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37 results for “fractional cover”
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Arctic vegetation cover fractions derived from Landsat time series (1984-2020) for the greater Mackenzie Delta Region (Western Canadian Arctic)
<p>Data to the publication by Nill et al. (2022) "<em>Arctic shrub expansion revealed by Landsat-derived multitemporal<br> vegetation cover fractions in the Western Canadian Arctic"</em></p> <p>The dataset features Landsat-derived fractional cover estimates of Arctic plant functional types (shrub, evergreen trees, herbaceous, lichen) and other land cover (barren, water) in the greater Mackenzie Delta Region, Canada.<br> We utilized regression-based unmixing based on synthetic training data in order to build multitemporal Kernel Ridge Regression (KRR) models for estimating fractional cover and validated our predictions based on independent very-high-resolution imagery (please be referred to publication for details).<br> <br> <strong>Dataset information</strong><br> The fraction cover predictions ("krr-avg") are provided separately for each epoch (1984-1990, 1991-1996, ..., 2015-2020) and class/cover type. The decadal change images ("dec-cng") between 1984 and 2020 are provided separately for each class/cover type. The naming convention of the files is as follows:</p> <p>XXXX-XXXX_YYY-YYY_int16-10e3_class-Z-Z</p> <ul> <li>XXXX-XXXX = epoch, e.g. 2015-2020</li> <li>YYY-YYY = dataset ("krr-avg" = fraction cover, "dec-cng" = decadal fraction cover change)</li> <li>Z-Z = class ID and associated class name (sh = shrub, cf = coniferous, hb = herbaceous, lc = lichen, wt = water, br = barren)</li> </ul> <p>The fraction cover values are % scaled by 10,000. For instance, a value of 1234 refers to 12.34%. Further image metadata:</p> <ul> <li><strong>Datatype:</strong> Signed 16-bit integer (Int16) </li> <li><strong>Data format: </strong>GeoTiff (.tif)</li> <li><strong>No data value:</strong> -9999</li> <li><strong>Projection:</strong> EPSG:3573 with custom central meridian; WKT string: 'PROJCS["WGS 84 / North Pole LAEA Canada",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",90],PARAMETER["longitude_of_center",-135],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</li> </ul> <p><strong>Publication</strong><br> Nill, L., Grünberg, I., Ullmann, T., Gessner, M., Boike, J. & Hostert, P. (2022): Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 2022, 281. https://doi.org/10.1016/j.rse.2022.113228</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Leon Nill (leon.nill@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/arctic-shrub/">here</a>.</p>
Dust mass fractions for dust bins covering the 0.1 to 100 µm size range
<p>This is a file containing the dust mass fractions for dust bins covering the 0.1 to 100 µm size range calculated from the BFT-supercoarse emitted dust PSD parameterization (Meng et al., 2021 GRL submitted). </p>
Land Cover Fraction Mapping with FORCE - Supplemental Data
<p> </p> <p>This upload contains data required to replicate a <a href="https://github.com/franzschug/force/blob/develop/docs/source/howto/lcf.rst">tutorial </a>that applies regression-based unmixing of spectral-temporal metrics for sub-pixel land cover mapping with synthetically created training data. The tutorial uses the <a href="https://github.com/davidfrantz/force">Framework for Operational Radiometric Correction for Environmental monitoring</a>.</p> <p>This dataset contains intermediate and final results of the workflow described in that tutorial as well as auxiliary data such as parameter files.</p> <p>Please refer to the above mentioned tutorial for more information.</p> <p> </p>
Vegetation cover fractions for the City of Berlin dervided from Landsat 5, Landsat 7 and Landsat 8 data between 1988 and 2018
<p>Fractional vegetation cover dataset used in the study "Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series".</p> <p>Landsat satellite imagery (Landsat-5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper) and Landsat-8 OLI (Operational Land Imager)) was acquired for seven years between 1988 and 2018. Imagery was pre-processed and a regression-based unmixing approach was performed in order to generate fraction maps of vegetated and non-vegetated surfaces. For complete method description please see:</p> <p>Wellmann, T., Schug, F., Haase, D., Pfulgmacher, D., van der Linden, S. (2020). Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series. <em>Landscape and Urban Planning</em></p>
Digital orthoimagery for fractional vegetation cover at site Alpha from Morgan et al Kuiseb River study
