# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Spectral Signal-to-Noise Ratio
The label column corresponds with Figure 14.  
welcome as it supports the use of this method as an approach to estimating noise. It should also be noted that this image is from the latter part of HICO's operational period, and the sensor is reported to have degraded with time. The noise estimation assumption in [51] is unrealistic for multispectral data. As one could expect from this, [PERSON]'s method gives a better SNR performance for the multispectral SeaHawk CubeSat. The reported SNR with [PERSON]'s method does not yield as good performance as expected in the blue-green region [19]. The SNR estimates are closer to the reported values in the longer wavelengths for SeaHawk. The HYPSO-1 and AVIRIS-NG data fit well with the assumptions of the noise estimation models [18; 52]. The mean acquisition elevation of the AVIRIS-NG scene is reported to be below 80 m. The sensor altitude means that the signal received is not made up of significant contributions from scattering in the atmosphere as the other sensors observe. Thus, it is reasonable to assume that the signal, or the number of photons, reaching the Focal Plane Array (FPA) is less than what is received by the other sensors. The flight path and altitude could explain why the AVIRIS-NG sensor is not performing better than the estimated SNR in Figure 16.  
The PRISMA sensor has a higher spatial resolution than the other sensors listed here [17]. The increased spatial resolution comes at the cost of a reduced SNR for a given aperture [7]. If one were to bin the PRISMA scene to match the spatial resolution of HICO or HYPSO-1, the SNR of PRISMA is expected to increase by a factor of \(\sqrt{n}\), where n is the number of bins [7; 8; 23]. For PRISMA, this would mean a doubling of the expected SNR. However, high spatial resolution can be necessary for some operational scenarios. Furthermore, the SNR reported here is of similar shape and magnitude as reported in a previous publication regarding PRISMA's noise characteristics over water bodies [7]. Even though there is a difference between the two noise estimation models for the PRISMA data, both results are within the range previously reported. This result further validates the use of the noise models given here. For the HYPSO-1 instrument, the center of the focal plane array is selected as there are fewer adverse effects from optical aberrations in this region [24]. This choice provides an SNR that is better than what the estimates is expected to yield near the edges of the image  
Figure 16: SNR estimates of sensor read-outs with two different approaches. The spectral approach estimates noise by the difference between the estimated spectral value as a linear regression between neighboring bands and the measured value. The spatial approach estimates noise by the variance between spatially neighboring pixels. Note that the \(y\)-axis is different for the two figures. (**a**) Spatial noise estimation [50]; (**b**) spectral noise estimation [51]. cube. The sensor performs in line with earlier modeling [5; 23]. As no radiometric correction or flat field corrections are applied to the data from HYPSO-1 for the SNR estimation, the assumptions in [PERSON]'s method has limited validity in the across-track direction. This dissonance should result in a poorer SNR. The homogeneous area for SNR estimation of HYPSO-1 in the across-track direction is chosen deliberately to be narrow to mitigate this limitation. Nevertheless, the SNR performance is still close to the values found in simulations [23]. As shown in Figure 6 in Section 3.1, images from HYPSO-1 are slightly more blurry in terms of spatial FWHM than the ideal values proposed by [37]. Blurring in the spatial dimensions could give an erroneous impression, given the assumptions for [PERSON]'s method. This section does not investigate whether or not this property provides any misguiding advantages in terms of the spatial SNR estimate. The radiometric corrections discussed in Section 3.3 can improve the SNR estimates of HYPSO-1 for the models used here.