Find research datasets worth reusing

opencc-by-4.0Oct 2023View details →

Data/code from: Long-term data reveal that grazer density mediates climatic stress in salt marshes

Understanding how climate and local stressors interact is paramount for predicting future ecosystem structure. The effects of stressors are often examined in small-scale and short-term field experiments, limiting understanding of the spatial and temporal generality of the findings. Using a 22-year observational dataset of plant and grazer abundance in a Southeastern US salt marsh, we analyzed how changes in drought and snail density combined to affect plant biomass. We found: (1) increased drought severity and higher snail density both correlated with lower plant biomass; (2) drought and snail effects interacted additively; and, (3) snail effects had a threshold, with additive top-down effects only occurring when snails were present at high densities. These results suggest that the emergence of multiple stressor effects can be density-dependent, and they validate short-term experimental evidence that consumers can exacerbate environmental stress. These findings have important implications for predicting future ecosystem structure and managing natural ecosystems.The data and code included here can be used to replicate all analyses and figures in our manuscript.

Data from: To lose is to win: long-term co-occurrence of two asexual populations realized by a dormant strategy of the inferior competitor

Asexual organisms are ubiquitous. While they are the same single species with the same ecological requirements, multiple asexual genotypes within a species are often found in a single habitat. Niche partitioning in time and space among genotypes has been proposed to explain this phenomenon. However, it is not clear whether these different genotypes co-occur in the long term. Therefore, we examined the population dynamics of two asexual Daphnia cf. pulex genotypes (JPN1 and JPN2) over nine years in a small mountain lake. These two genotypes consistently occurred in the same seasons and layers, suggesting that niche partitioning cannot explain their long-term co-occurrence. The abundance of JPN1 was typically higher in most years. However, the abundance of dormant eggs in lake sediments was at the same level between the genotypes. The laboratory experiment showed that JPN1 excluded JPN2. However, many JPN2 individuals produced the dormant eggs before JPN1 competitively excluded them from the experiment. Furthermore, the competitively inferior JPN2 produced the dormant eggs abundantly in the medium containing a crowding cue from JPN1. However, no such trend was observed for JPN1. These results showed that the competitively inferior asexual genotype could maintain the population with the competitively superior genotype for a long period of time because it had the ability to detect the increase in competitors and produce the dormant eggs each year before being competitively eliminated. Based on these results, we suggest that the variation in the genotype-specific response in dormant egg production to environmental change plays a key role in the long-term co-occurrence of different asexual genotypes in single habitats.

opencc-by-4.0Mar 2024View details →

Questionnaire and interview guidelines related to Süsser et al. (2024) on how the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term

The survey questionnaire and the interview guidelines have been designed in the framework of the EU H2020 projects SENTINEL to study the impact of the coronavirus disease 2019 (COVID-19) on the stakeholder engagement processes in susatinability research in the longterm. We conducted interviews with researchers and engaged stakeholders, using a semi-structured interview guideline. We designed the survey as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. The survey was reworked based on our previous survey. The study was a follow-up study on our own work (Süsser, D., Ceglarz, A., Stavrakas, V., & Lilliestam, J. COVID-19 vs. stakeholder engagement: the impact of coronavirus containment measures on stakeholder involvement in European energy research projects [version 1; peer review: awaiting peer review]. Open Research Europe 2021,1:57. doi: <a href="https://doi.org/10.12688/openreseurope.13683.1">https://doi.org/10.12688/openreseurope.13683.1</a>).  If you use this questionnaire or the interview guideline in an academic publication, please cite the following article: Süsser, D., Schibline, A. Ceglarz, A., Lilliestam, J., Stavrakas, V., & Schweizer, P.J. (2024), How the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term, F1000 Research (to be published).

Data from: Stable isotope evidence for long-term stability of large-scale hydroclimate in the Neogene North American Great Plains

The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US, and this gradient shapes the region's water availability, its ecosystems, and its economies. This climatic boundary is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains—the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains Low-Level Jet in the warmer months. Climate model simulations suggest that, as CO2 rises, this low- level jet will strengthen, leading to greater precipitation in the spring, but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ18O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ18O gradient with high (−6 to 0‰) δ18O in the southern Great Plains and low (−12 to −18‰) δ18O in the northern Plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ18O is indistinguishable from the spatial pattern of modern meteoric water δ18O. We use a recently developed vapor transport model to demonstrate that this δ18O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene—consistent with today. Our results suggest that the spatial extent of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains Low-Level Jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO2 and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene.

