Earth Observation Sensors Research Articles

Advantages of retrieving pigment content [μg/cm2] versus concentration [%] from canopy reflectance

Photosynthesis is essential for life on earth as it, inter alia, influences the composition of the atmosphere and is the driving mechanism of primary production. Photosynthesis is particularly controlled by leaf pigments such as chlorophyll, carotenoids or anthocyanins. Incoming solar radiation is mainly captured by chlorophyll, whereas plant organs are also protected from excess radiation by carotenoids and anthocyanins. Current and upcoming optical earth observation sensors are sensitive to these radiative processes and thus feature a high potential for mapping the spatial and temporal variation of these photosynthetic pigments. In the context of remote sensing, leaf pigments are either quantified as leaf area-based content [μg/cm2] or as leaf mass-based concentration [g/g or %]. However, these two metrics are fundamentally different, and until now there has been neither an in-depth discussion nor a consensus on which metric to choose. This is notable considering the amount of studies that do not explicitly differentiate between pigment content and concentration. We therefore seek to outline the differences between both metrics and thus show that the remote sensing of leaf pigment concentration [%] is unsubstantial. This is due to the fact that, firstly, pigment concentration is likely to primarily reflect variation in leaf mass per area and not pigments itself. Second, the radiative transfer in plant leaves is especially determined by the absolute content of pigments in a leaf and not its relative concentration to other leaf constituents. And third, as a ratio, pigment concentration is an ambiguous metric, which further complicates the quantification of leaf pigments at the canopy scale. Given these issues related to the use of chlorophyll concentration, we thus conclude that remote sensing of leaf pigments should be primarily performed on an area basis [μg/cm2].

Augmented Reality applications as digital experiments for education – An example in the Earth-Moon System

“You realise that the Earth is nothing but accumulated cosmic dust having formed a rock that is encompassed by a flimsy, fragile atmosphere. To grasp this, I needed the view out of the window.” German ESA Astronaut Alexander Gerst's perspective on Earth was changed sustainably by the view from the International Space Station (ISS) onto our home planet. It is possible to give pupils a very similar perspective within the means of public education due to the availability of Earth Observation data from the ISS. However, the data can be put to more educational use than providing a taste of the overview effect. Applying common remote sensing methods and modern teaching concepts to EO (Earth Observation) data from the National Aeronautics and Space Administration (NASA) High Definition Earth Viewing experiment, teaching modules for several STEM (Science, Technology, Engineering, and Mathematics) subjects could be implemented successfully. Building on this success, more ISS EO sensors are being implemented in teaching materials and new media techniques are explored. The more recent addition to the material pool are smartphone apps using Augmented Reality (AR) with which the pupils can experiment on their own. These apps are developed in a partial What You See Is What You Get (WYSIWYG) application development system called Unity with the Vuforia extension, the latter allowing the use of printed images as reference markers for AR. Complex theoretical topics can be visualised in 3-dimensional (3D) animations or turned into inexpensive, easy digital experiments. The app “The Earth-Moon System” applies this with experiments on the effects of changes in the distance between Earth and Moon and a 3D animation on the barycentre between two celestial bodies. Development of such apps is feasible for researchers to visualise their data even with no prior app development knowledge.

A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

ABSTRACT Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developed scheme is not tied to any particular sensor, it can also be implemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

Selection of the Key Earth Observation Sensors and Platforms Focusing on Applications for Polar Regions in the Scope of Copernicus System 2020–2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.

Study on the method of precision adjustment of star sensor

Star sensors are indispensable spatial measurement sensors for high-resolution earth observation and astronomical observations, and the demand for high measurement accuracy of satellite sensors continues to increase; thus, the star sensor optical machine adjustment error cannot be ignored. The commonly used installation error correction method cannot solely meet the precision analysis requirements. In this paper, the relationship between the optical machine installation and the star sensor measurement error is analyzed, and several common adjustment error correction methods are compared. An adjustment method for optical machines is proposed to meet the requirements of very high precision star sensors. The assembly precision requirements of the investigated very high precision star sensor are analyzed considering the whole machine, and then the optical components are controlled through optical precision adjustments to satisfy the precision requirements. Finally, through the complete machine calibration, the star sensor precision adjustment for an optical machine structure is verified. This method meets the requirements of very high precision sensors and is suitable for the precision adjustment of optical machine structures, which is of practical significance to improve the precision of star sensors.

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal observations. The increasing temporal capabilities of today’s sensors enable the use of temporal, along with spectral and spatial features.Domains such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results by using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells that reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, our experiments achieved state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing, compared to other classification approaches.

