output determine the data fusion level: (i) raw data level (e.g., pan-sharpening), where the input data from the various sources are integrated into one single, higher spatial resolution data; (ii) feature level (e.g., classification and change detection), where surface features can be extracted and re-arranged; and (iii) decision level (e.g., fusion of data acquired by multiple sensors—optical, SAR, and LiDAR), where the results from multiple algorithms are combined and fused into one single, fused decision [284,287,288]. Such an increase in the degree of data fusion level brings the current trend out, where this approach has become a fundamental tool for data analysis, partly due to the increase in the number of the launched satellites (eventually in constellation configuration), to the improvement of the spatial and spectral resolutions of space sensors, and to the opportunity of validating and integrating the remote sensing data with ground data [284].

In this framework, the DORA project can greatly benefit from the data fusion tech-
niques outlined above. On one hand, the current telescope prototype under development
is equipped with an optical sensor operating in the VIS to TIR spectral bands, for which
the various study cases requested a ~300-400 nm panchromatic filter and several ~50 nm
multispectral filters. On the other hand, a similar instrument equipped instead with an
SAR antenna is under study, as well as the possibility of having a constellation of small
platforms flying deployable optics. At the present state of activities, with only the optical
sensor under development, we will consider here only the pan-sharpening technique for
the data fusion application.

So far, large satellites, such as the WorldView, have optical sensors with very high resolutions (of the order of a meter or less) and multiple spectral bands, which allow for the pan-sharpening technique. However, since its initial conceptualization, data fusion has become an essential tool to process data acquired by small satellites, which can (i) guarantee a great ground coverage by means of constellations, and (ii) ensure a wider utilization of the final data products, because they are cheaper. An example can be provided by the SkySat constellation (in the case of SkySat 16 to 21, the resolutions of the panchromatic and the spectral bands are 0.57 m and 0.75 m, respectively). See Table 2 for more examples.

Here, we present the results of pan-sharpening applied to panchromatic and multi-
spectral images taken by Pléiades HR-1B, a large satellite that acquired images at the spatial
resolutions of 0.5 m and 2 m for the panchromatic and multispectral filters, respectively,
i.e., similar to those achievable with small satellites. Figure 1, showing the RGB compo-
sition of Lesbos Island (Greece), is taken as a sample image, where four pan-sharpening
techniques are tested: multiplicative, simple mean, intensity hue saturation (IHS), and
Brovey transformation. Figure 2a–d show the same scene of Figure 1 in RGB, each one
after the application of one of the pan-sharpening techniques listed above. Because the
best performing method cannot be fixed in an absolute way, but depends on the charac-
teristics of the study area, several methods must be compared each time to select the one
which provides suitable results for a defined purpose, by means of visual, spatial, and
spectral analysis [303,304].
<figure><img src="image_6.png" /><figcaption>**Figure 1.** RGB true color composition of a semi-urban area in Lesbos Island (Greece). Taken by Pléiades HR-1B instrumentations, which has a resolution of 0.5 m in the panchromatic and 2 m in the multispectral filters. The picture shows that the area taken by the instrument is at low resolutions, and details cannot be appreciated.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 2.** The image of the same region in Figure 1, after being processed by pan-sharpening techniques: (a) multiplicative method, (b) simple mean method, (c) IHS, and (d) Brovey transformation.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 1.** RGB true color composition of a semi-urban area in Lesbos Island (Greece). Taken by Pléiades HR-1B instrumentations, which has a resolution of 0.5 m in the panchromatic and 2 m in the multispectral filters. The picture shows that the area taken by the instrument is at low resolutions, and details cannot be appreciated.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** The image of the same region in Figure 1, after being processed by pan-sharpening techniques: (a) multiplicative method, (b) simple mean method, (c) IHS, and (d) Brovey transformation.</figcaption></figure>
## 5. Radiometric Model as Tool for DORA Feasibility Study

In this paper, we have presented a selection of study cases that are not exhaustive of all the possible marine, atmospheric, and oceanographic research topics, but they have been selected for being quite wide-ranging to carry out the feasibility study of the DORA project. In Table 3, we summarize the state-of-the-art of the presented study cases, which will highlight how new space missions, such as the one studied within the DORA project, can be of great benefit for their investigation and progress. Indeed, current remote sensing data are only partially available because of the high costs of the large satellite data acquisition and/or the still limited use of small satellites.

