{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T18:47:34Z","timestamp":1776538054808,"version":"3.51.2"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819609628","type":"print"},{"value":"9789819609635","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0963-5_22","type":"book-chapter","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T07:46:44Z","timestamp":1733557604000},"page":"366-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating Soil Organic Carbon from\u00a0Multispectral Images Using Physics-Informed Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9860-975X","authenticated-orcid":false,"given":"James","family":"Sargeant","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0347-3797","authenticated-orcid":false,"given":"Shyh","family":"Wei Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7079-9717","authenticated-orcid":false,"given":"Manzur","family":"Murshed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-5056","authenticated-orcid":false,"given":"Manoranjan","family":"Paul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Brennan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"issue":"1","key":"22_CR1","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1080\/17583004.2022.2106310","volume":"13","author":"U Acharya","year":"2022","unstructured":"Acharya, U., Lal, R., Chandra, R.: Data driven approach on in-situ soil carbon measurement. CARBON MANAGEMENT 13(1), 401\u2013419 (2022). https:\/\/doi.org\/10.1080\/17583004.2022.2106310","journal-title":"CARBON MANAGEMENT"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Badora, M., Bartosik, P., Graziano, A., Szolc, T.: Using physics-informed neural networks with small datasets to predict the length of gas turbine nozzle cracks. Adv. Eng. Inform. 58, 102232 (2023). https:\/\/doi.org\/10.1016\/j.aei.2023.102232, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1474034623003609","DOI":"10.1016\/j.aei.2023.102232"},{"key":"22_CR3","doi-asserted-by":"publisher","unstructured":"Ben-Dor, E., Inbar, Y., Chen, Y.: The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400\u20132500 nm) during a controlled decomposition process. Remote Sens. Environ. 61(1), 1\u201315 (1997). https:\/\/doi.org\/10.1016\/S0034-4257(96)00120-4, p33 Q60 F04 9732871 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23","DOI":"10.1016\/S0034-4257(96)00120-4"},{"key":"22_CR4","doi-asserted-by":"publisher","unstructured":"Budak, M., G\u00fcnal, E., K\u0131l\u0131\u00e7, M., \u00c7elik, I., S\u0131rr\u0131, M., Acir, N.: Improvement of spatial estimation for soil organic carbon stocks in yuksekova plain using sentinel 2 imagery and gradient descent-boosted regression tree. Environ. Sci. Pollut. Res. 30(18), 53253\u201353274 (2023). https:\/\/doi.org\/10.1007\/s11356-023-26064-8, https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85149019766&doi=10.1007%2fs11356-023-26064-8 &partnerID=40 &md5=935bbbf2cc962937244585318a271fb5, export Date: 04 August 2023; Cited By: 0","DOI":"10.1007\/s11356-023-26064-8"},{"key":"22_CR5","doi-asserted-by":"publisher","unstructured":"Cao, F., Gao, F., Guo, X., Yuan, D.: Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem. Computers & Mathematics with Applications 150, 229\u2013242 (2023). https:\/\/doi.org\/10.1016\/j.camwa.2023.09.030, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0898122123004157","DOI":"10.1016\/j.camwa.2023.09.030"},{"issue":"3","key":"22_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10712-019-09524-0","volume":"40","author":"S Chabrillat","year":"2019","unstructured":"Chabrillat, S., Ben-Dor, E., Cierniewski, J., Gomez, C., Schmid, T., van Wesemael, B.: Imaging spectroscopy for soil mapping and monitoring. Surv. Geophys. 40(3), 361\u2013399 (2019). https:\/\/doi.org\/10.1007\/s10712-019-09524-0","journal-title":"Surv. Geophys."},{"key":"22_CR7","doi-asserted-by":"publisher","DOI":"10.2760\/616084","author":"E Commission","year":"2020","unstructured":"Commission, E., Centre, J.R., Jones, A., Fern\u00e1ndez-Ugalde, O., Scarpa, S.: LUCAS 2015 topsoil survey - Presentation of dataset and results. Publications Office (2020). https:\/\/doi.org\/10.2760\/616084","journal-title":"Publications Office"},{"issue":"2","key":"22_CR8","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5194\/soil-4-101-2018","volume":"4","author":"JR England","year":"2018","unstructured":"England, J.R., Viscarra Rossel, R.A.: Proximal sensing for soil carbon accounting. SOIL 4(2), 101\u2013122 (2018). https:\/\/doi.org\/10.5194\/soil-4-101-2018","journal-title":"SOIL"},{"key":"22_CR9","unstructured":"European Space Agency: Sentinel-2 spectral