{"type": "FeatureCollection", "features": [{"id": "10.1111/gcb.12819", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:19:23Z", "type": "Journal Article", "created": "2014-12-05", "title": "Soil Warming And Co2 Enrichment Induce Biomass Shifts In Alpine Tree Line Vegetation", "description": "Abstract

Responses of alpine tree line ecosystems to increasing atmospheric CO2 concentrations and global warming are poorly understood. We used an experiment at the Swiss tree line to investigate changes in vegetation biomass after 9\uffc2\uffa0years of free air CO2 enrichment (+200\uffc2\uffa0ppm; 2001\uffe2\uff80\uff932009) and 6\uffc2\uffa0years of soil warming (+4\uffc2\uffa0\uffc2\uffb0C; 2007\uffe2\uff80\uff932012). The study contained two key tree line species, Larix decidua and Pinus uncinata, both approximately 40\uffc2\uffa0years old, growing in heath vegetation dominated by dwarf shrubs. In 2012, we harvested and measured biomass of all trees (including root systems), above\uffe2\uff80\uff90ground understorey vegetation and fine roots. Overall, soil warming had clearer effects on plant biomass than CO2 enrichment, and there were no interactive effects between treatments. Total plant biomass increased in warmed plots containing Pinus but not in those with Larix. This response was driven by changes in tree mass (+50%), which contributed an average of 84% (5.7\uffc2\uffa0kg\uffc2\uffa0m\uffe2\uff88\uff922) of total plant mass. Pinus coarse root mass was especially enhanced by warming (+100%), yielding an increased root mass fraction. Elevated CO2 led to an increased relative growth rate of Larix stem basal area but no change in the final biomass of either tree species. Total understorey above\uffe2\uff80\uff90ground mass was not altered by soil warming or elevated CO2. However, Vaccinium myrtillus mass increased with both treatments, graminoid mass declined with warming, and forb and nonvascular plant (moss and lichen) mass decreased with both treatments. Fine roots showed a substantial reduction under soil warming (\uffe2\uff88\uff9240% for all roots <2\uffc2\uffa0mm in diameter at 0\uffe2\uff80\uff9320\uffc2\uffa0cm soil depth) but no change with CO2 enrichment. Our findings suggest that enhanced overall productivity and shifts in biomass allocation will occur at the tree line, particularly with global warming. However, individual species and functional groups will respond differently to these environmental changes, with consequences for ecosystem structure and functioning.

", "keywords": ["0106 biological sciences", "2. Zero hunger", "Models", " Statistical", "Temperature", "Larix", "Carbon Dioxide", "15. Life on land", "Pinus", "Global Warming", "01 natural sciences", "Soil", "Species Specificity", "13. Climate action", "Biomass", "Tundra", "Switzerland"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12819", "name": "item", "description": "10.1111/gcb.12819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-01-30T00:00:00Z"}}, {"id": "10.1128/aem.02264-23", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-05-30T16:19:56Z", "type": "Journal Article", "created": "2024-02-19", "title": "Novel endolithic bacteria of phylum Chloroflexota reveal a myriad of potential survival strategies in the Antarctic desert", "description": "ABSTRACT

The ice-free McMurdo Dry Valleys of Antarctica are dominated by nutrient-poor mineral soil and rocky outcrops. The principal habitat for microorganisms is within rocks (endolithic). In this environment, microorganisms are provided with protection against sub-zero temperatures, rapid thermal fluctuations, extreme dryness, and ultraviolet and solar radiation. Endolithic communities include lichen, algae, fungi, and a diverse array of bacteria. Chloroflexota is among the most abundant bacterial phyla present in these communities. Among the Chloroflexota are four novel classes of bacteria, here named Candidatus Spiritibacteria class. nov. (=UBA5177), Candidatus Martimicrobia class. nov. (=UBA4733), Candidatus Tarhunnaeia class. nov. (=UBA6077), and Candidatus Uliximicrobia class. nov. (=UBA2235). We retrieved 17 high-quality metagenome-assembled genomes (MAGs) that represent these four classes. Based on genome predictions, all these bacteria are inferred to be aerobic heterotrophs that encode enzymes for the catabolism of diverse sugars. These and other organic substrates are likely derived from lichen, algae, and fungi, as metabolites (including photosynthate), cell wall components, and extracellular matrix components. The majority of MAGs encode the capacity for trace gas oxidation using high-affinity uptake hydrogenases, which could provide energy and metabolic water required for survival and persistence. Furthermore, some MAGs encode the capacity to couple the energy generated from H 2 and CO oxidation to support carbon fixation (atmospheric chemosynthesis). All encode mechanisms for the detoxification and efflux of heavy metals. Certain MAGs encode features that indicate possible interactions with other organisms, such as Tc-type toxin complexes, hemolysins, and macroglobulins.

IMPORTANCE

The ice-free McMurdo Dry Valleys of Antarctica are the coldest and most hyperarid desert on Earth. It is, therefore, the closest analog to the surface of the planet Mars. Bacteria and other microorganisms survive by inhabiting airspaces within rocks (endolithic). We identify four novel classes of phylum Chloroflexota , and, based on interrogation of 17 metagenome-assembled genomes, we predict specific metabolic and physiological adaptations that facilitate the survival of these bacteria in this harsh environment\uffe2\uff80\uff94including oxidation of trace gases and the utilization of nutrients (including sugars) derived from lichen, algae, and fungi. We propose that such adaptations allow these endolithic bacteria to eke out an existence in this cold and extremely dry habitat.

