A.

μSPOC principle and assets

We designed the NanoCarb payload to take advantage of the latest joint developments between ONERA and IPAG about compact spectrometry. Therefor it will be based on the integrated Fourier Transform Spectrometer (FTS) family so-called SPOC (SPectrometer On Chip) [4]. This concept consists in introducing a continuous modulation of the Optical Path Difference (OPD - δ -) in the chip in one direction, forming 2-waves interferometric fringes directly on the photo-sensitive surface. In the simple way the OPD modulation is introduced by an interferometric element, such as a glass slide or a prism glue to the sensor. The Fig. 1 show such a spectrometer, and we can see to the right an example of 2D-interferogram obtain with a monochromatic and uniform illumination (912nm strong line of Argon). A Fourier transform provides to obtain the incident light spectrum. By opposite to the SIFTI FTS [5], a μSPOC is a fully passive bulk spectrometer without mobile components. Hence it is potentially lesser sensitive to thermal and mechanical biases. It is also very simple to thermalized it with a drastic reduced power consumption due to its extreme compactness, which is promising for on-board spectrometry. The design Fig. 1 has been adapted in this way for the incoming ATISE nanosat mission of the Grenoble Spatial Center University (CSUG) to detect auroral emission lines in the upper atmosphere [6]. Parallel developments affecting the NanoCarb mission consist of an adaption of the concept to the spectro-imagery, with drastic miniaturization issues.

Fig. 1.

SPOC principle. Left: OPD modulation between glass prism and chip, responsible for 2-waves interferences on the sensor surface; Right: 2D-interferogram obtains with the strong line of Argon at 912 nm and a uniform illumination.

B.

Conversion into micro-imaging spectrometer for NanoCarb

We describe in the following paragraphs the SPOC imaging spectrometer of NanoCarb, giving its principle and the relevant design issues. Then we detail its implementation in a cubesat. To introduce some imagery feature we proceed to a discretization of the initially continuous OPD modulation. Thus the sensor is now divided into thumbnails or sub-apertures, each one coding for only one OPD of the interferogram. The field is imaged over the thumbnails and modulated according to the OPD. To retrieve the interferogram contrast at a given OPD for one point of the imaged field, we take advantage of the satellite scrolling over the time, inducing a pixel displacement on the thumbnail and so an intensity modulation according to the introduced OPD. A pixel line of the thumbnail corresponds to the ground satellite swath.

In the most up-to-date design, we expect to implement a 1024×1024 NGP sensor from SOFRADIR [7], sensitive from 400 nm to 3-4 μm with a Read Out Noise (RON) about 150 electrons. The photo-sensitive chip area will be divided into four 500×500 pixels independent sub-areas. Each one will be dedicated to the specific spectral bands described in the previous section, then divided into 100 50×50 pixels thumbnails (e.g. 100 OPD) as shown Fig. 2-right. The channel number 3 is not yet currently allocated and allows to adapt potentially the instrument to another spectral band for complementary measures as required (bias mitigation for example). One pixel represents a 3×3 km resolution element of the imaged field.

Fig. 2.

Left: optical design of the four band imaging spectrometer SPOC; Right: spectral band allocation over the chip. Each spectral band is divided into 100 square sub-apertures coding for 100 different OPD δ_k.

C.

Optical implementation

The Fig. 2-left shows the current optical design of the four-band imaging spectrometer. A very small 3 mm-diameter telescope is required in order to respect the Jaquinot criterion about aperture [4] and the field constraints. The device (including the chip) is easily contained in a 150×40×40 mm³ very compact area, which is also simple to be thermally regulated at 200 K.

Such a compact instrument is fully compatible with a 6U cubesat (30×20×10 cm³) as shown Fig. 3. About 2U are allocated to the optical bloc including the chip and the cooling device. The remaining space could be dedicated to the control electronic boards, communication and navigation systems, with most of the components available off-the-shelf. The estimated mass of the payload is about 6-7 kg, for a total power consumption of 10 W including cooling. We estimate a data flow about 21 Go/day with compression, and 3.6 Go/day with on-board data processing.

Fig. 3.

