capability Evaluation of Mars Mineralogical Spectrometer (MMS) Aboard HX-1 Orbiter

As a hyperspectral imager aboard the orbiter “HX-1” of China’s first Mars mission, the Mars Mineralogical Spectrometer (MMS) is designed with hyperspectral and multispectral operation modes to survey the mineral types and their distribution on the surface of Mars, and to study the overall chemical composition and evolution history of Mars. The multispectral modes of MMS are different from hyperspectral modes on the bands selection, spatial and spectral resolution, Signal-to-Noise Ratio (SNR) etc. So the spectral detection capability of each mode of MMS is also different. The ground validation experiment of MMS is conducted to evaluate the hyperspectral and multispectral data quality and detection capabilities. The main conclusions include: (1) The measured hyperspectra of typical mineral samples obtained by MMS agree well with the data acquired by the Standard Comparison Spectrometers (SCS) under the same measurement conditions, and the spectral uncertainty between MMS and SCS is less than 7% in the key spectral ranges ($0.7\sim2.2~\upmu \text{m}$ 0.7 ∼ 2.2 μm ). For some typical minerals, the absorption band positions deviation between MMS and SCS are within $0.69\sim14.86~\text{nm}$ 0.69 ∼ 14.86 nm , which are within the spectral resolution limits of MMS. (2) The six sets of band combinations designed for MMS multispectral modes are slightly superior to CRISM’s multispectral mode in terms of spectral resolutions and bands selection, the water-containing minerals will be more accurately distinguished and identified, such as montmorillonite and kaolinite. Besides, the SNR of each multispectral mode is greater than 400 in the 500–2600 nm spectral range, which meets the requirements for the subtle spectral characteristics of water-containing minerals. (3) Benefiting from the MMS ground validation experiment and the experience of the OMEGA and CRISM, it is recommended that MMS first adopt the spatial continuous 52-sample or 104-sample (spatial resolution is about $0.53\sim1.06~\text{km}$ 0.53 ∼ 1.06 km ) multispectral operation mode for typical minerals global mapping and finding target areas of interest. Then the 208-sample multispectral mode (spatial resolution is about $\sim265~\text{m}$ ∼ 265 m ) or 26-sample hyperspectral mode can be used to survey target areas of interest for the subtle mineral types characteristics and distribution. At last, 26-sample hyperspectral mode could be used to monitor the atmospheric composition of Mars by limb observations.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale

Methane (CH4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Remote sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of the instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance, and detector exposure times by using a realistic instrument noise model. In addition, we formally analyzed the impact of spectrally complex surface albedo features on retrievals using the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modeling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH4 plume over several natural and artificial surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 mole m−2 or 1.0 % of total atmospheric CH4 column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modeling framework provides the basis for assessing tradeoffs for future remote sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision, as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH4 concentration fields to determine methane sources accurately and efficiently at scale.

Evaluating daytime planetary boundary-layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions.

EVAA – das deutsche Konsortium zur Validierung der Wind- und Aerosolprodukte des ESA Satelliten Aeolus: Fallbeispiele und Langzeitstudien

&lt;p&gt;Der ESA-Satellit Aeolus wurde im August 2018 mit dem Ziel gestartet, durch globale Messungen von Windprofilen die Wettervorhersage zu verbessern. Dazu hat Aeolus das High-Spectral-Resolution (HSR) Doppler-Lidar ALADIN (Atmospheric Laser Doppler Instrument) an Bord, welches es erm&amp;#246;glicht, vertikale Profile einer Windkomponente (West-Ost) aktiv zu messen. Diese Messungen werden inzwischen von mehreren Wetterdiensten assimiliert und es konnte ein positiver Einfluss auf die Vorhersagen gezeigt werden. Zus&amp;#228;tzlich zu den Windprofilen k&amp;#246;nnen mit diesem Lidar auch Aerosol- und Wolkenprofile als Nebenprodukte gemessen werden. Es ist das erste Mal, dass so eine komplexe Technik vom Weltall aus zum Einsatz kommt und bedarf daher einer ausgiebigen Validierung.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Ein wichtiger Beitrag zur Validierung der Wind- und Aerosolprodukte von Aeolus wurde dabei in dem Kooperationsprojekt EVAA (Experimentelle Validierung und Assimilation von Aeolus-Beobachtungen) zwischen der Ludwig-Maximilians-Universit&amp;#228;t M&amp;#252;nchen, dem deutschen Zentrum f&amp;#252;r Luft- und Raumfahrt (DLR), dem Deutschen Wetterdienst (DWD) sowie dem Leibniz-Institut f&amp;#252;r Troposph&amp;#228;renforschung (TROPOS) geleistet. Anhand von bodengebundenen Wind- und Aerosol-Referenzmessungen als auch durch Radiosonden, konnten wichtige Erkenntnisse &amp;#252;ber den zeitlichen Verlauf sowie die Charakteristik des systematischen und zuf&amp;#228;lligen Fehlers der Aeolus-Beobachtungen gewonnen werden. Durch die Assimilation der Aeolus-Messungen im Wettermodell ICON des DWD, konnte ihr Einfluss auf die Wettervorhersage quantifiziert werden.&lt;/p&gt;&lt;p&gt;&lt;br&gt;In diesem Beitrag wollen wir die Ergebnisse von unseren Langzeit-Vergleichsmessungen mit Radiosonden in Leipzig, Punta Arenas (Chile) und Radar-Windprofilern &amp;#252;ber Deutschland pr&amp;#228;sentieren und das Potential und die Grenzen von Aeolus diskutieren. Um die Verbesserung der Wettervorhersage durch die neuartigen Windbeobachtungen zu quantifizieren, wird ihr Einfluss im Wettermodell ICON demonstriert.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Zus&amp;#228;tzlich werden wir einen Einblick in die M&amp;#246;glichkeiten der Aerosolprofilmessungen von Aeolus gegeben. Dazu wird als Beispiel der Transport von Rauchaerosol von den Br&amp;#228;nden in Kalifornien im Jahre 2020 bis nach Mitteleuropa diskutiert. Damals waren gro&amp;#223;e Mengen Rauch &amp;#252;ber Leipzig gemessen wurden, die f&amp;#252;r eine sichtliche Abschw&amp;#228;chung des Sonnenlichts sorgten. Diese Rauchschwaden konnten sowohl von Aeolus als auch mit einem bodengebundenen Forschungslidar, genannt PollyXT, beobachtet werden und sind daher ein hervorragendes Beispiel, um die Potentiale von Aeolus bzgl. Aerosol- und Wolkenmessungen zu diskutieren.&lt;/p&gt;