<table><caption>**Table 3.** Summary of the study cases presented in Section 3. The fourth column reports the current available data to study the specific research case, whereas the last one shows what it is still missing for a complete understanding of the related topics.</caption><thead><tr><td>ID</td><td>Scientific Area</td><td>Goals</td><td>State-of-the-Art</td><td>What We Need</td></tr></thead><tbody><tr><td>A1</td><td>Composition of the atmosphere (SO<sub>2</sub>)</td><td>Continuous monitoring of traces gases, e.g., SO<sub>2</sub>, from natural (volcanic) and anthropogenic (traffic, industry) sources</td><td>Large satellites (e.g., ESA Sentinel, NOAA/NASA POESS, or EUMESAT MetOp programs) and recently small satellites (e.g., PARASOL, HARP, and MIOSat) were inserted into polar and geostationary orbits to monitor the atmosphere</td><td><ul><li>Having the payload on a constellation of small satellites allows for a short revisiting time, critical to study real-time evolution of natural and anthropogenic events</li><li>MEMS technology can be used on integrated chips, avoiding complex and heavy mobile optics</li></ul></td></tr><tr><td>A2</td><td>Polynyas monitoring in polar areas</td><td>High resolution continuous monitoring of polynyas dynamics to study the evolution of the seasonal ice production</td><td>MODIS onboard Aqua EOS has a short revisiting time, but a low spatial resolution (1 km)</td><td>A short revisiting time provided by a constellation is associated with higher resolution to investigate the small-scale variability that characterizes the ice-water border</td></tr><tr><td>A3</td><td>Coastal area monitoring</td><td>Ocean color, sea surface temperature monitoring, and the sea state and altimetry</td><td>Available data are not at resolutions of space and time needed to have a comprehensive monitoring of the dynamics and variability of coastal natural phenomena</td><td>Monitor narrower regions, allowing the acquisition of data at higher spatial resolution (few hundred meters), in various spectral ranges, on a daily basis, allowing for instance to track the evolution of pollutant spillage</td></tr><tr><td>A4</td><td>P. oceanica monitoring</td><td>Before a gradual regression over the whole Mediterranean basin, an update of the dynamics and extension/distribution variation is needed</td><td>Instruments onboard large or medium sized satellites (e.g., QuickBird, WorldView, and Ikonos-2) have high spectral and spatial resolution for mapping the meadows, but long revisiting times</td><td><ul><li>Remote sensing images at spatial resolution of 1 m (PAN) and 2 m (MS), with a revisiting time of 5 days (on the nadir) and 7-8 days (off-nadir): (i) systematic monitoring of meadows, (ii) study of the coastal and estuarine biophysical dynamics</li><li>Marine Protected Areas effectiveness</li></ul></td></tr><tr><td>A5</td><td>Precipitations in the Mediterranean basin</td><td>The Mediterranean basin is a hot-spot: measuring vertical precipitation profiles in this area can be of essential relevance for capturing the entire development process of a thunderstorm clouds and, therefore, to reduce their impacts</td><td>In many regions, such as the Mediterranean basin, precipitation surveillance networks are still incomplete and inadequate: available data consist of rain-gauges punctual measurements, or ground-based weather radar, and very few satellite missions (e.g., TRMM)</td><td>Data with spatial and temporal resolutions good enough for a continuous and real-time characterization of fast-changing vertical structure of convective cells originated during extreme rainfall in the Mediterranean region</td></tr></tbody></table>
## 5. Radiometric Model as Tool for DORA Feasibility Study

In this paper, we have presented a selection of study cases that are not exhaustive of all the possible marine, atmospheric, and oceanographic research topics, but they have been selected for being quite wide-ranging to carry out the feasibility study of the DORA project. In Table 3, we summarize the state-of-the-art of the presented study cases, which will highlight how new space missions, such as the one studied within the DORA project, can be of great benefit for their investigation and progress. Indeed, current remote sensing data are only partially available because of the high costs of the large satellite data acquisition and/or the still limited use of small satellites.

