4.1

Payload assembly - measurement approach enabling high precision

Extensive literature exist that provide evidence on the errors incurred by ignoring aerosols, thin clouds and, for CH₄ plume measurements, local surface winds. All local information on aerosols loads, cirrus, thin clouds & winds can be obtained by our novel microlidar concept as already reported in the literature (https://spie.org/Publications/Proceedings/Paper/10.1117/12.2599360?SSO=1). A recent paper discussing the retrieval precision on the GHGsat -D1 satellite (D. Jervis et al.: Atmos. Meas. Tech., 14, 2127–2140, 2021), for the CO₂ and CH₄ gases, clearly shows that by neglecting aerosols (and local winds for Methane) will add an important error in the retrieval accuracy. Our payload and processing retrieval approach will remove these important errors source for GHG gases and mostly important for CH₄ plumes emission from local sources by adding local wind information. The basic idea behind the measurement approach goes along these steps:

Detecting the weak CO₂ and CH₄ band is the main target since there are few other gas absorption bands in this area, and it is much easier to separate the band from neighbouring bands. The chosen observation technique, to be compatible with Small Sats approach, is the SWIR spectrometry performed with a high-resolution Grating-Spectrometer, supplemented by a VIS Camera, for scene recognition & precise geolocation. SWIR Spectrometry is chosen since CO₂ has useful absorption bands in the 1.6-micron bands and very sensitive in GaAs array detectors are available. This technique relies on analysis of the reflected sunlight and can only be used while the satellite passes over illuminated areas of the planet. For this, solar-based spectroscopy is vulnerable to errors introduced by uncertainties in path length, atmospheric transmission, presence of thin clouds or aerosols, altitude/range errors, etc. For these reasons, here it is proposed to combine the Spectrometer with an atmospheric LiDAR to add and complement retrieval-important atmospheric information and minimize retrieval errors. Although LiDAR techniques exist for spectroscopy measurements of GHGs, within the frame of this project LiDAR is used as a supporting instrument to the SWIR spectrometer to enhance the accuracy and spatial resolution of the SWIR spectrometer. An only-LiDAR solution as is in Ascend or Merlin missions is not implementable on CubeSat in view of the power/mass/size constraints of the required LiDAR specifications for high-altitude orbits as being here considered (≈400-500 km). In fact, the selection of instrumentation for this mission is tightly constrained. The finalized design of the constellation satellites is subject to a volumetric constraint and must fit within a small satellite no larger than 12U-16U therefore minimizing the volume of the instruments within the satellite is of paramount importance. To ensure that the overall cost of the constellation is not prohibitively expensive, instruments that are commercially available will be selected as a matter of preference.

The proposed setup guarantees, for the first time, the possibility to retrieve CO₂ daytime and night time. For daytime, inversion algorithms, via radiative transfer & radiance reconstruction, will be used to determine CO₂ and CH₄ concentration based on Solar Irradiance as Source whose CO₂ absorptions bands provide the “imprint signature” which is then obtained via Radiance measured by the Satellite. Nighttime, the same process will be used but the Target area will be illuminated by an artificial light source, at the exactly the absorption wavelength of CO₂ (1.5716-15730 nm absorption band of CO₂): the laser source will be used both as an atmospheric LiDAR and specifically on night times, as the artificial “solar irradiance”. Night measurements for CO₂ are feasible with a fiber-laser at 1 W average. For CH₄ required absorption sources lie above 1.6 micron (1.645 to 1.655 micron) are difficult to be achieved with fiber lasers: other suitable candidate solid state sources will be considered if night CH₄ is required.

The LiDAR is not scanning but steering and generating Point Clouds images of the atmosphere (0 to 10 km): the images shall be processed for Aerosol & subvisible clouds, data that will be fed in the RTM models to match the measured radiances for spectral bands.

For the Data provided in this proposal, we have used designing & verification as provided by our Forward Model for the Grating spectrometer and a LiDAR simulator Tool which contains all system parameters from satellite height, input radiance to telescope, feed optics, detection chain & electronics providing raw Data & SNR for the various situations analyzed.

Table 3

Budgets for payload elements

4.2

Satellite constellation deployment

Since global coverage is required for GHG emissions monitoring, a CubeSat constellation of at least 12 satellites is selected in the sun synchronous orbit in 4 different orbital planes with separation of 45 degrees between planes. Spacecrafts will be launched in several launches while passive or active orbital phasing will deliver them to the dedicated orbital spots. The propulsion decision is to be made in the scope of the activity.