<p>Contains high-resolution digital orthoimagery (RGB and NIR) and derived products for site Alpha, from the following manuscript:</p> <p>Morgan et al. (in prep), Spatiotemporal analysis of vegetation cover change in a large ephemeral river: multi-sensor fusion of unmanned aerial vehicle (UAV) and Landsat imagery.</p>
GLOBMAP FTC China: an annual fractional tree cover dataset in China from 2000 to 2022
<p><span>We generated an annual fractional tree cover dataset (GLOBMAP) in China from 2000 to 2022 with a resolution of 250 m using the MODIS land surface reflectance products. The dataset is generated using a feedforward neural network. A total of 2000 samples are automatically extracted from the very high spatial resolution imagery on Google Earth through an auto-segmentation algorithm to calibrate and validate the model, with 80% for training and the remaining 20% for validation. Seven highly discriminative input features are extracted from</span> <span>the annual series of MODIS data, including values in the red (B01) and short wavelength infra-red 2.1 (SWIR 2.1: B07) bands for three key time phases, which are the day of year (DOY) of the maximum NDVI during the starting month (near 136~167), the medium month (near 197~228) and the ending month (near 259~289) of the growing season, and the yearly minimum NDVI. The root mean square error (RMSE)</span> <span>and Mean Absolute Error (MAE) of the dataset are 11.78% and 7.39%, respectively.</span></p> <p><span> </span></p> <p><span>The dataset provides fractional tree cover maps in China from 2000 to 2022. The data are projected by WGS 1984 and are stored with GeoTIFF format. The files are designated with the naming structure “GLOBMAP_YYYY_TreeCover_China.tif”, where “YYYY” represents the year of the data. The valid range of tree cover value is 0-100, with a scale factor of 1.0 and the unit of percentage (%).</span></p>
Fractional Snow Covered Area at Ny-Ålesund (Svalbard, Norway)
<p>The gridded datasets is a ensembled product obtained since 2020 processing imagery acquired by different time-lapse cameras located at the Zeppelin Observatory, at the Gruvebadet Snow Research Site and at the Amundsen-Nobile Climate Change Tower. </p>
Vegetation cover fraction in each town block across Japan
<p>Although the percent of green space is the most frequently used index for quantifying urban green, it is currently unavailable in most cities in Japan. Here we provide an open dataset of the index for each town block across the country using Sentinel-2 satellite images in Google Earth Engine. The dataset is calibrated and validated with airborne datasets obtained in nine cities in Tokyo.</p> <p> </p> <p><strong>日本全国の町丁目別の緑被率の公開</strong></p> <ul> <li>都市の緑を定量化する指標としては、緑被率が最もよく使われていますが、現在、日本のほとんどの都市では緑被率を利用することができません。ここでは、Sentinel-2の衛星画像をGoogle Earth Engineを用いて、日本全国の町丁目別の緑被率をオープンデータとして提供しています。このデータセットは、東京都内の9区の航空機データ(各区のWEBサイトで公開)を用いて校正・検証されています。</li> <li>ライセンスに関してデータは<a href="https://creativecommons.org/licenses/by/4.0/deed.ja">CC BY 4.0</a>で公開しています。</li> <li>校正・検証に関しての詳細は、<a href="https://doi.org/10.3130/aijt.28.521">日本建築学会技術報告集</a>に掲載しています。 </li> <li>「FRAC_VEG」が緑被率です。</li> <li>境界データは「平成27年国勢調査」を用いています。</li> </ul> <p> </p> <p><strong>データ概要</strong></p> <p>年:2020</p> <p>空間分解能:町丁目(平成27年国勢調査)</p> <p> </p> <p><strong>引用方法</strong></p> <p>・データの利用のみの場合</p> <p>Kiyono Tomoki, Fujiwara Kunihiko, & Tsurumi Ryuta. (2021). Vegetation cover fraction in each town block across Japan (1.0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5553516">https://doi.org/10.5281/zenodo.5553516</a></p> <p>・上記以外の場合(この取り組みそのものや,アルゴリズム等への言及)</p> <p>清野友規, 藤原邦彦, 鶴見隆太. Google Earth Engineを用いた町丁目別緑被率オープンデータ(全国版)の作成と評価, 日本建築学会技術報告集, 2022, 28 巻, 68 号, p. 521-526, https://doi.org/10.3130/aijt.28.521</p> <p> </p> <p><strong>問い合わせ</strong></p> <p>意見・要望・感想などお気軽にお問い合わせください。</p> <p>鶴見隆太(日建設計総合研究所) tsurumi.ryuta@nikken.jp</p> <p> </p> <p><strong>更新情報</strong></p> <p>v1.0.1 2021/10/7</p> <p>・エクセルでの文字化けを解消(文字コード:BOM付UTF-8)</p> <p>・町丁目のユニーク識別子であるKEY_CODE(11桁)を追加</p> <p>v0.1.0</p> <p>・初期バージョンをリリース</p> <p> </p> <p><strong>PI</strong></p> <p>Kiyono Tomoki</p>
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)