<table><caption>**Table 3.** Summary of the study cases presented in Section 3. The fourth column reports the current available data to study the specific research case, whereas the last one shows what it is still missing for a complete understanding of the related topics.</caption><thead><tr><td>ID</td><td>Scientific Area</td><td>Goals</td><td>State-of-the-Art</td><td>What We Need</td></tr></thead><tbody><tr><td>A1</td><td>Composition of the atmosphere (SO<sub>2</sub>)</td><td>Continuous monitoring of traces gases, e.g., SO<sub>2</sub>, from natural (volcanic) and anthropogenic (traffic, industry) sources</td><td>Large satellites (e.g., ESA Sentinel, NOAA/NASA POESS, or EUMESAT MetOp programs) and recently small satellites (e.g., PARASOL, HARP, and MIOSat) were inserted into polar and geostationary orbits to monitor the atmosphere</td><td><ul><li>Having the payload on a constellation of small satellites allows for a short revisiting time, critical to study real-time evolution of natural and anthropogenic events</li><li>MEMS technology can be used on integrated chips, avoiding complex and heavy mobile optics</li></ul></td></tr><tr><td>A2</td><td>Polynyas monitoring in polar areas</td><td>High resolution continuous monitoring of polynyas dynamics to study the evolution of the seasonal ice production</td><td>MODIS onboard Aqua EOS has a short revisiting time, but a low spatial resolution (1 km)</td><td>A short revisiting time provided by a constellation is associated with higher resolution to investigate the small-scale variability that characterizes the ice-water border</td></tr><tr><td>A3</td><td>Coastal area monitoring</td><td>Ocean color, sea surface temperature monitoring, and the sea state and altimetry</td><td>Available data are not at resolutions of space and time needed to have a comprehensive monitoring of the dynamics and variability of coastal natural phenomena</td><td>Monitor narrower regions, allowing the acquisition of data at higher spatial resolution (few hundred meters), in various spectral ranges, on a daily basis, allowing for instance to track the evolution of pollutant spillage</td></tr><tr><td>A4</td><td>P. oceanica monitoring</td><td>Before a gradual regression over the whole Mediterranean basin, an update of the dynamics and extension/distribution variation is needed</td><td>Instruments onboard large or medium sized satellites (e.g., QuickBird, WorldView, and Ikonos-2) have high spectral and spatial resolution for mapping the meadows, but long revisiting times</td><td><ul><li>Remote sensing images at spatial resolution of 1 m (PAN) and 2 m (MS), with a revisiting time of 5 days (on the nadir) and 7–8 days (off-nadir): (i) systematic monitoring of meadows, (ii) study of the coastal and estuarine biophysical dynamics</li><li>Marine Protected Areas effectiveness</li></ul></td></tr><tr><td>A5</td><td>Precipitations in the Mediterranean basin</td><td>The Mediterranean basin is a hot-spot: measuring vertical precipitation profiles in this area can be of essential relevance for capturing the entire development process of a thunderstorm clouds and, therefore, to reduce their impacts</td><td>In many regions, such as the Mediterranean basin, precipitation surveillance networks are still incomplete and inadequate: available data consist of rain-gauges punctual measurements, or ground-based weather radar, and very few satellite missions (e.g., TRMM)</td><td>Data with spatial and temporal resolutions good enough for a continuous and real-time characterization of fast-changing vertical structure of convective cells originated during extreme rainfall in the Mediterranean region</td></tr></tbody></table>
<table><caption>Table 3. *Cont.*</caption><thead><tr><th>ID</th><th>Scientific Area</th><th>Goals</th><th>State-of-the-Art</th><th>What We Need</th></tr></thead><tbody><tr><td>A6</td><td>Sea state from the vessel motions analysis</td><td><ul><li>Continuous monitoring of sea state conditions to improve the statistics of long-term wave data and optimize the route by balancing variations of the weather conditions and navigational constraints</li><li>Develop an integrated ship monitoring system, based on the integration of satellite positioning and radar imaging methods for determining the sea spectrum starting from the survey and analysis of vessel motions</li></ul></td><td>SAR imaging (e.g., Sentinel-1, TerraSAR-X) to accurately estimate the wave parameters such as height, average length, and direction</td><td><ul><li>Radar imaging</li><li>GNSS and onboard instrumentation to determine the sea states from the ship movements</li><li>Daily/hourly revisiting time</li></ul></td></tr><tr><td>A7</td><td>EO for vessel detection</td><td>All-day and all-weather capabilities, large area coverage, dense revisit time, fine spatial resolution details are key assets to generate added-value products to be delivered to end-users (e.g., policymakers, local authorities)</td><td>SAR observation of vessels is a well-established and mature application</td><td>The possibility of having a constellation of platforms with a very short revisiting time (2–3 h) VHR images at lower costs</td></tr></tbody></table>

Generally, for all the cases considered here, the remote sensing system should include instrumentation operating in the spectral range from VIS to NIR and in MW range, and on a global coverage, in order to accurately investigate and monitor the terrestrial environment. Such a system might be launched on a small satellite (mass ≤ 100 kg), operating on a circular polar orbit, with an altitude of ~400 to ~800 km (LEO), resulting in an orbital speed of ~7.5 km/s, which corresponds to a 6.91 km/s ground speed. The orbit should be also heliosynchronous, with an orbital period of about 100 min, i.e., about 15 orbits in one day, and equatorial passes at ~10:00 ÷ 14:00 local time.