Data from: Long-term climate and hydrologic regimes shape stream invertebrate community responses to a hurricane disturbance

Disturbances can produce a spectrum of short- and long-term ecological consequences that depend on complex interactions of the characteristics of the event, antecedent environmental conditions, and the intrinsic properties of resistance and resilience of the affected biological system. We used Hurricane Harvey's impact on coastal rivers of Texas to examine the roles of storm-related changes in hydrology and long-term precipitation regime on the response of stream invertebrate communities to hurricane disturbance. We detected declines in richness, diversity, and total abundance following the storm, but responses were strongly tied to direct and indirect effects of long-term aridity and short-term changes in stream hydrology. The amount of rainfall a site received drove both flood duration and flood magnitude across sites, but lower annual rainfall amounts (i.e., aridity) increased flood magnitude and decreased flood duration. Across all sites, flood duration was positively related to the time it took for invertebrate communities to return to a long-term baseline and flood magnitude drove larger invertebrate community responses (i.e., changes in diversity and total abundance). However, invertebrate response per unit flood magnitude was lower in sub-humid sites, potentially because of differences in refuge availability or ecological-evolutionary interactions. Interestingly, sub-humid streams had temporary large peaks in invertebrate total abundance and diversity following recovery period that may be indicative of the larger organic matter pulses expected in these systems because of their comparatively well-developed riparian vegetation. Our findings show that hydrology and long-term precipitation regime predictably affected invertebrate community responses and, thus, our work underscores the important influence of local climate to ecosystem sensitivity to disturbances.

Long-term individual-based records of mountain goat mortality and terrain use in relation to avalanches in coastal Alaska during 2005-2022

Snow is a major, climate-sensitive feature of the Earth's surface and a catalyst of fundamentally important ecosystem processes. Understanding how snow influences sentinel species in rapidly changing mountain ecosystems is particularly critical. Whereas the effects of snow on food availability, energy expenditure, and predation are well documented, we report how avalanches exert major impacts on an ecologically significant mountain ungulate - the coastal Alaskan mountain goat (Oreamnos americanus). Using long-term GPS data and field observations across four populations (421 individuals over 17 years), we show that avalanches caused 23-65% of all mortality, depending on area. Deaths varied seasonally and were directly linked to spatial movement patterns and avalanche terrain use. Population-level avalanche mortality, 61% of which comprised reproductively important prime-aged individuals, averaged 8% annually and exceeded 22% when avalanche conditions were severe. Our findings reveal a widespread but previously undescribed pathway by which snow can elicit major population-level impacts and shape demographic characteristics of slow-growing populations of mountain-adapted animals.

opencc-zeroApr 2024View details →

Below-ground root nutrient-acquisition strategies are more sensitive to long-term grazing than above-ground leaf traits across a soil nutrient gradient

Understanding how plant nutrient acquisition strategies respond to grazing at the community level is critical to understanding ecosystem structure and functioning in grasslands. However, few studies have simultaneously compared the difference in aboveground (leaf) and belowground (root) nutrient-acquisition strategies in response to long-term grazing, especially at the regional scale. Here, we measured a set of leaf and fine-root traits that correspond to the fast-slow economic spectrum at the community level in 10 experimental sites from paired grazed and ungrazed grasslands across a soil nutrient gradient covering three major types of grasslands in northern China. We found that patterns of variations of leaf and fine-root traits were consistent with both a leaf and root economic spectrum at the community level for both grazed and non-grazed plots. Grazing had a minor effect on community-level leaf nutrient-acquisition strategies but strongly influenced community-level root nutrient-acquisition strategies. Specifically, root nutrient-acquisition strategies were shifted to more exploitative resource use in grazed communities. Moreover, soil nutrients contributed to the changes in both leaf and root nutrient-acquisition strategies, which tended towards a more resource-acquisition strategy with increasing soil nutrient levels. Grazing significantly interacted with soil nutrients to affect root nutrient-acquisition strategies, and grazing contributed more to root nutrient-acquisition strategies than soil nutrients. Our results demonstrated completely inconsistent responses of community-level above- and below-ground resource acquisition strategies to long-term grazing, and below-ground acquisition strategies were more sensitive to long-term grazing. Our findings also suggest that high-intensity anthropogenic activities such as grazing may strongly modify below-ground resource acquisition strategies.

opencc-by-4.0Mar 2024View details →

XPS measurements on long-term stability of organic radical thin films

XPS raw data underlying Figures 1, 2, and 3 from the publication "Long-Term Degradation Mechanisms in Application-Implemented Radical Thin Films" (DOI: 10.1021/acsami.3c02057).

opencc-by-4.0Nov 2024View details →

A stocktaking of European mid- and long-term experiments dealing with the application of external organic matter (EJP SOIL - EOM4SOIL - D 3.1.1)

