The International Soil Moisture Network: serving Earth system science for over a decade

Hydrology and earth system sciences, Journal Year: 2021, Volume and Issue: 25(11), P. 5749 - 5804

Published: Nov. 9, 2021

Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by European Space Agency, to serve centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together collected and freely shared multitude of organisations, harmonises them terms units sampling rates, applies advanced quality control, stores database. Users can retrieve from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, has evolved into primary reference worldwide, evidenced more than 3000 active users over 1000 scientific publications referencing sets provided network. As July 2021, now contains 71 networks 2842 stations located all globe, with time period spanning 1952 present. number covered is still growing, approximately 70 % contained continue be updated on regular or irregular basis. main scope paper inform readers about evolution past decade, including description network set updates control procedures. A comprehensive review existing literature making use also order identify current limitations functionality usage shape priorities next decade operations unique community-based repository.

Language: Английский

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Critical adjustment of land mitigation pathways for assessing countries’ climate progress

Nature Climate Change, Journal Year: 2021, Volume and Issue: 11(5), P. 425 - 434

Published: April 26, 2021

Language: Английский

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Remote sensing of soil degradation: Progress and perspective

International Soil and Water Conservation Research, Journal Year: 2023, Volume and Issue: 11(3), P. 429 - 454

Published: March 15, 2023

Soils constitute one of the most critical natural resources and maintaining their health is vital for agricultural development ecological sustainability, providing many essential ecosystem services. Driven by climatic variations anthropogenic activities, soil degradation has become a global issue that seriously threatens environment food security. Remote sensing (RS) technologies have been widely used to investigate as it highly efficient, time-saving, broad-scope. This review encompasses recent advances state-of-the-art ground, proximal, novel RS techniques in degradation-related studies. We reviewed RS-related indicators could be monitoring properties. The direct (mineral composition, organic matter, surface roughness, moisture content soil) indirect proxies (vegetation condition land use/land cover change) evaluating were comprehensively summarized. results suggest these above are effective degradation, however, no system established date. also discussed RS's mechanisms, data, methods identifying specific phenomena (e.g., erosion, salinization, desertification, contamination). investigated potential relations between Sustainable Development Goals (SDGs) challenges prospective use assessing degradation. To further advance optimize technology, analysis retrieval methods, we identify future research needs directions: (1) multi-scale degradation; (2) availability data; (3) process modelling prediction; (4) shared dataset; (5) decision support systems; (6) rehabilitation degraded resource contribution technology. Because difficult monitor or measure all properties large scale, remotely sensed characterization related particularly important. Although not silver bullet, provides unique benefits studies from regional scales.

Language: Английский

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A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

Earth system science data, Journal Year: 2022, Volume and Issue: 14(12), P. 5267 - 5286

Published: Nov. 30, 2022

Abstract. High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing data often available at coarse resolution only surface soil. Here, we present a 1 km long-term dataset of derived through machine learning trained by in situ measurements 1789 stations over China, named SMCI1.0 (Soil Moisture China data, version 1.0). Random forest is used as robust approach to predict using ERA5-Land time series, leaf area index, land cover type, topography properties predictors. provides 10-layer with 10 cm intervals up 100 deep daily period 2000–2020. Using benchmark, two independent experiments were conducted evaluate estimation accuracy SMCI1.0: year-to-year (ubRMSE ranges from 0.041 0.052 R 0.883 0.919) station-to-station 0.045 0.051 0.866 0.893). generally has advantages other products, including ERA5-Land, SMAP-L4, SoMo.ml. However, high errors located North Monsoon Region. Overall, highly accurate estimations both ensure applicability study spatial–temporal patterns. As based on it can be useful complement existing model-based satellite-based datasets various hydrological, meteorological, ecological analyses models. The DOI link http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

Language: Английский

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A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: March 15, 2023

Abstract Global soil moisture estimates from current satellite missions are suffering inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine scale. This study developed a dataset of global gap-free surface (SSM) daily 1-km resolution 2000 to 2020. is achieved based on European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product 0.25° resolution. Firstly, an operational gap-filling method was fill missing data in ESA-CCI using ERA5 reanalysis dataset. Random Forest algorithm then adopted disaggregate coarse-resolution 1-km, with help International Soil Moisture Network in-situ other optical remote sensing datasets. The generated had good accuracy, high correlation coefficent (0.89) low unbiased Root Mean Square Error (0.045 m 3 /m ) by cross-validation. To best our knowledge, this currently only long-term far.

