Future Earth Observation Satellites Research Articles

Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications.

This study introduces a prototype end-to-end Simulator software tool for simulating two-dimensional satellite multispectral imagery for a variety of satellite instrument models in aquatic environments. Using case studies, the impact of variable sensor configurations on the performance of value-added products for challenging applications, such as coral reefs and cyanobacterial algal blooms, is assessed. This demonstrates how decisions regarding satellite sensor design, driven by cost constraints, directly influence the quality of value-added remote sensing products. Furthermore, the Simulator is used to identify situations where retrieval algorithms require further parameterization before application to unsimulated satellite data, where error sources cannot always be identified or isolated. The application of the Simulator can verify whether a given instrument design meets the performance requirements of end-users before build and launch, critically allowing for the justification of the cost and specifications for planned and future sensors. It is hoped that the Simulator will enable engineers and scientists to understand important design trade-offs in phase 0/A studies easily, quickly, reliably, and accurately in future Earth observation satellites and systems.

A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations

Abstract. One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG.

STRATEGIC OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL LAND COVER CLASSIFICATION

Abstract. Hyperspectral data recorded by future earth observation satellites will have up to hundreds of narrow bands that cover a wide range of the electromagnetic spectrum. The spatial resolution (around 30 meters) of such data, however, can impede the integration of the spatial domain for a classification due to spectrally mixed pixels and blurred edges in the data. Hence, the ability of performing a meaningful classification only relying on spectral information is important. In this study, a model for the spectral classification of hyperspectral data is derived by strategically optimizing a convolutional neural network (1D-CNN). The model is pre-trained and optimized on imagery of different nuts, beans, peas and dried fruits recorded with the Cubert ButterflEye X2 sensor. Subsequently, airborne hyperspectral datasets (Greding, Indian Pines and Pavia University) are used to evaluate the CNN's capability of transfer learning. For that, the datasets are classified with the pre-trained weights and, for comparison, with the same model architecture but trained from scratch with random weights. The results show substantial differences in classification accuracies (from 71.8% to 99.8% overall accuracy) throughout the used datasets, mainly caused by variations in the number of training samples, the spectral separability of the classes as well as the existence of mixed pixels for one dataset. For the dataset that is classified least accurately, the greatest improvement with pre-training is achieved (difference of 3.3% in overall accuracy compared to the non-pre-trained model). For the dataset that is classified with the highest accuracy, no significant transfer learning was observed.

Design and performance assessment of a distributed vibration suppression system of a large flexible antenna during attitude manoeuvres

One of the major needs of future Earth Observation satellites is to fulfil high demanding pointing requirements when using instrumentations supported by or mounted at the end of very large flexible truss structures. Those spacecraft often call for the capacity of performing fast and precise manoeuvres without exceeding deformation requirements related to their flexible parts. On-board mechanical disturbances can be generated by an attitude control equipment and pointing mechanisms. In this perspective, a network of smart actuators/sensors can be mounted on a passive structure to counteract undesired elastic vibrations. In this paper, the problem of micro-vibration control for a spacecraft equipped with a very large flexible antenna is addressed. The instrument is sustained by an active frame hosting an optimally distributed network of actuators and sensors dedicated to vibrations suppression. The fully coupled 3-D equations for a flexible spacecraft subjected to forces of the orbital environment are derived taking the presence of the smart materials into account. The 3-D flexible structure model is developed and validated via FEM formulation by using commercial codes. The model is then reduced to obtain a system to be easily handled by control systems algorithms. A placement strategy to identify the best location for vibration control devices is carried out by using Gramian-based techniques. Typical profiles of attitude manoeuvres are then simulated by applying a control torque while simultaneously suppressing unwanted vibrations. To achieve a high performance, feedback methods are implemented to coordinate the actuators and ensure that maximum tip displacement requirements are satisfied during operations.

Laser communication applied for EDRS, the European data relay system

Future earth observation satellites call for GEO relay links to make their data immediately available to the user. While RF communication limits the GEO relay’s data rate to roughly 1 Gbps (Gigabit per second) laser communication will extend its capacity into the 10 Gbps range as is required for the future systems. Laser communication will be applied for EDRS, the European data GEO relay system. At first, it is foreseen to provide the RF LEO-to-GEO link with an additional laser communication channel, still limiting, of course, the GEO relay’s performance to the RF bottleneck. However besides the operational service, the EDRS laser terminals shall also demonstrate the performance of high data rate links for both, the inter-satellite link and the GEO-to-ground link.

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R 2 of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R 2 of 0.63–0.71, RMSE of 32.0–28.2 Mg/ha up to biomass levels of 200–250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar–SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

The spaceborne lidar experiment

Lidar is one of the key sensors to understand global atmospheric environment, so lidar is one of the candidates to be flown on future earth observation satellites. But spaceborne lidar is our first trial and we have some technical issues to resolve before establishing operational sensor. So we start development of the Spaceborne Lidar Experimental Model as a first step of lidar program of NASDA. The Spaceborne Lidar Experimental Model will be launched in early 2001. The purpose of the Spaceborne Lidar Experiment is technological demonstration of system design and critical components to apply to future operational sensor. And we demonstrate availability of lidar observation data and scientific value. There are some critical points to resolve such as (1) thermal design of laser oscillator which remove locally-generated-heat effectively and (2) mirror design with little distortion.

Present status and future plans of the Japanese earth observation satellite program

Japan is now operating 3 earth observation satellites, i. e. MOS-1 (Marine Observation Satellite-1, Momo-1 in Japanese), EGS (Experimental Geodetic Satellite, Ajisai in Japanese) and GMS (Geostationary Meteorological Satellite, Himawari in Japanese). MOS-1 has 3 different sensors, MESSR (Multispectral Electronic Self Scanning Radiometer), VTIR (Visible and Thermal Infrared Radiometer) and MSR (Microwave Scanning Radiometer) in addition to DCS (Data Collection System). GMS has two sensors, VISSR (Visible and IR Spin Scan Radiometer) and SEM (Solar Environmental Monitor). EGS is equipped with reflecting mirrors of the sun light and laser reflecters. For the future earth observation satellites, ERS-1 (Earth Resources Satellite-1), MOS-1b, ADEOS (Advanced Earth Observing Satellite) are under development. Two sensors, AMSR (Advanced Microwave Scanning Radiometer) and ITIR (Intermediate Thermal IR Radiometer) for NASA's polar platform are initial stage of development. Study and planning are made for future earth observation satellites including Japanese polor platform, TRMM, etc.). The study for the second generation GMS has been made by the Committee on the Function of Future GMS under the request of Japan Meteorological Agency in FY 1987.