Find research datasets worth reusing

opencc-zeroOct 2023View details →

Adaptive and non-adaptive convergent evolution in feather reflectance of Channel Islands songbirds

Convergent evolution is widely regarded as a signature of adaptation. But testing the adaptive consequences of convergent phenotypes is challenging, making it difficult to exclude non-adaptive explanations for convergence. Here, we combined feather reflectance spectra and phenotypic trajectory analyses with visual and thermoregulatory modeling to test the adaptive significance of dark plumage in songbirds of the California Channel Islands. By evolving dark dorsal plumage, island birds are generally less conspicuous to visual-hunting raptors in the island environment than mainland birds. Dark dorsal plumage also reduces the energetic demands associated with maintaining homeothermy in the cool island climate. We also found an unexpected pattern of convergence, wherein the most divergent island populations evolved greater reflectance of near-infrared radiation. However, our heat flux models indicate that elevated near-infrared reflectance is not adaptive. Analysis of feather microstructure suggests that island-mainland differences are related to coloration of feather barbs and barbules rather than their structure. Our results indicate that adaptive and non-adaptive mechanisms interact to drive plumage evolution in this system. This study sheds light on the mechanisms driving the association between dark color and wet, cold environments across the tree of life, especially in island birds.

opencc-by-4.0Nov 2021View details →

Predicting medicinal phytochemicals of Moringa oleifera using hyperspectral reflectance of tree canopies

Research article:  <a href="https://doi.org/10.1080/01431161.2021.1887541">https://doi.org/10.1080/01431161.2021.1887541</a>New technique for processing hyperspectral data: 1)https://www.researchgate.net/publication/349663204_Computer_vision_and_hyperspectral_imagery_in_orchards_and_in_fields_Data_processing_and_analysis_methods2)https://www.researchgate.net/profile/Vjacheslav-Fisenko/publication/349663204/figure/fig13/AS:1139984849485825@1648804976658/3-D-visualization-of-reflectance-spectra-of-four-medicinal-plant-genotypesPredicting_W640.jpg

opencc-zeroNov 2023View details →

Data from: Disparate home range dynamics reflect nutritional inadequacies on summer range for a large herbivore

The spatial distribution of animals has consequences for nutrition, predator-prey dynamics, spread of diseases, and population dynamics in general. Animals must establish a home range to secure adequate resources to fuel their energetic needs. Home ranges, therefore, are temporally and spatially dynamic given the changing requirements of an animal and the availability of resources on the landscape. We used data from two populations of bighorn sheep with contrasting population dynamics following pneumonia epizootics and different habitat quality on their summer range to test the hypothesis that the distribution and size of home ranges are influenced by environmental conditions and reproductive status. We used a combination of data from 768 vegetation transects and remotely sensed metrics to index forage quality of consecutive biweekly home ranges for 27 bighorn sheep, June–August 2019–2021. There were population differences in space use that were consistent with resource limitations in the population declining in abundance. Animals in both populations increased the area of their space use through the summer in association with declining forage quality indexed by plant phenology. Furthermore, animals in the Whiskey Mountain population without live offspring used areas more than twice the size of animals with offspring, whereas there were no differences in the area of space use between animals with and without offspring in Jackson. We demonstrated that limitations young offspring impose on space use of a mother may have consequences for animals living where larger home ranges are needed to secure adequate resources—sheep in Whiskey Mountain had to travel 1,000 m from escape terrain to access the same amount of biomass that the Jackson sheep could access directly adjacent to escape terrain. Forage quality and availability influence movement and home range dynamics. In the presence of disease, movement and home range dynamics may influence pathogen transmission and persistence. Thus, forage availability may play an indirect role in population dynamics in the presence of disease, which is another line of evidence for how environmental and nutritional conditions may influence population dynamics of populations coping with disease.

opencc-by-4.0Oct 2022View details →

Learning From, Talking About and Reflecting on Data Loss: a Failure Report

Learning From, Talking About and Reflecting on Data Loss: a Failure Report Andrew Harvey Presented 7 October 2022 at the Berlin-Brandenburg Academy of Sciences and Humanities Where Do We Need to Go From Here? Language Documentation and Archiving in the International Decade of Indigenous Languages

opencc-zeroJan 2021View details →

Gregorian reflecting telescope (1700s)

