Fiducial Lower Confidence Limit of Reliability for a Power Distribution System

Reliability performance, especially the lower confidence limit of reliability, plays an important role in system risk and safety assessment. A good estimator of the lower confidence limit of system reliability can help engineers to make the right decisions. Based on the lifetime of the key component in a typical satellite intelligent power distribution system, the generalized fiducial method is adopted to estimate the lower confidence limit of the system reliability in this paper. First, the generalized pivotal quantity and the lower confidence limit of reliability for the key component are derived for the lifetimes of the exponential-type and Weibull-type components. Simulations show that the sample median is more appropriate than the sample mean when the lower confidence limit of reliability is estimated. Moreover, the lower confidence limit of reliability is obtained for the typical satellite intelligent power distribution system through the pseudo-lifetime data of the metallic oxide semiconductor field effect transistor. The lower confidence limit of reliability for this power distribution system at 15 years is 0.998, which meets the factory’s reliability requirement. Finally, through the comparison, a hot standby subsystem can be substituted with a cold standby subsystem to increase the lower confidence limit of the system reliability.

Virtual Prospecting in Paleontology Using a Drone-Based Orthomosaic Map: An Eye Movement Analysis

Paleontological fieldwork is often a time-consuming process and resource intensive. With unexplored and remote areas, the satellite images, geology, and topography of an area are analyzed to help survey for a site. A drone-based orthomosaic map is suggested as an additional tool for virtual paleontology fossil prospecting. The use of an orthomosaic map was compared to the use of a typical satellite map when looking for fossil sites to prospect. Factors were chosen for their impact when prospecting for a fossil site and availability of data. Eye movement data were captured for a convenience sample of paleontologists from a local university. Each band within the satellite map measures 7741 × 7821 with a ground resolution of 30 m/pix, and the ground resolution of the orthomosaic map is 2.86 cm/pix with a resolution of 52,634 × 32,383. Experts displayed a gaze behavior suggestive of high analysis levels as well as being able to identify and analyze features rapidly—this is illustrated through the presence of both longer and shorter fixations. However, experts appeared to look at both maps in more detail than novices. The orthomosaic map was very successful at both attracting and keeping the attention of the map reader on certain features. It was concluded that an orthomosaic-based drone map used in conjunction with a satellite map is a useful tool for high spatial density virtual prospecting for novices and experts.

Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained in other regions on typical satellite features, such as those from optical sensors, often exhibit low performance when transferred. Here we explore the use of NASA’s Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three major cropped regions (in China, France, and the United States) we first demonstrate that GEDI energy profiles are capable of reliably distinguishing maize, a crop typically above 2m in height, from crops like rice and soybean that are shorter. We further show that these GEDI profiles provide much more invariant features across geographies compared to spectral and phenological features detected by passive optical sensors. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84\%, and able to transfer across regions with accuracies higher than 82\% compared to 64\% for transfer of optical features. Finally, we show that GEDI profiles can be used to generate training labels for models based on optical imagery from Sentinel-2, thereby enabling the creation of 10m wall-to-wall maps of tall versus short crops in label-scarce regions. As maize is the second most widely grown crop in the world and often the only tall crop grown within a landscape, we conclude that GEDI offers great promise for improving global crop type maps.

Advanced data handling architecture for earth observation satellites

In this paper we describe how commercial open standards for embedded systems could affect the architecture of future satellite data handling systems. Traditionally, satellite data handling systems are based on the principles of a federated architecture, i. e. one function is implemented as one box. Each box has its own housing and power supply. In the paper we describe the transition path from the traditional federated architecture to a centralized but modular architecture based on adapted industrial standards. In the presented approach functional modules like on-board computer, Global Navigation Satellite System receiver, interface boards, etc. are combined in a rack communicating via a standard backplane using standardized communication links. The analysis performed during the Advanced Data Handling Architecture study showed that this approach contributes significantly to mass and power reduction (approx. 20 %) of a typical satellite data handling system. Another major point highlighted in the Advanced Data Handling Architecture study is the simplification of Assembly, Integration and Test activities. All this will help space industry to handle increasing system complexity while keeping costs at an acceptable level.

Snow Water Equivalent retrieval using L- & C-band InSAR.