Language: Английский

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A fine-resolution soil moisture dataset for China in 2002–2018

Earth system science data, Journal Year: 2021, Volume and Issue: 13(7), P. 3239 - 3261

Published: July 7, 2021

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become means to quickly obtain soil across large areas, but the coarse spatial resolution of data imposes great limitations on application these data. We provide a unique dataset (0.05∘, monthly) China from 2002 2018 based reconstruction model-based downscaling techniques using different passive products – including AMSR-E AMSR2 (Advanced Microwave Scanning Radiometer Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 SMOS-IC (Soil Moisture Ocean Salinity designed by Institut National de la Recherche Agronomique, INRA, Centre d’Etudes Spatiales BIOsphère, CESBIO) calibrated with consistent model in combination ground observation This new fine-resolution high overcomes multisource time matching problem between optical sources eliminates difference sensor errors. The validation analysis indicates that accuracy satisfactory (bias: −0.057, −0.063 −0.027 m3 m−3; unbiased root mean square error (ubRMSE): 0.056, 0.036 0.048; correlation coefficient (R): 0.84, 0.85 0.89 monthly, seasonal annual scales, respectively). was used analyze spatiotemporal patterns water content 2018. In past 17 years, China's shown cyclical fluctuations slight downward trend can be summarized as wet south dry north, increases west decreases east. reconstructed widely significantly improve hydrologic serve input ecological other geophysical are published Zenodo at https://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).

Language: Английский

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Separating the effects of climate change and human activities on drought propagation via a natural and human-impacted catchment comparison method

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 603, P. 126913 - 126913

Published: Sept. 7, 2021

Language: Английский

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Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Earth system science data, Journal Year: 2021, Volume and Issue: 13(3), P. 1385 - 1401

Published: March 31, 2021

Abstract. High-quality and long-term soil moisture products are significant for hydrologic monitoring agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) incomplete in global land (just about 30 %–80 % coverage ratio), due to satellite orbit limitations of retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) AMSR2 product gap-filling. Through proposed framework, generate seamless (SGD) from 2013 2019. further validate effectiveness these products, three verification methods used as follows: (1) situ validation, (2) time-series (3) simulated missing-region validation. Results show that have reliable cooperativity with selected values. The evaluation indexes reconstructed (original) dataset correlation coefficient (R) 0.685 (0.689), root-mean-squared error (RMSE) 0.097 (0.093), mean absolute (MAE) 0.079 (0.077). temporal consistency is ensured original distribution valid spatial continuity regions accordance information (R: 0.963–0.974, RMSE: 0.065–0.073, MAE: 0.044–0.052). This can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).

Language: Английский

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Re-thinking the environment in landscape genomics

Trends in Ecology & Evolution, Journal Year: 2022, Volume and Issue: 38(3), P. 261 - 274

