2.1.

Driven by the CubeSat Standard, Other Systems, and Implementation for Flight

The CubeSat standard sets limits for mass and volume to 1.33 kg and a 10-cm per side, respectively, per cubic unit (U); the satellite can be composed of one or more of these units (Quetzal-1 was a 1U CubeSat).⁶ All of the satellite’s subsystems-structure, EPS, ADCS, on-board computer (OBC), and other components required by the CDHS, as well as the payload must fit within these volume and mass constraints. A hierarchical approach was taken to Quetzal-1’s development in that a baseline design was produced for each subsystem, leaving the payload for last—mostly due to limited number of students available for this academic project. Hence, each of the systems-imposed requirements and constraints on the payload based on their own characteristics, capabilities, and limitations.

From the EPS perspective, these included that the motor could not require more than 1 A, 5.5 V, and 14.8 W of current, voltage, or power, respectively, and that the camera could not require more than 3.3 V.⁴ The ADCS, in turn, limited the motor’s torque to reduce the rotational disturbances introduced into the system. Similarly, and due to the passive nature of Quetzal-1’s ADCS, it was required that the motor accelerated and decelerated at the same rate to induce a net torque equal to zero over the $Z$ axis of the satellite (around which the payload’s light filter carousel rotated).⁵ If the motor-induced torque were to be different than zero, the satellite’s rotational energy would increase with each rotation of the carousel. Additionally, it was important to minimize the electromagnetic characteristics of the motor to be used. Typical direct current (DC) servo and most stepper motors contain permanent magnets that enable operation, which would potentially modify the alignment of the passive ADCS with Earth’s magnetic field. Note that a predictable alignment was important, as it was necessary to align the camera’s boresight to Earth at the desired locations to take relevant images.⁵

This mission was constrained to using the UHF band due to its amateur radio application and, most importantly, regulatory framework limitations in Guatemala. This consequently constrained how much data (file size and amount of files) could be transmitted down to the GCS during an overflight. Furthermore, the selection of the OBC (GOMSpace, Cat. No. NanoMind A3200) generated a series of requirements and constraints on the payload. These included that the motor control interface could not exceed nine general purpose input/output (GPIO) pins of the OBC and that the communication interface with the camera had to be I2C, CAN, SPI, or UART.

The launch and space environment, as well as the deployment of Quetzal-1 from the ISS, generated additional requirements that directly impacted the payload design. These included launch quasistatic loads and overall random vibration of 18.1 g and 4.825 Grms, respectively, and a volume to exhaust port area ratio of $<50.8\mathrm{m}$.⁶ Additionally, component materials had to have an “A” rating on NASA’s Materials and Processes Technical Information System⁷ (which translated into not being able to use lubricants of special impact for motor selection). The rationale for this is that out-gassing (the release of gases trapped within a solid when exposed to a vacuum) can result in condensation on the smooth optical surfaces (glass lens and light filters). No material with a total mass loss above 1.0% or a collected volatile condensable material above 0.1% could be used on the payload or in the satellite in general.⁶ Finally, on-orbit operations at an ISS-like orbit ($\sim 420\mathrm{km}$ altitude, 51.6 deg inclination) required that the satellite components could operate in a $-15°\mathrm{C}$ to $+60°\mathrm{C}$ temperature range in a near vacuum.⁶

2.2.

