المؤلفون: Walden, Lewis, Sepanta, Farid, Viscarra Rossel, Raphael

مصطلحات موضوعية: article, Verlagsveröffentlichung

الوصف: Soil carbon (C) is heterogeneous and exists in various forms along a decomposition continuum from labile fast-cycling compounds to more persistent forms of C, which can reside in the soil for centuries to millennia. Methods for soil organic C fractionation aim to account for this complexity by separating soil organic C into distinct groups that exhibit similar turnover. Our aims were to (a) fractionate three mineral soils with small C concentrations (<2.5 % C), different textures and mineralogy using a granulometric method to derive the particulate organic C in macroaggregates (POCmac), the particulate organic C in microaggregates (POCmic), and the mineral-associated organic carbon (MAOC), (b) test if mid-infrared (MIR) spectra can discriminate the distinct organic C fractions and characterise the critical organic and mineral functional groups, and c) explore the interactions between the dominant mineral and organic functional groups to elucidate C stabilisation. With a multivariate analyses we found that the MIR spectra use information from mineral and organic frequencies to discriminate the organic C fractions. Closer investigation on specific regions of the MIR spectrum showed that absorptions relating to silicates were more pronounced in the POCmac and POCmic fractions and clay mineral absorptions were stronger in the MAOC fraction. There was little organic C in the POCmic and POCmac fractions, respectively, and their spectra showed mostly mineralogical features. Most of the organic C in the soils was present as MAOC. The stretching vibration of the bonds in the alkyl CH2 molecule was most prominent. However, absorptions from C = C and C = O stretching vibrations, due to alkenes and amides were also present. These molecules are known to form MAOC. We found that the wavenumbers associated with the absorption of alkyl CH2 were positively correlated with the absorption of clay minerals, which may be used to infer the mineral association of organic C. Our results show that MIR spectroscopy can characterise ...

وصف الملف: electronic

العلاقة: https://doi.org/10.5194/egusphere-2023-2464Test; https://noa.gwlb.de/receive/cop_mods_00069769Test; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068140/egusphere-2023-2464.pdfTest; https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2464/egusphere-2023-2464.pdfTest

الإتاحة: https://doi.org/10.5194/egusphere-2023-2464Test
https://noa.gwlb.de/receive/cop_mods_00069769Test
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068140/egusphere-2023-2464.pdfTest
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2464/egusphere-2023-2464.pdfTest

المؤلفون: Walden, Lewis, Serrano, Oscar, Zhang, Mingxi, Shen, Zefang, Sippo, James Z., Bennett, Lauren T., Maher, Damien T., Lovelock, Catherine E, Macreadie, Peter I, Gorham, Connor, Lafratta, Anna, Lavery, P. S., Mosley, Luke, Reithmaier, Gloria M. S., Kelleway, Jeffrey J., Dittmann, Sabine, Adame, Fernanda, Duarte, Carlos M., Gallagher, John Barry, Waryszak, Pawel, Carnell, Paul, Kasel, Sabine, Hinko-Najera, Nina, Hassan, Rakib, Goddard, Madeline, Jones, Alice R., Viscarra Rossel, Raphael A.

المساهمون: Red Sea Research Center and Computational Biosciences Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Marine Science Program, Red Sea Research Center (RSRC), Biological and Environmental Science and Engineering (BESE) Division, Computational Bioscience Research Center (CBRC)

الوصف: The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the top 0–30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6–39.0 Gt), and blue carbon ecosystems 0.35 Gt (0.20–0.62 Gt). Tall open eucalypt and mangrove forests have the largest soil carbon content by area, while eucalypt woodlands and hummock grasslands have the largest total carbon stock due to the vast areas they occupy. Our findings suggest these are essential ecosystems for conservation, preservation, emissions avoidance, and climate change mitigation because of the additional co-benefits they provide. ; R.A.V.R., L.W., Z.S., and O.S. thank the Australian Government for funding this research via grant ACSRIV000077. O.S. thanks the additional support of I+D+i projects RYC2019-027073-I and PIE HOLOCENO 20213AT014 funded by MCIN/AEI/10.13039/501100011033 and FEDER20213AT014. We thank contributions from Dr. Andy Stevens, Lindsay Hutley, and the many colleagues who contributed to the collection of soil samples and data used in this research. This work is supported by the use of (i) Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) and (ii) computational resources in the Pawsey Supercomputing Centre, which is funded by the Australian Government and the Government of Western ...