Published: Nov. 17, 2022

The increasing availability of new, high-quality geo-referenced environmental data(bases) is stimulating landscape genomic studies terrestrial and aquatic organisms.Environmental data (e.g., climate, soil, topography) are now available at multiple spatial temporal scales and, together with environmentally genetically informed sampling designs, enable us to capture selection pressures high resolution in various organisms.Statistical advances genotype–environment association methods allow testing the response population variation complex environments, using nonredundant informative predictors.Our understanding constraints underlying local adaptation living organisms has provided insights into potential responses populations changes such as global warming. This a key component well-informed biodiversity conservation programmes. Detecting extrinsic selective shaping critical for better forecasting evolutionary natural changing conditions. With data, genomics provides unprecedented how gene functions affect traits potentially under selection. Yet, robustness associations used remains tempered due limitations, including characteristics used, designs employed, statistical frameworks applied. Here, we argue that complementary or new sources may help improve detection patterns environments. environment, particular its spatio-temporal changes, exerts strong on fitness-related phenotypic traits. If these controlled, leads locally adapted populations. Thus, drivers leave specific signature genomes species enables inference without directly measuring fitness traits, but instead determining effect factors (see Glossary) allele frequencies (GEA) [1.Rellstab C. et al.A practical guide analysis genomics.Mol. Ecol. 2015; 24: 4348-4370Crossref PubMed Scopus (412) Google Scholar]. research field contributes mechanisms adaptation, while detecting driving it. Landscape can be expanded assess possible maladaptation change (genomic offset [2.Capblancq T. al.Genomic prediction (mal)adaptation across current future climatic landscapes.Annu. Rev. Evol. Syst. 2020; 51: 245-269Crossref (98) Scholar,3.Rellstab al.Prospects limitations management.Evol. Appl. 2021; 14: 1202-1212Crossref (35) Scholar]). knowledge particularly valuable context human-induced alterations, climate land-use change, therefore relevant nature ecosystem management [4.Layton K.K.S. evidence past climate-linked loss migratory Arctic fish.Nat. Clim. Chang. 11: 158-165Crossref (24) Scholar,5.Bay R.A. signals predict climate-driven declines bird.Science (1979). 2018; 359: 83-86Crossref (237) In fact, concept inform assisted flow migration strategies heart programmes support climate-threatened strengthen resilience [6.Hoffmann A.A. al.Genetic mixing management: from genetic rescue provenancing.Evol. 634-652Crossref (57) emerged about 15 years ago publication first conceptual approach specifically designed GEA [7.Joost S. method (SAM) detect candidate loci selection: towards adaptation.Mol. 2007; 16: 3955-3969Crossref (332) Thereafter, advent next-generation sequencing techniques large datasets dramatically increased quality quantity both resources [8.Xia J. al.Research challenges opportunities big biology.Glob. Biol. 26: 6040-6061Crossref (33) Consequently, grown rapidly. According our literature survey (n = 278; Figure 1A ; see supplemental information online methodological details), more than 50% all articles published during period 2007–2021 were issued after 2017. A majority initially focused plants (especially trees), likely their sedentariness. trend become balanced recent years, resulting 51% 47% (2007–2021) examining plant animals, respectively (Figure 1A). also world (termed seascape riverscape [9.Liggins L. al.Seascape genomics: contextualizing adaptive neutral ocean environment.in: Oleksiak M.F. Rajora O.P. Population Genomics: Marine Organisms. 1st edn. Springer, 2020: 171-218Google Scholar,10.Grummer J.A. al.Aquatic effects variation.Trends 2019; 34: 641-654Abstract Full Text PDF (78) Scholar] subfields genomics), among which marine have frequent 2 years. development mirrored by three most studied ecosystems [forest (32.7%), (13.3%), agriculture (10.8%); 1A], account 56.8% studies. study typically relies on: (i) an appropriate design incorporate intraspecific diversity capturing differences, (ii) accurately describe putative interest acting individuals, (iii) genome-wide (iv) tools correlate (genomics, Y) predictor (environmental, X) variables, accounting confounding structure [11.Lotterhos K.E. Whitlock M.C. relative power genome scans depends method.Mol. 1031-1046Crossref (321) As result, manifold decisions need made early stage 2). While there numerous overviews issues approaches [12.Selmoni O. al.Sampling strategy optimization increase simulation-based approach.Mol. Resour. 