Monitoring of selected soil contaminants using proximal and remote sensing techniques: Background, state-of-the-art and future perspectives

ABSTRACTSoil degradation includes a number of processes, ranging from soil erosion to soil contamination, which reduce the capability of soil to work as a base for vegetation roots. Methods to quantify soil degradation due to contamination on a large area with a proper domain are needed and must be studied and developed. Proximal and remote sensing techniques are essential tools, well-suited for surveying large areas, and monitoring soil contamination at a high temporal and spatial interval. Recently developed and forthcoming satellites also dedicated to land monitoring and provide inimitable data streams, which have potential of soil contamination detection. This study peruses the potential of spectroscopy methods in various domains to assess selected soil contaminants including potentially toxic elements and petroleum hydrocarbons from reflectance information, plus a preliminary review of the new-generation orbital Earth observation sensors. An aim is to review the means to do so from spaceborne sensors, which are considered to be state-of-the-art Earth orbit observation technologies. This review will help to answer the question: how can spectral information from proximal and remote sensing techniques in different domains be used for soil contamination modelling? This direction will pave the way for soil contamination monitoring using these techniques.

Understanding Heterogeneous EO Datasets: A Framework for Semantic Representations

Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent information for a wide number of applications. However, dealing with the complexity of land cover is sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types of image data. Their properties dictate the category and nature of the perceptible land structures. The data heterogeneity hampers proper understanding, preventing the definition of universal procedures for content exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well as to the lack of coincidence between visual elements and similarities obtained by computation. In order to bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information extraction. The proposed approach acts like a common ground between the user’s understanding, who is visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider information. A hierarchical data representation is considered. At first, basic elements are automatically computed. Then, users can enforce their judgement on the data processing results until semantic structures are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as a dialogue, where communication is determined by a set of parameters guiding the computational process at each level of representation. The purpose is to maintain the data-driven observable connected to the level of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users, allowing them to generate those results that best fit their application scenario. The experiments performed on different satellite images demonstrate the ability to increase the performances in case of semantic annotation by adjusting a set of parameters to the particularities of the analyzed data.

FengYun-3C VIRR Active Fire Monitoring: Algorithm Description and Initial Assessment Using MODIS and Landsat Data

The Visible and Infra-Red Radiometer (VIRR) is an improved third-generation earth observation sensor that has channels ranging from the visible to thermal bands and is carried on board the Chinese FengYun-3C satellite. The VIRR instrument provides full global coverage at a spatial resolution of 1 km. It is neither appropriate nor correct to simply duplicate the parameters used in algorithms designed for other sensors. Hence, this paper proposes an active fire-monitoring algorithm designed for the VIRR sensor. A newly proposed concept—the “infrared channel slope”—is employed in the algorithm, which can detect active fire pixels by identifying deviations in their mid-infrared and thermal infrared brightness temperatures. “Dynamic thresholds” are also used to allow the algorithm to adapt to different situations. Different fire events that occurred in different parts of the world between 2014 and 2016 are selected to test the robustness of the algorithm. The testing consists of two parts: a rough assessment using products of a similar spatial resolution and then a detailed assessment based on fine spatial resolution images. In the rough assessment part, the results are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Fire and Thermal Anomaly product (MOD14A1) and the China Meteorological Administration’s Global Fire References (GFR) products from the National Satellite Meteorological Center. In the detailed assessment, imagery acquired by the Landsat-7/Enhanced Thematic Mapper Plus and the Landsat-8/Operational Land Imager is used. The MODIS and Landsat data are utilized as reference data to validate the accuracy while the comparison with the GFR products is carried out to demonstrate the advantages of this method. The assessment demonstrates that the algorithm produces comparable results to those obtained using MOD14A1 and is a significant improvement compared with GFR.

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

Cold regions affect global, regional and local climate; oftentimes they are relevant for water supply, host valuable ecosystems, and support human livelihood. They are thus eminently important for human society. In the context of ongoing climate change, monitoring and understanding cold region land surface dynamics is essential for environmental scientists, stakeholders and decision makers. However, the definition of cold regions remains inexplicit, and no up-to-date cold region maps or overarching spatial analyses exist. For example, Europe has densely populated cold regions, but hardly an article exists that provides a solid overview of Earth Observation (EO) based applications assessing cold region land surface dynamics in Europe. With this review article we aim at closing this gap by providing an overview of EO-based techniques for cold region observation in Europe, focusing on the dynamics of glaciers and snow. We present a novel spatial delineation of cold regions for Europe before analyzing the benefits and limitations of different EO sensor types and data processing methods for EO based cold region research. Furthermore, we identify research gaps and discuss challenges for future studies.