The emissions monitoring of GHGs needs to be constantly measured which calls for a satellite constellation to avoid latency while maintaining continuous coverage of areas of interest. The satellite configuration will follow the Walker Delta Pattern - circular orbits with set altitude and inclination for optimal coverage of land area which is the focus of observations to be made based on the literature review to observe mainly industrial areas. Aiming for 3-4 years maximum lifetime for the constellation to generate enough data, the altitude is fixed initially set at 450 km. The constellation altitude is 450 km as this gives each satellite enough room, both above and below the normal operations altitude. Most of the Satellites that historically have monitored GHG are known for the fact that they have big dimensions due to hosting several different instruments. For the design of the spacecraft the dimensions were a limiting factor. Thus, the base size of one satellite shall be coherent with 12U or 16U CubeSat. It will require deployable solar panels and an active attitude control system with low jitter to enable high pointing stability for image acquisition. During preliminary mission study the available satellite volume was assessed, and a sample CAD modelling allowed evaluation of satellite positioning and places for apertures. Besides that, data, power, mass and link budgets were evaluated, and satellite subsystems were selected. Table below outlines the satellite baseline design. It includes the main subsystems as well as their parameters.

Some of the initial findings for mission results are outlined below. The mission is composed of three segments, the space, ground and launch segment.

Figure 3

Mission Architecture

The satellite platform is a 12U platform based on the CubeSat form factor. From the 12 CubeSat Units, 8 will be occupied by the multiple payloads and the remaining 4 will be used by the bus.

Table 4

Budgets for satellite element

4.3

Data processing block

The retrieval of the desired species concentration is based on three steps algorithms:

During this process there are many sources of errors during computation of Step 1 & 3: primarily associated with poor knowledge of the atmospheric conditions, and mostly presence of thin clouds and Aerosols which are undetected by the VIS Camera, and which can lead to large errors in the vmr’s values. For this, in this Mission proposal, the Spectrometer & Camera are supported by a cloud/aerosol LiDAR, which is the simplest of all active LiDAR instruments, but will be able to detect and characterize with high vertical resolution and accuracy the presence of Aerosols and (subvisible) clouds & cirrus. The effect of this additional data, which is proposed for the 1st time for the CO₂ CubeSat mission, will improve the measurement accuracy which will be then independent from the atmospheric conditions.

The AIRMO Level 1B product list are calibrated and geo-located spectral radiances. Each Level 1B product will contain a unique record for every sounding that the payload instruments acquire while viewing the Earth during a single spacecraft orbit, consisting of atmospheric column spectra. The Level 1B product also includes error measures and indicators that assess the quality of each acquired spectrum. These indicators denote which spectra are suitable for subsequent processing. The full Constellation of AIRMO small satellites will be correspondingly generating thousands of Gbs of data daily. Certain pre-processing algorithms will enable on board compression and pre-processing (L01b) while the final intelligence products (L2) will be processed on the Ground segment. AIRMO shall develop a set of visualization, analysis, and processing tools for the exploitation of the L0 to L2 data from the mission. The Lidar and SWIR spectrometer produce a relative low data rate, major Data volume are generated by the RGB camera. For raw unprocessed instrumental Data, assuming each pixel # (from each instrument) being sampled at 10MH/sec with 12 Bit/sec, total ground “Tile” Data rate is calculated approximately as: 12x10E6 giving order of 10Gb

4.4

Lidar Impact on GHG gases Climatology

In this paragraph, we show the impact of Lidar-acquired Data on CO₂ Climatology. A simple compact lidar of the type presented at ICSO 2020 (Ref.) supplements the RTM models with information about presence of aerosols layers, cirrus clouds, presence of sub-visible clouds, which are not otherwise detected by the on board SWIR Camera. This added information remove the inaccuracy in the retrieval of GHG concentrations from the Spectrometer measured Radiances. Additional important contribution provided by this lidar concept is that it can provides the local surface winds (see Paper 009_Armandillo in this Conference) which will allow to determine without error the local CO₂ or CH₄ out flow rates from industrial sites.

Figure 4

shows typical profiles from the lidar: Fig. 4a shows vertical atmospheric profile. Fig. 4b shows the typical Point-Cloud pattern which is then processed for information retrieval.

Fig. 5:

local winds processed by the Point-Clouds pattern of Fig. 4b

Fig. 6:

Backscattering & extinction coefficients retrieved from the Point-cloud pattern of Fig. 4

Fig. 7 below shows clearly the impact of lidar data on Radiance measurements. For these Aerosols AOD values:

Fig. 7:

Effect of aerosols of varying optical density on the Absolute radiance retrieval from Spectrometer.