", "keywords": ["570", "Bacteria", "Fungi", "Antarctic Regions", "Chloroflexi", "15. Life on land", "Survival strategies", "Cold Temperature", "Extremophiles", "13. Climate action", "Antarctica", "Endolithic communities", "Metagenomics", "14. Life underwater", "Sugars", "Settore BIO/19 - MICROBIOLOGIA GENERALE"]}, "links": [{"href": "https://doi.org/10.1128/aem.02264-23"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20and%20Environmental%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1128/aem.02264-23", "name": "item", "description": "10.1128/aem.02264-23", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1128/aem.02264-23"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-19T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:23:57Z", "type": "Dataset", "title": "HWSD2_Climate_and_Socioeconomic_agriculturalsoil_dataset_mainland_portugal", "description": "The study uses the Harmonized World Soil Database (HWSD v2.0) developed by FAO and IIASA for biophysical models and agroecological queries. This database consolidates information from various sources, including the European Soil Database, the 1:1 million soil map of China, and national soil maps from Afghanistan, Ghana, and T\u00fcrkiye. It has a spatial resolution of around 1 km and is revised in 2013 and 2023. HWSD v2.0 includes detailed information on soil mapping units, general soil unit information, and specific physical and chemical soil unit characteristics across seven depth layers. The database fields cover a wide range of attributes, such as soil texture, bulk density, organic carbon content, pH, and cation exchange capacity. The harmonization process ensures that data from different sources is standardized and integrated, providing a consistent and reliable dataset for various applications. However, the HWSD v2.0 has some limitations, such as combining soil inventories gathered at different times, scales, and precision, which may affect its reliability for national studies. It is recommended to use national-level harmonized soil databases for more accurate results in specific regions. For Portugal's mainland, the data presented in the HWSD v2.0 dataset is sourced from the European Soil Data Centre (ESDAC), which contains various metrics of chemical and physical soil properties. Out of the 2882 Portuguese parishes, only 22 are left out, representing 0.76% percent of the total number of parishes. The study uses several datasets to analyze land use and occupation in Portugal. The Land Use and Occupation Map (COS2007v3.0) is a detailed thematic map of land use and occupation for mainland Portugal, developed by the Directorate-General for Territory (DGT). The data is organized hierarchically and includes 83 classes of land use and occupation. The CHELSA database, maintained by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), provides bioclimatic indexes for precipitation and average temperature over various temporal intervals and variables. The National Institute of Statistics (INE) provides data on agricultural machinery distribution across different geographical locations. The dataset covers the total number of agricultural machines, as well as specific categories such as wheeled and tracked tractors, motor cultivators, power hoes, motor mowers, and combine harvesters. The dataset also examines the distribution of farms with access to irrigation based on geographical location. The burned land data from 1975 to 2023 provides a comprehensive overview of fire occurrences and their impact over time. This data is crucial for understanding long-term patterns, assessing the effectiveness of fire prevention measures, and informing future land management and policy decisions. Lastly, the population density dataset from the 2021 Census and the 2011 Census provides a decennial comparison of total population density across different geographical regions. These data are essential for understanding the evolution of land use and occupation in Portugal and their implications for environmental and agricultural consequences.", "keywords": ["Soil", "Total organic carbon", "Land use", "Soil use", "Atmospheric precipitation", "Soil type", "Organic carbon", "Land surface temperature"], "contacts": [{"organization": "Almeida Santos, R. G. F.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15096788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15096788", "name": "item", "description": "10.5281/zenodo.15096788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15096788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-27T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:24:45Z", "type": "Dataset", "title": "Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis", "description": "Open AccessData description This dataset includes detailed information about five different types of land use change reported in \u201cThe effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; https://doi.org/10.1016/j.agee.2023.108402)\u201d. Lists of five different types of land use change 1) conversion of primary forest to cropland 2) conversion of primary forest to grassland 3) conversion of cropland to forest 4) conversion of grassland to forest 5) conversion of grassland to cropland Lists of detailed information Land use change (pre-LUC, post-LUC) Country, Location, Geographic position (Longitude, Latitude) Altitude (m) Climate zone Weather [rainfall (mm yr-1) and temperature (\u00b0C)] Reported time of change (years) Vegetation type (pre-LUC, post-LUC) Fertilizer (pre-LUC, post-LUC: type, application; change) Soil sampling depth (cm) Soil type [units, pre-LUC, post-LUC, change rate (%)] Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)] Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)] Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)] Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)] Soil C:N [units, pre-LUC, post-LUC, change rate (%)] Soil C:P [units, pre-LUC, post-LUC, change rate (%)] Soil N:P [units, pre-LUC, post-LUC, change rate (%)] Reference Data collection method We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland. We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases. While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation. When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents. Acknowledgements We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand\u2019s Ministry of Business, Innovation and Employment.", "keywords": ["2. Zero hunger", "13. Climate action", "land-use change", " greenhouse gas emissions", " soil", " carbon", " nitrogen", " phosphorus", " stoichiometry", " time", " temperature", " rainfall", " forest type", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7656722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7656722", "name": "item", "description": "10.5281/zenodo.7656722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7656722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-20T00:00:00Z"}}, {"id": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:26:24Z", "type": "Journal Article", "created": "2024-03-21", "title": "Environmental drivers and remote sensing proxies of post-fire thaw depth in Eastern Siberian larch forests", "description": "