Driven by the Water Color Monitoring Mission

The selection of Quetzal-1 payload’s data collection wavelengths is here described in the context of its water color monitoring mission. However, by changing the commercial-off-the-shelf (COTS) bandpass filters, others may implement the same or equivalent payload to serve different remote sensing applications in 1U or larger CubeSats. The initial step to determine the requirements imposed by the water color monitoring mission, per se, was to identify the number of channels (wavelengths where images are acquired) that were needed to enable water quality characterization from space. Specific wavelengths provide information on different constituents in the water, stressing the importance of the appropriate selection of the sensor’s channels. Mertes et al.,⁸ a review of over 1000 journal publications, books, and reports focused on rivers and lakes, was used as the guiding reference for this purpose. Remote sensing enables the estimation of several water quality properties, including colored dissolved organic matter (CDOM), concentration of total suspended matter, chlorophyll-a content, and phycocyanin, a pigment indicative of the presence of cyanobacteria, among others. The presence of algae (and chlorophyll in general) in water results in changes in absorption, mostly between 400 and 500 nm, and between 600 to 700 nm wavelength range. In general, the range of 400 to 700 nm is known as the photosynthetically active radiation range.⁸ A literature survey showed that, originally, most of the algorithms—equations developed to quantify a parameter from images or other types of data—for chlorophyll concentration determination depended on the ratio of two bands. However, newer algorithms that used three bands have shown to increase algorithm tolerance to atmospheric correction, especially important for short wavelengths.⁹ For example, IOCCG¹⁰ indicate that the OCM sensor on board the Indian satellite IRS-P4 uses data from the 768 and 867 nm wavelengths for atmospheric corrections. Hu et al.¹¹ presented an algorithm used on SeaWiFS data using the 443, 555, and 670 nm wavelengths. It is suggested that this three-band approach increases performance, especially for low chlorophyll concentrations ($<0.25{\mathrm{mg}/\mathrm{m}}^3$). Similarly, the use of red ($\sim 650\mathrm{nm}$) and/or near-infrared (NIR) ($>700\mathrm{nm}$) bands help avoid CDOM contamination problems.⁹ Hence, some algorithms have been based on the use of four bands.⁹ Table 3 (Appendix A) summarizes the functional parameters of sensors used for water color monitoring applications. Starting with the coastal zone color scanner (CZCS) sensor onboard the Nimbus-7 satellite (launched in 1978), these devices have been used for purposes beyond chlorophyll-a concentration determination, and this is reflected in their use of multiple channels (e.g., detecting oil spills or assessing fishing resources).

Data from the CZCS was used to calculate chlorophyll-a concentration through “blue-green band ratio” models, where the reflectance at 550 nm was compared to that at 443 or 520 nm. In this case, the 550 nm band is used as a reference, as it is minimally affected by chlorophyll-a concentration.⁹ Table 3 shows that data collected at 443 nm has been a common numerator among the analyzed algorithms. Similarly, data acquired at a channel between 547 and 560 nm have been used as the denominator for the blue-green band ratio among these algorithms. Additionally, three-channel algorithms used to determine chlorophyll-a concentration from SeaWiFS data utilized the 670 nm wavelength.¹¹ As indicated by Hu and Campbell,⁹ this wavelength helps in differentiating CDOM from chlorophyll-a in the collected light.

In conclusion, a literature survey on algorithms used to determine chlorophyll-a concentration in lakes, and on the sensors used in satellites with this objective, showed channels centered at (a) the 443 nm and (b) in between 547 and 560 nm as numerators and denominators, respectively. The use of a third channel around the red ($\sim 650\mathrm{nm}$) and/or NIR ($>700\mathrm{nm}$) bands help to differentiate signals from CDOM and chlorophyll-a.⁹ Hence, and to facilitate the development of an algorithm for Quetzal-1’s data, the three channels used in the SeaWiFS sensors—443, 555, and 670 nm—were chosen as the water color monitoring mission specific requirements. Additionally, as nature of the hardware design allowing for data acquisition at four wavelengths (see Sec. 3.1), 700 nm was included to acquire additional data, potentially extendable to land applications. The wavelengths implemented on the satellite’s payload varied by a few nanometers in most cases as described in Sec. 3.1 based on which light filters were commercially available. Figures 2 and 3 show the wavelength location and bandpass (range of wavelengths into which it allows photons to be transmitted) of Quetzal-1’s light filters in the electromagnetic spectrum in the context of chlorophyll-a characterization.

Fig. 2

Adapted from Gons et al.¹². Line without symbols, total absorption coefficient; solid circles, plankton biomass absorption; open triangles, gilvin; and solid diamonds, water. The subsurface irradiance reflectance is shown with open squares and uses the right axis. Solid vertical lines were placed over the MERIS bands. Dotted green vertical lines represent the center of Quetzal-1’s bands: 450, 550, 680, and 700 nm. The green boxes around them represent the bandpass for each of the four light filters (25 nm in all cases except for the 680 nm filter, for which it was 10 nm). Adapted with permission from Ref. 12, copyright (2005).