Imaging micro-SPOC implementation example in a 6U cubesat plateform from ISIS. IV. NUMERICAL SIMULATIONS

The main issue of the instrument design is now to identify some interest fringes on the interferogram, that will be sampled at the OPD δ_k. Thus we have to optimize the instrumental sensitivity to a given variation of the CO₂ or CH₄ concentration. We have also to identify in the interferogram some geophysical bias (aerosols) signatures. This study is based on numerical simulations, presented in the next section.

We simulated atmospheric absorption spectra at the ONERA using MODTRAN (Release 5.2.1) [8] radiative transfer code, in several typical observation scenarios. We use these synthetic spectra at IPAG to compute theoretical interferograms and derive sensibility and design assessments. In the following paragraphs we firstly give the simulation parameters, then the observational scenario. We finally present the theoretical interferogram in the interest spectral bands in order to discuss about measurement strategy for concentration retrieval.

MODTRAN 5 reaches a spectral resolution of 0.1 cm^-1 which is consistent with atmospheric absorption line width. In this paper we focus only on the measurement of CO₂ concentration in the averaged column density. We have not yet consistent simulated data with different profiles of concentrations of CH₄. The atmosphere composition model is the US standard. We simulated spectra with CO₂ concentration from 400 to 430 ppm. The nominal concentration is taken at 400 ppm, near to the actual annual and global averaged value. Different kind of aerosol models have been tested as reported in Tab. 1. We have reported also the averaged luminance in the spectral bands 1 and 2, obtained with a surface albedo of 0.3, and a solar zenithal angle of 20°. We compared these values to a CNES scenario and we could report it as a medium flux condition, such as above emerged land at a tempered latitude with vegetation for example.

Tab. 1.

Simulation parameters for MODTRAN radiative transfer code.

We present on Fig. 4 the simulated 1.6 μm (left) and 2.06 μm (right) bands without aerosol, for a nominal CO₂ concentration of 400 ppm. The 1.6 μm band presents several regularly spaced unsaturated absorption lines, allowing to measure the CO₂ concentration. On the opposite the lines at 2.06 μm are saturated. Nevertheless, they can potentially bring some information about scattering effects as we will discuss in the next section.

Fig. 4.

MODTRAN simulations of absorption lines of atmospheric CO₂ at 1.6 μm (Left) and 2.06 μm (Right), for a concentration of 400 ppm.

Finally, we computed on Fig. 5 the corresponding theoretical interferogram for the full 1.6 μm band. Given the limited number of OPD per spectral band on the spectrometer, we can’t sample all the interferogram and thus it is not possible to retrieve the full spectrum. Nevertheless, the FTS allows to recognize some specific motif in the spectrum. In our case we can observe Fig. 5 some bumps at 5.5 mm then 11 mm and 16.5 mm of OPD, corresponding in the dual space to the absorption line comb (period ~1.8 cm^-1). The fringe contrast in these region is directly proportional to the energy absorbed by CO₂, and thus to the CO₂ concentration (assuming small variations of concentration). Our samples will be placed between 5 and 6mm for the 1.6 μm band. In order to maximize the contrast in this region and the sensibility to the concentration of CO₂, we have to optimize the spectral window, that will be study in the next section. To conclude, we can also notice that a 1 ppm variation of column density CO₂ (goal) causes a luminance variation about 10^-11 W/cm²/sr/cm^-1 for a mean level about 10^-7W/cm²/sr/cm^-1 at 1.6 μm. We study also in the next section the capability of the instrument to measure such a small variation.

Fig. 5.

Synthetic interferogram for the full band at 1.6μm, a concentration of CO₂ of 400 ppm, and without taking account of aerosols. The bumps at 5.5, 11 and 16.5 mm are the signature in the direct space of the absorption line comb in the Fourier space.

A.

