Earth Observation Satellites Research Articles

Operational Google-Earth-Engine Workflow to Monitor Irrigated Areas in a Semi-Arid Climate

Abstract. Sustainable water resources management relies on advanced hydrological models calibrated from long-term satellite observations of water cycles. Monitoring water resources from Earth-observing satellites is enabled by cloud computing environments like Google Earth Engine (GEE) that support easy processing and analysis of archived data. While there are different satellite-based indicators of water resources availability, this paper focuses on mapping irrigated areas for each summer harvest season. The paper presents an automated workflow to extract yearly irrigated areas from imagery made public by Landsat and Sentinel-2. This method uses a normalised difference vegetation index (NDVI) to identify fields that exhibit higher vegetation greenness than their surroundings and are potentially being irrigated. As a first step, Google Earth Engine was used to create a seasonal maximum NDVI composite on which a grid-based thresholding algorithm was applied to identify irrigated areas in different parts of a catchment. The irrigated areas were then refined using morphological image processing and a quality-control step was conducted with ancillary datasets such as the most recent landuse classification, evapotranspiration data and crop phenology information. Finally, real-world use cases are presented to demonstrate the ability of satellite remote sensing to provide critical information on irrigation practices over the last 4 decades at the catchment scale.

The EL-BIOS Earth Observation Data Cube for Supporting Biodiversity Monitoring in Greece

In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide.

Earth observation reveals the shifting patterns of China's lake colour driven by climate change and land cover

Water colour has been recognized as one of the most important Essential Climate Variables of the lake ecosystem, as it is directly related to changes in water constituents and almost all of the lake's ecological changes could alter water colour. Given the high retrieval accuracy from existing Earth observation satellite data, water colour, in terms of Forel Ule Index (FUI), can be a realistic indicator to track the long-term changes in the lake ecosystem and further explore the lake response to environmental changes. This paper aims to comprehensively investigate the spatiotemporal variation patterns of FUI in 159 large lakes (≥25 km2) across China during 2000–2022 based on the MODIS data and detect the climatic and anthropogenic driving forces of these changes. The 23 years of MODIS records revealed an overall downward trend of lake FUI across China, indicating the lakes in China shifted to bluer colour during the past two decades. Through driving factor analyses, the complicated interplay among lake colour, lake morphology, regional climate shifts and human interference dynamics was uncovered. In the long term, it was found the pronounced change in lake colour in the western lake zones was primarily attributed to climate warming and humidification, whereas that in the eastern lake zones was mainly related to the alterations in regional land cover during the past two decades. Seasonally, lake basin's air temperature was identified as the main factor impacting the seasonal patterns of lake colour, followed by wind speed and runoff. Spatially, there was high spatial variability in lake colour across China, which was mainly associated with lake elevation and lake basin's precipitation rate, although the factors exhibited considerable divergence across different zones. Based upon the above findings, the implications for lake environment protection and management in different regions of China were further discussed.

A Hybrid Spatial Fuzzy Inference and CNN Approach for Forecasting Changes in Remote Sensing

Satellite cloud images, captured by Earth observation satellites, provide crucial information for weather forecasting, aviation, natural resource management, and disaster response. This study presents a novel method for detecting changes in satellite cloud images, combining convolutional neural networks (CNNs) for feature extraction with a spatial complex fuzzy inference system (CFIS). The CNNs effectively capture color channel features in the image blurring and deblurring processes, while the CFIS models the spatiotemporal relationships in the data. Experiments conducted on a US Navy satellite image dataset demonstrate the effectiveness and stability of our proposed method, outperforming related studies in terms of both root mean square error (RMSE) and R-squared (R2) metrics.