Fig. 3

(a) Water and (b) phytoplankton (chlorophyll-a) absorptivity (solid lines) and particulate backscattering coefficient (dotted lines) as a function of wavelength; (c) CDOM absorptivity, which can help differentiate in between phytoplankton and other organic material; and (d) reflectance. These figures show the importance of the channels selected for Quetzal-1 (450, 550, 680, and 700 nm, with green bands indicating their respective bandpasses) for determining chlorophyll-a concentration. Adapted with permission from Ref. 9, copyright (2014).

3.1.

Light Filter Carousel

Based on the selection of wavelengths for data acquisition, light filters were chosen taking into account their dimensions, mass, closeness to target wavelength, bandpass, and price. Hence, for the target 443, 555, 670, and 700 nm, light filters centered at 450, 550, 680, and 700 nm were selected, respectively (each with a 25 nm bandpass with the exception of the 680 nm filter, for which it was 10 nm) (Edmund Optics, Cat. Nos. 86-653, 86-655, 86-571, and 86-658, respectively). This is summarized in Table 1.

Table 1

Summary of the wavelengths selected for the water color monitoring mission of the payload prototype, and their rationale for selection. Desired and actual wavelengths vary due to COTS hardware availability.

Different approaches were considered to implement these light filters and the overall multispectral imaging payload within the limitations and constraints of a 1U CubeSat bus, including having multiple cameras (each with a designated light filter), using a “stack” held by an axle, and a “carousel” or disk holding the light filters (changed via a motor). Based on the volume, mass, torque and power needed for implementation, cost, and complexity of spaceflight operations, the light filter carousel was chosen via a trade study. Additionally, the carousel approach enabled the inclusion of a fourth light filter given its dimensions. Al7075 was chosen as the carousel material, which per requirements, this (and all other) aluminum components were hard anodized following specifications by MIL-A-8625 type 3 class 1, which formed a coating with a thickness of at least $10\mu \mathrm{m}$; in the case of the carousel specifically, it was black-anodized to minimize reflections. The carousel was composed of three main parts: two “top” and one “bottom” plates (see items 1 and 2, respectively, in Fig. 4). These plates housed the light filters (shown in Fig. 4 as items 8 to 11) using Viton O-rings (The O-Ring Store LLC, Cat. No. V1.00X021.5) (one per light filter) to dampen the launch vibrations that could otherwise jeopardize these glass components. The carousel’s engineering drawings are included as an appendix and the computer assisted design (CAD) models can be downloaded from Ref. 13.

Fig. 4

Exploded view of Quetzal-1’s light filter carousel: (1) top plates (×2), (2) bottom plate, (3) O-rings (×4), (4) to (7) fasteners, and (8) to (11) light filters. Engineering drawings are included as an appendix.

3.2.

Motor

To rotate the light filter carousel so that a single camera could be used, different types of motors—DC, stepper, continuous rotation servo, and piezoelectric—were assessed. As described in Sec. 2.1, the requirements that no electromagnetic motors or lubricants be used impacted motor selection. Piezoelectric motors convert an electrical field into mechanical strain, which in turn, translates into motion using fewer parts compared to their electromagnetic counterparts, while being free of lubricants. Hence, it was determined that a piezoelectric motor was ideal for this application, with the caveat that no previous use of these types of devices on CubeSats could be found. A trade study was used to select the specific piezoelectric motor to use in spaceflight. The variables considered for the selection included volume (dimensions), power consumption, operational temperature range, whether it had spaceflight heritage (for risk mitigation), mass, angular accuracy, acceleration profile, and cost. TEKCELEO’s WLG-30 model was chosen through this process, due to the following beneficial characteristics:

The main drawbacks included the lack of spaceflight heritage and the need for an additional 7.5 V power rail dedicated to the motor, which had to be implemented on the payload circuit board. Otherwise, the selection of a piezoelectric motor proved to be the best alternative to reduce mass, volume, and electromagnetic interference, while also guaranteeing a $<0.13\mathrm{deg}$ carousel positioning accuracy and fast operating response.