We performed a feasibility study only of those applications that demanded a sensor in the VIS to IR spectral range. We carried out a radiometric analysis on the system, which allowed to quantify the radiation flux gathered by the optical system, at varying observing conditions and needed resolutions, and the signal-to-noise ratio (SNR). The SNR is then compared to the specific values given by each application to determine whether or not DORA can reach the requested requirements. In Table 4, the columns 2 to 5 and 7 summarize the requirements, from each of the applications listed in Section 3, to accomplish their specific goals. We also included for completeness the MW-related applications, listing all their requirements, and for a couple of them we assessed their feasibility.

To evaluate the characteristics and performances of the optical instrument, a radiometric model has been developed. The core of such a model represents the response of the instrument, by correctly assessing the spectral response of all its constituting optical elements [305]. On the other hand, the final signal gathered by the detector also depends on the observed sources, which varied from one application to another, and the targets of which have been thus used to compute the output signal and related SNR. Due to the lack of a physical model of the instrument we are considering, in order to produce reliable and meaningful results, the spectral properties of the instrument were assumed based on previous similar projects or instruments in operation [305,306].
<table><caption>Table 3. *Cont.*</caption><thead><tr><th>ID</th><th>Scientific Area</th><th>Goals</th><th>State-of-the-Art</th><th>What We Need</th></tr></thead><tbody><tr><td>A6</td><td>Sea state from the vessel motions analysis</td><td><ul><li>Continuous monitoring of sea state conditions to improve the statistics of long-term wave data and optimize the route by balancing variations of the weather conditions and navigational constraints</li><li>Develop an integrated ship monitoring system, based on the integration of satellite positioning and radar imaging methods for determining the sea spectrum starting from the survey and analysis of vessel motions</li></ul></td><td>SAR imaging (e.g., Sentinel-1, TerraSAR-X) to accurately estimate the wave parameters such as height, average length, and direction</td><td><ul><li>Radar imaging</li><li>GNSS and onboard instrumentation to determine the sea states from the ship movements</li><li>Daily/hourly revisiting time</li></ul></td></tr><tr><td>A7</td><td>EO for vessel detection</td><td>All-day and all-weather capabilities, large area coverage, dense revisit time, fine spatial resolution details are key assets to generate added-value products to be delivered to end-users (e.g., policymakers, local authorities)</td><td>SAR observation of vessels is a well-established and mature application</td><td>The possibility of having a constellation of platforms with a very short revisiting time (2–3 h) VHR images at lower costs</td></tr></tbody></table>

Generally, for all the cases considered here, the remote sensing system should include instrumentation operating in the spectral range from VIS to NIR and in MW range, and on a global coverage, in order to accurately investigate and monitor the terrestrial environment. Such a system might be launched on a small satellite (mass ≤ 100 kg), operating on a circular polar orbit, with an altitude of ~400 to ~800 km (LEO), resulting in an orbital speed of ~7.5 km/s, which corresponds to a 6.91 km/s ground speed. The orbit should be also heliosynchronous, with an orbital period of about 100 min, i.e., about 15 orbits in one day, and equatorial passes at ~10:00 ÷ 14:00 local time.

We performed a feasibility study only of those applications that demanded a sensor in the VIS to IR spectral range. We carried out a radiometric analysis on the system, which allowed to quantify the radiation flux gathered by the optical system, at varying observing conditions and needed resolutions, and the signal-to-noise ratio (SNR). The SNR is then compared to the specific values given by each application to determine whether or not DORA can reach the requested requirements. In Table 4, the columns 2 to 5 and 7 summarize the requirements, from each of the applications listed in Section 3, to accomplish their specific goals. We also included for completeness the MW-related applications, listing all their requirements, and for a couple of them we assessed their feasibility.