Toward Seamless Multiview Scene Analysis From Satellite to Street Level

In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis. Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks like land cover mapping, object detection, or scene understanding. What makes the combination of overhead and street-level images challenging, is the strongly varying viewpoint, different scale, illumination, sensor modality and time of acquisition. Direct (dense) matching of images on a per-pixel basis is thus often impossible, and one has to resort to alternative strategies that will be discussed in this paper. We review recent works that attempt to combine images taken from the ground and overhead views for purposes like scene registration, reconstruction, or classification. Three methods that represent the wide range of potential methods and applications (change detection, image orientation, and tree cataloging) are described in detail. We show that cross-fertilization between remote sensing, computer vision and machine learning is very valuable to make the best of geographic data available from Earth Observation sensors and ground imagery. Despite its challenges, we believe that integrating these complementary data sources will lead to major breakthroughs in Big GeoData.

Social Media: New Perspectives to Improve Remote Sensing for Emergency Response

Remote sensing is a powerful technology for Earth observation (EO), and it plays an essential role in many applications, including environmental monitoring, precision agriculture, resource managing, urban characterization, disaster and emergency response, etc. However, due to limitations in the spectral, spatial, and temporal resolution of EO sensors, there are many situations in which remote sensing data cannot be fully exploited, particularly in the context of emergency response (i.e., applications in which real/near-real-time response is needed). Recently, with the rapid development and availability of social media data, new opportunities have become available to complement and fill the gaps in remote sensing data for emergency response. In this paper, we provide an overview on the integration of social media and remote sensing in time-critical applications. First, we revisit the most recent advances in the integration of social media and remote sensing data. Then, we describe several practical case studies and examples addressing the use of social media data to improve remote sensing data and/or techniques for emergency response.

Radiometric sensitivity contrast metrics for spectral remote sensors

The calculation, interpretation, and implications of radiometric sensitivity metrics for Earth-observing multispectral and hyperspectral imaging sensors are discussed. The most commonly used sensor performance metric is signal-to-noise ratio, from which additional noise equivalent quantities can be computed, including noise equivalent spectral radiance (NESR), noise equivalent delta reflectance ( NE Δ ρ ), noise equivalent delta emittance ( NE Δ ϵ ), and noise equivalent delta temperature ( NE Δ T ). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and nontarget (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on a contrast signal-to-noise ratio, including their noise equivalent counterparts (CNESR, CNE Δ ρ , CNE Δ ϵ , and CNE Δ T ), is developed. These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.

Space technology meets policy: An overview of Earth Observation sensors for monitoring of cultural landscapes within policy framework for Cultural Heritage

A wide range of satellite sensors that provide potentially useful imagery for digital documentation, mapping and monitoring of archaeological sites and cultural landscapes. Although some satellites have stopped acquiring new data, their archived satellite imagery can still be accessed, downloaded and exploited for monitoring of changes and therefore useful for research domain of archaeology and cultural landscapes. The aim of this paper is 1) to make an overview of past and current satellite earth observation optical sensors useful for land monitoring, with focus on cultural landscapes and 2) to illustrate a policy framework that goes beyond recommendations, suggesting the need of valuable information possibly provided by the in satellite imagery. Paper will put focus on Copernicus programme as the most recent mission that provides imagery on the global scale and free of charge. Paper, furthermore, highlights the need for a more structured consideration of the contribution that space technologies services and products can offer to the non-space sectors. The actions for implementation of strategies regarding the currently renewed attention towards cultural heritage protection and management, could soon benefit from the technological achievements of satellite technologies in terms of dedicated operational services and applications, tailored to the needs of end-users such as archaeologists, landscape professionals, public administration, researchers and students.

Physically Based Model for Multispectral Image Simulation of Earth Observation Sensors

Physically based multispectral image simulation consists of sensor system modeling, bottom-of-atmosphere (BOA) image generation, and top-of-atmosphere (TOA) image calculation. TOA radiance images are usually generated using a lookup table (LUT) for computational efficiency, which is calculated by means of atmospheric radiative transfer codes with different combination of input variables, including viewing zenith, solar zenith, and relative azimuth angles; visibility; columnar water vapor; and ground elevation. In this paper, a new strategy is proposed for TOA radiance image simulation, where transmitted surface radiance and atmospheric radiance at the TOA are calculated, respectively, to improve accuracy as well as efficiency. The transmitted surface radiance image is obtained from pixel-by-pixel calculation of BOA radiance and path transmittance. In calculating the atmospheric radiance of TOA, two LUTs are built for the emitted and the scattered radiance from each atmospheric layer, respectively. The effects of visibility and columnar water vapor on the atmospheric radiance are characterized by means of an equivalent path transmittance, which is related to the scene geometry as well as the thickness of atmospheric layer. In this way, when a new scene is simulated, except for three variables, i.e., viewing and solar zenith angles and atmospheric layer number, other parameters are set as constants in building the LUTs, enabling more combinations of input variables without adding excessive computational burden. Multispectral images in different bands with moderate spatial resolution are simulated and compared with the moderate-resolution imaging spectroradiometer (MODIS) images to demonstrate the accuracy and the usefulness of the proposed strategy.

Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China

AbstractCloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari‐8 is a multispectral Earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari‐8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy, or hazy. Based on the observations that haze occurs near the ground and accumulates in low‐elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model data with traditional indicators, such as the R0.86/R0.64, R0.86/R1.6, and BT11‐BT3.9, improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari‐8 observations from August 2015, November 2015, January 2016, and May 2016 and validated against the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation vertical feature mask results. The validation shows that the average leakage rate, false alarm rate, and haze missing rate of the HCHM algorithm are 3.95%, 5.88%, and 4.17%, respectively.

Discussion on Application of Polarimetric Synthetic Aperture Radar in Marine Surveillance

Synthetic Aperture Radar (SAR), an important earth observation sensor, has been used in a wide range of applications for land and marine surveillance. Polarimetric SAR (PolSAR) can obtain abundant scattering information of a target to improve the ability of target detection, classification, and quantitative inversion. In this paper, the important role of PolSAR in ocean monitoring is discussed with factors such as sea ice, ships, oil spill, waves, internal waves, and seabed topography. Moreover, the future development direction of PolSAR is put forward to get an inspiration for further research of PolSAR in marine surveillance applications.

Establishing metrological traceability for radiometric calibration of earth observation sensor in Malaysia

The space borne earth observation (EO) sensor provides a continuous large spatial coverage over the earth at relatively low cost (cost-effective) and can be practically accessible worldwide. The daily synoptic view offered by instrument in earth orbit is tremendously useful in various applications, particularly long term global monitoring that needs multi-disciplinary, multi-temporal and multi-sensor data. Due to the indirect measurement nature of the EO sensor, calibration and validation (cal/val) are essentially required to establish the linkage between the acquired raw data and the actual target of interest. Ultimately, EO sensor provider must strive to deliver “the right information, at the right time, to the right people”. This paper is authored with the main aim to report the process of establishing metrological traceability for radiometric calibration of EO sensor at Optical Calibration Laboratory (OCL), National Space Agency of Malaysia (ANGKASA). The paper is structured into six sections. The first section introduces the context of EO and background of radiometric calibration. The next section discusses the requirements for metrological traceability in radiometric calibration while the following third section outlines ANGKASA efforts in setting up the metrological traceability laboratory in radiometric calibration. Meanwhile, the uncertainty estimation results is reported in the fourth section and the fifth section explains some of the continuous efforts made in order to improve the current metrological traceability set up. Lastly, the summary of this paper is provided in the last section.

A Sensor Web and Web Service-Based Approach for Active Hydrological Disaster Monitoring

Rapid advancements in Earth-observing sensor systems have led to the generation of large amounts of remote sensing data that can be used for the dynamic monitoring and analysis of hydrological disasters. The management and analysis of these data could take advantage of distributed information infrastructure technologies such as Web service and Sensor Web technologies, which have shown great potential in facilitating the use of observed big data in an interoperable, flexible and on-demand way. However, it remains a challenge to achieve timely response to hydrological disaster events and to automate the geoprocessing of hydrological disaster observations. This article proposes a Sensor Web and Web service-based approach to support active hydrological disaster monitoring. This approach integrates an event-driven mechanism, Web services, and a Sensor Web and coordinates them using workflow technologies to facilitate the Web-based sharing and processing of hydrological hazard information. The design and implementation of hydrological Web services for conducting various hydrological analysis tasks on the Web using dynamically updating sensor observation data are presented. An application example is provided to demonstrate the benefits of the proposed approach over the traditional approach. The results confirm the effectiveness and practicality of the proposed approach in cases of hydrological disaster.

Permanent Stations for Calibration/Validation of Thermal Sensors over Spain

The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the set-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05 N, 2.1 W), the marshland area in the National Park of Doñana (36.99 N, 6.44 W), and the semi-arid area of the National Park of Cabo de Gata (36.83 N, 2.25 W). This work presents the performance of the permanent stations installed over the different test areas, as well as the cal/val results obtained for a number of Earth Observation sensors: SEVIRI, MODIS, and TIRS/Landsat-8.