وصف الملف: application/pdf

العلاقة: https://www.nature.com/articles/s43247-023-00838-xTest; Walden, L., Serrano, O., Zhang, M., Shen, Z., Sippo, J. Z., Bennett, L. T., Maher, D. T., Lovelock, C. E., Macreadie, P. I., Gorham, C., Lafratta, A., Lavery, P. S., Mosley, L., Reithmaier, G. M. S., Kelleway, J. J., Dittmann, S., Adame, F., Duarte, C. M., Gallagher, J. B., … Viscarra Rossel, R. A. (2023). Multi-scale mapping of Australia’s terrestrial and blue carbon stocks and their continental and bioregional drivers. Communications Earth & Environment, 4(1). https://doi.org/10.1038/s43247-023-00838-xTest; Communications Earth & Environment; http://hdl.handle.net/10754/692388Test

الإتاحة: https://doi.org/10.1038/s43247-023-00838-xTest
https://doi.org/10.5281/zenodo.7787493Test
http://hdl.handle.net/10754/692388Test

المؤلفون: Abramoff, Rose, Z, Guenet, Bertrand, Zhang, Haicheng, Georgiou, Katerina, Xu, Xiaofeng, Viscarra Rossel, Raphael, Yuan, Wenping, Ciais, Philippe

المساهمون: Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire de géologie de l'ENS (LGENS), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL), Université libre de Bruxelles (ULB), Stanford University, Lawrence Livermore National Laboratory (LLNL), San Diego State University (SDSU), School of Earth and Planetary Science Curtin university, Curtin University, Sun Yat-sen University Guangzhou (SYSU), R.Z.A. thanks Jinyun Tang and Elisa Thébault for helpful discussion about model formulation. R.Z.A. was supported by the French government grant “Make Our Planet Great Again” and by a Marie Skłodowska–Curie Individual Fellowship (Grant no. 834-169) from the European Union's Horizon 2020 program. K.G. was supported as a Lawrence Fellow at Lawrence Livermore National Lab (LLNL) by the LLNL-LDRD Program under Project No. 21-ERD-045 and the US DOE Office of Science, Office of Biological and Environmental Research, Genomic Science Program as part of the LLNL Microbes Persist Scientific Focus Area, SCW1632. Work at LLNL was conducted under the auspices of US DOE Contract DE-AC52-07NA27344. X.X. has been supported by the ORNL Terrestrial Ecosystem Science Scientific Focus Area (ORNL TES-SFA) and NGEE Arctic projects and DE-SC0014416, which are supported by the Office of Biological and Environmental Research in the Department of Energy Office of Science., European Project: 834169,H2020,H2020-MSCA-IF-2018,FlexMod(2020)

المصدر: ISSN: 0038-0717 ; Soil Biology and Biochemistry ; https://hal.science/hal-03419469Test ; Soil Biology and Biochemistry, 2022, 164, pp.108466. ⟨10.1016/j.soilbio.2021.108466⟩.

مصطلحات موضوعية: Soil carbon modeling, Microbial decomposition, Mineral association, Soil organic carbon stocks, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment

الوصف: International audience ; Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, it is necessary for soil C models to represent these measurable quantities so that model processes can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and low molecular weight C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three independent data sets of soil fractionation measurements spanning a range of climate and geochemistry in Australia (N = 495), Europe (N = 175), and across the globe (N = 659). When using all the data together (N = 1329), the Millennial V2 model predicted SOC (RMSE = 3.3 kg C m−2, AIC = 675, = 0.31, = 0.26) better than the widely-used first-order decomposition model Century (RMSE = 3.4 kg C m−2, AIC = 696, = 0.21, = 0.18) across sites, despite the fact that Millennial V2 has an increase in process complexity and number of parameters compared to Century. Millennial V2 also reproduced the observed fraction of C in MAOM and larger particle size fractions for most latitudes and biomes, and allows for a more detailed understanding of the pools and ...

العلاقة: info:eu-repo/grantAgreement//834169/EU/A Flexible, Data-driven Model Framework to Predict Soil Responses to Land-use and Climate Change/FlexMod; hal-03419469; https://hal.science/hal-03419469Test; https://hal.science/hal-03419469/documentTest; https://hal.science/hal-03419469/file/1-s2.0-S0038071721003400-main.pdfTest

الإتاحة: https://doi.org/10.1016/j.soilbio.2021.108466Test
https://hal.science/hal-03419469Test
https://hal.science/hal-03419469/documentTest
https://hal.science/hal-03419469/file/1-s2.0-S0038071721003400-main.pdfTest