Full name: **Astronomical telescope with a Gregorian mirror system** This type of telescope was first described in 1663 by James Gregory (1638-1675), a Scottish mathematician and astronomer, professor at the University of Edinburgh and St. Andrews University in Scotland. Inventor of the first reflecting telescope. He proposed a method of determining an astronomical unit based on observations of the transit of Venus in front of the disc of the Sun. This particular telescope was engineered by Jesse Ramsden (1735–1800), optician, maker of scientific instruments, mainly astronomical and navigational ones, Fellow of the Royal Society in London. **For more images and further information, visit:** https://muzea.malopolska.pl/en/objects-list/2778 Inventory number: 4044; 42/V Localisation of the physical object: Jagiellonian University Museum Collegium Maius, Kraków, Poland **Digitalisation: Regional Digitalisation Lab, Małopolska Institute of Culture in Kraków, Poland; "Virtual Museums of Małopolska" project** Source: Objaverse 1.0 / Sketchfab

opencc-zeroJan 2024View details →

Environmental DNA reflects spatial distribution of a rare turtle in a lentic wetland assisted colonisation site

Conservation translocations require robust post-release monitoring to evaluate their success, which can be challenging to implement and maintain. Monitoring techniques that can account for the dispersal and cryptic nature of translocated animals are necessary to provide critical information on persistence and distribution. In this study, we developed a highly sensitive environmental DNA (eDNA) assay specific to the Critically Endangered western swamp turtle (Pseudemydura umbrina), a species currently undergoing trials of assisted colonisation. Actively filtering sufficient volumes of water in lentic systems is difficult due to high concentrations of clogging particulates, therefore we assessed the viability of passive sampling in a controlled environment by submerging filter membranes and directly extracting DNA. Active sampling detected P. umbrina with a 97.6% detection rate, whereas passive sampling resulted in an 8.3% detection rate. We then used a fine-scale eDNA sampling design and radio tracked translocated P. umbrina at the assisted colonisation wetland to investigate eDNA dispersal and spatial monitoring resolution. We detected P. umbrina at 42% (7 / 17) of eDNA sample sites, and the probability of a positive eDNA detection was negatively associated with the distance of P. umbrina from the sampling site, indicating limited eDNA dispersal from the source. Systems with low natural mixing and limited eDNA dispersal provide an opportunity for high resolution spatial and temporal monitoring via targeted eDNA approaches. This is beneficial for monitoring rare species in these systems, as such high-resolution results can provide insights on species presence, distribution, and microhabitat use.

opencc-zeroFeb 2024View details →

Ancient insect vision tuned for flight amongst rocks and plants underpins natural flower colour diversity - rock, mineral, stick, bark, leaf, bird- and insect-flower petal reflectance spectra

Understanding the origins of flower colour signalling to pollinators is fundamental to evolutionary biology and ecology. Flower colour evolves under pressure from visual systems of pollinators, like birds and insects, to establish global signatures among flowers with similar pollinators. However, an understanding of the ancient origins of this relationship remains elusive. Here, we employ computer simulations to generate artificial flower backgrounds assembled from real material sample spectra of rocks, leaves, and dead plant materials, against which to test flowers' visibility to birds and bees. Our results indicate how flower colours differ from their backgrounds in strength, and the distributions of salient reflectance features when perceived by these key pollinators, to reveal the possible origins of their colours. Since Hymenopteran visual perception evolved before flowers, the terrestrial chromatic context for its evolution to facilitate flight and orientation consisted of rocks, leaves, sticks, and bark. Flowers exploited these pre-evolved visual capacities of their visitors, and in response evolved chromatic features to signal to bees, and differently to birds, against a backdrop of other natural materials. Consequently, it appears that today's flower colours may be an evolutionary response to the vision of diurnal pollinators navigating their world millennia prior to the first flowers.

opencc-zeroFeb 2024View details →

Contrasting patterns of sequence variation in steelhead populations reflect distinct evolutionary processes