Sub-band optimization for CO₂ concentration measurement

The first work consists in optimizing the spectrometer sensibility to the concentration of CO₂. To do that we fit a sub-band of the 1.6 μm spectral band in order to maximize the contrast of the fringes between the 5 mm et 6 mm OPD according to the previous section. More the sub-band is large, more the collected flux is important, but potentially lesser the fringes are contrasted around 5.5 mm due to jitter in the line period. We scan the 1.6 μm band with increasingly wide windows, and we compute for each the maximum value of the interferogram between 5 mm and 6 mm of OPD. The obtained figure of merit is shown Fig. 6. Each vertically-delimited area represents a given window width. The optimal value is found for a 30 cm^-1-wide window, centered around 6220 cm^-1. The corresponding Jacobian is shown Fig. 7 for a variation of the CO₂ concentration of 1 ppm. We can now place our 100 samples of OPD in the fringes around the maximum of the Jacobian (excluding the unity pic at 0 mm), where the sensibility to a variation of concentration is maximal.

Fig. 6.

Maximum of the Jacobian around 5.5 mm for a variation of 1 ppm of CO₂ according to the sub-band position and width in 1.6 μm band.

Fig. 7.

Jacobian for a variation of 1 ppm of CO₂, and for the optimal sub-band of the 1.6 μm band. The fringes around the maximum of the Jacobian are sampled with the 100 OPD of the spectrometer.

B.

Effective sensitivity, photometric elements

We can derive now from this study some photometric elements. To do that we introduce the Sensitivity-to-Noise Ratio (SNR_j), corresponding to the maximum of the Jacobian in electron compared to noise. For a Jacobian obtained with a variation of concentration of 1 ppm, a SNR_j greater than 1 indicates that the instrument can detect such a variation, given photometric data. SNR_j is given by:

With n_pix the number of pixel in the swath, n_OPD the number of sub-aperture per band, N_e the mean number of electron per frame and per pixel (derives from the spectral luminance), σ_RON the sensor RON and N_jmax the maximum illumination of the Jacobian. The SNR_j takes into account both photon and read out noises. We report on the Tab. 2 the results of this study. Given the SNR_j of 2.5 in our setup, we have demonstrated here the full capability of NanoCarb to detect a variation of the CO₂ concentration lesser than 1 ppm in medium flux conditions. We can notice also the convenient mean number of electron per frame and per pixel on the interferogram N_e for 1.6 μm band, despite of the very small telescope aperture (3 mm).

Tab. 2.

Photometric elements for CO2 concentration measurement.

C.

Aerosol effects and mitigation

The previously computed intrinsic sensibility is not directly effective due to geophysical biases, affecting all the dedicated missions. The aerosols in the atmosphere are responsible for the main biases over the concentration estimation. Indeed, the induced scattering effects disrupt the optical path of light in the atmosphere, and introduced artificial variations of the measured concentration. They are mitigated only during the model inversion with priors extracted from in situe measurements. We propose in this paragraph an approach to extract information about aerosol from our data.

On the Fig. 8-top two Jacobians on the 1.6μm band are represented, the first one with a null differential of concentration, but with the introduction of rural-type aerosol. The second one without aerosol and a differential of the CO₂ concentration of 20 ppm. We notice that the aerosol induced a contrast variation at the same OPD than CO₂ absorption, and furthermore introduce a ~20 ppm-bias over the concentration measurement! Thus we can effectively not clearly discriminate scattering effects due to aerosols to absorption by CO₂ on the 1.6 μm band. However, we notice a scattering signature on the 2.06 μm band, quasi-independent of the CO₂ absorption. We can observe it on the Fig. 8-bottom, where the same Jacobians as top of figure are represented, but this time on the 2.06 μm band. We can reasonably assume that the fringe’s contrast variations at the 6 mm OPD depends mainly of the scattering effects. This contrast variation is caused by the differential evolution of the edge of saturated lines of the 2.06 μm band affected by scattering, in contrast to the bottom. Thus a differential measurement of fringe contrast on 2.06 μm band at 6 mm and 12 mm (apparently not affected by scattering) allows to detect aerosol effects. This is a very promising mitigation way, with our limited number of samples over the interferogram. We are currently investigating the 760 nm band for mitigate the other potential geophysical biases. The next step will consist in studying the instrumental biases such as thermal variations.

Fig. 8.

Effects of scattering on the 1.6 μm (top) and 2.06 μm (bottom) bands. We notice a potential aerosol signature on the 2.06 μm band independent of CO₂ absorption. An interesting way to mitigated the relevant biases over the measure.