Abstract. Boreal fire regimes are intensifying because of climate change and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in Eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean = 127.3 cm, standard deviation (sd) = 27.7 cm) than in unburned (98.1 cm, sd = 26.9 cm) landscapes one year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained landscape positions, in open larch forest often intermixed with Scots pine, and in high severity burns. The environmental drivers, site moisture, forest type and density, and fire severity explained 73.4 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced Normalized Burn Ratio (dNBR), land surface temperature (LST), and pre-fire Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar, and found that LST displayed particularly strong correlations with post-fire thaw depth (r = 0.65, p < 0.01). Our study reveals some of the governing processes of post-fire thaw depth development and shows the capability of Landsat imagery to estimate thaw depth at a landscape scale.

", "keywords": ["Dynamic and structural geology", "QE1-996.5", "13. Climate action", "Science", "Q", "Geology", "QE500-639.5", "Deforestation", "15. Life on land", "Landsat", "Multiple linear regression", "Atmospheric temperature"]}, "links": [{"href": "https://esd.copernicus.org/articles/15/1459/2024/esd-15-1459-2024.pdf"}, {"href": "https://doi.org/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Dynamics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "name": "item", "description": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-21T00:00:00Z"}}, {"id": "1fb8c60c-4140-4c8b-ba82-6cfd17bfaf91", "type": "Feature", "geometry": null, "properties": {"license": "https://spdx.org/licenses/CC-BY-4.0.html", "updated": "2025-06-24T06:22:31.561869Z", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Temperatura del suolo (C) - (2025-06-23 ore 12 UTC).", "description": "Temperatura del suolo (C). Corsa del 2025-06-23 ore 12 UTC - Valido dalle ore 12 UTC del 2025-06-23 alle ore 00 UTC del 2025-06-27. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["120000", "2025-06-23", "20250623t120000000z", "3km", "arw", "below", "between", "depths", "it", "lamma", "layer", "soil", "surface", "temperature", "two"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN12/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-temperatura-del-suolo-c-2025-06-23-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250623T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/1fb8c60c-4140-4c8b-ba82-6cfd17bfaf91"}, {"rel": "self", "type": "application/geo+json", "title": "1fb8c60c-4140-4c8b-ba82-6cfd17bfaf91", "name": "item", "description": "1fb8c60c-4140-4c8b-ba82-6cfd17bfaf91", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1fb8c60c-4140-4c8b-ba82-6cfd17bfaf91"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.5281/zenodo.4542881", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:24:29Z", "type": "Dataset", "title": "Total and partitioned soil respiration and below-ground carbon budget in SAFE intensive carbon plots", "description": "Description: This dataset contains two parts:
1) 'data' worksheet: measured soil respiration, values of individual measurements across all plots.
2) 'Soil C cycle' worksheet: calculated summaries of the components of the below-ground carbon cycle, including total and component soil respiration (this study), soil carbon pools and flows of organic carbon (previous studies). These data form the basis of the below-ground carbon cycle in Riutta et al 2021 GBC. This sheet contains mean values in each 1 ha carbon plot. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project.

SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/.
Total soil respiration is measured at 25 points per plot, in the middle of each subplot (16 points per plot in OP, in subplot corners), using PVC collars of 10.65 cm internal diameter, inserted into approximately 5 cm depth.
Partitioned respiration is measured at four points per plot, a using a cluster of six collars (see below).
Disturbance experiment in the plot centre to assess the potential bias on fluxes caused by the collar installation.
All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf.
The aim is to measure monthly, but in practice the measurement interval is almost always longer (problems with access, staffing and instruments).
EGM-4 infrared CO2 analyser and SRC-1 respiration chamber (PP Systems).
Chamber closure time is 124 seconds, CO2 concentration inside the chamber is recorded every 5 s. Flux is calculated from the linear change in concentration in the chamber headspace.


Conversion from parts per million (ppm) of total gas volume per second per unit collar area to mega grams (1 Mg = 10^6 g) of carbon per hectare per month.

