Calibration of a Dust Scattering Instrument Using Tomographic Techniques and Its Application to a Dust Sensor Instrument.

The characterization of suspended dust near the Martian surface is extremely relevant to understand the climate of Mars. In this frame, a Dust Sensor instrument, an infrared device designed to obtain the effective parameters of Martian dust using the scattering properties of the dust particles, was developed. The purpose of this article is to present a novel methodology to calculate, from experimental data, an instrumental function of the Dust Sensor that allows solving the direct problem and providing the signal that this instrument would provide given a distribution of particles. The experimental method is based on recording the signal measured when a Lambertian reflector is gradually introduced into the interaction volume at different distances from the detector and source and applying tomography techniques (inverse Radon transform) to obtain the image of a section of the interaction volume. This method provides a complete mapping of the interaction volume experimentally, which determines the Wf function. The method was applied to solve a specific case study. Among the advantages of this method, it should be noted that it avoids assumptions and idealizations of the dimensions of the volume of interaction and reduces the time required to carry out simulations.

New Method to Calculate the Angular Weighting Function for a Scattering Instrument: Application to a Dust Sensor on Mars.

Suspended dust above the Martian surface is an important element in Martian climatology. In the frame of the Exomars'22 mission, we developed a dust sensor instrument, designed to provide size parameters of dust particles suspended in Mars surface from the light scattered by the particles. Thus, to interpret the data of the dust sensor, we need a method to calculate the theoretical optical power dispersed by the particles and, therefore, the theoretical signal obtained by the instrument. This signal depends on the suspended particles and on the instrument configuration. In this paper, we present a new method to calculate the angular weighting function (Wf) for scattering sensors. Wf encompasses the scattering angles measured by the sensor and depends only on the instrument and not on the suspended particles. To calculate this Wf, we use fundamental radiometry principles and an appropriate coordinate system, where one coordinate is the scattering angle. The method is applied to the dust sensor instrument and compared with other methods. The comparison highlights the advantages of the proposed method since it avoids using an ideal sampling volume, preserves the radiometric meaning, and avoids instrument calibration. The effectiveness of the method makes it a valuable tool for the design of scattering instruments and also for the interpretation of their data.

Virtual Spectral Selectivity in a Modulated Thermal Infrared Emitter with Lock-In Detection.

The need for affordable low-power devices has led MEMS-based thermal emitters to become an interesting option for optical gas sensors. Since these emitters have a low thermal mass, they can be easily modulated and combined with a lock-in amplifier for detection. In this paper, we show that the signal measured by a lock-in amplifier from a thermal emitter that varies its temperature periodically can have different spectral profiles, depending on the reference signal used. These virtual emitters appear because the Fourier series expansion of the emitted radiance, as a function of time, has different coefficients for each wavelength, and this spectral signature, which is different for each harmonic, can be retrieved using a reference signal that corresponds to its frequency. In this study, the effect is first proved theoretically and then is measured experimentally. For this purpose, we performed measurements with an IR camera provided with six different spectral filters of a modulated emitter, in combination with lock-in amplification via software. Finally, we show a potential application of this effect using multiple virtual emitters to gain spectral selectivity and distinguish between two gases, CO2 and CH4.

Bi-Spectral Infrared Algorithm for Cloud Coverage over Oceans by the JEM-EUSO Mission Program.

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges.