Earth Observing System Satellite Research Articles

Results of the first Wave Glider experiment in the southern Tyrrhenian Sea

<p>A wave-propelled autonomous vehicle (Wave Glider) instrumented with a variety of oceanographic and meteorological sensors was launched from Gulf of Naples on the 12<sup>th </sup>of September 2012 for a two-week mission in the Southern Tyrrhenian Sea. The main objective of the mission was a preliminary evaluation of the potential of commercial autonomous platforms to provide reliable measurements of sea surface parameters which can complement existing satellite based products moving from the local to the synoptic scale. To this aim Wave Glider measurements were compared to equivalent, or near-equivalent, satellite products achieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors onboard the EOS (Earth Observing System) satellite platforms and from AVISO (Archiving Validation and Interpretation of Satellite Oceanographic Data). Level-3 near real time and Level-4 reprocessed sea surface foundation temperature products provided by the CMEMS (Copernicus Marine Environment Monitoring Service) were also included in this study as well as high resolution model output supplied by NEMO (Nucleus for European Modelling of the Ocean). The Wave Glider was equipped with sensors to measure temperature, salinity, currents, as well as Colored Dissolved Organic Matter (CDOM), turbidity and refined fuels fluorescence. The achieved results confirmed the emerging value of Wave Gliders in the framework of multiplatform monitoring systems of the ocean surface parameters. In particular, they showed that Wave Glider measurements captured the southern Tyrrhenian Sea major surface oceanographic features, including the coast to open sea haline gradient and the presence of a cyclone-anticyclone system in the southeastern sub-region. The Wave Glider also had the capability to monitor upper ocean currents at finer spatial and temporal scales than satellite altimetric observations and model outputs. Nonetheless, results stressed the existence of several limits in the combined use of satellite and Wave Glider observations and the necessity of further analyses concerning the monitoring of the ocean optical properties. In fact, Wave Glider and satellite-based products agree in terms of sea surface temperature and currents patterns, while bio-optical properties turned out to be less well correlated. No significant traces of refined fuels have been detected along the WG track.</p>

Overview of China Earth Observation Satellite Programs [Space Agencies

China has developed some satellite series such as meteorological satellite series Fengyun (FY), ocean satellite series Haiyang (HY), Earth resources satellite series Ziyuan (ZY), environment and disaster monitoring small satellite constellation (HJ), as well as Shijian satellite series (SJ) for new technological experiments, and has formed a complete Earth observation satellite and ground application system. Until now, 20 Chinese Earth observation satellites have been launched. Chinese Earth observation system has not only largely contributed to the rapid economic development of China, but also become a crucial part of international Earth observation system.

Comparison of in-situ, aircraft, and satellite land surface temperature measurements over a NOAA Climate Reference Network site

Land surface temperature (LST) is a key variable for studying the energy and water vapor exchange at the biosphere–atmosphere interface. In an effort to better quantify the spatial variability and overall representativeness of single-point LST measurements being recorded at NOAA's Climate Reference Network (CRN) sites and to improve the accuracy of satellite LST measurements, airborne flight campaigns were conducted over a CRN site in Crossville, Tennessee, USA during 2010 to 2011. Multiple measurements of LST were made using infrared temperature sensors at micrometeorological tower sites and onboard an instrumented Piper Navajo airborne research aircraft. In addition to this, coincident LST products from the moderate resolution imaging spectroradiometer (MODIS) instruments (Collection 5), onboard NASA Terra and Aqua Earth Observing System satellites were used. In this paper the comparison of LST measurements made from multiple platforms are presented. Our study showed that the temporal and spatial variability of surface temperature as indicated by the standard deviation of the brightness temperature (Tb) during the flight periods were <1.7°C. Aircraft and tower-based Tb during the flight periods agreed well with a root mean square error (RMSE) of <1.3°C. The daytime MODIS LST was lower than the tower and aircraft-based LST, but higher than the daytime near surface air temperature (Ta). MODIS LST showed a positive and lower bias with the nighttime tower-based LST, but with slightly higher RMSE than the daytime dataset. The MODIS LST showed better correlation with the tower-based LST than Ta during clear sky conditions due to the complex relationship between air and surface temperature. Including both day and nighttime data the MODIS LST showed a bias of −0.56°C, RMSE of 2.84°C, and standard deviations of the difference of 2.79°C when compared to the mean tower-based LST at the site. Tower-based Ta explained 98% of the variance in LST during nighttime conditions with a bias of ~0.8°C and RMSE of 0.86°C.