Idea gas law: pV=nRT --> n=pV/(RT)
Mass-Mole: n=m/M --> m=n*M
Combined: m=MpV/(RT)

p (constant) 101,325
R (constant) 8.314472
T temperature in Kelvins --> AirT_Use + 273.15
V headspace volume
M_carbon 12.01

parts per million to absolute units 10^-6
A collar area, m2 0.008825
m2 to hectare 10^4
grams to megagrams 10^-6
seconds to months 2592000

Flux_MgCha-1month-1 = Slope_ppm_s-1 * M* p* V /(R*T) * 10^-6 / A * 10^4 * 10^-6 * 2592000
Soil collar codes Partitioned respiration
C1 All soil respiration components: litter, roots, mycorrhiza, soil organic matter (SOM)
C2 Roots excluded (litter, mycorrhiza, SOM)
C3 Roots and mycorrhiza excluded (litter, SOM)
S1 Litter excluded (roots, mycorrhiza, SOM)
S2 Litter and roots excluded (mycorrhiza, SOM)
S3 Litter, roots and mycorrhiza excluded (only SOM)
D1 Double litter, roots, mycorrhiza, soil organic matter (SOM)
D2 Roots excluded (double litter, mycorrhiza, SOM)
D3 Roots and mycorrhiza excluded (double litter, SOM)
X Organic layer of the soil removed