المؤلفون: Wetterlind, Johanna, Viscarra Rossel, Raphael A., Steffens, Markus

المساهمون: Australian Research Council, Svenska Forskningsrådet Formas

المصدر: European Journal of Soil Science ; volume 73, issue 4 ; ISSN 1351-0754 1365-2389

الوصف: Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid‐state 13 C nuclear magnetic resonance (NMR) spectroscopy, are time‐consuming and expensive. Diffuse reflectance spectroscopy in the visible, near infrared and mid‐infrared regions (vis–NIR: 350–2500 nm; mid‐IR: 4000–400 cm −1 ) can also be used to characterise SOC chemistry; however, it is difficult to know the frequencies where the information occurs. Thus, we correlated the C functional groups from the 13 C NMR to the frequencies in the vis–NIR and mid‐IR spectra using two methods: (1) 2‐dimensional correlations of 13 C NMR spectra and the diffuse reflectance spectra, and (2) modelling the NMR functional C groups with the reflectance spectra using support vector machines (SVM) (validated using 5 times repeated 10‐fold cross‐validation). For the study, we used 99 mineral soils from the agricultural regions of Sweden. The results show clear correlations between organic functional C groups measured with NMR and specific frequencies in the vis–NIR and mid‐IR spectra. While the 2D correlations showed general relationships (mainly related to the total SOC content), analysing the importance of the wavelengths in the SVM models revealed more detail. Generally, models using mid‐IR spectra produced slightly better estimates than the vis–NIR. The best estimates were for the alkyl C group (R 2 = 0.83 and 0.85, vis–NIR and mid‐IR, respectively), and the O/N‐alkyl C group was the most difficult to estimate (R 2 = 0.34 and 0.38, vis–NIR and mid‐IR, respectively). Combining 13 C NMR with the cost‐effective diffuse reflectance methods could potentially increase the number of measured samples and improve the spatial and temporal characterisation of SOC. However, more studies with a wider range of soil types and land management systems are needed to further evaluate the conditions under which these ...

الإتاحة: https://doi.org/10.1111/ejss.13263Test

المؤلفون: Viscarra Rossel, Raphael, Behrens, Thorsten, Ben-Dor, Eyal, Chabrillat, Sabine, Demattê, José Alexandre Melo, Ge, Yufeng, Gomez, Cecile, Guerrero, César, Peng, Yi, Ramirez-Lopez, Leonardo, Shi, Zhou, Stenberg, Bo, Webster, Richard, Winowiecki, Leigh, Shen, Zefang

المساهمون: School of Molecular and Life Sciences Curtin University, Curtin University, Swiss Competence Center for Soils, Tel Aviv University (TAU), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), Leibniz Universität Hannover=Leibniz University Hannover, Universidade de São Paulo = University of São Paulo (USP), University of Nebraska–Lincoln, University of Nebraska System, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD), Universidad Miguel Hernández Elche (UMH), Food and Agriculture Organization of the United Nations Rome, Italie (FAO), BÜCHI Labortechnik AG, Partenaires INRAE, Zhejiang University, Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet (SLU), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), World Agroforestry Center CGIAR, Kenya (ICRAF), Consultative Group on International Agricultural Research CGIAR (CGIAR), Raphael A. Viscarra Rossel received funding from the Australian Government via grant ACSRIV000077.

المصدر: ISSN: 1351-0754.

مصطلحات موضوعية: calibration, machine learning, model localization, reflectance spectroscopy, regression, soil constituents, spectral libraries, validation, [SDV]Life Sciences [q-bio]

الوصف: International audience ; Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one-tenth of the cost of a chemical determination. Spectroscopy is cost-effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over-fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over-fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights Spectroscopy is reliable because it is a highly repeatable and ...

العلاقة: hal-03738798; https://hal.inrae.fr/hal-03738798Test; https://hal.inrae.fr/hal-03738798/documentTest; https://hal.inrae.fr/hal-03738798/file/European%20J%20Soil%20Science%20-%202022%20-%20Viscarra%20Rossel%20-%20Diffuse%20reflectance%20spectroscopy%20for%20estimating%20soil%20properties%20A.pdfTest; IRD: fdi:010085407; WOS: 000824425000001

الإتاحة: https://doi.org/10.1111/ejss.13271Test
https://hal.inrae.fr/hal-03738798Test
https://hal.inrae.fr/hal-03738798/documentTest
https://hal.inrae.fr/hal-03738798/file/European%20J%20Soil%20Science%20-%202022%20-%20Viscarra%20Rossel%20-%20Diffuse%20reflectance%20spectroscopy%20for%20estimating%20soil%20properties%20A.pdfTest

المؤلفون: Yang, Yuanyuan, Shen, Zefang, Bissett, Andrew, Viscarra Rossel, Raphael A.