Multiple evolutionary processes influence genome‐wide allele frequencies and quantifying effects of genetic drift, and multiple forms of selection remain challenging in natural populations. Here, we investigate variation at major effect loci in contrast to patterns of neutral drift across a wide collection of steelhead (Oncorhynchus mykiss) populations that have declined in abundance due to anthropogenic impacts. Whole‐genome resequencing of 74 populations of steelhead revealed genome‐wide patterns (~8 million SNPs) consistent with expected neutral population structure. However, allelic variation at major effect loci associated with adult migration timing (chromosome 28: GREB1L/ROCK1) and age at maturity (chromosome 25: SIX6) reflected how selection has acted on phenotypic variation in contrast with neutral structure. Variation at major effect loci was influenced by evolutionary processes with differing signals between the strongly divergent Coastal and Inland lineages, while allele frequencies within and among populations within the Inland lineage have been driven by local natural selection as well as recent anthropogenic influences. Recent anthropogenic effects appeared to have influenced the frequency of major effect alleles including artificial selection for specific traits in hatchery stocks with subsequent gene flow into natural populations. Selection from environmental factors at various scales has also likely influenced variation for major effect alleles. These results reveal evolutionary mechanisms that influence allele frequencies at major effect loci that are critical for conservation of phenotypic traits and life history variation of this protected species.

opencc-by-4.0Oct 2022View details →

Data for "Stratospheric Downward Wave Reflection Events Modulate North American Weather Regimes and Cold Spells"

Wave reflection index and regimes time series for DJFM 1979/80 - 2020/21 as used in <a href="https://doi.org/10.5194/wcd-3-1215-2022" target="_blank" rel="noopener">Messori et al. (2022)</a> "Stratospheric Downward Wave Reflection Events Modulate North American Weather Regimes and Cold Spells", Weather and Climate Dynamics. This dataset has been updated following the publication of a corrigendum: <a href="https://doi.org/10.5194/wcd-3-1215-2022-corrigendum" target="_blank" rel="noopener">https://doi.org/10.5194/wcd-3-1215-2022-corrigendum</a>. The correction only applies to the reflection index (the regimes time series are unchanged).

opencc-by-4.0Apr 2024View details →

Fourier transformed infrared reflectance (FTIR) spectra of peat soils collected from the top and bottom of peatland erosion gullies

Peat soil was randomly  collected  from gullies within two eroding blanket bogs. Balmoral (BAM) is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl) and Glensaugh (GSA) is an upland livestock farm  with sections of and blanket bog peatland in the Grampian foothills (56.55° N, 2.33° E, 412 m asl). Both sites have undergone extensive degradation and peat erosion, and both have, in some parts, recently undergone restoration practices, including bunding and reprofiling. Peat samples were collected at Glensaugh and Balmoral as follows. At Glensaugh, peat at the top 1 cm of exposed gully sides (approximately 10-20cm from the vegetated surface) and at the gully bottom were taken, air dried and passed on for FTIR analysis. These gullies correspond to four erosion pin measurement areas and their corresponding peat sediment trap areas at Glensaugh. At Balmoral, the same approach was taken except six ‘gully top’ and ‘gully bottom’ sites were randomly selected and not geographically paired in the same way at Glensaugh. Samples were air dried and finely ball milled, prior to FTIR analysis. FTIR spectra were recorded using a Bruker Vertex 70 FTIR spectrometer (Bruker, Ettlingen, Germany) and OPUS 7.2 software. To record the FTIR spectra, each of the samples were placed, in turn, on a Diamond Attenuated Total Reflectance (DATR) sampling accessory, with a single reflectance system. Data points in the range of 4000-400 cm-1 were recorded with a resolution of 4 cm-1 and average of 64 scans. A spectrum of the empty sampling accessory, with the same resolution and number of scans, was recorded as the background spectrum before each measurement.  Since the penetration depth for the DATR accessory is different for each wavelength and is directly proportional to the wavelength of the incident light (The higher the wavenumber the lower the penetration), an ATR correction was applied to the spectra to correct this effect, using the OPUS software. No correction was required for water vapour and CO2 as the spectrometer is continuously purged with dry air.   In the dataset, columns correspond to the following: Site: Balmoral or Glensaugh Gully Position: Top or bottom Gully Number: Replicate gullies within the site Sample date: Date Sample ID: Unique identifier Remaining columns: Reflectance at a given wavelength

opencc-by-4.0May 2021View details →

FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia

FIGURE 7 (caption on next page).

