Standard Data Products Research Articles

LOFAR PL610 Station Data Product Specification

ABSTRACT Since 2017, the Space Research Centre of the Polish Academy of Sciences in Warsaw, Poland, has measured and gathered over 35,000 hours of observations with the use of the LOw Frequency ARray (LOFAR) radio telescope. This paper outlines the Standard Data Product acquired from the LOFAR PL610 station located in Borówiec, Poland. Within this context, we detail the data products that are accessible, provide metadata descriptions for them, and include an example of the data under both quiet and disturbed ionospheric conditions.

Long-term assessment and analysis of the radiometric quality of standard data products for Chinese Gaofen-1/2/6/7 optical remote sensing satellites

The first Chinese Gaofen (GF) remote sensing satellite was launched in August 2013 and has been in orbit for more than 10 years, providing a rich variety of image product data for remote sensing applications in various industries, with other remote sensing satellites of the GF series. To ensure the reliability of the information generated via remote sensing applications, all remote sensing images must undergo systematic quality evaluation. The quality of GF satellites is evaluated almost comprehensively during in-orbit testing at the early stage after launch; however, the remaining evaluations of product quality including geometric or radiometric calibrations are performed only annually. Thus, long-term systematic image quality evaluations are lacking, and the quality level and long-term change trends of the image products are unknown. These deficits have introduced uncertainty in the application of these products. This study aimed to establish a radiometric quality evaluation index framework for GF satellites to comprehensively measure the radiometric quality of GF image products. Multiple types of sample data, such as uniform field, knife-edge target, and radiometric calibration field data with different reflectance features, were acquired globally. Four core indexes, namely, the relative radiometric correction accuracy (RRCA), absolute radiometric correction accuracy (ARCA), modulation transfer function (MTF), and signal-to-noise ratio (SNR), were used to analyze and evaluate the long-term radiometric quality of the images from four GF optical remote sensing satellites: GF1, GF2, GF6, and GF7. The four GF satellites had high SNR and RRCA but low dynamic MTF and variable ARCA for different reflectance features. For example, the average SNRs for the four satellites reached 42.66, 43.91, 44.33, and 47.46 dB; the average RRCA (@root-mean-square errors) values reached 1.6%, 1.0%, 1.6%, and 0.1%; the average MTF values (at Nyquist frequency) of the panchromatic bands were only 0.15, 0.12, 0.07 and 0.04; and the difference in ARCA for different reflectance targets was approximately 10%. In the long time series, the SNRs for each sensor of the four GF satellites were high and stable for high-reflectance features, whereas the RCA and SNR values for low-reflectance features and the MTF of the sensors fluctuated in a cyclic manner. Meanwhile, large dynamic fluctuations and episodic image quality problems occurred at certain local time points, which must be considered during practical applications. The findings presented here will lay a foundation for the in-depth application of GF images.

Maximizing the Volume of Collocated Data from Two Coordinated Suborbital Platforms

Abstract Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud Meteorology Interactions Over the western Atlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically resolved lidar + polarimeter–derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest-neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open-source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms. Significance Statement This study describes a data collocation (i.e., selection) process that aims to maximize the volume of data identified to be simultaneously collected in time and space from two coordinated measurement platforms. The functional utility of the resultant dataset is also demonstrated by extending the validation of aerosol particle number concentration derived from standard lidar and polarimeter data products from a suborbital mission that used two aircraft platforms.