مصطلحات موضوعية: article, Verlagsveröffentlichung

الوصف: Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental-scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. The method relies on the development of spectrotransfer functions with state-of-the-art machine learning and uses publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared (vis–NIR) wavelengths, to estimate the relative abundances of Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community diversity measured with the abundance-based coverage estimator (ACE) index. The algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), extreme gradient boosting (XGBoost) and one-dimensional convolutional neural networks (1D-CNNs). The spectrotransfer functions were validated with a 10-fold cross-validation (n=577). The 1D-CNNs outperformed the other algorithms and could explain between 45 % and 73 % of fungal relative abundance and diversity. The models were interpretable, and showed that soil nutrients, pH, bulk density, ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R2≥0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R2 values of 0.5 and 0.58 respectively. The spectrotransfer functions for the Glomeromycota and diversity were the poorest with R2 values of 0.48 and 0.45 respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the ...

وصف الملف: electronic

العلاقة: SOIL -- http://www.soil-journal.netTest/ -- http://www.bibliothek.uni-regensburg.de/ezeit/?2834892Test -- 2199-398X; https://doi.org/10.5194/soil-8-223-2022Test; https://noa.gwlb.de/receive/cop_mods_00060436Test; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00060071/soil-8-223-2022.pdfTest; https://soil.copernicus.org/articles/8/223/2022/soil-8-223-2022.pdfTest

الإتاحة: https://doi.org/10.5194/soil-8-223-2022Test
https://noa.gwlb.de/receive/cop_mods_00060436Test
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00060071/soil-8-223-2022.pdfTest
https://soil.copernicus.org/articles/8/223/2022/soil-8-223-2022.pdfTest

المؤلفون: Baumann, Philipp, Lee, Juhwan, Behrens, Thorsten, Biswas, Asim, Six, Johan, McLachlan, Gordon, Viscarra Rossel, Raphael A.

المصدر: European Journal of Soil Science, 73 (2)

مصطلحات موضوعية: available soil water, machine learning, soil water retention, visible–near-infrared spectroscopy, water retention models

الوصف: We need measurements of soil water retention (SWR) and available water capacity (AWC) to assess and model soil functions, but methods are time-consuming and expensive. Our aim here was to investigate the modelling of AWC and SWR with visible–near-infrared spectra (vis–NIR) and the machine-learning method cubist. We used soils from 54 locations across Australian agricultural regions, from three depths: 0–15 cm, 15–30 cm and 30–60 cm. The volumetric water content of the samples and their vis–NIR spectra were measured at seven matric potentials from −1 kPa to −1500 kPa. We modelled the following: (i) AWC directly with the average spectra of the samples measured at different water contents, (ii) water contents at field capacity and permanent wilting point and calculated AWC from those estimates, (iii) AWC with spectra of air-dried soils, and (iv) parameters of the Kosugi and van Genuchten SWR models, then reconstructed the SWR curves to calculate AWC. We compared the estimates with those from a local pedotransfer function (PTF) and an established Australian PTF. The accuracy of the spectroscopic approaches varied but was generally better than the PTFs. The spectroscopic methods are also more practical because they do not require additional soil properties for the modelling. The root-mean squared-error (RMSE) of the spectroscopic methods ranged from 0.033 cm3 cm−3 to 0.059 cm3 cm−3. The RMSEs of the PTFs were 0.050 cm3 cm−3 for the local and 0.077 cm3 cm−3 for the general PTF. Spectroscopy with machine learning provides a rapid and versatile method for estimating the AWC and SWR characteristics of diverse agricultural soils. Highlights - Soil available water capacity can be estimated with vis-NIR specta. - Parameters of water retention models can be estimated with vis-NIR spectra. - vis-NIR spectroscopy performed better than pedotransfer functions. - The results apply to a diverse range of soils. ; ISSN:1351-0754 ; ISSN:1365-2389

وصف الملف: application/application/pdf

العلاقة: info:eu-repo/semantics/altIdentifier/wos/000787185400008; http://hdl.handle.net/20.500.11850/546105Test

الإتاحة: https://doi.org/20.500.11850/546105Test
https://doi.org/10.3929/ethz-b-000546105Test
https://doi.org/10.1111/ejss.13220Test
https://hdl.handle.net/20.500.11850/546105Test

المؤلفون: Shen, Zefang, D'Agui, Haylee, Walden, Lewis, Zhang, Mingxi, Yiu, Tsoek Man, Dixon, Kingsley, Nevill, Paul, Cross, Adam, Matangulu, Mohana, Hu, Yang, Viscarra Rossel, Raphael A

