Formation flying of small satellites using suboptimal MPSP guidance

Using the recently developed model predictive static programming (MPSP), a suboptimal guidance logic is presented in this paper for formation flying of small satellites. Due to the inherent nature of the problem formulation, MPSP does not require the system dynamics to be linearized. The proposed guidance scheme is valid both for high eccentricity chief satellite orbits as well as large separation distance between chief and deputy satellites. Moreover, since MPSP poses the desired conditions as a set of `hard constraints', the final accuracy level achieved is very high. The proposed guidance scheme has been tested successfully for a variety of initial conditions and for a variety of formation commands as well. Comparison with standard Linear Quadratic Regulator (LQR) solution (which serves as a guess solution for MPSP) and another nonlinear controller, State Dependent Riccati Equation (SDRE) reveals that MPSP guidance achieves the objective with higher accuracy and with lesser amount of control usage as well.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.

Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics, at Global Scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography)

Up to now, high-resolution mapping of surface water extent from satellites has only been available for a few regions, over limited time periods. The extension of the temporal and spatial coverage was difficult, due to the limitation of the remote sensing technique e.g., the interaction of the radiation with vegetation or cloud for visible observations or the temporal sampling with the synthetic aperture radar (SAR)]. The advantages and the limitations of the various satellite techniques are reviewed. The need to have a global and consistent estimate of the water surfaces over long time periods triggered the development of a multi-satellite methodology to obtain consistent surface water all over the globe, regardless of the environments. The Global Inundation Extent from Multi-satellites (GIEMS) combines the complementary strengths of satellite observations from the visible to the microwave, to produce a low-resolution monthly dataset () of surface water extent and dynamics. Downscaling algorithms are now developed and applied to GIEMS, using high-spatial-resolution information from visible, near-infrared, and synthetic aperture radar (SAR) satellite images, or from digital elevation models. Preliminary products are available down to 500-m spatial resolution. This work bridges the gaps and prepares for the future NASA/CNES Surface Water Ocean Topography (SWOT) mission to be launched in 2020. SWOT will delineate surface water extent estimates and their water storage with an unprecedented spatial resolution and accuracy, thanks to a SAR in an interferometry mode. When available, the SWOT data will be adopted to downscale GIEMS, to produce a long time series of water surfaces at global scale, consistent with the SWOT observations.

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS (TM) libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

Tracking Pacific bluefin tuna (Thunnus thynnus orientalis) in the northeastern Pacific with an automated algorithm that estimates latitude by matching sea-surface-temperature data from satellites with temperature data from tags on fish

Data recovered from 11 popup satellite archival tags and 3 surgically implanted archival tags were used to analyze the movement patterns of juvenile northern bluefin tuna (Thunnus thynnus orientalis) in the eastern Pacific. The light sensors on archival and pop-up satellite transmitting archival tags (PSATs) provide data on the time of sunrise and sunset, allowing the calculation of an approximate geographic position of the animal. Light-based estimates of longitude are relatively robust but latitude estimates are prone to large degrees of error, particularly near the times of the equinoxes and when the tag is at low latitudes. Estimating latitude remains a problem for researchers using light-based geolocation algorithms and it has been suggested that sea surface temperature data from satellites may be a useful tool for refining latitude estimates. Tag data from bluefin tuna were subjected to a newly developed algorithm, called “PSAT Tracker,” which automatically matches sea surface temperature data from the tags with sea surface temperatures recorded by satellites. The results of this algorithm compared favorably to the estimates of latitude calculated with the lightbased algorithms and allowed for estimation of fish positions during times of the year when the lightbased algorithms failed. Three near one-year tracks produced by PSAT tracker showed that the fish range from the California−Oregon border to southern Baja California, Mexico, and that the majority of time is spent off the coast of central Baja Mexico. A seasonal movement pattern was evident; the fish spend winter and spring off central Baja California, and summer through fall is spent moving northward to Oregon and returning to Baja California.

A new prototype of self-pressurizing fuel tank for micro and nano-satellites

To increase effective load, light-weight micro-propulsion system is necessary for micro-satellites. Traditional propulsion systems including large and heavy high-pressure vessels are difficult to be scaled down to fulfill the demand of micro-satellites. In this article, a novel self-pressurizing fuel tank without high-pressure gas vessel is proposed. When some liquid propellant is consumed, pressure is compensated with CO2 released by heating NH4HCO3 powder in the fuel tank. Comparing with other types of self-pressurizing liquid fuel tank, a gas generator with special and simple structure was designed to stop or continue the NH4HCO3 decomposition reaction easily, and consumed a small amount of energy to heat the powder effectively. Performance tests showed that this new prototype is very suitable for micro-thrusters.