The eROSITA upper limits

Context. The soft X-ray instrument eROSITA on board the Spectrum-Roentgen-Gamma (SRG) observatory has successfully completed four of the eight planned all-sky surveys, detecting almost one million X-ray sources during the first survey (eRASS1). The catalog of this survey will be released as part of the first eROSITA data release (DR1). Aims. Based on X-ray aperture photometry, we provide flux upper limits for eRASS1 in several energy bands. We cover galactic longitudes between 180° ≲ l ≲ 360° (eROSITA-DE). These data are crucial for studying the X-ray properties of variable and transient objects, as well as non-detected sources in the eROSITA all-sky survey data. Methods. We performed aperture photometry on every pixel of the SRG/eROSITA standard pipeline data products for all available sky tiles in the single detection band (0.2–2.3 keV). Simultaneously, we performed the same analysis in the three-band detection at soft (0.2–0.6 keV), medium (0.6–2.3 keV), and hard (2.3–5.0 keV) energy bands. Based on the combination of products for the individual bands, we are also able to provide aperture photometry products and flux upper limits for the 0.2–5.0 keV energy band. The upper limits were calculated based on a Bayesian approach that utilizes detected counts and background within the circular aperture. Results. The final data products consist of tables with the aperture photometry products (detected counts, background counts, and exposure time), a close-neighbor flag, and the upper flux limit based on an absorbed power-law spectral model (Γ = 2.0, NH = 3 × 1020 cm−2). The upper limits are calculated using the one-sided 3σ confidence interval (CL) of a normal distribution, representing CL = 99.87%. The aperture photometry products allow for an easy computation of upper limits at any other confidence interval and spectral model. These data are stored in a database with hierarchical indexes in order to offer a fast query option. Conclusions. We provide a detailed description of the process of retrieving SRG/eROSITA upper limits for a large set of input positions, as well as of the eROSITA data, the X-ray aperture photometry, the upper limit calculation, and the final data products. The eROSITA upper flux limits represent either the maximum flux of potential non-detections or the 3σ upper flux uncertainty of detected sources. We emphasize the importance of choosing the right spectral model that ought to match the spectral shape of the source of interest: the wrong spectral model can produce discrepancies of up to 30% in the final flux upper limit value. We also describe the architecture of the database and the web tool, which are designed to handle large queries on input positions.

Framework for custom event sample augmentations for ATLAS analysis data

For HEP event processing, data is typically stored in column-wise synchronized containers, such as most prominently ROOT’s TTree, which have been used for several decades to store by now over 1 exabyte. These containers can combine row-wise association capabilities needed by most HEP event processing frameworks (e.g. Athena for ATLAS) with column-wise storage, which typically results in better compression and more efficient support for many analysis use-cases. One disadvantage is that these containers, TTree in the HEP use-case, require to contain the same attributes for each entry/row (representing events), which can make extending the list of attributes very costly in storage, even if those are only required for a small subsample of events. Since the initial design, the ATLAS software framework features powerful navigational infrastructure to allow storing custom data extensions for subsamples of events in separate, but synchronized containers. This allows adding event augmentations to ATLAS standard data products (such as DAOD-PHYS or PHYSLITE) avoiding duplication of those core data products, while limiting their size increase. For this functionality, the framework does not rely on any associations made by the I/O technology (i.e. ROOT), however it supports TTree friends and builds the associated index to allow for analysis outside of the ATLAS framework. A prototype based on the Long-Lived Particle search is implemented and preliminary results with this prototype will be presented. At this point, augmented data are stored within the same file as the core data. Storing them in separate files will be investigated in future, as this could provide more flexibility, e.g. certain sites may only want a subset of several augmentations or augmentations can be archived to tape once their analysis is complete.

Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry

Snow depth is highly variable across basins, yet most snow depth data in the western U.S. come from sparse in situ point measurements. The water resources community needs accurate snow depth data for improved basin-wide snow depth estimates. The NASA ICESat-2 mission has provided over four years of global satellite laser altimetry measurements since October 2018. Previous studies have shown that standard ICESat-2 data products, when combined with snow-off digital terrain models (DTMs) from airborne laser scanning, have the potential to provide snow depth measurements with varying accuracy depending on factors such as surface slope and canopy cover. In this study we show that ICESat-2 snow depth measurements can be improved with customized data products generated using the SlideRule Earth service. Here we investigate the accuracy of our ICESat-2 SlideRule snow depth method using four years (2019–2022) of reference in situ snow depth measurements and airborne lidar snow depth observations for two watersheds with varying terrain characteristics: the Tuolumne River basin above Hetch Hetchy, CA and the Methow Valley, WA. We observe median differences of −0.14 m (RMSE of 0.18 m) and −0.20 m (RMSE of 0.33 m) between our ICESat-2 snow depth measurements and reference snow depth measurements for the Tuolumne Basin and Methow Valley sites, respectively. While individual ICESat-2 elevation measurements can contain noise, basin-scale aggregation offers robust statistics for snow depth. Differences in accuracy between sites are attributed to terrain characteristics and their spatial distributions. The customized ICESat-2 SlideRule data products used in this study resulted in more accurate median snow depth values, including under canopy, than those found by previous studies using standard ICESat-2 data products in mid-latitude mountainous regions. When combined with snow-off DTMs, the aggregated snow-on ICESat-2 SlideRule observations could provide a new snow depth dataset across the western U.S. and potentially global land surfaces.

Improving Cloud Detection in WFV Images Onboard Chinese GF-1/6 Satellite

We have developed an algorithm for cloud detection in Chinese GF-1/6 satellite multispectral images, allowing us to generate cloud masks at the pixel level. Due to the lack of shortwave infrared and thermal infrared bands in the Chinese GF-1/6 satellite, bright land surfaces and snow are frequently misclassified as clouds. To mitigate this issue, we utilized MODIS standard snow data products for reference data to determine the presence of snow cover in the images. Subsequently, our algorithm was utilized to correct misclassifications in snow-covered mountainous regions. The experimental area selected was the perpetually snow-covered Western mountains in the United States. The results indicate the accurate labeling of extensive snow-covered areas, achieving an overall cloud detection accuracy of over 91%. Our algorithm enables users to easily determine whether pixels are affected by cloud contamination, effectively improving accuracy in annotating data quality and greatly facilitating subsequent data retrieval and utilization.

Updated observations of clouds by MODIS for global model assessment

Abstract. This paper describes a new global dataset of cloud properties observed by MODIS relying on the current (collection 6.1) processing of MODIS data and produced to facilitate comparison with results from the MODIS observational proxy used in climate models. The dataset merges observations from the two MODIS instruments into a single netCDF file. Statistics (mean, standard deviation, and number of observations) are accumulated over daily and monthly timescales on an equal-angle grid for viewing and illumination geometry, cloud detection, cloud-top pressure, and cloud properties (optical thickness, effective particle size, and water path) partitioned by thermodynamic phase and an assessment as to whether the underlying observations come from fully or partly cloudy pixels. Similarly partitioned joint histograms are available for (1) optical thickness and cloud-top pressure, (2) optical thickness and particle size, and (3) cloud water path and particle size. Differences with standard data products, caveats for data use, and guidelines for comparison to the MODIS simulator are described. Data are available on daily (https://doi.org/10.5067/MODIS/MCD06COSP_D3_MODIS.062; NASA, 2022b) and monthly (https://doi.org/10.5067/MODIS/MCD06COSP_M3_MODIS.062; NASA, 2022a) timescales.

Application of a β-mannanase enzyme in diets with a reduced net energy content in post-weaning piglets resulted in equal performance and an additional economic benefit