20: 154-169Crossref (22) Scholar, 13.Wold al.Expanding toolbox: incorporating structural variants enhance concern.Mol. 30: 5949-5965Crossref (14) 14.Bourgeois Y.X.C. Warren B.H. An overview whole-genome resequencing eukaryotes.Mol. 6036-6071Crossref (23) Scholar]), hitherto no general guidelines use genomics. fill this gap review types applications highlight promising avenues characterising heterogeneous habitats, aim improving outcomes. Notably, revealed only 36% reported values explanatory presented models. Always taking best model each study, median resulted R2 0.38 93, standard deviation 0.29) ranging 0.01 0.98 overall low small sample sizes 1B). Although maximum achievable models unknown, indicate analyses moderate yet. Hence, decisions, example, based prior (i.e., choice, type, source, scale predictors; 2), should anticipate confidence To end, trends present four main pursued Researchers conditions locations: situ measurements, remote sensing, interpolation latter being clearly widely date WorldClim; [15.Fick S.E. Hijmans R.J. WorldClim 2: 1-km surfaces land areas.Int. Climatol. 2017; 37: 4302-4315Crossref (6863) open access user-friendly interpolation-based, databases greatly improved characterisation exploration ecological gradients identify Their convenience additional collected field. been facilitated initiatives led international consortia acquire, model, store, share scale. For World Ocean Database [16.Boyer T.P. al.World Database.in: Mishonov A.V. NOAA Atlas NESDIS. 87. NOAA, 2018: 1-207Google Scholar], powered over 20 000 datasets, centralises coordinated effort uniformly curated oceanographic, research. resources, ambitious tackled continental-scale areas [17.Babin al.RAD-Seq reveals additive polygenic caused spatially-varying American eel (Anguilla rostrata).Genome 9: 2974-2986Crossref (28) Scholar]; 1C), leaving cost bottleneck. Nevertheless, further efforts acquisition modelling still needed grasp variation, poorly sampled freshwater ecosystems. parallel, highly accurate pocket-sized devices developed measure biophysical properties locations, allowing measurements grain. These powerful studying habitats organisms, especially those short generation times dynamics associated pressures. However, often represent snapshots inadequately reflect long-term pressure within timescale. Nearly 90% abiotic pressures, largely neglecting biotic relevance 1D; [18.Maselko al.Long-lived resilient variability through portfolio effect.Ecol. 10: 6435-6448Crossref (1) Scholar,19.Fraik A.K. al.Disease swamps molecular signatures genetic-environmental Tasmanian devil (Sarcophilus harrisii) populations.Evolution (N Y). 74: 1392-1408Crossref (13) operate different features, thus providing distinct forces act [20.Descombes P. al.Spatial indicator improves predictions distributions landscapes.Ecography. 43: 1448-1463Crossref instance, mean derived floristic compositions locations ascribe not humidity, light availability, soil organic matter content, pH [21.Leempoel K. al.Multiscale alpine Biscutella laevigata.Ecol. 8: 1794-1806Crossref (7) interactions. Of employing factors, vast (91.4%; 1D) considered analyses, followed topography (35.3%) (11.5%) factors. focus reflects desire gain (micro-) they respond change. frequently supplemented other 1D). Yadav al. [22.Yadav al.Microgeographical corresponds elevational congeneric montane grasshoppers.Mol. 481-498Crossref (12) investigated two grasshopper endemic Australian Alps found significant GEAs, precipitation seasonality, number frost days, terrain ruggedness, illustrates benefit diverse conditioned hypotheses multifactorial adaptation. plays role establishment persistence many fungi, plants, few below-ground because missing. great generate comprehensive interpolated descriptors decent complement measurements. bacterial established provide reference atlas dominant bacteria Earth [23.Delgado-Baquerizo M. soil.Science. 325: 320-325Crossref (1072) random forest 250 m [24.Hengl al.SoilGrids250m: gridded machine learning.PLoS One. 12e0169748Crossref (1977) Similarly, variables explain abundance nematodes links fertility functioning [25.van den Hoogen al.Soil nematode functional group composition scale.Nature. 572: 194-198Crossref (512) chemical physical usually released cross-validation scores uncertainty maps, users perceive level thereby, observations target geographical https://soilgrids.org). Recently, Lembrechts [26.Lembrechts J.J. al.Global maps temperature.Glob. 2022; 28: 3110-3144Crossref (76) highlighted difference between temperature atmospheric air (up 10°C some areas, 3.0 ± 2.1°C) scale, cold dry biomes. authors advocated collect yet unsampled density essential interpolation. Remote sensing time, surface moisture [27.Chen Y. al.An remote-sensing-based (RSSSM) dataset covering 2003-2018.Earth Sci. Data. 13: 1-31Crossref (38) Generation time influence ability 3A,B ). tree known long produce numbers seeds, dense seedling layers subjected competition nutrients [28.Petit Hampe A. Some consequences tree.Annu. 2006; 187-214Crossref (765) Characterising site juvenile establishment, strongest (sometimes 100 ago), helps adaptation-relevant GEA, shown Swiss stone pine (Pinus cembra) [29.Dauphin B. vulnerability rapid warming time.Glob. 27: 1-15Crossref (26) achieved tuning grain well extent desired 3A). breeding range yellow warbler (Setophaga petechia) [5.Bay bird GEAs averaged 1970–2000, highlighting impact climate-induced birds' habitat. Interestingly, same bioclimatological (reference 1970–2000), charr (Salvelinus alpinus) irrespective individuals spanning several age classes (2005–2017) lag predictors 1E), intentional not, primary importance vary antagonistic [30.Troth al.Selective trade-offs maintain alleles underpinning trait plants.Science. 