Scheduling of Earth observing satellites using distributed constraint optimization

Earth observation satellites (EOS) are satellites equipped with optical sensors that orbit the Earth to take photographs of particular areas at the request of users. With the development of space technology, the number of satellites has increased continuously. Yet still, the number of satellites cannot meet the explosive growth of applications. Thus, scheduling solutions are required to satisfy requests and obtain high observation efficiency. While the literature on multi-satellite scheduling is rich, most solutions are centralized algorithms. However, due to their cost, EOS systems are often co-funded by several agents (e.g., countries, companies, or research institutes). Central solutions require that these agents share their requests for observations with others. To date, there has yet to be a solution for EOS scheduling that protects the private information of the interested parties. In this study, we model the EOS scheduling problem as a distributed constraint optimization problem (DCOP). This modeling enables the generation of timetables for the satellites in a distributed manner without a priori sharing users’ private information with some central authority. For solving the resulting DCOP, we use and compare the results of two different local search algorithms—Distributed Stochastic Algorithm and Maximum Gain Message—which are known to produce efficient solutions in a timely manner. The modeling and solving of the resulting DCOP constitute our new solution method, which we term Distributed Satellite Timetable Solver (DSTS). Experimental evaluation reveals that the DSTS method provides solutions of higher quality than a commonly used centralized greedy algorithm and is comparable to an additional centralized algorithm that we propose.

SchoolHEAT: Racial and Ethnic Inequity in School Temperature.

Exposure to high environmental temperature is detrimental to health through multiple pathways. This paper describes disparities in school-based high-temperature exposure at metropolitan schools in the United States. Using school location and sociodemographic data from the National Center for Education Statistics, neighborhood data from the US Census Bureau, and land surface temperature (LST) data from the Aqua Earth-observing satellite mission, we find that for every 10% more Black or Hispanic residents in the neighborhood, schools have LST 0.25°C and 0.38°C hotter, respectively. When the Black or Hispanic student population is greater than the neighborhood population, LST is an additional 0.20°C and 0.40°C for each 10% increase in students over neighborhood population, respectively. Black and Hispanic students are overrepresented in the hottest schools, making up 58.7% of students in the hottest 20% of schools, compared to only 30.0% of students in the coolest 20% of schools.

A Distributed Deadlock-Free Task Offloading Algorithm for Integrated Communication–Sensing–Computing Satellites with Data-Dependent Constraints

Integrated communication–sensing–computing (ICSC) satellites, which integrate edge computing servers on Earth observation satellites to process collected data directly in orbit, are attracting growing attention. Nevertheless, some monitoring tasks involve sequential sub-tasks like target observation and movement prediction, leading to data dependencies. Moreover, the limited energy supply on satellites requires the sequential execution of sub-tasks. Therefore, inappropriate assignments can cause circular waiting among satellites, resulting in deadlocks. This paper formulates task offloading in ICSC satellites with data-dependent constraints as a mixed-integer linear programming (MILP) problem, aiming to minimize service latency and energy consumption simultaneously. Given the non-centrality of ICSC satellites, we propose a distributed deadlock-free task offloading (DDFTO) algorithm. DDFTO operates in parallel on each satellite, alternating between sub-task inclusion and consensus and sub-task removal until a common offloading assignment is reached. To avoid deadlocks arising from sub-task inclusion, we introduce the deadlock-free insertion mechanism (DFIM), which strategically restricts the insertion positions of sub-tasks based on interval relationships, ensuring deadlock-free assignments. Extensive experiments demonstrate the effectiveness of DFIM in avoiding deadlocks and show that the DDFTO algorithm outperforms benchmark algorithms in achieving deadlock-free offloading assignments.

Enhancing Surface Water Monitoring through Multi-Satellite Data-Fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling.

Optimal Agile Satellite Target Scheduling with Learned Dynamics

Target sequencing is an important aspect of agile Earth-observing satellite scheduling. The objective of the problem is to find a feasible imaging sequence that maximizes the cumulative value of unique heterogeneously valued requests; no expendable resource constraints (power and data storage) are considered. Two challenges are encountered: transition times between targets are dictated by dynamics of an arbitrary controller, and the solution space is combinatorial with respect to request count. To find dynamically accurate time-dependent attitude maneuver transition times, a neural network is trained on simulated slew data; this allows for the generation of more physically accurate, and thus better performing, schedules than when using the traditional approach of lower-order models for slew feasibility. Next, slews between requests are represented by a sparsified graph, and a mixed-integer linear program is formulated to solve them; the sparse formulation keeps the problem tractable over longer planning horizons and larger numbers of requests when compared to the standard approach. The solutions are optimal up to the quality of the transition time estimator and a time discretization. Finally, the solutions are verified in a high-fidelity simulation, demonstrating validity when deployed on a lifelike system. Time-dependent request values and multiple satellites are also considered.