β-Mannans are strongly anti-nutritive polysaccharide fibers found in most vegetable feed ingredients. The estimated content of soluble β-mannans in common swine diets range from 0.15 to 0.40%. In vitro studies have demonstrated that as little as 0.05% soluble β-mannan content in feed can elicit a strong innate immune response. Hemicell HT (Elanco Animal Health) is a β-mannanase enzyme for animal feed that breaks down β-mannans, thereby preventing economic losses from the wasteful immune response to β-mannans. The present study aimed to compare pig performance on a control diet and a reformulated diet with a lower energy content – 45 kcal/kg NE reduction – and the inclusion of a β-mannanase enzyme. A six-week feeding trial was conducted on a commercial post-weaning facility with DanBred x Belgian Piétrain pigs starting at 21 days of age. Standard three-phase control diets were compared to reformulated diets with an energy reduction of 45 kcal NE/kg and inclusion of a β-mannanase enzyme (Hemicell HT; Elanco) at 300 g/tonne. Standard production data were collected. The data were analyzed using JMP 15.0 statistical program. Overall, performance data did not differ significantly between trial groups in both Phase 1 and Phase 2, and overall, during the entire post-weaning period. Mortality was only numerically, but not significantly higher in the Control as compared to the Hemicell HT group. Hemicell HT had an overall benefit of € 1.69 per piglet and € 15.18 per tonne of feed due to the 45 kcal/kg NE reduction. The current trial demonstrated that the inclusion of Hemicell HT in reformulated diets with a lower energy content (45 kcal NE/kg) was able to retain production performance in post-weaned piglets with an economic benefit.

Application of a β -Mannanase Enzyme in Diets with a Reduced Net Energy Content Results in Reduced Production Costs Per Kg of Carcass Weight

β-Mannans are strongly anti-nutritive polysaccharide fibres found in most vegetable feed ingredients. They belong to the hemicellulose fraction and have a backbone composed entirely of mannose, as in mannans and galactomannans, or of mannose and glucose, as in glucomannans and galacto-glucomannans. The estimated content of soluble β-mannans in common fattening diets is only 0.20-0.35%, and in vitro studies have demonstrated that as little as 0.05% soluble β-mannan in feed can elicit a strong innate immune response. This innate response is often referred to as a feed induced immune response or FIIR, which suppresses growth to protect the liver and reserve energy and nutrients for high priority immune functions. Hemicell HT (Elanco Animal Health) is a β-mannanase enzyme for animal feed that breaks down β-mannans and thereby prevents economic losses from the wasteful immune response to β-mannans. The aim of the present study was to compare pig performance and carcass parameters on a control diet and a reformulated diet with lower energy content - a 65 kcal reduction in net energy per kg – and inclusion of a β-mannanase enzyme. An eighteen-week feeding trial was conducted on a commercial fattening unit with DanBred x Belgian Piétrain pigs starting at 10 weeks of age. Standard two-phase control diets (Control group) were compared to reformulated diets with an energy reduction of 65 kcal NE/kg and inclusion of a β-mannanase enzyme (Hemicell HT; Elanco) at 300 g/tonne (Enzyme treated group). Standard production and health data were collected including days in fattening, bodyweight at start, at 28 days and at 127 days (prior to slaughter), feed intake, mortality and antibiotic use. Additionally, Average Daily Weight Gain (ADWG), Average Daily Feed Intake (ADFI) and Feed Conversion Rate (FCR) were calculated from the collected data. Furthermore, pigs were slaughtered on separate slaughter days to reliably collect relevant carcass parameters at slaughterhouse level. The data were analysed using JMP 15.0 statistical program. Overall, performance data did not differ significantly between trial groups in both Phase 1 and Phase 2, except for mortality that was significantly higher during Phase 1 in the Control group (3.19% vs. 0.00% in the Enzyme treated group). Carcass quality did only significantly (P < 0.05) differ for muscle depth (73.58 ± 0.66 mm vs. 75.33 ± 0.59 mm in the Control and Enzyme treated group, respectively). Following calculation of feed costs per kg carcass weight and considering the cost of enzyme inclusion, Enzyme treated pigs had € 0.033 lower feed costs per kg carcass weight as compared to Control pigs. The trial demonstrated that inclusion of Hemicell HT in reformulated diets with a lower energy content (65 kcal NE/kg) was able to degrade β-mannans in diets and therefore maintain production performance and overall carcass quality. Reduction in mortality may indicate an improved overall immunity due to decreased FIIR by the breakdown of β-mannans in the feed. Inclusion of a β-mannanase enzyme resulted in an overall reduction in production costs of € 0.033 per kg of carcass weight, which is an additional advantage considering the current feed prices globally.

