Earth Observation Applications Research Articles

Increasing the Awareness of the Benefits of Earth Observation Applications through the Space Information Infrastructure Capacity Building and Training Program (SIICaP)

Abstract. Earth observation (EO) applications offer significant potential for addressing challenges and driving sustainable development in the Philippines. Despite the positive effect EO services can bring, its utilization encounter barriers that includes the lack of awareness of its benefits. The Philippine Space Agency (PhilSA) initiated the Space Information Infrastructure Capacity Building and Training Program (SIICaP) that aims to promote awareness of EO applications through free and inclusive online webinar platforms. This paper highlights the various webinars conducted by SIICaP showing their relevance in raising awareness of EO data, services, and programs. The analysis of evaluation results from the webinar participants further illustrates the program’s reach and success. By showcasing SIICaP, this paper contributes to efforts in developing capacity building programs that aim to have the same objectives of SIICaP.

An Empirical Line approach for Agrowing Camera Aerial Images of inland waters based on exponential fit and in situ water measurements

Abstract. With the advancement of technology in the area of remote sensing, monitoring the Earth's surface using multispectral cameras attached to unmanned aerial vehicles (UAV) has become promising. However, in many Earth observation applications, it is needed to make compatible the spatial data of images captured by high spatial resolution multispectral sensors with the spectral response of targets on the Earth's surface. This relation is obtained through a radiometric calibration. The empirical line method is commonly used to calibrate the spectral bands of sensors. Thus, applications of this method using linear fit have retrieved negative values in water bodies. So that, attempting different adjustments, as well different reference targets, can solve this issue. In this study, the water quality of small bodies of water was analysed using a Agrowing multispectral camera, which derived negative values when applying linear fit. The aim of this study, therefore, was to fit a radiometric calibration based on empirical line method for Agrowing camera in inland water applications. Besides of standard reference targets, water samples also were attempted because showed a lower radiance response than the darkest (black) calibration target. Two empirical line methods were applied to convert the digital number (DN) from the Agrowing images into remote sensing reflectance (Rrs): linear and exponential. The exponential method showed to be more appropriate, with greater accuracy, unlike the linear method.

Thermal Infrared Image Georeferencing using an Ensemble of Land Cover Predictions

Abstract. Precise georeferencing of remote sensing data is usually implemented in a post-processing fashion and is a crucial step for Earth observation applications such as change detection, natural hazard management, and ground target tracking. It is particularly important for small satellites intending to perform temperature monitoring and wildfire detection on a global scale wherein the precise location of fire is to be communicated. The cost-efficient navigating systems on board such satellites are often not capable of providing accurate geolocation information directly due to space and power limitations. Therefore, it is very important to have a globally applicable georeferencing refinement framework that is robust against illuminational and time-relevant scene changes. In this paper, we propose a georeferencing framework for thermal infrared images that consists of ensemble matching of deep learning-based land cover predictions to archival, well-georeferenced land cover maps. We verify the proposed framework on the georeferencing of single-band Landsat thermal imagery. Experimental results show the efficiency and practicality of the method with 72% of the test images geolocated within 1-pixel accuracy with no trajectory information available.

The Adoption and Applications of Earth Observations and Allied Geoinformation Systems for Environmental Management in Ghana

Earth Observations (EOs) and allied technologies were initially adopted and applied to inform sustainable environmental resource management in Ghana. This paper examines the initial adoption of EOs in the early 1990s and subsequent applications. It highlights the transformative potentials of EO technologies in addressing critical environmental issues while also identifying barriers to their widespread adoption and options for optimal up-scaling. Literature review, in-depth interview, and personal experiences of the author were analyzed. The initial optimism bias generated may have masked the need for establishing the necessary sustainability arrangements. The adoption of EOs was driven by the need to generate data to inform natural and environmental resource management in the mid-1990s. Initially, EOs facilities were established in public organizations, including the Council for Scientific and Industrial Research (CSIR), Survey Department, and the University of Ghana. Since then, EOs have been applied in various sectors for research and environmental resource management. Granted, there have been successes. Yet, challenges undermine the optimization and scaling-up of actual benefits. These include the misconceived technocentric view of EOs, which is a restricted domain for the technically initiated and map-making tool. Others, such as unfavourable organizational processes, unsustainable funding, and limitations in research and applications, have adversely contributed to suboptimal outcomes. Suboptimal institutionalization of EOs into management decisions has constrained EOs infrastructure acquisition, maintenance and data procurement. To sustainably enhance EOs applications in resource management, research, and governance, a shift from present misconceptions for effective mainstreaming and a leveraging of EOs as integral parts of decision-making processes are prerequisites. Decision-makers and management personnel should be educated on the relevance of EOs’ functionalities to the core strategies of environmental management.