Extending and optimizing recycling of organic wastes in agriculture is a key element in shifting conventional agriculture towards systems adapted to both energy depletion and climate change. Long-term field experiments (LTEs) play a crucial role in assessing and modelling the effects of repeated exogenous organic matter (EOM) application to soil, which allows to formulate locally adapted recommendations of use.   Nevertheless, a specific database focussing on LTEs dealing with organic fertilization in Europe were missing. To close this gap, we listed LTEs dealing with repeated application of EOM from existing online databases, and collected and harmonized all available metadata. The aim of this work was threefold: (1) to facilitate connections between comparable LTEs to foster data harmonization and compilation, (2) to map the diversity of pedoclimatic contexts and experimental designs in the LTE list and, (3) to highlight current knowledge gaps and research needs.  Data were collected from five online databases, allowing us to describe 201 LTEs. Key characteristics such as trial name, responsible institution, location, pedoclimatic context, duration, crop type and availability of online resources are well-described in contrast to LTE goal and owner contact, experimental design, soil type, studied EOM and monitored parameters (EOM characteristics, soil and crop properties), which are more difficult to gather and harmonize. The analysis of LTE metadata highlighted first that substantial harmonization efforts are required, particularly regarding the reporting of soil, crop and EOM properties over time. Second, the survey outlines that some European regions are poorly represented in the database, which may result either from an absence of LTE or from a lack of reporting. To close this gap, we call LTE managers to complete the current database with any missing relevant LTE or additional metadata, using the editable online repository attached to this document. In the future, improvement of predictive models could contribute to provide recommendations of EOM use to uncovered situations, whether in terms of soil, climate or type of EOM. Third, long-term effects on soil properties such as changes in soil biology composition or accumulation of organic contaminants (PFAS, microplastics, antibiotics, ...) appear to be poorly documented. LTEs have a key role to play in answering these emerging questions, having the potential to provide the rationale to fix acceptable thresholds in soils and EOMs for emerging pollutants and accordingly provide the best possible guidelines for the use of EOM in agriculture.  See related report at <a href="https://doi.org/10.5281/zenodo.14161379" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14161379</a>.

opencc-by-4.0Nov 2024View details →

Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations

This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations". The results and data contain: <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (Hala_surges_aster**.nc): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period  (Hala_surges_[sensor]_[period]_dh_final.tif).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference (   strm-c_x_n37_39_e96_e98_pentration_dh_final.tif)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements (     Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results (     Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc)</li> </ol>

opencc-by-4.0Nov 2024View details →

Dataset: Lodgepole pine Pinus contorta Douglas ex Loudon invasion in subarctic Iceland: evidence from a long-term study.

This dataset supports the paper Lodgepole Pine (Pinus contorta Douglas ex Loudon) Invasion in Subarctic Iceland: Evidence from a Long-Term Study, published in NeoBiota. It includes the following files: <ol> <li>Point_data_distribution.csv: Contains distribution point data for Pinus contorta in Steinadalur, using the WGS 84 coordinate reference system (EPSG:4326).</li> <li>Plot_data_pinus.csv: provides data on plant species richness within study plots.</li> <li>Transects_data.csv: Includes information on the density of P. contorta along transects.</li> </ol>

opencc-by-4.0Dec 2024View details →

Exo-Metabolomics Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"

Exo-Metabolomics Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"

opencc-by-4.0Dec 2024View details →

Illumina Sequencing Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"

Illumina Sequencing Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"

opencc-by-4.0Sep 2024View details →

(12)-Pereyra2024A-DS0001--0009 – Nine Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy

(12)-Pereyra2024A-DS0001--0009 – Nine Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy

opencc-by-4.0Sep 2024View details →

An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning

This dataset contains two improved surface pCO2 products, along with surface pCO2  from the INCOIS-BIO-ROMS model (pCO2_model) and other input variables. It is a long-term, high-resolution dataset developed for the Indian Ocean region (30°E - 120°E, 30°S - 30°N), covering the period from 1980 to 2019. The dataset features a monthly temporal resolution and a spatial resolution of 1/12 degree.  The file includes INCOIS-BIO-ROMS model outputs (sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), nitrate (NO3), dissolved inorganic carbon (DIC), and chlorophyll-a (CHL)). These variables are used as inputs for machine learning models to improve the pCO2_model. The machine learning model predicts the surface pCO2 deviants (pCO2_obs - pCO2_model). The file also provides spatiotemporally varying uncertainties associated with the predicted pCO2 deviants. **Users are advised to download Version v2 of the data product, as Version v1 has been deprecated and is no longer recommended for use.

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.