<p>The Asia Water Tower region, with the Qinghai-Tibet Plateau at its core, is the most widespread region of snow cover on Earth, except for the North and South Poles. The topographic heterogeneity of the Asian Water Tower region is so great that the snow cover is thin and patchy, resulting in a highly time-varying snow cover in the region, and therefore daily-scale fractional snow cover data are urgently needed. This dataset is based on the MODIS surface reflectance product MO/YD09GA product, and the MODIS daily cloud-free fractional snow cover dataset for the Asian Water Tower region from 2000 to 2022 was produced using the MESMA-AGE algorithm and the MSTI algorithm. The high spatial resolution Landsat-8 image was taken as the "ground truth", the RMSE was 0.16, and the MAE was 0.10. This dataset has a time series from 26 February 2000 to 31 December 2022 with a spatial resolution of 0.005°, which can provide quantitative snow cover information on the spatial distribution of snow for mountain hydrological models, land surface models, numerical weather forecasts, etc.</p>
Validation dataset of 30 m resolution Landsat-8 fractional snow cover in the Asian Water Tower region (2013-2022)
<p>This dataset is the validation dataset for the article 'MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)', which is a 30m resolution Landsat-8 fractional snow cover dataset for the time period 2013-2022 in the Asian Water Tower Region. This dataset is based on 3046 scene Landsat-8 surface reflectance data and uses the MESMA-AGE algorithm to retrieve the fractional snow cover. Gaofen-2 images with higher resolution were used to evaluate the accuracy, and the results showed that the accuracy was better, with OA of 94.46% and RMSE of 0.094. The cloud cover of each image in this dataset is less than 10% and the snow cover is more than 30%, which can be used to validate medium- or coarse-scale snow products and to study the spatial distribution of snow at high spatial resolution.</p>
30 Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel using Landsat Timeseries
<p>30m resolution historically consistent land cover and cover fraction maps over the Sudano-Sahel for the period 1986-2015. These land cover / cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification / regression model, while historical consistency is achieved using the Hidden Markov Model.</p> <p>Validated land cover / cover fraction maps covering the full Sudano-Sahel are provided for 2015 (2015_Sahel.zip), while historical maps are available for four focus areas. The extent of the areas are displayed in 11_study_area.jpeg</p> <p>Each of the zip files contains 14 GeoTIFF files for the respective period and area:</p> <ul> <li>Landsat_LC30_epochYYYY_AREA_bare-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_crops-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif [# overpasses that are used as input for the creation of the maps for this region / epoch]</li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif [temporally cleaned discrete classification map using the Hidden Markov Model; legend see below] </li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification.tif [original discrete classification map; legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_forest-type-layer.tif [legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_grass-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_moss-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_shrub-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_snow-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_tree-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_urban-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-permanent-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-seasonal-coverfraction-layer.tif [0-100%]</li> </ul> <p>Discrete classification legend:</p> <ul> <li>0: Unknown. No or not enough satellite data available.</li> <li>20: Shrubs. Woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous.</li> <li>30: Herbaceous vegetation. Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %.</li> <li>40: Cultivated and managed vegetation / agriculture. Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.</li> <li>50: Urban / built up. Land covered by buildings and other man-made structures.</li> <li>60: Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year.</li> <li>70: Snow and ice. Lands under snow or ice cover throughout the year.</li> <li>80: Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies.</li> <li>90: Herbaceous wetland. Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.</li> <li>100: Moss and lichen.</li> <li>111: Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>112: Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>113: Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>114: Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>115: Closed forest, mixed.</li> <li>116: Closed forest, not matching any of the other definitions.