Ice–ocean coupled computations for sea-ice prediction to support ice navigation in Arctic sea routes

With the recent rapid decrease in summer sea ice in the Arctic Ocean extending the navigation period in the Arctic sea routes (ASR), the precise prediction of ice distribution is crucial for safe and efficient navigation in the Arctic Ocean. In general, however, most of the available numerical models have exhibited significant uncertainties in short-term and narrow-area predictions, especially in marginal ice zones such as the ASR. In this study, we predict short-term sea-ice conditions in the ASR by using a mesoscale eddy-resolving ice–ocean coupled model that explicitly treats ice floe collisions in marginal ice zones. First, numerical issues associated with collision rheology in the ice–ocean coupled model (ice–Princeton Ocean Model [POM]) are discussed and resolved. A model for the whole of the Arctic Ocean with a coarser resolution (about 25 km) was developed to investigate the performance of the ice–POM model by examining the reproducibility of seasonal and interannual sea-ice variability. It was found that this coarser resolution model can reproduce seasonal and interannual sea-ice variations compared to observations, but it cannot be used to predict variations over the short-term, such as one to two weeks. Therefore, second, high-resolution (about 2.5 km) regional models were set up along the ASR to investigate the accuracy of short-term sea-ice predictions. High-resolution computations were able to reasonably reproduce the sea-ice extent compared to Advanced Microwave Scanning Radiometer–Earth Observing System satellite observations because of the improved expression of the ice–albedo feedback process and the ice–eddy interaction process.Keywords: Arctic sea ice; numerical simulation; floe collision; Arctic sea routes.(Published: 23 November 2015)To access the supplementary material for this article, please see supplementary files in the column to the right (under Article Tools).Citation: Polar Research 2015, 34, 25008, http://dx.doi.org/10.3402/polar.v34.25008

Experiments with Human Integration in Asynchronous and Sequential Multi-agent Frameworks for Architecture Optimization

Architecting large complex systems is a challenging task due to the presence of uncertainty, ambiguity, and subjectivity as well as the extremely large space of candidate architectures. While the traditional approach to system architecting is a 100% human process, there has been a relatively recent trend to incorporate computational tools to different degrees in the process, thus making it more interactive. Tradespace exploration and optimization tools are among the most frequently used decision-support tools for systems architecting. From a mathematical perspective, architecture optimization problems are usually non-linear, non-convex, and multi-objective combinatorial optimization problems that are at least NP-hard. For these reasons, heuristics are often used to solve architecture optimization problems. These heuristics can be domain-specific, leveraging human knowledge or experience about the problem, or domain-independent, utilizing very little or no domain knowledge. Most applications of optimization in systems architecting consist of a single heuristic or meta-heuristic. However, this approach often results in suboptimal performance and premature convergence, in many cases due to the inability of a single heuristic to adapt to the changing environment of the search space. Humans, on the other hand, can also be considered as heuristics that may be able to adapt more easily to new situations and can naturally recognize patterns in information that can contribute to creating high performing architectures. A hyper-heuristic approach is proposed to combine multiple heuristics, including the human, and adapt the search strategy over time by applying heuristics that display good performance. In order to achieve a beneficial cooperation between humans and computers, this paper discusses and compares two different modes of a multi-agent optimization framework (asynchronous and sequential) that attempt to integrate human and computational agents in a hyper-heuristic architecture optimization algorithm. Experiments are conducted on a real-world problem to architect an Earth observing satellite system with a focus on gathering climate data.