Satellite observed oceanographic drivers of Mobulidae fisheries catch in the Southeast Indian Ocean

Indonesian coastal waters include several marine megafauna biodiversity hotspots. Several fish populations of ecological and socio-economic importance, such as elasmobranchs (sharks and rays), have experienced rapid decline due to unsustainable human activities, primarily overfishing. Small-scale fisheries (SSF) are currently exempt from governmental fisheries management measures despite contributing a significant proportion of a total catch. The Generalised Additive Models were used to investigate the effect of variations in oceanographic parameters of the Teluk Penyu fishing ground, south of central Java, on the magnitude of Mobulidae (Mobula spp.) catch based on its landings data over ten years (2009–2018) from one of Indonesia's largest ports, Cilacap, Central Java, Indonesia. Mobulidae catch from Teluk Penyu fishing ground was generally higher from June to November when the water exhibited relatively high sea surface salinity (sal >34.1 ‰), chlorophyll (0.32–0.45 mg/m3), and nitrate (nit >0.0045 mg NO3/m3), water speed (>0.29 m/s) and eddy kinetic energy (>0.04 m3/s2) levels, and relatively low sea surface temperature (<28 °C), oxygen (<0.182 mg O2/m3) and sea surface height (<0.9 m) levels than the other months of the year. This study reveals that satellite Earth Observation (EO) data provided a preliminary relationship between oceanographic conditions and the amount of catch for developing more effective management and conservation measures for endangered species like Mobulidae. Utilizing EO data may also be applied to help inform much-needed ecosystem-based management measures, including habitat protection and bycatch reduction for conserving endangered Mobulidae species in the Southeast Indian Ocean. The in-situ onboard ocean observation and temporal species-specific catch data will greatly complement the current work.

Oued Lakhdar watershed (Morocco), monitoring land use/cover changes: remote sensing and GIS approach

ABSTRACT Landsat is a series of Earth-observation satellites jointly managed by NASA and the United States Geological Survey (USGS) that capture multispectral images of the Earth’s surface dating back to the 1970s, providing a valuable tool for tracking changes in land cover over time. In this study, Landsat satellite data were combined with remote sensing and Geographic Information System (GIS) technologies to monitor land cover changes in the Oued Lakhdar watershed, which covers an area of 1,638 km2. Landsat satellite data for the years 1987, 1994, 2004, 2014, and 2021 were used, and the maximum likelihood method was applied in ArcGIS software version 10.4.1 to classify land components. The classification was validated using total accuracy, the kappa index, Google Earth, and terrain knowledge. The results show remarkable changes in land cover, with an increase in bare soil and irrigated crops between 1987 and 2021, especially in the last six years, totaling 0.672 km2/yr and 15.028 km2/yr of the study area, respectively, and a decrease in water bodies and dense forests, totaling −0.941 km2/yr and −30.046 km2/yr, respectively. Water retention coefficient analysis indicates that the watershed is anthropized and has a low retention capacity. This study provides valuable information for land use planning and management in the Oued Lakhdar watershed (Morocco).

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part I: Background and Methods

Abstract Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm−1), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm−1) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are difficult to model, like non-Local Thermodynamic Equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In part one of this study, as presented here, the amount of unique information (as determined by Empirical Orthogonal Decomposition (EOD)) made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA’s Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part two of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.