الوصف: Mining can cause severe disturbances to the soil, which underpins the viability of terrestrial ecosystems. Post-mining rehabilitation relies on measuring soil properties that are critical indicators of soil health. Soil visible–near-infrared (vis–NIR) spectroscopy is rapid, accurate, and cost-effective for estimating a range of soil properties. Recent advances in infrared detectors and microelectromechanical systems (MEMSs) have produced miniaturised, relatively inexpensive spectrometers. Here, we evaluate the spectra from four miniaturised visible and NIR spectrometers, some combinations, and a full-range vis–NIR spectrometer for modelling 29 soil physical, chemical, and biological properties used to assess soil health at mine sites. We collected topsoil samples from reference, undisturbed native vegetation, and stockpiles from seven mines in Western Australia. We evaluated the spectrometers' repeatability and the accuracy of spectroscopic models built with seven statistical and machine learning algorithms. The spectra from the visible spectrometer could estimate sand, silt, and clay with similar or better accuracy than the NIR spectrometers. However, the spectra from the NIR spectrometers produced better estimates of soil chemical and biological properties. By combining the miniaturised visible and NIR spectrometers, we improved the accuracy of their soil property estimates, which were similar to those from the full-range spectrometer. The miniaturised spectrometers and combinations predicted 24 of the 29 soil properties with moderate or greater accuracy (Lin's concordance correlation, ρc≥0.65). The repeatability of the NIR spectrometers was similar to that of the full-range, portable spectrometer. The miniaturised NIR spectrometers produced comparably accurate soil property estimates to the full-range portable system which is an order of magnitude more expensive, particularly when combined with the visible range sensor. Thus, the miniaturised spectrometers could form the basis for a rapid, cost-effective soil ...

وصف الملف: fulltext

العلاقة: http://purl.org/au-research/grants/arc/IC150100041Test; http://hdl.handle.net/20.500.11937/88962Test

الإتاحة: https://doi.org/20.500.11937/88962Test
https://doi.org/10.5194/soil-8-467-2022Test
https://hdl.handle.net/20.500.11937/88962Test

المؤلفون: Shen, Zefang, Ramirez-Lopez, Leonardo, Behrens, Thorsten, Cui, Lei, Zhang, Mingxi, Walden, Lewis, Wetterlind, Johana, Shi, Zhou, Sudduth, Kenneth, Baumann, Philipp, Song, Yongze, Catambay, Kevin, Viscarra Rossel, Raphael

الوصف: There is global interest in spectroscopy and the development of large and diverse soil spectral libraries (SSL) to model soil organic carbon (SOC) and monitor, report, and verify (MRV) its changes. The reason is that increasing SOC can improve food production and mitigate climate change. However, ‘global’ modelling of SOC with such diverse and hyperdimensional SSLs do not generalise well locally, e.g. at a field scale. To address this challenge, we propose deep transfer learning (DTL) to leverage useful information from large-scale SSLs to assist local modelling. We used one global, three country-specific SSLs and data from three local sites with DTL to improve the modelling and localise the SOC estimates in individual fields or farms in each country. With DTL, we transferred instances from the SSLs, representations from one-dimensional convolutional neural networks (1D-CNNs) trained on the SSLs, and both instances and representations to improve local modelling. Transferring instances effectively used information from the global SSL to most accurately estimate SOC in each site, reducing the root mean square error (RMSE) by 25.8% on average compared with local modelling. Our results highlight the effectiveness of DTL and the value of diverse, global SSLs for accurate local SOC predictions. Applying DTL with a global SSL one could estimate SOC anywhere in the world more accurately, rapidly, and cost-effectively, enabling MRV protocols to monitor SOC changes.

وصف الملف: fulltext

العلاقة: http://hdl.handle.net/20.500.11937/88543Test

الإتاحة: https://doi.org/20.500.11937/88543Test
https://doi.org/10.1016/j.isprsjprs.2022.04.009Test
https://hdl.handle.net/20.500.11937/88543Test

المؤلفون: St. Luce, Mervin, Ziadi, Noura, Viscarra Rossel, Raphael A.

المصدر: Geoderma ; volume 425, page 116048 ; ISSN 0016-7061

مصطلحات موضوعية: Soil Science

الإتاحة: https://doi.org/10.1016/j.geoderma.2022.116048Test
https://api.elsevier.com/content/article/PII:S001670612200355X?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S001670612200355X?httpAccept=text/plainTest