Development of a data science CURE in microbiology using publicly available microbiome datasets.

Scientific and technological advances within the life sciences have enabled the generation of very large datasets that must be processed, stored, and managed computationally. Researchers increasingly require data science skills to work with these datasets at scale in order to convert information into actionable insights, and undergraduate educators have started to adapt pedagogies to fulfill this need. Course-based undergraduate research experiences (CUREs) have emerged as a leading model for providing large numbers of students with authentic research experiences including data science. Originally designed around wet-lab research experiences, CURE models have proliferated and diversified globally to accommodate a broad range of academic disciplines. Within microbiology, diversity metrics derived from microbiome sequence information have become standard data products in research. In some cases, researchers have deposited data in publicly accessible repositories, providing opportunities for reproducibility and comparative analysis. In 2020, with the onset of the COVID-19 pandemic and concomitant shift to remote learning, the University of British Columbia set out to develop an online data science CURE in microbiology. A team of faculty with collective domain expertise in microbiome research and CUREs developed and implemented a data science CURE in which teams of students learn to work with large publicly available datasets, develop and execute a novel scientific research project, and disseminate their findings in the online Undergraduate Journal of Experimental Microbiology and Immunology. Analysis of the resulting student-authored research articles, including comments from peer reviews conducted by subject matter experts, demonstrate high levels of learning effectiveness. Here, we describe core insights from course development and implementation based on a reverse course design model. Our approach to course design may be applicable to the development of other data science CUREs.

HMI Ring-Diagram Analysis: Effects of Tracking, Noise, and Resolution

Ring-diagram analysis is a standard local helioseismic technique. Data from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) are routinely used for ring-diagram analysis, and fits to the power spectra as well as inversion results are standard data products. In this article we examine the effects of different tracking rates, noise, and resolution on ring-diagram results. Most of the analysis is for \(15^{\circ}\) tiles, but we also examine the effects of different tile sizes. The largest effect that we find is that of spatial resolution. Doppler noise has very little effect on the results, except perhaps at the deepest regions for which the tiles can give reliable results; variations in the tracking rate have a similar effect.

Monitoring and Analysis of Water Level–Water Storage Capacity Changes in Ngoring Lake Based on Multisource Remote Sensing Data

Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.

Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset

Quantifying atmospheric aerosols and their linkages to climatic repercussions is necessary to understand the dynamics of climate forcing and enhance our knowledge of climate change. Because of this reactivity to precipitation, temperature, topography, and human activity, the atmospheric boundary layer (ABL) is one of the most dynamic atmospheric regions: ABL aerosols have a big impact on the evolution of climate change’s radiative forcing, human health, food security, and, eventually, the local and global economy. Continuous monitoring and instrumental and computational approaches are required for the detection and analysis of ABL pattern behavior. This paper provides a deep learning-based outer layer aerosol detection system based on Light Detection and Ranging (LiDAR) data fusion. The suggested method applies sequential models to turn low-level data into compressed features using object-based analysis, feature-level fusion, and autoencoder-based dimensionality reduction. Convolutional neural networks (CNNs) were used to convert compressed data into high-level properties that could be used to categorize air particles in the outer layer. This research describes deep learning approaches that allowed for detecting 40% more atmospheric features at a horizontal resolution of 5 km during daytime operations when applied to LiDAR data. Compared to existing deep learning algorithms for edges and complicated near-surface sceneries during the day, a convolutional autoencoder (CAE) trained using LiDAR dataset standard data products showed the potential for improved aerosol discrimination with 98% accuracy.