This motor included a driver, which allowed to control the piezoelectric device in a simple fashion since its inputs were GPIOs. Aside from the power on and off pins, the driver had pins to select the direction of rotation (clockwise or counterclockwise), an increment pin (to count steps), and an index pin (to count full rotations).

3.3.

Camera

Requirements for the camera selection included interfacing with the OBC and EPS, maximum peak and nominal power consumption, volume (dimensions), mass, operational temperature range, capability to store images, and generated file type and size. Given that Quetzal-1’s payload was a prototype—for test demonstration, verification, and subsequent maturation—and not designed to be used for systematic water color monitoring data acquisition, no spatial resolution requirements were imposed—but were considered—during the camera selection process. Different types of sensors were analyzed for this mission, including red–green–blue (RGB) sensors, multispectral cameras, and monochromatic sensors. RGBs are the simplest to integrate but selectively receive light at those three wavelengths, exclusively. Multispectral cameras with Bayer patterns are ideal as they can be procured to acquire data specifically in the wavelengths of the mission’s interest; however, these types of sensors can be two or more orders of magnitude more expensive (as well as larger and more power-consuming), reasons for which they could not be further considered for the Quetzal-1 satellite. Finally, monochromatic sensors are ideal to be used behind light filters, such as the ones housed in this payload’s carousel. These types of sensors do not have a color filter array, i.e., each pixel receives light through the whole range of wavelengths the sensor is set up to receive (e.g., 350 to 950 nm), thus the light-filtering function is achieved with the light filters. Through the design and development phases of the Quetzal-1 project, a monochromatic camera was included. However, the camera arrived half a year after expected, it had different dimensions and mass than what was conveyed in the drawings, and most importantly, it did not have the communication bus that was advertised, all of which required the swift selection of a new device—while all other modules were ready to transition to the integrated testing phase and with handover date prior to launch a few months away. Given its open-source nature, low complexity, and compatibility with the other, already established satellite systems (namely, the other payload’s components), Arducam’s OV5642 camera was the new selection for flight. This device uses a 5 MP complementary metal oxide semiconductor $2592\times 1944$ array size, $1.4\mu \mathrm{m}\times 1.4\mu \mathrm{m}$ pixel size, 63-pin CSP3 digital image sensor (OmniVision Technologies Inc., Cat. No. OV05642-A63A) and was launched set up with autoexposure control (AEC), autogain control, auto white balance, autofocus control, black and white, and JPG compression. Although this was a deviation from the original payload design, it enabled to continue towards launch and testing of the prototype approach based on a piezoelectric motor rotating a light filter carousel in a 1U CubeSat. According to the manufacturer, the minimum and maximum exposure times for this family of sensors are $53.39\mu \mathrm{s}$ and 66.63 ms, respectively, and saving times as short as 816 ms.¹⁴ Ultimately, captured images were stored in an SD memory.

3.4.

Optical Design and Lens

In order to design an imaging payload, several key optics and photography concepts need to be considered, which are described here and noted in italics, and graphically represented in Fig. 8 (Appendix B). The distance between the lens and the sensor depends on the system’s focal length. This is the distance from the point where light rays converge to form an image, to the digital sensor and has a role on the area that is captured; the longer the focal length is, the narrower the angle of view and therefore the higher the magnification is. The focal length is defined by the following equation:

The $F$ number is a measure of the lens opening and amount of light that goes through a lens (inversely proportional to aperture) and impacts focus and depth of field (range of distances in which objects are still in focus). In the case of remote sensing applications, it is important to have a high $F\#$ (small aperture), as it yields a greater depth of field.^16,17 The $F\#$ can be calculated as the ratio of the focal length and the lens’ diameter $d$:

For a sensor to capture an entire image and not just part of it, it is recommended to select a lens larger than the sensor size optical format—approximately the diagonal length of the sensor multiplied by 2/3. For example, a sensor with a ¼ in. optical format could be used with a 1/3 in. sensor. If the lens and the sensor’s optical format are of the same dimension, some optical problems as dark or blur edged or copped areas on the image may occur.¹⁸ Another important parameter is the ISO value, a measure of the sensor’s ability to capture (or sensitivity to) light.¹⁹ A brighter light environment requires a lower ISO number or a higher shutter speed—how long the shutter is open, exposing the sensor to light—and is usually used under bright conditions (e.g., sunlight or for long-exposure photos). Conversely, low-light environments require slower shutter speeds are a higher ISO value. Something to consider is that high ISO numbers tend to increase the noise level in the image.¹⁹ To better understand how the ISO number, the shutter speed, and the aperture interact between each other, readers are encouraged to seek more information on the “exposure triangle.”

Finally, the type of global shutter or rolling shutter needs to be considered. The prior [common in charge-couple device sensors] exposes every pixel to the light simultaneously, resulting in slower transfer rates. The latter exposes one column or row of pixels at a time, resulting in faster transfer rates but can potentially result in distortion when capturing a fast-moving target.²⁰ In addition to the optical design-driven requirements, the lens selection took into account the space available to the overall payload subsystem, including the distance between the camera and the light filter carousel and the light filters’ dimensions. From this, the lens integrated to the selected camera (DLSCorp., Cat. No. M2508ZH02) was confirmed to be appropriate for this mission’s application with its 1/2 in. maximum sensor format, $f/2.5$ aperture, 8.0 mm effective focal length, and M12 × 0.5 S mount.

3.5.

Interfaces with Other Satellite Subsystems and Governing Software

It was decided to use a single microcontroller to operate both the camera and the motor so that this secondary OBC acted as an extension of the main OBC. Its only task was to collect data and activate actuators in the payload board. Meanwhile, the main OBC was the one deciding when to perform the individual tasks. In terms of communication protocol, I2C was implemented for the payload. Quetzal-1’s software allowed it to operate the payload under either of two modes: autonomous or manual. Under autonomous mode, the satellite would take a picture and download it at a predetermined periodicity (which could be changed via a GCS command). Under the manual mode, pictures were taken exclusively at the moment of receipt of a GCS command requesting the satellite to acquire an image. Since the camera was initially placed between two light filters (protected from sunlight), the operation was to rotate 45 deg to set the first lens in front of the camera, take a picture and rotate 135 deg to cover the lens again. Meanwhile, the image was stored in a SanDisk Industrial Grade 8 GB micro SD card (ScanDisk, Cat. No. SDSDQAF3-008G-XI) for subsequent download. Hardware checks were done during this operation (see Fig. 9 in Appendix C), including the verification of current and voltage flags and confirmation of motor position, to ensure that a successful picture would be taken. A command to rotate the carousel without taking pictures was not considered and therefore not included.

3.6.

Picture Capture, Download, and Use

The picture download process was considered a high-risk procedure because it involved the transmission of image files from the payload microcontroller to the OBC through the I2C main bus. This increased the risk of bus latch-ups due to potential bus collisions between the transmitted payload data and data coming from other subsystems, based on preflight testing observations. Due to time constraints—as these collisions were discovered close to the date of satellite handover to JAXA—no redundancy or watchdog safeguards were implemented for the I2C bus. Therefore, a bus latch-up could only be solved via an external reset caused by battery depletion in space. The download process was also considered of high risk because it involved a sudden increase in the power consumed by the transceiver (GOMSpace, Cat. No. AX100) during 1 to 4 min when transmitting pictures to the ground. The EPS was designed to be capable of providing this expected amount of power, but the sudden power peak could translate into unexpected failures. For these reasons, it was determined before handover that the automatic download mode of the payload subsystem was not going to be used on orbit, and that the picture and download process was going to be manually executed via GCS commands when the satellite passed over Guatemala.