Satellite Scheduling Problems: A survey of applications in Earth and outer space observation

Satellite Scheduling Problems: A survey of applications in Earth and outer space observation

Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation

AbstractThe ability to compute the exact divergence between two high-dimensional distributions is useful in many applications, but doing so naively is intractable. Computing the $$\alpha \beta $$ α β -divergence—a family of divergences that includes the Kullback–Leibler divergence and Hellinger distance—between the joint distribution of two decomposable models, i.e., chordal Markov networks, can be done in time exponential in the treewidth of these models. Extending this result, we propose an approach to compute the exact $$\alpha \beta $$ α β -divergence between any marginal or conditional distribution of two decomposable models. In order to do so tractably, we provide a decomposition over the marginal and conditional distributions of decomposable models. We then show how our method can be used to analyze distributional changes by first applying it to the benchmark image dataset QMNIST and a dataset containing observations from various areas at the Roosevelt Nation Forest and their cover type. Finally, based on our framework, we propose a novel way to quantify the error in contemporary superconducting quantum computers.

Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Current open implementations require moving large amounts of EO data to the algorithms. We introduced Google Earth Engine native modules that bring conformal prediction to the data and compute, facilitating the integration of uncertainty quantification into existing traditional and deep learning modelling workflows. To demonstrate the versatility and scalability of these tools we apply them to valued EO applications spanning local to global extents, regression, and classification tasks. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that accessible and easy-to-use tools, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of downstream uses such as operational monitoring and decision-making.

Comparative analysis of different supervised methods for satellite-based land-use classification: a case study of Reyhanlı

Satellite-based land-use classification plays a crucial role in various Earth observation applications, ranging from environmental monitoring to disaster management. This study presents a comparative analysis of machine learning techniques applied to land cover classification using Landsat-9 and Sentinel-2 satellite imagery in the Reyhanlı district in southern Türkiye. Three different classification algorithms, Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classification (MLC), were evaluated for their ability to distinguish different land cover classes. High resolution multispectral satellite imagery processed under the same conditions using Geographic Information System (GIS) software was utilized in this study. Visual inspection and statistical evaluation, including overall accuracy and kappa coefficient, were employed to assess classification performance. The classification of Sentinel-2 and Landsat-9 satellite imagery using different machine learning algorithms resulted in the highest overall accuracy (OA = 0.911, Kappa = 0.879) for Sentinel 2 imagery with the RF algorithm. These findings highlight the importance of satellite image selection and algorithm optimization for accurate land cover mapping. This study provides valuable insights for local planners and authorities and underscores the potential of Sentinel-2 imagery combined with machine learning techniques for effective land-use classification and monitoring.

Proposal and investigation of a distributed learning strategy in Orbital Edge Computing-endowed satellite networks for Earth Observation applications

One of the key enabling solutions to in-orbit extract information from Earth Observation images is given by deep learning techniques. However, the accuracy of these algorithms is strictly related to the availability of large datasets of satellite images for training purposes. Limitations on the available transmission bandwidth in the orbital context may prevent the possibility to downlink all acquired images to a node where centralized training happens. Instead, Federated Learning (FL) could be fruitfully leveraged in this scenario, since it provides for each satellite to train a local model only with its own dataset, and then to share its trained model with a central server, which receives models trained by the different satellites and aggregates them into a new global model being eventually shared with all the satellites, and this repeats until convergence is reached. However, because communication with a node acting as a central parameter server may be still limited by short visibility time, the described process may need a long time because of limited communication windows, negatively impacting the time needed to reach model convergence. For this reason, we propose a communication strategy to support a completely distributed learning technique to train a deep learning model in-orbit, by leveraging the fact that satellites may form a network thanks to the potential availability of Inter-Satellite Links (ISLs) within and between orbital planes. Our proposal is different from a FL approach since we provide for each satellite to receive all the information needed to calculate an updated global model by itself, without leaning on a central parameter server. Numerical results show that distributed learning outperforms FL in number of learning rounds completed in the unit time, allowing for reaching validation accuracy convergence in a shorter time, as it has been verified on a land coverage classification task based on the EuroSAT dataset.