361: 475-478Crossref (66) advocate optimising match periods ensure optimally anticipated exposed. species' life history hand, it explore test adaptation-focused going beyond often-chosen conveniently related bioclimatology geography. probably single source meets needs terms grain, set heterogeneity space 3). when means versus minimum/maximum extreme events, gradual average sum annual 1970–2010) could teamed hourly daily growing season 2018) singular pulses enough induce evolution addition, size commonly increases back diminishing observations, trade-off stages long-lived accuracy tested GEA. appears challenging events droughts, although recently substantial improvement combining independent [31.Cook E.R. al.Old megadroughts pluvials Common Era.Sci. Adv. 1: 1-22Crossref (371) At what operates seems impractical question, acts phenotypes explicit Instead, planning detectable certain taxonomic groups histories, intimately linked outlined next section. There awareness applying Sampling generally follows one following strategies: uninformed, environment-informed, environment- genetics-informed (Box 1), using, priori and/or variation. alternatives do rely regular, scattered, transect (67.6% surveyed; 1C) assumed adequately IA Box 1). contrast, incorporates beforehand establish design. Considering paired wet/dry low/high elevation) along gradient(s) strives maximising covered keeping good representation distribution (23.7%; Figures 1C IB correlated demographic [32.Yeaman al.Convergent distantly conifers.Science. 2016; 353: 23-26Crossref (196) later), phenomenon accounted downstream analysis. limiting false positive rarely implemented (3.4%, [33.Buehler D. outlier locus habitat-mediated regional replicates.Evol. 2013; 285-300Crossref limited sites similar contrast. simulation-supported mitigates selecting closely consistently contrasting environments Another drawback data) contrast situated locations. Finally, takes up suitable IC optimised zones) clusters, lineages), stratified components sharing background. conclusion, choosing contribute outcomes.Box 1Sampling studiesLandscape conducted six I). Definitions review, examples, next.Uninformed designRegular: according regular grid.Scattered: unstructured way randomly depending locations).Transect: linear Euclidian distances every km axis).Environment-informedPaired: warm habitats). special case categorical pairs replicated times.Gradient: gradient elevation/latitude, temperature, conditions).Environment- genetics-informedStratified: evenly (often randomly) defined subsets (strata; i.e., equivalent samples per cluster group). Stratification even biogeographical regions fair previously characterised lineages). next. Uninformed Regular: grid. Scattered: locations). Transect: axis). Environment-informed Paired: times. Gradient: conditions). Environment- Stratified: Sample p

Language: Английский

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Analyzing urban scaling laws in the United States over 115 years

Environment and Planning B Urban Analytics and City Science, Journal Year: 2024, Volume and Issue: 51(9), P. 2249 - 2263

Published: April 10, 2024

The scaling relations between city attributes and population are emergent ubiquitous aspects of urban growth. Quantifying these understanding their theoretical foundation, however, is difficult due to the challenge defining boundaries a lack historical data study dynamics over time space. To address this issue, we analyze infrastructure across 857 metropolitan areas in conterminous United States an unprecedented 115 years (1900–2015) using dasymetrically refined estimates, road network models, multi-temporal settlement define dynamic boundaries. We demonstrate that exponents closely match models century. Despite some close quantitative agreement with theory, empirical unexpectedly vary regions. Our analysis coefficients, meanwhile, reveals contemporary cities use more developed land kilometers than similar 1900, which has serious implications for development impacts on local environment. Overall, our results provide new way systems based novel, geohistorical data.

Language: Английский

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