Disturbance The purpose of the disturbance experiment is to quantify how much disturbance the removal of the roots and mixing the soil causes, compared to just hammering in the deep collar
ND1 Roots severed, not removed and soil not mixed at the installation
ND2 ND1-ND5 are replicates, same treatmet
ND3
ND4
ND5
D1 Roots removed, soil mixed at the installation
D2 D1-D5 are replicates
D3
D4
D5
Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modificationFunding: These data were collected as part of research funded by: Sime Darby Foundation (Grant, SAFE Core data)European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)NERC standard grant: The multi-year impacts of the 2015/2016 El Ni\u00f1o on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1)HSBC Malaysia (Grant)The University of Zurich (Grant)This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.Permits: These data were collected under permit from the following authorities:Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76))XML metadata: GEMINI compliant metadata for this dataset is available hereFiles: This dataset consists of 2 files: SAFE_SoilRespiration_Data_SAFEdatabase_update_2021-01-11.xlsx, SAFE_soil_DATA.zipSAFE_SoilRespiration_Data_SAFEdatabase_update_2021-01-11.xlsxThis file contains dataset metadata and 2 data tables:Soil respiration data (described in worksheet data)Description: Soil respiration data by individual measurementsNumber of fields: 21Number of data rows: 20602Fields: ForestType: Old-growth, Logged or Oil palm (Field type: categorical)SAFEPlotName: SAFE plot name (Field type: location)PlotName: Plot name (Field type: id)ForestPlotsCode: Plot code in the ForestPlots database (this should be used in publications, instead of plot name). OP plot is not in the ForestPlots database (ForestPlotsCode = NA) (Field type: id)Date: Measurement date (dd/mm/yyyy) (Field type: date)Observers: Observers (Field type: comments)Subplot: Subplot number within each plot, 1-25 (in OP, because the total respiration collars are in subplot corners, no subplot numbers are used, but the collars are refered to as SR1 - SR16. Subplot numbers are used for the partitioned respiration) (Field type: id)MeasurementType: Total, Partitioned or Disturbance (Field type: categorical)CollarType: Total; Partitioned: C1, C2, C3, S1, S2, S3, D1, D2, D3, X; Disturbance: ND1, ND2, ND3, ND4, ND5, D1, D2, D3, D4, D5 (see metadata description for codes) (Field type: id)EGM_RecordNumber: Record number in of the raw flux file. (Field type: id)SoilMoisture: Volumetric soil moisture content (% of pore space) next to the collar. measured with Campbell Scientific Hydrosense sensor with 12 cm rods. (Field type: numeric)SoilT: Soil temperature (\u00b0C) is measured with a handheld digital thermometer next to the collar, inserted into 10 cm depth (Field type: numeric)AirT: Air temperature (\u00b0C) is measured with a handheld digital thermometer outside the chamber, at the chamber height, in a shaded spot (Field type: numeric)Slope: Slope of the linear regression between time (seconds) from the chamber closure and CO2 concentration (parts per million, ppm) in the chamber headspace. (Field type: numeric)Remarks: Any notes in the field or at data entry stage. 0 = no remarks. If the measurement is repeated in the field multiple times, the other flux estimates are sometimes written in the remarks (not consistent). 2x, 3x etc. indicate multiple repeats. (Field type: comments)CollarHeight: Height from the top of the soil to the top of the collar, mm. This is used for calculating the total headspapce volume (chamber volume + collar volume above the soil surface). (Field type: numeric)HeadspaceVolume: Total headspace volume, sum of the chamber volume (0.001229 m3) and collar volume (d=0.106 m, h=CollarHeight_mm/1000) (Field type: numeric)AirT_Use: Gap filled air temperature data, missing air temperatures replaced with average temperature in logged (27.1), old-growth forest (26.2) and OP (28.7). This is needed for calculating the flux, but should not be used in response functions etc. (Field type: numeric)Flux: Flux corverted from ppm s-1 to Mg carbon per hectare per month. See conversion below. (Field type: numeric)Quality: 1 - good flux; 0 - missing data or bad measurement; 2 - outlier (Field type: numeric)Girdling_0_1: In Tower Plot (SAF-05), subplots 14-25, all trees were girdled in late January - early February 2016. Post-girdling data = 1, if no girdling = 0. (Field type: numeric)Soil carbon cycle (described in worksheet Soil C cycle)Description: Estimates of soil carbon pools (fine and coarse root biomass, root and litter necromass, soil organic carbon); fluxes of organic carbon into and respiration out of the different pools. Values are means for each intensive carbon plot.Number of fields: 41Number of data rows: 11Fields: ForestType: Old-growth, Logged or Oil palm (Field type: categorical)SAFEPlotName: SAFE plot name, as in the SAFE Gazetteer (Field type: location)PlotName: Plot name (used in field work) (Field type: id)ForestPlotsCode: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id)SOC_0to100cm: Soil carbon stock, 0-100 cm layer (Field type: numeric)CanopyStock: Biomass stock of the canopy (Field type: numeric)LitterStock: Necromass stock of the litter layer (leaf, branch and reproductive litter) (Field type: numeric)NPP_Canopy: Canopy net primary productivity (Field type: numeric)CanopyHerbivory: Canopy herbivory (Field type: numeric)Litterfall: Canopy litterfall (leaves, reproductive parts, twigs < 2 cm diameter) (Field type: numeric)Frassfall1: Frassfall, 1st literature estimate (Field type: numeric)Frassfall2: Frassfall, 2nd literature estimate (Field type: numeric)Frassfall_Mean: Frassfall, mean of the Frassfall 1 and Frassfall 2 (Field type: numeric)FineRootStock: Fine root biomass stock (Field type: numeric)CoarseRootStock: Coarse root biomass stock (Field type: numeric)Delta_FRstock: Change in fine root biomass stock in logged forest (old-growth forest stock assumed to be in quasi-equilibrium) (Field type: numeric)Delta_CRstock: Change in coarse root biomass stock in logged forest (old-growth forest stock assumed to be in quasi-equilibrium) (Field type: numeric)NPP_FR: Fine root net primary productivity (Field type: numeric)NPP_CR: Coarse root net primary productivity (Field type: numeric)Mortality_FR: Fine root mortality (Field type: numeric)Mortality_CR: Coarse root mortality (Field type: numeric)R_Tot: Total soil respiration (litter, roots, mycorrhiza, soil organic matter) (Field type: numeric)R_SOM: Soil organic matter respiration (Field type: numeric)R_Litter: Litter layer respiration (Field type: numeric)R_Root: Root and priming respiration (Field type: numeric)R_Myc: Mycorrhizal respiration (Field type: numeric)R_Rhizosphere: Rhizosphere respiration (Field type: numeric)R_FRdebris: Fine root debris (recently dead, < 1 year) respiration (Field type: numeric)R_CRdebris: Coarse root debris (recently dead, < 1 year) respiration (Field type: numeric)R_RootTurnover: Root turnover respiration (sum of fine and coarse root debris respiration) (Field type: numeric)R_Het: Heterotrophic respiration (Field type: numeric)Exudation1: Root exudation, 1st literature estimate (Field type: numeric)Exudation2: Root exudation, 2nd literature estimate (Field type: numeric)Exudation3: Root exudation, 3rd literature estimate (Field type: numeric)Exudation4: Root exudation, 4th literature estimate (Field type: numeric)Exudation5: Root exudation, 5th literature estimate (Field type: numeric)Exudation_Mean: Root exudation, mean of the five literature-based estimates (Field type: numeric)LitterToSOM: Litter inputs (> 1 year old material) to soil organic matter (Field type: numeric)FRdebrisToSOM: Fine root debris inputs (> 1 year old material) to soil organic matter (Field type: numeric)CRdebrisToSOM: Coarse root debris inputs (> 1 year old material) to soil organic matter (Field type: numeric)OrgInputsToSOM: Total labile organic inputs (> 1 year old material) to soil organic matter (Field type: numeric)SAFE_soil_DATA.zipDescription: Raw EGM files and a ReadMe doc explaining how to interpret Date range: 2011-08-25 to 2018-07-17Latitudinal extent: 4.1830 to 5.0700Longitudinal extent: 114.0190 to 117.8200", "keywords": ["570", "Soil carbon cycle", "Soil organic matter", "Flux", "Total respiration", "Soil respiration", "15. Life on land", "Partioned respiration", "Autotrophic respiration", "Roots", "6. Clean water", "630", "SAFE core data", "Below-ground", "Carbon dioxide", "SAFE project", "Heterotropchic respiration", "Litter", "Soil temperature", "CO2", "Carbon plot", "Soil moisture"], "contacts": [{"organization": "Riutta, Terhi, Ewers, Robert M, Malhi, Yadvinder, Majalap, Noreen, Khoon, Kho Lip,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4542881"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4542881", "name": "item", "description": "10.5281/zenodo.4542881", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4542881"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-16T00:00:00Z"}}, {"id": "10.1007/s13595-016-0547-4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:15:45Z", "type": "Journal Article", "created": "2016-03-24", "title": "Effects Of Experimental Warming On Soil Respiration And Biomass In Quercus Variabilis Blume And Pinus Densiflora Sieb. Et Zucc. Seedlings", "description": "AbstractKey messageIn the open-field warming experiment using infrared heaters, 3\u00a0\u00b0C warming affected soil respiration more in the deciduousQuercus variabilisBlume plot than in the evergreenPinus densifloraSieb. et Zucc. plot, but did not affect the plant biomass in either species.ContextUnderstanding the species-specific responses of belowground carbon processes to warming is essential for the accurate prediction of forest carbon cycles in ecosystems affected by future climate change.AimsThis study aimed to investigate the effect of experimental warming on soil CO2 efflux, soil-air CO2 concentration, and plant biomass for two taxonomically different temperate tree species.MethodsExperimental warming was conducted in an open-field planted with Q. variabilis and P. densiflora seedlings. Infrared heaters increased the air temperature by 3\u00a0\u00b0C in the warmed plots compared with the air temperature in the control plots over a 2-year period.ResultsThe increase in air and soil temperature stimulated soil CO2 efflux by 29 and 22\u00a0% for the Q. variabilis and P. densiflora plots, respectively. Seasonal variation in the warming effect on soil CO2 efflux was species-specific. Soil CO2 efflux was also positively related to both soil temperature and soil water content. The soil moisture deficit decreased the difference in soil CO2 efflux between the control and warmed plots. Warming did not affect soil CO2 concentration and plant biomass in either species; however, the mean soil CO2 concentration was positively correlated with root and total biomass.ConclusionWarming increased soil CO2 efflux in both Q. variabilis and P. densiflora plots, while the increase showed remarkable seasonal variations and different magnitudes for the two species.", "keywords": ["0106 biological sciences", "soil temperature", "evergreen tree", "soil water", "Red pine", "seedling", "soil respiration", "01 natural sciences", "experimental study", "Pinus resinosa", "Climate change", "Pinus densiflora", "seasonal variation", "concentration (composition)", "Quercus variabilis", "Oriental oak", "carbon dioxide", "Soil respiration", "04 agricultural and veterinary sciences", "15. Life on land", "air temperature", "carbon flux", "[SDV] Life Sciences [q-bio]", "climate change", "13. Climate action", "coniferous tree", "phytomass", "0401 agriculture", " forestry", " and fisheries", "Experimental warming", "soil moisture", "deciduous tree"]}, "links": [{"href": "https://doi.org/10.1007/s13595-016-0547-4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Annals%20of%20Forest%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s13595-016-0547-4", "name": "item", "description": "10.1007/s13595-016-0547-4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s13595-016-0547-4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-03-24T00:00:00Z"}}, {"id": "10138/569454", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:25:44Z", "type": "Journal Article", "created": "2023-12-21", "title": "Radiation and temperature drive diurnal variation of aerobic methane emissions from Scots pine canopy", "description": "