To evaluate the characteristics and performances of the optical instrument, a radiometric model has been developed. The core of such a model represents the response of the instrument, by correctly assessing the spectral response of all its constituting optical elements [305]. On the other hand, the final signal gathered by the detector also depends on the observed sources, which varied from one application to another, and the targets of which have been thus used to compute the output signal and related SNR. Due to the lack of a physical model of the instrument we are considering, in order to produce reliable and meaningful results, the spectral properties of the instrument were assumed based on previous similar projects or instruments in operation [305,306].
The radiometric model is developed from the solar radiation flux at the terrestrial upper atmosphere, which was shaped as the Plank function, assuming the sun as a black body at a temperature of 5800 K (BB model), and decreased in intensity to consider the radiation travel to the Earth's orbit. The characteristics of the telescope and platform were assumed on the basis of the most recent optical design. The deployable telescope is assumed with an aperture of 300 m, with an obstruction of 60 mm due to the presence of a secondary mirror. This is coupled with panchromatic and multispectral filters, ranging from about 0.3 to 2.5 µm, with a bandwidth from about 0.05 to 0.2 µm, depending on the considered application. In order to derive the final flux, we considered the optical efficiency (varying between 0.65 and 0.85 in the 350 to 1100 nm wavelength range, and 0.001 otherwise) and the detector quantum efficiency (set to 0.6). The reflected solar flux was obtained by assuming the albedo (which was different for each application, cf. Table 4), whereas no corrective factor was included to account for the terrestrial atmosphere effects, assuming to avoid the stronger absorption bands. The total radiation was derived using the maximum integration time, set to 70% of the dwell time. The SNR was estimated assuming as the main error source the shot noise, and was therefore computed as a square root of the signal.

In Table 4, column 8, the results of the radiometric analysis are provided. We draw attention to the fact that these SNR values are functions of the *maximum integration time*, which is in turn computed from the ground resolutions (depending on the requirements requested by each applications), and the ground track speed (depending on the spacecraft speed and the altitude). We found that the DORA system is capable of resolving the majority of the targets and features described by the various applications. One of the most problematic observations is given by the *P. oceanica*. We therefore performed a parametric study to evaluate the downgrading of the ground resolution (Table 4, column 9) to have an SNR sufficient for observations. In this case, we obtained that *P. oceanica* colonies can be observed and monitored by the DORA system with a ground resolution three times larger than requested.

## 6. Conclusions

In this paper, we reviewed several environmental applications in marine, atmospheric, and oceanographic thematics, and selected both for having pivotal advancements by using remote sensing data and for being quite comprehensive to evaluate the capabilities of deployable optical systems. In particular, we focused on the Deployable Optics for Remote sensing Applications (DORA) project, which is currently under development, and tested its optical performances through radiometric analysis.

The analyzed study cases show how past and current small satellites can be of great benefit to improve such topics, in terms of data resolutions, revisiting times, and lower acquisition costs. On the other hand, manufacturing constraints imposed by small satellites, i.e., size and weight limitations, might severely limit data quality (high spatial and spectral resolutions) with respect to the requirements needed for a specific application. We showed that the DORA deployable telescope is a valuable solution for EO, in particular for monitoring ecological issues, so as to be able to implement arrangements in a timely manner to protect our natural environment.

The successful development of deployable remote-sensing payloads onboard small satellites might also represent a pathway for the exploration of planets and small bodies, whose variety of properties, in terms of size, shape, composition, and internal structure, calls for newer space missions to fill the gaps in the understanding of our solar system.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14092066/s1, Table S1: Lists of all the medium to large space missions cited in the text, reporting all their 34 relevant characteristics for the study cases in the spectral range from VIS to MW. References [43,56,57,63,307–317] are cited in supplementary materials.
The radiometric model is developed from the solar radiation flux at the terrestrial upper atmosphere, which was shaped as the Plank function, assuming the sun as a black body at a temperature of 5800 K (BB model), and decreased in intensity to consider the radiation travel to the Earth's orbit. The characteristics of the telescope and platform were assumed on the basis of the most recent optical design. The deployable telescope is assumed with an aperture of 300 m, with an obstruction of 60 mm due to the presence of a secondary mirror. This is coupled with panchromatic and multispectral filters, ranging from about 0.3 to 2.5 µm, with a bandwidth from about 0.05 to 0.2 µm, depending on the considered application. In order to derive the final flux, we considered the optical efficiency (varying between 0.65 and 0.85 in the 350 to 1100 nm wavelength range, and 0.001 otherwise) and the detector quantum efficiency (set to 0.6). The reflected solar flux was obtained by assuming the albedo (which was different for each application, cf. Table 4), whereas no corrective factor was included to account for the terrestrial atmosphere effects, assuming to avoid the stronger absorption bands. The total radiation was derived using the maximum integration time, set to 70% of the dwell time. The SNR was estimated assuming as the main error source the shot noise, and was therefore computed as a square root of the signal.

In Table 4, column 8, the results of the radiometric analysis are provided. We draw attention to the fact that these SNR values are functions of the *maximum integration time*, which is in turn computed from the ground resolutions (depending on the requirements requested by each applications), and the ground track speed (depending on the spacecraft speed and the altitude). We found that the DORA system is capable of resolving the majority of the targets and features described by the various applications. One of the most problematic observations is given by the *P. oceanica*. We therefore performed a parametric study to evaluate the downgrading of the ground resolution (Table 4, column 9) to have an SNR sufficient for observations. In this case, we obtained that *P. oceanica* colonies can be observed and monitored by the DORA system with a ground resolution three times larger than requested.