Data from: Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest-association

Aim: Understanding how the landscape influences gene flow is important in explaining biodiversity, especially when co-distributed taxa across heterogeneous landscapes exhibit species-specific habitat associations. Here, we test predictions about the effects of forest-type on population connectivity in two sympatric species of spiny rats that differ in their forest associations. Specifically, we evaluate the hypothesis that seasonal floodplain forests (várzea) provide linear connectivity, facilitating gene flow among individuals, while non-flooded forests (terra-firme) may diminish the functional connectivity. Location: Western Amazon, South America. Taxon: Proechimys simonsi (non-flooded forests, terra-firme) and Proechimys steerei (seasonal floodplain forests, várzea). Methods: We analyze about 13,000 SNPs along with characterizations of landscape heterogeneity for two forest types to test for differences in the functional connectivity. Influence of the landscape and environmental variables are quantified using maximum-likelihood population effect (MLPE) models to identify the relative importance of variables in explaining the gene flow. Results: There are significant differences in functional connectivity between species. However, the genomic data does not support the conventional hypotheses of higher connectivity for inhabitants of várzea than those of terra-firme. Stronger genetic structure in P. steerei than P. simonsi based on IBD models suggests reduced gene flow in species associated with várzea forests. Isolation by resistance reinforces that wetland habitats inhibit and promote the functional connectivity in P. simonsi and P. steerei, respectively, although large distances along the rivers can prevent gene flow in P. steerei. Main conclusions: Interpreting differences between connectivity in taxa apparent from genetic analyses through the lens of a single dimension of Amazonian heterogeneity – that is, forest type – may be an oversimplification. Our statistical modeling and fit of the data to different models points to specific environmental and habitat differences between the ecological divergent spiny rat species that may contribute to differences in the genetic structure of these sympatric taxa.

Exploring the impact of varied background on quantification of soil carbon content using microwave and millimeter wave signal reflectance

The quantification of soil carbon content is paramount to the advancement of soil carbon management practices, serving as the bedrock for the development and implementation of various carbon-negative and carbon-neutral technologies. These technologies are crucial in the battle against climate change and in enhancing soil health to boost agricultural productivity. Therefore, we employ an innovative sensor technology comprised of a compact array, including 18 pairs of radar transmitters (TX) and receivers (RX) within a microwave radar array sensor, alongside a configuration of 20 TX and 20 RX pairs in a millimeter wave radar array sensor. For this kind of setup to work, the sensors need to be carefully tested to make sure they can capture changes in the soil's carbon content over time and space. This dataset presents an exhaustive series of raw data aimed at evaluating the efficacy of these two sensor technologies in detecting soil carbon content. A distinctive aspect of this study is the examination of sensor performance across four divergent backgrounds—glass, metal, sponge, and wood surfaces—to ascertain the influence of background material on radar wave reflectance and, consequently, measurement accuracy. Furthermore, the investigation extends to the analysis of sensing distance as a critical parameter for classification accuracy, with the microwave radar array sensor tested across distances ranging from 1 inch to 4 inches and the millimeter wave radar sensor evaluated at intervals of 2.5 inches, 5 inches, 7.5 inches, and 10 inches. By providing a comprehensive dataset on the performance evaluation of cutting-edge sensor technologies, this study aims to facilitate further advancements in the field of soil carbon management, ultimately supporting the global effort to combat climate change through innovative and sustainable agricultural practices.

Local reflects global: Life-stage dependent changes in the phenology of coastal habitat use by North Sea herring

Climate warming is affecting the suitability and utilisation of coastal habitats by marine fishes around the world. Phenological changes are an important indicator of population responses to climate-induced changes but remain difficult to detect in marine fish populations. The design of large-scale monitoring surveys does not allow fine-grained temporal inference of population responses, while the responses of ecologically and economically important species groups such as small pelagic fish are particularly sensitive to temporal resolution. Here, we use the longest, highest-resolution time series of species composition and abundance of marine fishes in northern Europe to detect possible phenological shifts in the small pelagic North Sea herring. We detect a clear forward temporal shift in the phenology of nearshore habitat use by small juvenile North Sea herring. This forward shift can best be explained by changes in water temperatures in the North Sea. We find that reducing the temporal resolution of our data to reflect the resolution typical of larger surveys makes it difficult to detect phenological shifts and drastically reduces the effect sizes of environmental covariates such as seawater temperature. Our study therefore shows how local, long-term, high-resolution time series of fish catches are essential to understand the general phenological responses of marine fishes to climate warming and to define ecological indicators of system-level changes.