Methane emissions from plant foliage may play an important role in the global methane cycle, but their size and the underlying source processes remain poorly understood. Here, we quantify methane fluxes from the shoots of Scots pine trees, a dominant tree species in boreal forests, to identify source processes and environmental drivers, and we evaluate whether these fluxes can be constrained at the ecosystem-level by eddy covariance flux measurements. We show that shoot-level measurements conducted in forest, garden, or greenhouse settings; on mature trees and saplings; manually and with an automated CO 2 -, temperature-, and water-controlled chamber system; and with multiple methane analyzers all resulted in comparable daytime fluxes (0.144 \uffc2\uffb1 0.019 to 0.375 \uffc2\uffb1 0.074 nmol CH 4 g \uffe2\uff88\uff921 foliar d.w. h \uffe2\uff88\uff921 ). We further find that these emissions exhibit a pronounced diurnal cycle that closely follows photosynthetically active radiation and is further modulated by temperature. These diurnal patterns indicate that methane production is associated with diurnal cycle of sunlight, indicating that this production is either a byproduct of photosynthesis-associated biochemical reactions (e.g., the methionine cycle) or produced through nonenzymatic photochemical reactions in plant biomass. Moreover, we identified a light-dependent component in stand-level methane fluxes, which showed order-of-magnitude agreement with shoot-level measurements (0.968 \uffc2\uffb1 0.031 nmol CH 4 g \uffe2\uff88\uff921 h \uffe2\uff88\uff921 ) and which provides an upper limit for shoot methane emissions. Arctic and alpine tundra ecosystems are large reservoirs of organic carbon1,2. Climate warming may stimulate ecosystem respiration and release carbon into the atmosphere3,4. The magnitude and persistency of this stimulation and the environmental mechanisms that drive its variation remain uncertain5\uffe2\uff80\uff937. This hampers the accuracy of global land carbon\uffe2\uff80\uff93climate feedback projections7,8. Here we synthesize 136 datasets from 56 open-top chamber in situ warming experiments located at 28 arctic and alpine tundra sites which have been running for less than 1\uffe2\uff80\uff89year up to 25\uffe2\uff80\uff89years. We show that a mean rise of 1.4\uffe2\uff80\uff89\uffc2\uffb0C [confidence interval (CI) 0.9\uffe2\uff80\uff932.0\uffe2\uff80\uff89\uffc2\uffb0C] in air and 0.4\uffe2\uff80\uff89\uffc2\uffb0C [CI 0.2\uffe2\uff80\uff930.7\uffe2\uff80\uff89\uffc2\uffb0C] in soil temperature results in an increase in growing season ecosystem respiration by 30% [CI 22\uffe2\uff80\uff9338%] (n\uffe2\uff80\uff89=\uffe2\uff80\uff89136). Our findings indicate that the stimulation of ecosystem respiration was due to increases in both plant-related and microbial respiration (n\uffe2\uff80\uff89=\uffe2\uff80\uff899) and continued for at least 25\uffe2\uff80\uff89years (n\uffe2\uff80\uff89=\uffe2\uff80\uff89136). The magnitude of the warming effects on respiration was driven by variation in warming-induced changes in local soil conditions, that is, changes in total nitrogen concentration and pH and by context-dependent spatial variation in these conditions, in particular total nitrogen concentration and the carbon:nitrogen ratio. Tundra sites with stronger nitrogen limitations and sites in which warming had stimulated plant and microbial nutrient turnover seemed particularly sensitive in their respiration response to warming. The results highlight the importance of local soil conditions and warming-induced changes therein for future climatic impacts on respiration.Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real\uffe2\uff80\uff90world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first\uffe2\uff80\uff90order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth\uffe2\uff80\uff90dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool\uffe2\uff80\uff90 and flux\uffe2\uff80\uff90based data sets through data assimilation is among the highest priorities for near\uffe2\uff80\uff90term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