Setigen: Simulating Radio Technosignatures for the Search for Extraterrestrial Intelligence

The goal of the search for extraterrestrial intelligence (SETI) is the detection of nonhuman technosignatures, such as technology-produced emission in radio observations. While many have speculated about the character of such technosignatures, radio SETI fundamentally involves searching for signals that not only have never been detected, but also have a vast range of potential morphologies. Given that we have not yet detected a radio SETI signal, we must make assumptions about their form to develop search algorithms. The lack of positive detections also makes it difficult to test these algorithms’ inherent efficacy. To address these challenges, we present setigen, a Python-based, open-source library for heuristic-based signal synthesis and injection for both spectrograms (dynamic spectra) and raw voltage data. setigen facilitates the production of synthetic radio observations, interfaces with standard data products used extensively by the Breakthrough Listen project, and focuses on providing a physically motivated synthesis framework compatible with real observational data and associated search methods. We discuss the core routines of setigen and present existing and future use cases in the development and evaluation of SETI search algorithms.

A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.

Verification and Validation of Hybridspectral Radiometry Obtained from an Unmanned Surface Vessel (USV) in the Open and Coastal Oceans

The hardware and software capabilities of the compact-profiling hybrid instrumentation for radiometry and ecology (C-PHIRE) instruments on an unmanned surface vessel (USV) are evaluated. Both the radiometers and USV are commercial-off-the-shelf (COTS) products, with the latter being only minimally modified to deploy the C-PHIRE instruments. The hybridspectral C-PHIRE instruments consist of an array of 18 multispectral microradiometers with 10 nm wavebands spanning 320–875 nm plus a hyperspectral compact grating spectrometer (CGS) with 2048 pixels spanning 190–1000 nm. The C-PHIRE data were acquired and processed using two architecturally linked software packages, thereby allowing lessons learned in one to be applied to the other. Using standard data products and unbiased statistics, the C-PHIRE data were validated with those from the well-established compact-optical profiling system (C-OPS) and verified with the marine optical buoy (MOBY). Agreement between algorithm variables used to estimate colored dissolved organic matter (CDOM) absorption and chlorophyll a concentration were also validated. Developing and operating novel technologies, such as the C-PHIRE series of instruments, deployed on a USV increase the frequency and coverage of optical observations, which are required to fully support the present and next-generation validation exercises in radiometric remote sensing of aquatic ecosystems.

Missing Burns in the High Northern Latitudes: The Case for Regionally Focused Burned Area Products

Global estimates of burned areas, enabled by the wide-open access to the standard data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), are heavily relied on by scientists and managers studying issues related to wildfire occurrence and its worldwide consequences. While these datasets, particularly the MODIS MCD64A1 product, have fundamentally improved our understanding of wildfire regimes at the global scale, their performance may be less reliable in certain regions due to a series of region- or ecosystem-specific challenges. Previous studies have indicated that global burned area products tend to underestimate the extent of the burned area within some parts of the boreal domain. Despite this, global products are still being regularly used by research activities and management efforts in the northern regions, likely due to a lack of understanding of the spatial scale of their Arctic-specific limitations, as well as an absence of more reliable alternative products. In this study, we evaluated the performance of two widely used global burned area products, MCD64A1 and FireCCI51, in the circumpolar boreal forests and tundra between 2001 and 2015. Our two-step evaluation shows that MCD64A1 has high commission and omission errors in mapping burned areas in the boreal forests and tundra regions in North America. The omission error overshadows the commission error, leading to MCD64A1 considerably underestimating burned areas in these high northern latitude domains. Based on our estimation, MCD64A1 missed nearly half the total burned areas in the Alaskan and Canadian boreal forests and the tundra during the 15-year period, amounting to an area (74,768 km2) that is equivalent to the land area of the United States state of South Carolina. While the FireCCI51 product performs much better than MCD64A1 in terms of commission error, we found that it also missed about 40% of burned areas in North America north of 60° N between 2001 and 2015. Our intercomparison of MCD64A1 and FireCCI51 with a regionally adapted MODIS-based Arctic Boreal Burned Area (ABBA) shows that the latter outperforms both MCD64A1 and FireCCI51 by a large margin, particularly in terms of omission error, and thus delivers a considerably more accurate and consistent estimate of fire activity in the high northern latitudes. Considering the fact that boreal forests and tundra represent the largest carbon pool on Earth and that wildfire is the dominant disturbance agent in these ecosystems, our study presents a strong case for regional burned area products like ABBA to be included in future Earth system models as the critical input for understanding wildfires’ impacts on global carbon cycling and energy budget.