</li> <li>121: Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>122:Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>123: Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>124: Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>125: Open forest, mixed.</li> <li>126: Open forest, not matching any of the other definitions.</li> <li>200: Oceans, seas. Can be either fresh or salt-water bodies.</li> </ul> <p>Forest type legend:</p> <ul> <li>0: Unknown</li> <li>1: Evergreen needle leaf</li> <li>2: Evergreen broad leaf</li> <li>3: Deciduous needle leaf</li> <li>4: Deciduous broad leaf</li> <li>5: Mix of forest types</li> </ul> <p>More detail on the classification algorithm and the resulting maps can be found in the accompanying paper: </p> <p>Souverijns, N.; Buchhorn, M.; Horion, S.; Fensholt, R.; Verbeeck, H.; Verbesselt, J.; Herold, M.; Tsendbazar, N.-E.; Bernardino, P.N.; Somers, B.; Van De Kerchove, R. Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. <em>Remote Sens.</em> <strong>2020</strong>, <em>12</em>, 3817. https://doi.org/10.3390/rs12223817</p> <p>Please note that a quality layer is available for each of the historical areas / periods (Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif). In case a value of 4 or lower is achieved here, the discrete land cover classification / cover fraction for this period / area is highly uncertain. Take this into account when analysing the maps. Furthermore, take note that there is a large difference between the temporally cleaned (Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif) and original discrete land cover classification (Landsat_LC30_epochYYYY_AREA_discrete-classification.tif). We recommend to use the temporally cleaned version in combination with the quality layer.</p>
Cover crops improve soil structure and change OC distribution in aggregate fractions
<p>Data set and R script on the statistic evaluation of soil data. The data derived from a long-term field trial at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52°45′48.4′′N 9°01′24.3′′E). Data include soil data (OC, TN, bulk density, texture) as well as data from soil aggregate fractionation and evaluation of their aggregate stability. All methods and data will be described in an upcoming journal article (DOI will be provided soon).</p>
Beyond 'greening' and 'browning': trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation
<p class="MsoNormal">Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long‑term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modelling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally-uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non‑photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote‑sensing‑based monitoring of trends in grasslands so that underlying processes can be discerned.</p>
Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands
Open the record for dataset details and reuse information.
Beyond ‘greening’ and ‘browning’: trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation
Open the record for dataset details and reuse information.
Fractional Snow Covered Area at Ny-Ålesund (Svalbard, Norway) 2015-2019
<p>The produced dataset (in netCDF v4 format) contains the estimation of the Factional Snow Cover (FSC) in different sites located in Svalbard islands. We considered areas that complete the already available datasets and we focused the attention to the Ny-Ålesund area (Svalbard - Norway) (78.917° N, 11.933° E) where different facilities are available for supporting the use of terrestrial photography. One asset is the Zeppelin observatory, located on a panoramic spot where cameras are operating since 2000, and one is the Climate Change Tower (CCT) where we deployed a camera in 2018 (Figure 1). While the Zeppelin camera, operated by the Norwegian Polar Institute, offers a long time-series coupled with a pan-tilt-zoom device (4 different views daily), the CCT device was installed in order to cover the hidden side of the coastal plain not visible from the observatory. Furthermore, the CCT camera provided highly spatial and time resolved (hourly) images that were also below the cloud layer.</p>
Data for: Cover crop functional types differentially alter the content and composition of soil organic carbon in particulate and mineral-associated fractions