**6. Conclusions**

In this paper, we reviewed several environmental applications in marine, atmospheric, and oceanographic thematics, and selected both for having pivotal advancements by using remote sensing data and for being quite comprehensive to evaluate the capabilities of deployable optical systems. In particular, we focused on the Deployable Optics for Remote sensing Applications (DORA) project, which is currently under development, and tested its optical performances through radiometric analysis.

The analyzed study cases show how past and current small satellites can be of great benefit to improve such topics, in terms of data resolutions, revisiting times, and lower acquisition costs. On the other hand, manufacturing constraints imposed by small satellites, i.e., size and weight limitations, might severely limit data quality (high spatial and spectral resolutions) with respect to the requirements needed for a specific application. We showed that the DORA deployable telescope is a valuable solution for EO, in particular for monitoring ecological issues, so as to be able to implement arrangements in a timely manner to protect our natural environment.

The successful development of deployable remote-sensing payloads onboard small satellites might also represent a pathway for the exploration of planets and small bodies, whose variety of properties, in terms of size, shape, composition, and internal structure, calls for newer space missions to fill the gaps in the understanding of our solar system.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14092066/s1, Table S1: Lists of all the medium to large space missions cited in the text, reporting all their 34 relevant characteristics for the study cases in the spectral range from VIS to MW. References [43,56,57,63,307–317] are cited in supplementary materials.
**Author Contributions:** Conceptualization, E.M., G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), V.D.C., P.P., I.B. and L.I.; Data curation, E.M., E.A., L.A., G.A., V.C., E.C. (Elena Catucci), P.D.P., F.E., E.F., A.I. (Anna Innac), S.P., S.S. and G.T.; Formal analysis, E.M., A.M.P., V.D.C., E.A., L.A., G.A., V.C., E.C. (Elena Catucci), P.D.P., F.E., E.F., A.I. (Anna Innac), S.P., S.S. and G.T.; Funding acquisition, V.D.C.; Investigation, E.M. and A.M.P.; Methodology, E.M., A.M.P., G.F. (Giampaolo Ferraioli), V.D.C. and P.P.; Project administration, G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), P.P., S.C., E.C. (Elena Chianese), P.P.F., G.F. (Giannetta Fusco), S.G., A.I. (Agostino Iadicicco), F.N., C.P., V.P., A.R. (Angelo Riccio), G.F.R. and E.Z.; Resources, E.M., I.B. and Z.D.; Supervision, A.R. (Alessandra Rotundi), V.D.C., P.P., S.C., P.P.F., G.F. (Giannetta Fusco), S.G., A.I. (Agostino Iadicicco), F.N., C.P., V.P., A.R. (Angelo Riccio), G.F.R. and E.Z.; Validation, P.P., I.B. and E.C. (Elena Chianese); Writing—original draft, E.M., A.M.P., G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), V.D.C., P.P., E.A., L.A., G.A., I.B., V.C., E.C. (Elena Catucci), P.D.P., F.E., E.F., A.I. (Anna Innac), S.P., S.S. and G.T.; Writing—review & editing, E.M., A.M.P., G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), V.D.C., P.P. and I.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was developed within the framework of "DORA – Deployable Optics for Remote sensing Applications" (project no. ARS01_00653), specialisation area "aerospace", a project funded by MIUR-PON "Research & Innovation"/PNR 2015-2020. The authors received funding from the Department of Sciences and Technologies, University of Napoli "Parthenope" for the OA publication.

**Acknowledgments:** The authors thank the Editor and anonymous Reviewers for critical discussion on this and on an earlier version of the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

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13. Sepuru, T.K.; Dube, T. An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring. *Remote Sens. Appl. Soc. Environ.* **2018**, *9*, 1–9. [CrossRef]

14. Wright, R.; Garbeil, H.; Harris, A.J.L. Using infrared satellite data to drive a thermorheological/stochastic lava flow emplacement model: A method for near-real-time volcanic hazard assessment. *Geophys. Res. Lett.* **2008**, *35*, L19307. [CrossRef]

15. Santilli, G.; Venditozzi, C.; Cappelletti, C.; Battistini, S.; Gessimi, P. CubeSat constellations for disaster management in remote areas. *Acta Astronaut.* **2018**, *145*, 11–17. [CrossRef]