An introduction to factor analysis for radio frequency interference detection on satellite observations

A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C- and X-band radiometer channels as effectively as the conventional PCA method.

Earth Observation Plan Looks Toward Balancing U.S. Federal Priorities

The U.S. federal government funds about $2.5 billion for Earth observation satellite systems and more than $1 billion for airborne, terrestrial (including freshwater), and marine networks and surveys among 11 departments and agencies. Combined, those Earth observation activities add about $30 billion to the U.S. economy each year.

Rule-Based System Architecting of Earth Observing Systems: Earth Science Decadal Survey

This paper presents a methodology to explore the architectural trade space of Earth observing satellite systems, and applies it to the Earth Science Decadal Survey. The architecting problem is formulated as a combinatorial optimization problem with three sets of architectural decisions: instrument selection, assignment of instruments to satellites, and mission scheduling. A computational tool was created to automatically synthesize architectures based on valid combinations of options for these three decisions and evaluate them according to several figures of merit, including satisfaction of program requirements, data continuity, affordability, and proxies for fairness, technical, and programmatic risk. A population-based heuristic search algorithm is used to search the trade space. The novelty of the tool is that it uses a rule-based expert system to model the knowledge-intensive components of the problem, such as scientific requirements, and to capture the nonlinear positive and negative interactions between instruments (synergies and interferences), which drive both requirement satisfaction and cost. The tool is first demonstrated on the past NASA Earth Observing System program and then applied to the Decadal Survey. Results suggest that the Decadal Survey architecture is dominated by other more distributed architectures in which DESDYNI and CLARREO are consistently broken down into individual instruments.

A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models

A technique is presented to produce real-time, daily vegetation composites at 0.01 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> resolution ( ~ 1 km) over the Conterminous United States (CONUS) for use in the NASA Land Information System (LIS) and weather prediction models. Green vegetation fraction (GVF) is derived from direct-broadcast swaths of normalized difference vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observing System satellites. The real-time data and increased resolution compared to the 0.144 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ( ~ 16 km) resolution monthly GVF climatology in community models result in an improved representation of vegetation in high-resolution models, especially in complex terrain. The MODIS GVF fields show seasonal variations that are similar to the community model climatology, and respond realistically to temperature and precipitation anomalies. The wet spring and summer 2010 over the U.S. Plains led to higher regional GVF than in the climatology. The GVF substantially decreased over the U.S. Southern Plains from 2010 to 2011, consistent with the transition to extreme drought in summer 2011. LIS simulations depict substantial sensitivity to the MODIS GVF, with regional changes in heat fluxes around 100 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> over the northern U.S. in June 2010. CONUS LIS simulations during the 2010 warm season indicate that the larger MODIS GVF in the western U.S. led to higher latent heat fluxes and initially lower sensible heat fluxes, with a net drying effect on the soil. With time, the drier soil eventually lead to higher mean sensible heat fluxes such that the total surface energy output increased by late summer 2010 over the western U.S. A sensitivity simulation of a severe weather event using real-time MODIS GVF data results in systematic changes to low-level temperature, moisture, and instability fields, and improves the evolution of simulated precipitation.

Global Distribution of Water Vapor and Cloud Cover—Sites for High-Performance THz Applications

Absorption of terahertz radiation by atmospheric water vapor is a serious impediment for radio astronomy and for long-distance communications. Transmission in the THz regime is dependent almost exclusively on atmospheric precipitable water vapor (PWV). Though much of the Earth has PWV that is too high for good transmission above 200 GHz, there are a number of dry sites with very low attenuation. We performed a global analysis of PWV with high-resolution measurements from the Moderate Resolution Imaging Spectrometer (MODIS) on two NASA Earth Observing System (EOS) satellites over the year of 2011. We determined PWV and cloud cover distributions and then developed a model to find transmission and atmospheric radiance as well as necessary integration times in the various windows. We produced global maps over the common THz windows for astronomical and satellite communications scenarios. Notably, we show that up through 1 THz, systems could be built in excellent sites of Chile, Greenland and the Tibetan Plateau, while Antarctic performance is good to 1.6 THz. For a ground-to-space communication link up through 847 GHz, we found several sites in the Continental United States where mean atmospheric attenuation is less than 40 dB; not an insurmountable challenge for a link.