Comparative study on satellite based image encryption methods: a survey

The availability of high-resolution satellite images increases with advancements in remote sensing technology. These satellite images are used in various earth observation applications such as disaster management, military applications, weather forecasting, land use and cover, and many more. Satellite images have large volumes stored in memory devices. These satellite images are transmitted to the ground station for processing and analysis. In these cases, images are vulnerable to privacy issues. As technology advances, onboard processing of satellite images using intelligent systems processes the images faster. A model such as field programmable gate arrays (FPGA) is used in onboard processing to process satellite images. However, images are susceptible to faults induced by harsh radiation environments in space. Encryption is one of the most assured methods to provide privacy to satellite images. Hence, encryption of satellite images during processing, storage, and transmission is the present rising demand. There are various encryption methods implemented using algorithms such as advanced encryption standard (AES), homomorphic, advanced encryption standard-counter (AES-CTR), and chaotic maps. Concurrent processing and encryption of images using MapReduce with Hadoop Framework perform the task faster. The focus of this paper is a comparative study of the various encryption methods used in recent years.

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Abstract. Light detection and ranging (LiDAR), as an innovative remote sensing tool, not only captures target reflectance but also provides its morphological parameters. Traditional single/multi-band LiDAR and multispectral LiDAR (MSL) are presently employed in applications such as 3D modeling and plant biochemical parameter inversion albeit with effectiveness limited. Moreover, hyperspectral LiDAR (HSL) distinguished by its expanded array of spectral detection channels and enhanced spectral resolution, has proven more effective in meeting these requirements and also exhibits superior capabilities in both feature and land cover classification tasks. Nevertheless, point clouds acquired through HSL frequently exhibit quality deficiencies, including uneven density and excessive noise. Meanwhile, there exists a notable absence of technical specifications and operational standards governing the measurement protocols for HSL systems globally. To address this gap, this study constructed a systematic analysis framework of data quality in hyperspectral point clouds and endeavors to qualitatively analyse 30 tree point clouds continuously scanned with Finnish Geospatial Research Institute (FGI) 8-band hyperspectral laser scanner. Furthermore, this research validated the theoretical feasibility of employing the 8-band HSL system for inversion processes aimed at quantifying chlorophyll leaf content. Apart from detecting the time-varying patterns of reflectance within birch canopy point clouds, the results of this study also effectively pinpointed the band exhibiting heightened noise level of the HSL system, demonstrating the efficacy of our proposed quality analysis methodology. The endeavor presented in this study can serve as a cornerstone for advancing hyperspectral LiDAR across a diverse array of related remote sensing and earth observation applications.

Enhanced wavelet based spatiotemporal fusion networks using cross-paired remote sensing images

Spatiotemporal fusion can provide remote sensing images with both high temporal and high spatial resolution for earth observation applications. Most of the state-of-the-art models require three or even five images as input, which may lead to difficulties in practical applications due to bad weather or data missing. In this paper, the enhanced cross-paired wavelet based spatiotemporal fusion networks (ECPW-STFN) are proposed to improve the accuracy and quality of the fusions with fewer remote sensing images as inputs. Wavelet transform is introduced into spatiotemporal image fusion to achieve separate training of the high and low frequency components of the image to better extract features of different levels. In addition, a compound loss function containing wavelet loss is proposed to facilitate the preservation of details. Also, an enhancement module with convolutional block attention is put forward to further refine the prediction quality. Experiments were conducted to compare the proposed ECPW-STFN with five state-of-the-art methods on the public CIA and Daxing datasets. The results show ECPW-STFN is better than GAN-STFM which also requires two images, and not inferior to the methods requiring at least three inputs, even exceeds the optimal MLFF-GAN in some cases, proving its great superiority and potential. The code will be available at https://github.com/lixinghua5540/ECPW-STFN.

Earth Observation to Address Inequities in Post‐Flood Recovery

AbstractFloods impact communities worldwide, resulting in loss of life, damaged infrastructure and natural assets, and threatened livelihoods. Climate change and urban development in flood‐prone areas will continue to worsen flood‐related losses, increasing the urgency for effective tools to monitor recovery. Many Earth Observation (EO) applications exist for flood‐hazard monitoring and provide insights on location, timing, and extent in near real‐time and historically to estimate flood risk. Less attention has been paid to flood recovery, even though differing recovery rates and outcomes can have immediate and enduring distributional effects within communities. EO data are uniquely positioned to monitor post‐flood recovery and inform policy on hazard mitigation and adaptation but remain underutilized. We encourage the EO and flood research community to refocus on developing flood recovery applications to address growing risk. Translation of EO insights on flood recovery among flood‐affected communities and decision‐makers is necessary to address underlying social vulnerabilities that exacerbate inequitable recovery outcomes and advocate for redressing injustices where disparate recovery is observed. We identify an unequivocal need for EO to move beyond mapping flood hazard and exposure toward post‐flood recovery monitoring to inform recovery across geographic contexts. This commentary proposes a framework for remote sensing scientists to engage community‐based partners to integrate EO with non‐EO data to advance flood recovery monitoring, characterize inequitable recovery, redistribute resources to mitigate inequities, and support risk reduction of future floods.

AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images

Super-resolution (SR), a technique to increase the resolution of images, is a pre-processing step in the pipelines of applications of Earth observation (EO) data. The manual design and optimisation of SR models that are specific to every possible EO use case is a laborious process that creates a bottleneck for EO analysis. In this work, we develop an automated machine learning (AutoML) method to automate the creation of dataset-specific SR models. AutoML is the study of the automatic design of high-performance machine learning models. We present the following contributions. (i) We propose AutoSR4EO, an AutoML method for automatically constructing neural networks for SR. We design a search space based on state-of-the-art residual neural networks for SR and incorporate transfer learning. Our search space is extendable, making it possible to adapt AutoSR4EO to future developments in the field. (ii) We introduce a new real-world single-image SR (SISR) dataset, called SENT-NICFI. (iii) We evaluate the performance of AutoSR4EO on four different datasets against the performance of four state-of-the-art baselines and a vanilla AutoML SR method, with AutoSR4EO achieving the highest average ranking. Our results show that AutoSR4EO performs consistently well over all datasets, demonstrating that AutoML is a promising method for improving SR techniques for EO images.

CNC-Machined and 3D-Printed Metal G-band Diplexers for Earth Observation Applications

CNC-Machined and 3D-Printed Metal G-band Diplexers for Earth Observation Applications

Wet etch methods to achieve submicron active area self-aligned vertical Sb-heterostructure backward diodes

Sb-heterostructure backward diodes are attractive for passive millimeter-wave sensing techniques in Earth Observation applications, facilitating superior sensitivity (non-linearity) and low noise as compared to traditional zero-bias Schottky diodes. Reducing diode active area is imperative to achieve both increased sensitivity and operation at higher frequencies. In this work, we report a wet etch method utilizing citric acid, phosphoric acid, and hydrogen peroxide for simplified fabrication of vertical (250 nm height) Sb-heterostructure (InAs/AlSb/AlGaSb/GaSb) devices down to active area of 0.04 μm2 (critical dimensions 0.2 × 0.2 μm2). While conventional citric/peroxide etch chemistries are ineffective for removal of Sb-based etch products, including Sb2O5, the addition of phosphoric acid has shown to promote dissolution of etch stop layers, maintaining a linear etch rate throughout the device heterostructure. Furthermore, we found that citric acid plays a crucial role in the underlying etch mechanism which can be attributed to the formation of soluble Sb(V)-citrate complexes. Conducting the etching at reduced temperatures has also shown to promote slow-etching facets at reduced etch temperature (4 °C) drastically improving anisotropy. Finally, the etch method is evaluated by current-voltage characterization of the fabricated diodes achieving predictible active area scaling of performance-metrics across 190 devices indicating formation of defect free sidewalls.

The Impact and Correction of Sensitive Environmental Factors on Spectral Reflectance Measured In Situ

The spectral reflectance measured in situ is often regarded as the “truth” of objects, which plays an important role in Earth observation applications. However, in situ measurements are influenced by several factors such as atmospheric conditions, illumination and view geometry (I&VG), cloud coverage, and adjacency effects. In order to avoid the influence of these factors, in situ measurements are usually carried out under sunny days and close to noon. However, the impact of I&VG is still present in most cases. At present, people still know little about the influence mechanism of I&VG. Moreover, correcting the impact of I&VG is also a problem that needs to be urgently solved in reflectance spectroscopy. In this work, experiments are carried out using the multi-directional hyperspectral remote sensing simulation facility (MHSRS2F), which allows adjustment and control of the I&VG parameters. This paper proposes an uncertainty evaluation model for I&VG and quantifies the uncertainty caused by different I&VG parameters. Then, the sensitivity of reflectance to I&VG at different wavelengths is explored based on uncertainty models. Finally, a correction model for reflectance under different I&VG conditions is proposed. The results reveal that the uncertainty and sensitivity caused by observation height are relatively high, regardless of the surface heterogeneity. It directly affects the size of the field of view and the physicochemical characteristics of the object. For objects that approximate the Lambertian surface, more attention should be paid to the selection and variation of solar and view zenith angles and view azimuth angles. For objects with surface heterogeneity, the selection and variation of solar azimuth angle, view azimuth angle, and solar zenith angle are more crucial. The correction model proposed in this paper has a 41.25% correction effect on different view zenith angles, but the correction effect on other environmental factors is not significant.