16. Boccardo, P.; Tonolo, F.G. Remote Sensing Role in Emergency Mapping for Disaster Response. In *Engineering Geology for Society and Territory*; Lollino, G., Manconi, A., Guzzetti, F., Culshaw, M., Bobrowsky, P., Luino, F., Eds.; Springer: Cham, Switzerland, 2015; Volume 5. [CrossRef]
**Author Contributions:** Conceptualization, E.M., G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), V.D.C., P.P., I.B. and L.I.; Data curation, E.M., E.A., L.A., G.A., V.C., E.C. (Elena Catucci), P.D.P., F.E., E.F., A.I. (Anna Innac), S.P., S.S. and G.T.; Formal analysis, E.M., A.M.P., V.D.C., E.A., L.A., G.A., V.C., E.C. (Elena Catucci), P.D.P., F.E., E.F., A.I. (Anna Innac), S.P., S.S. and G.T.; Funding acquisition, V.D.C.; Investigation, E.M. and A.M.P.; Methodology, E.M., A.M.P., G.F. (Giampaolo Ferraioli), V.D.C. and P.P.; Project administration, G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), P.P., S.C., E.C. (Elena Chianese), P.P.F., G.F. (Giannetta Fusco), S.G., A.I. (Agostino Iadicicco), F.N., C.P., V.P., A.R. (Angelo Riccio), G.F.R. and E.Z.; Resources, E.M., I.B. and Z.D.; Supervision, A.R. (Alessandra Rotundi), V.D.C., P.P., S.C., P.P.F., G.F. (Giannetta Fusco), S.G., A.I. (Agostino Iadicicco), F.N., C.P., V.P., A.R. (Angelo Riccio), G.F.R. and E.Z.; Validation, P.P., I.B. and E.C. (Elena Chianese); Writing—original draft, E.M., A.M.P., G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), V.D.C., P.P., E.A., L.A., G.A., I.B., V.C., E.C. (Elena Catucci), P.D.P., F.E., E.F., A.I. (Anna Innac), S.P., S.S. and G.T.; Writing—review & editing, E.M., A.M.P., G.F. (Giampaolo Ferraioli), A.R. (Alessandra Rotundi), V.D.C., P.P. and I.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was developed within the framework of "DORA – Deployable Optics for Remote sensing Applications" (project no. ARS01_00653), specialisation area "aerospace", a project funded by MIUR-PON "Research & Innovation"/PNR 2015-2020. The authors received funding from the Department of Sciences and Technologies, University of Napoli "Parthenope" for the OA publication.

**Acknowledgments:** The authors thank the Editor and anonymous Reviewers for critical discussion on this and on an earlier version of the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

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204. Carruthers, T.J.B.; Dennison, W.C.; Kendrick, G.A.; Waycott, M. Seagrasses of south-West Australia: A conceptual synthesis of the world's most diverse and extensive seagrass meadows. *J. Exp. Mar. Biol. Ecol.* **2007**, *350*, 21–45. [CrossRef]

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209. Häyhä, T.; Franzese, P.P. Ecosystem services assessment: A review under an ecological-economic and systems perspective. *Ecol. Modell.* **2014**, *289*, 124–132. [CrossRef]

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212. Cozza, R.; Rende, F.; Ferrari, M.; Bruno, L.; Pacenza, M.; Dattola, L.; Bitonti, M.B. Biomonitoring of *Posidonia oceanica* beds by a multiscale approach. *Aquat. Bot.* **2019**, *156*, 14–24. [CrossRef]

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214. Veettil, B.K.; Ward, R.D.; Lima, M.D.A.C.; Stankovic, M.; Hoai, P.N.; Quang, N.X. Opportunities for seagrass research derived from remote sensing: A review of current methods. *Ecol. Indic.* **2020**, *117*, 106560. [CrossRef]

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217. Pham, T.D.; Xia, J.; Ha, N.T.; Bui, D.T.; Le, N.N.; Takeuchi, W. A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrasses and Salt Marshes during 2010–2018. *Sensors* **2019**, *19*, 1933. [CrossRef] [PubMed]

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219. Pasqualini, V.; Pergent-Martini, C.; Pergent, G.; Agreil, M.; Skoufas, G.; Sourbes, L.; Tsirika, A. Use of SPOT 5 for mapping seagrasses: An application to *Posidonia oceanica*. *Remote Sens. Environ.* **2005**, *94*, 39–45. [CrossRef]

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221. Rende, S.F.; Bosman, A.; Di Mento, R.; Bruno, F.; Lagudi, A.; Irving, A.D.; Dattola, L.; Giambattista, L.D.; Lanera, P.; Proietti, R.; et al. Ultra-High-Resolution Mapping of *Posidonia oceanica* (L.) Delile Meadows through Acoustic, Optical Data and Object-based Image Classification. *J. Mar. Sci. Eng.* **2020**, *8*, 647. [CrossRef]