Assimilation of microwave brightness temperature in a land data assimilation system with multi‐observation operators

AbstractA radiative transfer model (RTM) that provides a link between model states and satellite observations (e.g., brightness temperature) can act as an observation operator in land data assimilation to directly assimilate brightness temperatures. In this study, a microwave Land Data Assimilation System (LDAS) was developed with three RTMs (The radiative transfer model for bare field (QH), land emissivity model (LandEM), and Community Microwave Emission Model (CMEM)) as its multi‐observation operators (LDAS‐MO). Assimilation experiments using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) satellite brightness temperature data from July 2005 to December 2008 were then conducted to investigate the impact of the RTMs on the assimilated results over China. It was found that the assimilated volumetric soil‐water content using each of the three observation operators improved the estimation of soil moisture content in the top soil layer (0–10 cm), with reduced root mean square errors (RMSEs), and increased correlation coefficients with field observations (OBS) as compared to a control run with no assimilation for the absence of frozen or snow‐covered conditions. The assimilated soil moisture for the QH model, which was more sensitive to dry soil than the other models, produced closer correlations with OBS in arid and semi‐arid regions while smaller RMSEs were observed for LandEM. CMEM agreed most closely with OBS over the middle and lower reaches of the Yangtze River due to its better simulation of the brightness temperature over densely vegetated areas. To improve assimilation accuracy, a Bayesian model averaging (BMA) scheme for the LDAS‐MO was developed. The BMA scheme was found to significantly enhance assimilation capability producing the soil moisture analysis, showing the lowest RMSEs and highest correlations with OBS over all areas. It was demonstrated that the BMA scheme with LDAS‐MO has the potential to estimate soil moisture with high accuracy.

Optimization of earth observation satellite system based on parallel systems and computational experiments

Earth observation satellite system (EOSS) is the main space platform to collect ground information. Optimization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experiments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.

A Multi-Sensor Exportable Approach for Automatic Flooded Areas Detection and Monitoring by a Composite Satellite Constellation

Timely and frequently updated information about flood-affected areas and their space-time evolution are often crucial in order to correctly manage the emergency phases. In such a context, optical data provided by meteorological satellites, offering the highest available temporal resolution (from hours to minutes), could have a great potential. As cloud cover often occurs reducing the number of usable optical satellite images, an appropriate integration of observations coming from different satellite systems will surely improve the probability to find cloud-free images over the investigated region. To make this integration effective, appropriate satellite data analysis methodologies, suitable for providing congruent results, regardless of the used sensor, are envisaged. In this paper, a sensor-independent approach (RST, Robust Satellites Techniques-FLOOD) is presented and applied to data acquired by two different satellite systems (Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration platforms and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System satellites) at different spatial resolutions (from 1 km to 250 m) in the case of Elbe flood event occurred in Germany on August 2002. Results achieved demonstrated as the full integration of AVHRR and MODIS RST-FLOOD products allowed us to double the number of satellite passes daily available, improving continuity of monitoring over flood-affected regions. In addition, the application of RST-FLOOD to higher spatial resolution MODIS (250 m) data revealed to be crucial not only for mapping purposes but also for improving RST-FLOOD capability in identifying flooded areas not previously detected at lower spatial resolution.