Design and on-orbit performance evaluation of Ethiopian earth observation satellite multispectral optical imaging payload

The Ethiopian Earth observation micro-satellite was successfully launched into a sun-synchronous orbit equipped with an optical multispectral imaging payload system. This optical multispectral imaging payload camera weighs 1.4 kg with four spectral bands: RGB and NIR, each having a unique spectral range. Furthermore, it has a field of view of 7.4°×0.3°, a relative F-number of 9.8°, and a spectral range of 0.45∼0.9μm. The present article covers the design and on-orbit performance analysis of the ETRSS-1 electro-optical imaging payload system, which is based on the COTS compact and unobscured off-axis three-mirror anastigmatic system for high-resolution imaging. Additionally, selecting an appropriate imaging sensor, optomechanical architecture, and performance characteristics for optical instruments is investigated to evaluate spatial, spectral, and radiometric requirements and the payload’s on-orbit performance and capability.

Applying MBSE to Optimize Satellite and Payload Interfaces in Early Mission Phases

The use of model-based systems engineering (MBSE) has been increasingly explored recently in industries that require multi-discipline engineering coordination. In the European space industry, applying MBSE for the engineering of space systems has been an ongoing undertaking on many missions. In the following paper, the MBSE activities in CAMEO conducted during the A/B1 phases of a typical Earth observation satellite engineered by Airbus are discussed in detail in the form of a case study. The analyses shown are based around the modeling of the spacecraft electrical interfaces in CAMEO. This model was used to automate electrical interface control documents (EICDs) and enable the control of electrical interface development. These methodologies were further put in the context of Airbus’ satellite design processes to assess the benefits of an MBSE approach to the current electrical interface engineering procedure and the potential for the reuse of CAMEO models between satellite projects. The reduction in system engineering effort through the reuse of models to modularly create similar satellite systems for efficient concept evaluation and comparison is a clear benefit.

Three multi-objective memetic algorithms for observation scheduling problem of active-imaging agile earth observation satellites

Three multi-objective memetic algorithms for observation scheduling problem of active-imaging agile earth observation satellites

Evaluating Lakes Boundary Change: A Case Study from Ward Number 26 of Pokhara Metropolitan City

Lakes are crucial for their multi-faceted services, including drinking water, irrigation, tourism, and more. Understanding their status, including area coverage, is important, and continuous monitoring is essential. Lakes can be monitored through various approaches, ranging from traditional ground-based surveys to advanced earth observation satellites. This study aimed to monitor several lakes in Ward Number 26 of Pokhara Metropolitan City, including Khaste, Neurini, and Gunde Lakes, using high-resolution cadastral maps, ground-based field surveys, and Sentinel-2 earth observation satellites. Additionally, focus group discussions and key informant interviews were conducted. Supervised land use and land cover classification and the Normalized Difference Water Index method were employed to determine the area of each lake in 2017 and 2021. The focus group discussions and key informant interviews investigated factors affecting lake dynamics. The study found that climate change, land use change, floods, water abstraction, and landslides are major factors contributing to changes in lake areas. The results showed that the satellite-derived lake boundaries were smaller than those indicated on the cadastral map. Ground surveys using total station equipment also revealed that Khaste Lake shrunk by 6122.73 m² (0.01 km²), Neurini Lake lost 474.95 m² (0.00005 km²), and Gunde Lake shrunk by 4226 m² (0.004 km²) due to encroachment. Although satellite images are useful for detecting large-scale changes, this study indicates that the lake boundary areas identified through satellite imagery were smaller than those delineated by cadastral maps and ground surveys. This suggests that remote sensing analysis alone is insufficient for studying small lake boundary changes and should be combined with ground-based surveys. Therefore, a combination of satellite images and ground-based surveys is recommended for accurately and comprehensively detecting changes in lake areas.

Comparative analysis of multispectral data between GF-1 WFV4 and GF-6 WFV sensors

ABSTRACT In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity.