Mapping Spatiotemporal Dynamics of Akure Industrial Layout for Sustainable Development

This research aimed at mapping the spatiotemporal dynamics of the Industrial Layout located in Akure Ondo State Nigeria. The dataset used are the administrative map of Ondo State, Akure Industrial Layout Boundary,various Landsat imageries of 32m resolution which are Thematic Mapper (TM) of 1986 & 1991, Enhanced Thematic Mapper Plus (ETM)+ of 2002, Operational Land Imager / Thermal Infrared Sensor (OLI/TIRS) of 2014, 2017, 2020; and Worldview 3 image 2020 of 1.24m resolution. The Landsat data were used to extract the different Land use/Land cover (LULC) within the study area. GPS receiver and Worldview 3 image were used to obtain the coordinates of the different LULC classes, which aided in the classification of image, and also for accuracy assessment of the classified image. All the Landsat standard data products were processed, to ascertain that they are free of radiometric and geometric errors using the Level 1 Product Generation System (LPGS) and extracted to obtain the landsat image bands. The extracted Landsat images (bands) were used in the processing and calculating the Normalized Difference Vegetation Index (NDVI) and calculation of LULC changes. Evaluation the accuracy of the results produced from the land cover classification was carried out by comparing the results of ground coordinates with the coordinates obtained from a higher resolution image (Worldview 3 image) in order to determine the accuracy of the land cover classification in the study area. The trend of changes of land cover in these areas was assessed and also, the prediction for the future condition both in terms of development was determined based on the results obtained from the initial results. Results from various maps produced and numerical data generated showed that Akure Industrial Layout was mainly dominated by shrub and grass land in 1986 and has in 34 years experienced transformation of 604% in the built environment (18% /year), 119% of Bareland (3.5%/year), and -29% of Grassland (0.9%/year), -66% of Shrub (2%/year). The forecast of the probable spatial extent for the years 2025 and 2030 were estimated to be 175.3Ha and 214.8Ha respectively, which shows there will be a continuous increase in the future development in Akure Industrial layout. The research recommended a proactive action from the government and end-users that will ensure a sustained manageability of the layout.

Cool, CALM, collected: the Circumpolar Active Layer Monitoring program and network

ABSTRACT The Circumpolar Active Layer Monitoring (CALM) program is the primary global-change monitoring program concerned with the seasonally frozen active layer above permafrost. The active layer has been designated by the Global Climate Observing System and the Global Terrestrial Observing Network as an ‘Essential Climate Variable’. CALM was launched in 1991 on a volunteer basis in cooperation with the International Tundra Experiment. CALM observatories in Russia and Alaska have been supported since 1998 by the U.S. National Science Foundation through five consecutive five-year funding cycles. In its current configuration, the CALM network includes observation sites throughout the circum-Arctic region and a substantial number of sites in Antarctica. Open access to data and data harmonization are hallmarks of the program. In addition to its ongoing emphasis on field observations of active-layer thickness, temperature, soil moisture, and thaw subsidence are currently being monitored at many sites. Increased emphasis is being placed on observing the dynamics of other landscape and ecosystem parameters, including vegetation, landscape patterns, and soils. Other developing features of the program include expanded education and outreach activities, close cooperation with other international programs, and provision of quality-controlled, standardized data products that meet the needs of the wider scientific community.