If images of the Earth’s surface were received, it would confirm that the team was successful in implementing this concept of operations, by developing and operating a carousel with light filters rotated via a piezoelectric motor, aligning a light filter to a sensor to acquire data at a specific wavelength. A picture would be needed to confirm this given that the carousel had a default position where the sensor was covered by the solid aluminum block of the carousel, i.e., a light filter was not aligned to the sensor, with the intent of protecting it from direct sunlight. Should images of water bodies be received, these would be used to test the chlorophyll-a concentration algorithms after appropriate calibration, albeit this was limited with the last-minute sensor change as described in Sec. 3.3. Nevertheless, this was not a direct objective given our limitation to a passive ADCS, as described in detail by Alvarez et al.,⁵ which would not allow for active targeting of lakes. This first mission would be focused in assessing whether this approach of multispectral imaging acquisition could be implemented in a 1U CubeSat.

5.1.

Revised Picture Capture and Download Protocol

After the commissioning phase of the mission, communication tests were performed to determine the most effective way of commanding the satellite during payload operations. It was determined that each command needed to be sent as a batch of six repeating command packets. This maximized the probability that the command would be successfully received by the omnidirectional antennas of the satellite. It was determined that passes with a maximum elevation $>40\mathrm{deg}$ were adequate to execute picture capture and download processes. The satellite could be easily commanded at elevations $>30\mathrm{deg}$. At lower elevations, the communication link became irregular. A series of steps needed to be executed during payload operations to minimize the aforementioned failure risks. A pass with a $+40\mathrm{deg}$ maximum elevation typically gave a 3-min window to execute this process. Table 4 (Appendix B) lists these steps and the rationale for their executions.

The watchdog reset timer that rebooted the OBC every 2 h ensured that the satellite always returned to its nominal configuration even if the reconfiguration commands sent after picture download were not successfully received by the satellite due to low elevation, or otherwise. The value of the parameters modified during payload operations was only stored on the satellite’s volatile RAM memory, thus they returned to their default values saved on the satellite’s flash memory upon reboot. Furthermore, another safeguard was implemented to guarantee that the payload subsystem would not execute an automatic picture download if the subsystem was not successfully disabled after a payload operation. The payload’s automode cycle time was permanently changed from 30 to 255 min (4 h and 15 min). The subsystem’s time counter was reset to zero by the OBC’s watchdog timer every 2 h. Therefore, the new cycle time ensured that the counter never reached 4 h and 15 min, triggering an automatic picture download.

5.2.

Payload Performance Related to EPS and ADCS

The camera typically demanded 60 mA on standby mode when powered on during payload operations and demanded 195 mA when executing a picture capture and save, as detailed in Ref. 4. This corresponded to nominal operation of the camera, i.e., no differences were observed in the camera’s per consumption with respect to preflight ground-based characterization tests. The carousel motor power consumption could not be evaluated because the power transient was too fast to be detected with the configured EPS sampling period. However, no short-circuit or overcurrent alert was triggered during any payload operation throughout the mission, evidencing the correct operation of the subsystem in space.⁴ The piezoelectric motor rotated $\sim 1812$ times in space. This estimate was calculated by counting the times it rotated when it reconfigured itself to its 0 deg position after an OBC watchdog timer reset, and adding the successful payload operations detailed in the next section. A 2.41 W power consumption increase was measured when the transceiver (GOMSpace, Cat. No. AX100) downloaded an image.

As detailed in Ref. 5, the carousel generated a gyroscopic torque over the satellite when it rotated around the $Z$ axis. This transient gyroscopic torque temporarily increased the satellite’s oscillation over its $X$ and $Y$ axes. This in turn, increased the $H$-field oscillations on the satellite’s hysteresis rods, thus providing rotational energy dampening. The carousel was activated during every OBC watchdog timer reboot because the motor was reset to its zero position. This phenomenon may have acted as a momentum dumping method that prevented a greater increase in rotational velocity over the satellite’s uncontrolled axis. The effect of the rotations of the carousel caused by payload operations was negligible compared to the effect of the rotations caused by the watchdog timer reboots every 2 h.

5.3.