HYPSTAR: a hyperspectral pointable system for terrestrial and aquatic radiometry

Optical Earth observation satellites provide vast amounts of data on a daily basis. The top-of-atmosphere radiance measured by these satellites is usually converted to bottom-of-atmosphere radiance or reflectance which is then used for deriving numerous higher level products used for monitoring environmental conditions, climate change, stock of natural resources, etc. The increase of available remote sensing data impacts decision-making on both regional and global scales, and demands appropriate quality control and validation procedures. A HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR®) has been designed to provide automated, in-situ multiangular reflectance measurements of land and water targets. HYPSTAR-SR covers 380–1020 nm spectral range at 3 nm spectral resolution and is used at water sites. For land sites the HYPSTAR-XR variant is used with the spectral range extended to 1680 nm at 10 nm spectral resolution. The spectroradiometer has multiplexed radiance and irradiance entrances, an internal mechanical shutter, and an integrated imaging camera for capturing snapshots of the targets. The spectroradiometer is mounted on a two-axis pointing system with 360° range of free movement in both axes. The system also incorporates a stable light emitting diode as a light source, used for monitoring the stability of the radiometric calibration during the long-term unattended field deployment. Autonomous operation is managed by a host system which handles data acquisition, storage, and transmission to a central WATERHYPERNET or LANDHYPERNET server according to a pre-programmed schedule. The system is remotely accessible over the internet for configuration changes and software updates. The HYPSTAR systems have been deployed at 10 water and 11 land sites for different periods ranging from a few days to a few years. The data are automatically processed at the central servers by the HYPERNETS processor and the derived radiance, irradiance, and reflectance products with associated measurement uncertainties are distributed at the WATERHYPERNET and LANDHYPERNET data portals.

Enhanced Satellite Analytics for Mussel Platform Census Using a Machine-Learning Based Approach

Mussel platforms are big floating structures made of wood (normally about 20 m × 20 m or even a bit larger) that are used for aquaculture. They are used for supporting the growth of mussels in suitable marine waters. These structures are very common near the Galician coastline. For their maintenance and tracking, it is quite convenient to be able to produce a periodic census of these structures, including their current count and position. Images from Earth observation satellites are, a priori, a convenient choice for this purpose. This paper describes an application capable of automatically supporting such a census using optical images taken at different wavelength intervals. The images are captured by the two Sentinel 2 satellites (Sentinel 2A and Sentinel 2B, both from the Copernicus Project). The Copernicus satellites are run by the European Space Agency, and the produced images are freely distributed on the Internet. Sentinel 2 images include thirteen frequency bands and are updated every five days. In our proposal, remote-sensing normalized (differential) indexes are used, and machine-learning techniques are applied to multiband data. Different methods are described and tested. The results obtained in this paper are satisfactory and prove the approach is suitable for the intended purpose. In conclusion, it is worth noting that artificial neural networks turn out to be particularly good for this problem, even with a moderate level of complexity in their design. The developed methodology can be easily re-used and adapted for similar marine environments.

Analysis of inter-satellite optical wireless communication systems for enhanced data transmission in satellite constellations

This research focuses on the comprehensive performance analysis of inter-satellite optical wireless communication (IsOWC) systems, specifically tailored for high data rate applications in Earth observation satellites. The research employs OptiSystem 21.0 simulation software to model and analyze the IsOWC systems. The simulation scenario is set in a low Earth orbit (LEO) constellation, typical for Earth observation satellites. Various system parameters are systematically varied to assess their impact on performance. Modulation types, such as non-return-to-zero (NRZ) and return-to-zero (RZ) are analyzed for their influence on the quality (Q) factor. The study further explores the effects of different wavelengths, antenna apertures, transmitter output powers, and link distances on the IsOWC system performance. This research provides valuable insights into optimizing IsOWC systems for Earth observation satellites, contributing to the advancement of communication technologies in satellite networks. The research shows that the NRZ modulation consistently outperforms RZ modulation in terms of maximum Q factor and minimum BER across different antenna apertures and power levels. Additionally, shorter wavelengths exhibit improved performance, while larger antenna apertures contribute to enhanced signal quality. The relationship between transmitter power and data rate is also investigated, demonstrating the potential for achieving higher data rates with increased transmitter power. The findings guide the selection of parameters to achieve optimal performance, ensuring efficient data transfer and meeting the demands of contemporary Earth observation missions. This study provides conclusive evidence that the 1350 nm wavelength outperforms the 1550 nm wavelength in reliable and high-data-rate optical wireless communications, offering higher received power, superior signal quality, and lower bit error rates. This makes it a preferred choice for systems in challenging space environments and could enhance future satellite communication architectures.