", "keywords": ["550", "LAND MODELS", "Oceanography", "HETEROTROPHIC RESPIRATION", "01 natural sciences", "Atmospheric Sciences", "LITTER DECOMPOSITION", "ORGANIC-CARBON", "Geoinformatics", "GLOBAL CLIMATE-CHANGE", "DATA-ASSIMILATION", "Meteorology & Atmospheric Sciences", "TEMPERATURE SENSITIVITY", "CMIP5", "MICROBIAL MODELS", "0105 earth and related environmental sciences", "2. Zero hunger", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "500", "Earth system models", "04 agricultural and veterinary sciences", "15. Life on land", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "TERRESTRIAL ECOSYSTEMS", "Climate Action", "Geochemistry", "Climate change impacts and adaptation", "realistic projections", "13. Climate action", "recommendations", "Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "soil carbon dynamics", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Climate Change Impacts and Adaptation", "Environmental Sciences", "PARAMETER-ESTIMATION"]}, "links": [{"href": "https://escholarship.org/content/qt1pw7g2r2/qt1pw7g2r2.pdf"}, {"href": "https://doi.org/10.1002/2015gb005239"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Biogeochemical%20Cycles", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2015gb005239", "name": "item", "description": "10.1002/2015gb005239", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2015gb005239"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-01T00:00:00Z"}}, {"id": "10.1002/2017JD027346", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:14:25Z", "type": "Journal Article", "created": "2017-12-28", "title": "Soil Moisture-Temperature Coupling in a Set of Land Surface Models", "description": "Abstract

The land surface controls the partitioning of water and energy fluxes and therefore plays a crucial role in the climate system. The coupling between soil moisture and air temperature, in particular, has been shown to affect the severity and occurrence of temperature extremes and heat waves. Here we study soil moisture\uffe2\uff80\uff90temperature coupling in five land surface models, focusing on the terrestrial segment of the coupling in the warm season. All models are run off\uffe2\uff80\uff90line over a common period with identical atmospheric forcing data, in order to allow differences in the results to be attributed to the models' partitioning of energy and water fluxes. Coupling is calculated according to two semiempirical metrics, and results are compared to observational flux tower data. Results show that the locations of the global hot spots of soil moisture\uffe2\uff80\uff90temperature coupling are similar across all models and for both metrics. In agreement with previous studies, these areas are located in transitional climate regimes. The magnitude and local patterns of model coupling, however, can vary considerably. Model coupling fields are compared to tower data, bearing in mind the limitations in the geographical distribution of flux towers and the differences in representative area of models and in situ data. Nevertheless, model coupling correlates in space with the tower\uffe2\uff80\uff90based results (r = 0.5\uffe2\uff80\uff930.7), with the multimodel mean performing similarly to the best\uffe2\uff80\uff90performing model. Intermodel differences are also found in the evaporative fractions and may relate to errors in model parameterizations and ancillary data of soil and vegetation characteristics.

", "keywords": ["ENVIRONMENT SIMULATOR JULES", "FLUXES", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "CO2 EXCHANGE", "models", "WATER", "SCALE", "Research Articles", "0105 earth and related environmental sciences", "land surface", "CARBON-DIOXIDE EXCHANGE", "eartH2Observe", "temperature", "15. Life on land", "DECIDUOUS FOREST", "CLIMATE", "EVAPORATION", "VARIABILITY", "13. Climate action", "Earth and Environmental Sciences", "BALANCE", "land surface models", "SENSIBLE HEAT", "land-atmosphere interactions", "soil moisture"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JD027346"}, {"href": "https://doi.org/10.1002/2017JD027346"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2017JD027346", "name": "item", "description": "10.1002/2017JD027346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2017JD027346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-02-01T00:00:00Z"}}, {"id": "10.1002/2016rg000543", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:14:25Z", "type": "Journal Article", "created": "2017-03-23", "title": "A review of spatial downscaling of satellite remotely sensed soil moisture", "description": "Abstract