Image Results

A total of 20 pictures were captured and 16 of those were downloaded. Five were all-black images due to reduced lighting during eclipse conditions, another five were all-white images showing little to no details, suggesting a potential limitation on the camera’s AEC capabilities. To interrogate this aspect, subsequent image acquisition was timed on overflights of the GCS in Guatemala to take place during local sunrise or sunset. It was considered that this would provide more amicable lighting conditions to the sensor’s AEC. However, the initial four of these pictures only showed out-of-focus lens flares caused by solar rays shining over the camera boresight. Finally, two pictures were captured at a time when the satellite was about to enter Earth’s shadow and the planet’s surface in front of the camera was slightly illuminated. This seems to have prevented the camera’s sensor from being overexposed, allowing Quetzal-1 to capture images of hurricane Iota as it was impacting Guatemala (November 17, 2020 23:15 and 23:16 UTC) (see Fig. 7).

Fig. 7

Image of Hurricane Iota over Guatemala and the Caribbean Sea, respectively, taken by Quetzal-1 with the 450 nm light filter on (a) 2020-11-17-231511 UTC and (b) 2020-11-17-231626 UTC.

After analyzing the downloaded images, it was hypothesized that the time between carousel rotation to uncover the camera lens and the picture capture (50 ms) was not enough for the camera to automatically adjust its exposure and focus parameters according to lighting conditions. Unfortunately, the delay between these two steps was hard-coded into the payload software and could not be changed via GCS commands. The autoexposure and autofocus configuration of the camera was also hard-coded, thus these parameters could not be manually configured during the mission. Therefore, the payload operations relied on the lighting conditions during satellite passes over Guatemala to capture images. This proved to be a difficult task due to all the orbital conditions needed to guarantee the capture of a picture that was neither overexposed nor all dark.

These two images were captured during the same payload operation on November 17, 2020 at 23:15:11 UTC and 23:16:26 UTC, respectively. As observed in the pictures, the satellite was aligned to Earth’s terminator, which likely provided an adequate balance between illuminated and obscured surfaces. This allowed the camera to pick up details from clouds that were later determined to be part of hurricane Iota due to the location of the satellite when the pictures were captured.

5.4.

Ground Sampling Distance Analysis for Hurricane Iota Pictures

A ground sampling distance (GSD) analysis—used to determine the distance between two pixels measured on the ground—was performed for the Hurricane Iota picture taken on November 17, 2020 at 23:16:26 UTC (16-UVG-2020-11-17-231626, Fig. 10 right). Quetzal-1’s coordinates during the capture of image were 18.651° N, 86.228° W, 408.49 km, i.e., it was over the Caribbean Sea, 157 km to the east of the coast of Quintana Roo, Mexico. The GSD analysis was carried out taking into account the following characteristics of the payload’s 5MP OV5642 image sensor and the M2508ZH02 lens: sensor height and width were 2738.4 and $3673.6\mu \mathrm{m}$, respectively, the lens was a 1/2.5 in. 8.0 mm M12 × P0.5, with 8 mm focal length and $640\times 480$ video graphics array (VGA) resolution. The GSD was calculated separately for the vertical and horizontal axes of the image following Eq. (5). For simplification, it was assumed that the camera’s line of sight was perpendicular to the surface of the Earth at the time of the picture capture:

The resulting GSD for the horizontal axis was 291.30 m per pixel, and the GSD for the vertical axis was 293.09 m per pixel. This meant that, at a resolution of $640\times 480$, the horizontal axis covered a total of 188 km and the vertical axis covered 140 km. Thus the picture of Hurricane Iota captured over the Caribbean Sea covered $\mathrm{26,228}{\mathrm{km}}^2$ of the Earth’s surface.

5.5.

End of Mission

Subsequent to the taking of the images in Fig. 7, the Quetzal-1 mission reached its end of mission due to battery failure following repeated subzero temperature exposures. Although the battery heaters showed appropriate performance during the life of the satellite, it is believed the OBC failed to command the heater to activate as a result of a Payload-OBC I2C bus hang, based on preflight testing as described in Sec. 3.6.