Cosmo-Skymed Ground Segment Ram Analysis

The COSMO-SkyMed is a low-orbit, dual-use, earth observation satellite system operating in the X-band funded by the Italian Ministry of Research (MIUR) and Ministry of Defense and managed by the Italian Space Agency (ASI). SkyMed will make use of the most advanced remote sensing technology in the construction of four SAR (Synthetic Aperture Radar) satellites and in the realization of a complex and geographically distributed Ground Segment infrastructure manufactured by Italian industries. In the context of the Agreement between ASI and CONAE (National Commission of Spatial activities) two Argentine missions are in progress SAOCOM-1-A & 1-B, two SAR L-band satellites. This set of six SAR satellites comprised the SIASGE (Italian-Argentine System for Emergency Management). The COSMO-SkyMed ground segment includes: A constellation management infrastructure (Mission Control Centres and Satellite Control Centres), a decentralized ground-based user facilities for direct data collection from the satellites and a Centralized archiving facility. Falda del Carmen CONAE Ground Station (CGS) in Cordoba city, Argentina was upgraded in order to fulfill Cosmo-SkyMed RAM (Reliability, Availability & Maintainability) requirements. In this paper a complete RAM analysis for all the services provided by CGS (CGSS) was performed, providing the RAM figures that demonstrate that, with the design concept and the architecture proposed, system requirements were achieved.

Atmospheric aerosol layers over Bangkok Metropolitan Region from CALIPSO observations

Previous studies suggested that aerosol optical depth (AOD) from the Earth Observing System satellite retrievals could be used for inference of ground-level air quality in various locations. This application may be appropriate if pollution in elevated atmospheric layers is insignificant. This study investigated the significance of elevated air pollution layers over the Bangkok Metropolitan Region (BMR) from all available aerosol layer scenes taken from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) for years 2007 to 2011. The results show that biomass burning smoke layers alone were the most frequently observed. The smoke layers accounted for high AOD variations and increased AOD levels. In the dry seasons, the smoke layers alone with high AOD levels were likely brought to the BMR via northeasterly to easterly prevailing winds and found at altitudes above the typical BMR mixing heights of approximately 0.7 to 1.5km. The smoke should be attributed to biomass burning emissions outside the BMR.

From BASE-ASIA toward 7-SEAS: A satellite-surface perspective of boreal spring biomass-burning aerosols and clouds in Southeast Asia

In this paper, we present recent field studies conducted by NASA's SMART-COMMIT (and ACHIEVE, to be operated in 2013) mobile laboratories, jointly with distributed ground-based networks (e.g., AERONET, http://aeronet.gsfc.nasa.gov/ and MPLNET, http://mplnet.gsfc.nasa.gov/) and other contributing instruments over northern Southeast Asia. These three mobile laboratories, collectively called SMARTLabs (cf. http://smartlabs.gsfc.nasa.gov/, Surface-based Mobile Atmospheric Research &Testbed Laboratories) comprise a suite of surface remote sensing and in-situ instruments that are pivotal in providing high spectral and temporal measurements, complementing the collocated spatial observations from various Earth Observing System (EOS) satellites. A satellite-surface perspective and scientific findings, drawn from the BASE-ASIA (2006) field deployment as well as a series of ongoing 7-SEAS (2010–13) field activities over northern Southeast Asia are summarized, concerning (i) regional properties of aerosols from satellite and in-situ measurements, (ii) cloud properties from remote sensing and surface observations, (iii) vertical distribution of aerosols and clouds, and (iv) regional aerosol radiative effects and impact assessment. The aerosol burden over Southeast Asia in boreal spring, attributed to biomass burning, exhibits highly consistent spatial and temporal distribution patterns, with major variability arising from changes in the magnitude of the aerosol loading mediated by processes ranging from large-scale climate factors to diurnal meteorological events. Downwind from the source regions, the tightly coupled-aerosol–cloud system provides a unique, natural laboratory for further exploring the micro- and macro-scale relationships of the complex interactions. The climatic significance is presented through large-scale anti-correlations between aerosol and precipitation anomalies, showing spatial and seasonal variability, but their precise cause-and-effect relationships remain an open-ended question. To facilitate an improved understanding of the regional aerosol radiative effects, which continue to be one of the largest uncertainties in climate forcing, a joint international effort is required and anticipated to commence in springtime 2013 in northern Southeast Asia.