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224. Giardino, C.; Bresciani, M.; Fava, F.; Matta, E.; Brando, V.E.; Colombo, R. Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations. *Remote Sens.* **2016**, *8*, 2. [CrossRef]

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226. Giorgi, F. Climate change hot spots. *Geophys. Res. Lett.* **2006**, *33*, L08707. [CrossRef]

227. Alexander, L.V.; Zhang, X.; Peterson, T.C.; Caesar, J.; Gleason, B.; Klein Tank, A.M.G.; Haylock, M.; Collins, D.; Trewin, B.; Rahimzadeh, F.; et al. Global observed changes in daily climate extremes of temperature and precipitation. *J. Geophys. Res.* **2006**, *111*, D05109. [CrossRef]

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229. Donat, M.G.; Alexander, L.V.; Yang, H.; Durre, I.; Vose, R.; Dunn, R.J.H.; Willett, K.M.; Aguilar, E.; Brunet, M.; Caesar, J.; et al. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. *J. Geophys. Res.* **2013**, *118*, 2098–2118. [CrossRef]

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231. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The Global Precipitation Measurement Mission. *Bull. Am. Meteorol. Soc.* **2014**, *95*, 701–722. [CrossRef]

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237. Panegrossi, G.; Casella, D.; Dietrich, S.; Marra, A.C.; Sanò, P.; Mugnai, A.; Baldini, L.; Roberto, N.; Adirosi, E.; Cremonini, R.; et al. Use of the GPM constellation for monitoring heavy precipitation events over the Mediterranean region. *IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.* **2016**, *9*, 2733–2753. [CrossRef]

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239. McCabe, M.F.; Rodell, M.; Alsdorf, D.E.; Miralles, D.G.; Uijlenhoet, R.; Wagner, W.; Lucieer, A.; Houborg, R.; Verhoest, N.E.C.; Franz, T.E.; et al. The future of Earth Observation in hydrology. *Hydrol. Earth Syst. Sci.* **2017**, *21*, 3879–3914. [CrossRef]

240. Caspi, A.; Barthelemy, M.; Bussy-Virat, C.D.; Cohen, I.J.; DeForest, C.E.; Jackson, D.R.; Vourlidas, A.; Nieves-Chinchilla, T. Small satellite mission concepts for space weather research and as pathfinders for operations. *Space Weather* **2022**, *20*, e2020SW002554. [CrossRef]

241. Berg, W.; Brown, S.T.; Lim, B.H.; Reising, S.C.; Goncharenko, Y.; Kummerow, C.D.; Gaier, T.C.; Padmanabhan, S. Calibration and Validation of the TEMPEST-D CubeSat Radiometer. *IEEE Trans. Geosci. Remote Sens.* **2021**, *59*, 4904–4914. [CrossRef]

242. Blackwell, W.J.; Braun, S.; Bennartz, R.; Velden, C.; DeMaria, M.; Atlas, R.; Dunion, J.; Marks, F.; Rogers, R.; Annane, B.; et al. An overview of the TROPICS NASA Earth Venture Mission. *Q. J. R Meteorol. Soc.* **2018**, *144* (Suppl. S1), 16–26. [CrossRef]

243. Mehta, A.V.; Yang, S. Precipitation climatology over Mediterranean Basin from ten years of TRMM Measurement. *Adv. Geosci.* **2008**, *17*, 87–91. [CrossRef]

244. Murphy, M.S.; Konrad, C.E., II. Spatial and Temporal Patterns of Thunderstorm Events that Produce Cloud-to-Ground Lightning in the Interior Southeastern United States. *Mon. Weather Rev.* **2005**, *133*, 1417–1430. [CrossRef]

245. Retalis, A.; Katsanos, D.; Michaelides, S. Precipitation climatology over the Mediterranean Basin—Validation over Cyprus. *Atmos. Res.* **2016**, *169*, 449–458. [CrossRef]

246. Stampoulis, D.; Emmanuel, N.A. Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events. *J. Hydrometeorol.* **2013**, *14*, 1500–1514. [CrossRef]

247. Ruf, C.S.; Chew, C.; Lang, T.; Morris, M.G.; Nave, K.; Ridley, A.; Balasubramaniam, R. A New Paradigm in Earth Environmental Monitoring with the CYGNSS Small Satellite Constellation. *Sci. Rep.* **2018**, *8*, 8782. [CrossRef] [PubMed]

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