First Assessment of Bistatic Geometric Calibration and Geolocation Accuracy of Innovative Spaceborne Synthetic Aperture Radar LuTan-1

LuTan-1 (LT-1) is a bistatic synthetic aperture radar (BiSAR) system consisting of two identical L-band SAR satellites. The bistatic mode of LT-1 plays a critical role in generating high-precision digital elevation models (DEMs), which requires precise geometric calibration of initial range and azimuth times for both SARs to ensure the reliability and quality of geolocation. However, existing geometric calibration methods predominantly focus on monostatic SAR systems, with limited literature on slave SAR calibration in bistatic systems. This research addresses this gap by establishing geometric calibration models for both SARs based on signal echo history and the range–Doppler model. The geometric errors are effectively resolved using corner reflector data from Xinjiang, China. Through statistical analysis of LT-1 SAR images acquired between July and November in bistatic mode, this paper has demonstrated range delay accuracy of better than 5 ns and azimuth time accuracy of better than 0.1 ms. This level of precision translates into a positional accuracy better than 0.8 m. The proposed models have been successfully applied to geometric calibration, providing precise geolocation for LT-1, thus enhancing its utility for a wide range of Earth observation applications. This paper is the first endeavor to present the assessment of the geometric calibration and geolocation accuracy of LT-1 and discuss the results of the bistatic geometric calibration of the master and slave SARs in a BiSAR formation.

Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape

The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring the continuity of Landsat’s calibrated Earth observations. This study comprehensively assesses the continuity of reflectance and the Normalized Difference Vegetation Index (NDVI) between Landsat-8 and Landsat-9 Operational Land Imagers (OLIs) over diverse Chinese landscapes. It reveals that sensor discrepancies minimally impact reflectance and NDVI consistency. Although Landsat-9’s top-of-atmosphere (TOA) reflectance is slightly lower than that of Landsat-8, small root-mean-square errors (RMSEs) ranging from 0.0102 to 0.0248 for VNIR and SWIR bands (and larger RMSE for NDVI at 0.0422) fall within acceptable ranges for Earth observation applications. Applying atmospheric corrections markedly enhances reflectance uniformity and brings regression slopes closer to unity. Further, Bidirectional Reflectance Distribution Function (BRDF) adjustments improve comparability, ensuring measurement reliability, and the NDVI maintains robust consistency across various reflectance types, time series, and land cover classes. These findings affirm Landsat-9’s success in achieving data continuity within the Landsat program, allowing interchangeable use of Landsat-8 and Landsat-9 OLI data for diverse Earth observation purposes. Future research may explore specific sensor correlations across different vegetation types and seasons while integrating data from complementary platforms, such as Sentinel-2, to enhance the understanding of data continuity factors.

An efficient algorithm for solving the constellation-to-ground coverage problem based on latitude strip division

The issue of constellation-to-ground coverage is a research focus in Earth observation applications. Traditional calculations often rely on grid point methods or their derivatives, but these can be limited in their application, relatively costly, inefficient, and often do not account for potential errors. This paper proposes a novel, efficient method based on latitude strip division for calculating the ground area coverage of satellite constellations, capable of providing the upper and lower bounds of coverage ratio for any ground area. Initially, the ground target area is divided into several latitude strips, and the target area range is utilized to determine the longitude range of each latitude strip. Subsequently, the upper and lower bounds of coverage of each strip are calculated according to the satellite ground coverage range. On this basis, the coverage boundary function is defined and the coverage ratio is derived through comprehensive statistics analysis. Finally, depending on the accuracy of the latitude strip division, the precise coverage area and coverage ratio with upper and lower bounds are determined for instantaneous, continuous, and cumulative coverage problems. Numerical simulation experiments were carried out and compared with the traditional grid point method to validate the effectiveness and computational efficiency of this algorithm in addressing the coverage issue for arbitrarily shaped ground areas. When compared to the longitude strip method, it was confirmed that for ground areas where the longitude exceeds the latitude range, this approach offers superior computational efficiency than the latitude strip method.