Urban green spaces analysis for development planning in Colombo, Sri Lanka, utilizing THEOS satellite imagery – A remote sensing and GIS approach

Upon conclusion of the 30 years of civil conflict, Sri Lanka has entered into a new era of development. The beautification project of Colombo, Sri Lanka's commercial capital, can be considered as one of the most important projects among urban planning and restoration endeavours implemented throughout the island. In such urban development endeavour, green spaces planning play a crucial role in improving the environmental quality and standard of living of the residents. The objective of this study is to analyse the environmental quality in Colombo city based on green spaces and to provide appropriate recommendations to raise the environmental quality to international standards.In this study, green space areas were extracted from Thailand Earth Observation System (THEOS) satellite imagery using Normalized Difference Vegetation Index (NDVI). Extracted green space areas were further analysed quantitatively with air quality indicators and population density utilizing deductive indexing method at Grama Niladhari (GN) administrative division level to identify correlations of these factors. An environmental criticality map based on population density and percentage green spaces has been constructed to identify the areas with high criticality. The study revealed that 24% of the entire Colombo Municipal Council (CMC) area consists of green spaces. This study also revealed that 34 GN divisions out of 55 lack the minimum per capita green space recommended by World Health Organization for healthy living. Recommendations are presented regarding regions where new green spaces should be established to enhance the environmental standard of living up to international standards.

Application of Kriging Surrogate Model to Optimization of Earth Observation Satellite System

Optimized system configuration is the key point to improve performance of earth observation satellite system(EOSS).However,its performance can not be calculated analytically.To solve the problem of EOSS optimization,we propose a simulation based optimization method,in which Kriging surrogate model is built to approximate simulation data.Points with optimized values or maximal expected improvement are selected to update our surrogate model.And a measure named objective improvement versus distance is defined to filtrate the selected points.To get the optimized solution of the surrogate model,we construct an improved generalized pattern search algorithm.In the search step,genetic algorithm and sequential quadratic programming are used to find potential update points.In the poll step,dynamic incompletion poll is carried out to find points with greater value.Finally,through a series of test cases and contrastive experiments,the results prove that our method is e?ective.

Step Forward to the Next Earth Observation Satellite Systems in JAXA

Step Forward to the Next Earth Observation Satellite Systems in JAXA

Assessment of remotely sensed drought features in vulnerable agriculture

Abstract. The growing number and effectiveness of Earth observation satellite systems, along with the increasing reliability of remote sensing methodologies and techniques, present a wide range of new capabilities in monitoring and assessing droughts. A number of drought indices have been developed based on NOAA-AVHRR data exploiting the remote sensing potential at different temporal scales. In this paper, the remotely sensed Reconnaissance Drought Index (RDI) is employed for the quantification of drought. RDI enables the assessment of hydro-meteorological drought, since it uses hydrometeorological parameters, such as precipitation and potential evapotranspiration. The study area is Thessaly, central Greece, which is a drought-prone agricultural region characterized by vulnerable agriculture. Several drought features are analyzed and assessed by using monthly RDI images over the period 1981–2001: severity, areal extent, duration, periodicity, onset and end time. The results show an increase in the areal extent during each drought episode and that droughts are classified into two classes, namely small areal extent drought and large areal extent drought, respectively, lasting 12 or 13 months coinciding closely with the hydrological year. The onset of large droughts coincides with the beginning of the hydrological year, whereas the onset of small droughts is in spring. During each drought episode, the maximum occurs usually in the summer and they all last until the end of the hydrological year. This finding could justify an empirical prognostic potential of drought assessment.