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

", "keywords": ["TIME-DOMAIN REFLECTOMETRY", "550", "IN-SITU", "downscaling", "MODIS TOA RADIANCES", "AMSR-E", "15. Life on land", "551", "01 natural sciences", "LAND-SURFACE TEMPERATURE", "REMEDHUS NETWORK SPAIN", "6. Clean water", "3. Good health", "[SDU] Sciences of the Universe [physics]", "L-BAND RADIOMETER", "remote sensing", "EVAPORATIVE FRACTION", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth and Environmental Sciences", "soil moisture", "SOUTHERN GREAT-PLAINS", "spatial resolution", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016RG000543"}, {"href": "https://doi.org/10.1002/2016rg000543"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Reviews%20of%20Geophysics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2016rg000543", "name": "item", "description": "10.1002/2016rg000543", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2016rg000543"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-18T00:00:00Z"}}, {"id": "10.1002/ecy.2199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:14:29Z", "type": "Journal Article", "created": "2018-02-27", "title": "Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe", "description": "Abstract

The relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change.Understanding anthropogenic influences on soil respiration (Rs) is critical for accurate predictions of soil carbon fluxes, but it is not known how Rs responds to grazing exclusion (GE). Here, we conducted a manipulative experiment in a meadow grassland on the Tibetan Plateau to investigate the effects of GE on Rs. The exclusion of livestock significantly increased soil moisture and above\uffe2\uff80\uff90ground biomass, but it decreased soil temperature, microbial biomass carbon (MBC), and Rs. Regression analysis indicated that the effects of GE on Rs were mainly due to changes in soil temperature, soil moisture, and MBC. Compared with the grazed blocks, GE significantly decreased soil carbon release by 23.6% over the growing season and 21.4% annually, but it increased the temperature sensitivity (Q10) of Rs by 6.5% and 14.2% for the growing season and annually respectively. Therefore, GE may reduce the release of soil carbon from the Tibetan Plateau, but under future climate warming scenarios, the increases in Q10 induced by GE could lead to increased carbon emissions.

", "keywords": ["570", "MICROBIAL RESPIRATION", "Environmental Sciences & Ecology", "Plant Productivity", "Temperature Sensitivity", "ALPINE GRASSLAND", "630", "Microbial Biomass Carbon", "NORTHERN CHINA", "SEASONAL PATTERNS", "MOUNTAIN GRASSLANDS", "Grazing Exclusion", "Tibetan Plateau", "PLANT-COMMUNITIES", "Original Research", "2. Zero hunger", "Science & Technology", "CLIMATE-CHANGE", "CO2 EFFLUX", "Ecology", "04 agricultural and veterinary sciences", "15. Life on land", "INNER-MONGOLIA", "BELOW-GROUND BIOMASS", "Soil Respiration", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Life Sciences & Biomedicine"]}, "links": [{"href": "https://doi.org/10.1002/ece3.1867"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology%20and%20Evolution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ece3.1867", "name": "item", "description": "10.1002/ece3.1867", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ece3.1867"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-11T00:00:00Z"}}, {"id": "10.1007/s00248-009-9508-x", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-05-30T16:14:50Z", "type": "Journal Article", "created": "2009-03-30", "title": "Comparative Resistance And Resilience Of Soil Microbial Communities And Enzyme Activities In Adjacent Native Forest And Agricultural Soils", "description": "Degradation of soil properties following deforestation and long-term soil cultivation may lead to decreases in soil microbial diversity and functional stability. In this study, we investigated the differences in the stability (resistance and resilience) of microbial community composition and enzyme activities in adjacent soils under either native tropical forest (FST) or in agricultural cropping use for 14 years (AGR). Mineral soil samples (0 to 5 cm) from both areas were incubated at 40 degrees C, 50 degrees C, 60 degrees C, or 70 degrees C for 15 min in order to successively reduce the microbial biomass. Three and 30 days after the heat shocks, fluorescein diacetate (FDA) hydrolysis, cellulase and laccase activities, and phospholipid-derived fatty acids-based microbial community composition were measured. Microbial biomass was reduced up to 25% in both soils 3 days after the heat shocks. The higher initial values of microbial biomass, enzyme activity, total and particulate soil organic carbon, and aggregate stability in the FST soil coincided with higher enzymatic stability after heat shocks. FDA hydrolysis activity was less affected (more resistance) and cellulase and laccase activities recovered more rapidly (more resilience) in the FST soil relative to the AGR counterpart. In the AGR soil, laccase activity did not show resilience to any heat shock level up to 30 days after the disturbance. Within each soil type, the microbial community composition did not differ between heat shock and control samples at day 3. However, at day 30, FST soil samples treated at 60 degrees C and 70 degrees C contained a microbial community significantly different from the control and with lower biomass regardless of high enzyme resilience. Results of this study show that deforestation followed by long-term cultivation changed microbial community composition and had differential effects on microbial functional stability. Both soils displayed similar resilience to FDA hydrolysis, a composite measure of a broad range of hydrolases, supporting the concept of high functional redundancy in soil microbial communities. In contrast, the resilience of the substrate-specific activities of laccase and cellulase were lower in AGR soils, indicating a less diverse community of microorganisms capable of producing these enzymes and confirming that specific microbial functions are more sensitive measurements for evaluating change in the ecological stability of soils.", "keywords": ["2. Zero hunger", "Hot Temperature", "Laccase", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Fluoresceins", "6. 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