<p>Cover crops (CCs) can increase soil organic carbon (SOC) sequestration by providing additional OC residues, recruiting beneficial soil microbiota, and improving soil aggregation and structure. The various CC species that belong to distinct plant functional types (PFTs) may differentially impact SOC formation and stabilization. Biogeochemical theory suggests that selection of PFTs with distinct litter quality (C:N ratio) should influence the pathways and magnitude of SOC sequestration. Yet, we lack knowledge on the effect of CCs from different PFTs on the quantity and composition of physiochemical pools of SOC. We sampled soils under monocultures of three CC PFTs (legume [crimson clover]; grass [triticale]; and brassica [canola]) and a mixture of these three species, from a long-term CC experiment in Pennsylvania, USA. We measured C content in bulk soil and C content and composition in contrasting physical fractions: particulate organic matter, POM; and mineral-associated organic matter, MAOM. The bulk SOC content was higher in all CC treatments compared to the fallow. Compared to the legume, monocultures of grass and brassica with lower litter quality (wider C:N) had higher proportion of plant-derived C in POM, indicating selective preservation of complex structural plant compounds. In contrast, soils under legumes had greater accumulation of microbial-derived C in MAOM. Our results for the first time, revealed that the mixture contributed to a higher concentration of plant-derived compounds in POM relative to the legume, and a greater accumulation of microbial-derived C in MAOM compared to monocultures of grass and brassica. Mixtures with all three PFTs can thus increase the short- and long-term SOC persistence balancing the contrasting effects on the chemistries in POM and MAOM imposed by monoculture CC PFTs. Thus, despite different cumulative C inputs in CC treatments from different PFTs, the total SOC stocks did not vary between CC PFTs, rather PFTs impacted whether C accumulated in POM or MAOM fractions. This highlights that CCs of different PFTs may shift the dominant SOC formation pathways (POM vs. MAOM), subsequently impacting short- and long-term SOC stabilization and stocks. Our work provides a strong applied field test of biogeochemical theory linking litter quality to pathways of C accrual in soil.</p>
Towards efficient N cycling in intensive maize: role of cover crops and application methods of digestate liquid fraction
<p class="MsoNormal"><span>Digestate, a by-product of biogas production, is widely recognized as a promising renewable nitrogen (N) source with high potential to replace synthetic fertilizers. Yet, inefficient digestate use can lead to pollutant N losses as ammonia (NH<sub>3</sub>) volatilization, nitrous oxide (N<sub>2</sub>O) emissions and nitrate (NO<sub>3</sub><sup>-</sup>) leaching. Cover crops may reduce some of these N losses and recycle the N back into the soil after incorporation, but the net effect on the N balance depends on the cover crop species. In a one-year field study, we tested the effects of two application methods (i.e., surface broadcasting, BDC; and shallow injection, INJ) of the liquid fraction of separated co-digested cattle slurry (DLF), combined with different winter cover crop options (CCs, i.e., rye, white mustard or bare fallow), as starter fertilizer for maize. Later in the season, side-dressing with urea was required to fulfill maize N-requirements. We tested treatment effects on yield, N-uptake, N-use efficiency parameters, and N-losses in the form of N<sub>2</sub>O emissions and NO<sub>3</sub><sup>-</sup> leaching. Cover crop development and biomass production were strongly affected by their contrasting frost tolerance, with spring-regrowth for rye, while mustard was winter killed. After the cover crops, injection of DLF increased N<sub>2</sub>O emissions significantly compared with BDC (emission factor of 2.69 <em>vs.</em> 1.66%). Nitrous oxide emissions accounted for a small part (11-13%) of the overall yield-scaled N losses (0.46 - 0.97 kg N Mg grain<sup>-1</sup>). The adoption of CCs reduced fall NO<sub>3</sub><sup>-</sup> leaching, <span>being 51% and 64% lower for mustard and rye than under bare soil. In addition, rye reduced </span>NO<sub>3</sub><sup>-</sup> leaching during spring and summer after termination by promoting N immobilization, thus leading to -57% lower annual leaching losses compared to mustard.<span> Our study confirms the potential of CCs to reduce </span>NO<sub>3</sub><sup>-</sup><span> leaching, but it also highlights that their residue can increase N losses once terminated and that they may cause yield reductions. DLF application method modified N-loss pathways, but not the cumulative yield-scaled N losses. Overall, these insights contribute to inform an evidence-based design of cropping systems in which nutrients are recycled more efficiently.</span></span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.