Short-term dietary changes are reflected in the cerebral content of adult Ring-billed Gulls

Long-chain omega-3s (n3-LCPUFAs) are produced primarily in aquatic ecosystems and are essential for the structural integrity of vertebrates' brains. Carnivorous mammals must consume n3-LCPUFAs throughout their lifespan to maintain optimal cerebral functions; it is unknown whether other taxa are under similar nutritional pressures. This is concerning because many avian predators now rely on anthropogenic foods lacking n3-LCPUFAs. Here, we tested whether a recent or longer-term diet explains the encephalic fatty acid composition of a seabird, the Ring-billed Gull, that now thrives in cities. During the breeding season, rural gulls exploiting marine organisms had significantly higher encephalic levels of n3-LCPUFAs (mean±SD: 32±1%) than city-nesting gulls exploiting garbage (27±1%). Stable isotope analysis of blood and feathers grown during different seasons showed that urban and rural nesters consumed similar fall and winter diets, suggesting that the difference in cerebral n3-LCPUFA of breeding adults was due to concomitant and transient dietary differences. We also experimentally manipulated gulls' diets throughout incubation with a n3-LCPUFA supplement, a n3-LCPUFA-free caloric equivalent, or nothing, and found evidence that supplemental n3-LCPUFA increased urban nesters' cerebral levels of n3-LCPUFAs. These complementary analyses provide strong evidence that the brain of a seabird remains plastic during adulthood and responsive to short-term dietary changes.

opencc-by-4.0Nov 2024View details →

Seismic Reflection Data from the Kentland Impact Structure, Indiana from Robitaille MSc (2024)

This repository contains the correlated and stacked shot gathers and the final unmigrated and migrated files (all in SGY format) collected near the Kentland Crater Impact Structure.  These data are associated with the MSc thesis of Brian Robitaille at Purdue University (2024). See citation below.

Long-term Continuous Red and Near-infrared Channel Reflectance from MODIS, 2001-2023 (LCREF-MODIS)

Usage Notes: This is the updated LCREF-MODIS dataset (v3.2) consists of BRDF-normalized MODIS red and near-infrared surface reflectance. The LCREF-MODIS product was used to calibrate and benchmark the AVHRR surface reflectance to produce a temporally consistent record of surface reflectance prior to the MODIS era. It was also used to generate LCSPP-MODIS (previously known as LCSIF-MODIS) as a benchmark. Key updates in version 3.2 include: <ul> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension: to include observations from the year of 2023.</li> <li>Snow mask: we note that all pixels marked with percent_snow >0 in the original MCD43C1.v061 have been removed. This conservative approach was applied to reduce bias during cross-calibration, since unlike MODIS, AVHRR does not have a reliable snow detection algorithm. Therefore, surface reflectance values in high latitude regions are almost entirely gap-filled and should never be used for analysis for both LCREF-AVHRR and LCREF-MODIS. We encourage users to use only QA=0 and QA=1 pixels for their analysis. Alternatively, users can use LCREF-MODIS from the previous version for high latitude regions (v3.1), which did not mask out snow-covered pixles. </li> </ul> The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test.  <ul> <li>The LCREF-AVHRR v3.2 (1982-2023) is available at <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> </ul> The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 2001-2023

Usage Notes: This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals. Key updates in version 3.2 include: <ul> <li>Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension to include observations from the year of 2023.</li> </ul> Other LCSPP repositories can be accessed via the following links: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test.  In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links: <ul> <li>LCREF-AVHRR v3.2 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

Long-term Continuous Red and Near-infrared Channel Reflectance from AVHRR, 1982-2023 (LCREF-AVHRR)

Usage Notes: This is the updated LCREF dataset (v3.2) consists of calibrated AVHRR surface reflectance record for the red and near-infrared channel.  Key updates in version 3.2 include: <ul> <li>Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension to include observations from the year of 2023.</li> </ul> The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test.  <ul> <li>The LCREF-MODIS v3.2 (2001-2023) is available at <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a>.</li> </ul> The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with MODIS surface reflectance (LCSPP-MODIS), 2001-2023

Usage Notes: This is the updated LCSPP dataset (v3.2), reconstructed using the MODIS record from 2001–2023. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals. The MODIS-based LCSPP is generated as an ancillary product to complement and benchmark the LCSPP-AVHRR product from 1982-2023. Key updates in version 3.2 include: <ul> <li>Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension to include observations from the year of 2023.</li> </ul> LCSPP-AVHRR repositories can be accessed via the following links: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> </ul> The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test.  In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links: <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.