<p>Interferometric Synthetic Aperture Radar (InSAR) imagery is a promising technique for retrieving Snow Water Equivalent (SWE). It exploits the relation of the interferometric phase to the amount and density of the snow in the radar signal path, leading to a quasi-linear relation with SWE (Guneriussen et al., 2001; Leinss et al., 2015). Here, we analyze timeseries of Sentinel-1 and ALOS-2 interferometric image pairs, collected over a test site in Sodankylä, Northern Finland, during the winter of 2019-2020. The satellite imagery is complemented by tower-based SAR observations using SodSAR (Sodankylä SAR) a 1-10GHz fully polarimetric SAR instrument. Typical satellite visit times (7 and 14 days) are compared with the 12-hour temporal resolution provided by SodSAR. Interferometric pairs from the three sensors are generated, and the interferograms are used to estimate the increase in SWE between the image acquisitions. Retrieved SWE is compared with measurements of an in-situ SWE scale, as well as manual ground observations made in the area. Coherence conservation and its relation with various meteorological events are also analyzed.</p>

TROPOMI observations of total column water vapor

<p>We present the total column water vapor (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the blue band. The retrieval was first developed to retrieve TCWV from Global Ozone Monitoring Experience 2 (GOME-2). We have modified the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV retrieval algorithm consists of two major steps. The first step is the retrieval of water vapor slant columns by applying the differential optical absorption spectroscopy (DOAS) technique to TROPOMI observations in the blue band. The retrieved water vapor slant columns are then converted to vertical columns using air mass factors (AMFs). An iterative optimization has been developed to dynamically find the optimal a priori water vapor profile for AMF calculation. The dynamic search algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column amount. This makes the algorithm better suited for climate studies compared to typical satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). Details of the TCWV retrieval are presented. The TCWV retrieval algorithm is applied to TROPOMI observations. The results are validated by comparing to Ozone Monitoring Instrument (OMI), GOME-2 and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.94 to 0.99. The correlation is slight better during winter time of the northern hemisphere. Small discrepancies are found among TROPOMI, OMI, GOME-2 and SSMIS observations. The discrepancies are mainly due to differences in measurement time and cloud filtering. More detailed validation against ground based sun-photometer observations are presented separately in this session*.</p><p> </p><p>*see the respective abstract by Katerina Garane.</p>

Upscaling from Instantaneous to Daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for Satellite Products

The fraction of absorbed photosynthetically active radiation (FAPAR) is an essential climate variable (ECV) widely used for various ecological and climate models. However, all the current FAPAR satellite products correspond to instantaneous FAPAR values acquired at the satellite transit time only, which cannot represent the variations in photosynthetic processes over the diurnal period. Most studies have directly used the instantaneous FAPAR as a reasonable approximation of the daily integrated value. However, clearly, FAPAR varies a lot according to the weather conditions and amount of incoming radiation. In this paper, a temporal upscaling method based on the cosine of the solar zenith angle (SZA) at local noon ( c o s ( S Z A n o o n ) ) is proposed for converting instantaneous FAPAR to daily integrated FAPAR. First, the diurnal variations in FAPAR were investigated using PROSAIL (a model of Leaf Optical Properties Spectra (PROSPECT) integrating a canopy radiative transfer model (Scattering from Arbitrarily Inclined Leaves, SAIL)) simulations with different leaf area index (LAI) values corresponding to different latitudes. It was found that the instantaneous black sky FAPAR at 09:30 AM provided a good approximation for the daily integrated black sky FAPAR; this gave the highest correlation (R2 = 0.995) and lowest Root Mean Square Error (RMSE = 0.013) among the instantaneous black sky FAPAR values observed at different times. Secondly, the difference between the instantaneous black sky FAPAR values acquired at different times and the daily integrated black sky FAPAR was analyzed; this could be accurately modelled using the cosine value of solar zenith angle at local noon ( c o s ( S Z A n o o n ) ) for a given vegetation scene. Therefore, a temporal upscaling method for typical satellite products was proposed using a cos(SZA)-based upscaling model. Finally, the proposed cos(SZA)-based upscaling model was validated using both the PROSAIL simulated data and the field measurements. The validated results indicated that the upscaled daily black sky FAPAR was highly consistent with the daily integrated black sky FAPAR, giving very high mean R2 values (0.998, 0.972), low RMSEs (0.007, 0.014), and low rMAEs (0.596%, 1.378%) for the simulations and the field measurements, respectively. Consequently, the cos(SZA)-based method performs well for upscaling the instantaneous black sky FAPAR to its daily value, which is a simple but extremely important approach for satellite remote sensing applications related to FAPAR.

Low cost satellite constellations for nearly continuous global coverage

Satellite services are fundamental to the global economy, and their design reflects a tradeoff between coverage and cost. Here, we report the discovery of two alternative 4-satellite constellations with 24- and 48-hour periods, both of which attain nearly continuous global coverage. The 4-satellite constellations harness energy from nonlinear orbital perturbation forces (e.g., Earth’s geopotential, gravitational effects of the sun and moon, and solar radiation pressure) to reduce their propellant and maintenance costs. Our findings demonstrate that small sacrifices in global coverage at user-specified longitudes allow operationally viable constellations with significantly reduced mass-to-orbit costs and increased design life. The 24-hour period constellation reduces the overall required vehicle mass budget for propellant by approximately 60% compared to a geostationary Earth orbit constellation with similar coverage over typical satellite lifetimes. Mass savings of this magnitude permit the use of less expensive launch vehicles, installation of additional instruments, and substantially improved mission life.