response functions (s2-srf) (2023), https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-2-msi\/document-library\/-\/asset_publisher\/Wk0TKajiISaR\/content\/sentinel-2a-spectral-responses"},{"key":"22_CR10","doi-asserted-by":"publisher","unstructured":"Garosi, Y., Ayoubi, S., Nussbaum, M., Sheklabadi, M.: Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of iran. Geoderma Reg. 29, e00513 (2022). https:\/\/doi.org\/10.1016\/j.geodrs.2022.e00513, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352009422000335","DOI":"10.1016\/j.geodrs.2022.e00513"},{"key":"22_CR11","doi-asserted-by":"publisher","unstructured":"Gholizadeh, A., \u017di\u017e\u017eala, D., Saberioon, M., Bor\u016fvka, L.: Soil organic carbon and texture retrieving and mapping using proximal, airborne and sentinel-2 spectral imaging. Remote Sens. Environ. 218, 89\u2013103 (2018). https:\/\/doi.org\/10.1016\/j.rse.2018.09.015, https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85053836935&doi=10.1016%2fj.rse.2018.09.015 &partnerID=40 &md5=26c21702d4fb56fb7d2541b5f23594ef, export Date: 03 August 2023; Cited By: 209","DOI":"10.1016\/j.rse.2018.09.015"},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Guo, H.L., Zhang, R.R., Dai, W.H., Zhou, X.W., Zhang, D.J., Yang, Y.H., Cui, J.: Mapping soil organic matter content based on feature band selection with zy1-02d hyperspectral satellite data in the agricultural region. AGRONOMY-BASEL 12(9) (2022). https:\/\/doi.org\/10.3390\/agronomy12092111","DOI":"10.3390\/agronomy12092111"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Hateffard, F., Szatm\u00e1ri, G., Nov\u00e1k, T.J.: Applicability of machine learning models for predicting soil organic carbon content and bulk density under different soil conditions. Soil Science Annual 74(1) (2023). https:\/\/doi.org\/10.37501\/soilsa\/165879, https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85165428835&doi=10.37501%2fsoilsa%2f165879 &partnerID=40 &5=f2e4eca72f08465999d8c9c338818d39, export Date: 04 August 2023; Cited By: 0","DOI":"10.37501\/soilsa\/165879"},{"key":"22_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12071062","author":"F Kaya","year":"2022","unstructured":"Kaya, F., Keshavarzi, A., Francaviglia, R., Kaplan, G., Ba\u015fayi\u011fit, L., Dedeo\u011flu, M.: Assessing machine learning-based prediction under different agricultural practices for digital mapping of soil organic carbon and available phosphorus (2022). https:\/\/doi.org\/10.3390\/agriculture12071062","journal-title":"Assessing machine learning-based prediction under different agricultural practices for digital mapping of soil organic carbon and available phosphorus"},{"key":"22_CR15","doi-asserted-by":"publisher","unstructured":"Liu, B., Wang, Y., Rabczuk, T., Olofsson, T., Lu, W.: Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks. Renewable Energy 220, 119565 (2024). https:\/\/doi.org\/10.1016\/j.renene.2023.119565, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960148123014805","DOI":"10.1016\/j.renene.2023.119565"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Liu, Q., He, L., Guo, L., Wang, M., Deng, D., Lv, P., Wang, R., Jia, Z., Hu, Z., Wu, G., Shi, T.: Digital mapping of soil organic carbon density using newly developed bare soil spectral indices and deep neural network. CATENA 219, 106603 (2022). https:\/\/doi.org\/10.1016\/j.catena.2022.106603, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0341816222005896","DOI":"10.1016\/j.catena.2022.106603"},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Madugundu, R., Al-Gaadi, K.A., Tola, E., Edrris, M., Edrees, H., Alameen, A., Fulleros, R.B.: Estimation of soil organic carbon in agricultural fields: A remote sensing approach. Journal of Environmental Biology 43(1), 73\u201384 (2022). https:\/\/doi.org\/10.22438\/jeb\/43\/1\/MRN-1873, https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85122220559&doi=10.22438%2fjeb%2f43%2f1%2fMRN-1873 &partnerID=40 &md5=552edc18dab4d38c845aea5708e250eb, export Date: 02 August 2023; Cited By: 1","DOI":"10.22438\/jeb\/43\/1\/MRN-1873"},{"key":"22_CR18","doi-asserted-by":"publisher","unstructured":"Ng, W., Minasny, B., Montazerolghaem, M., Padarian, J., Ferguson, R., Bailey, S., McBratney, A.B.: Convolutional neural network for simultaneous prediction of several soil properties using visible\/near-infrared, mid-infrared, and their combined spectra. Geoderma 352, 251\u2013267 (2019). https:\/\/doi.org\/10.1016\/j.geoderma.2019.06.016, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0016706119300588","DOI":"10.1016\/j.geoderma.2019.06.016"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Nguyen, T.T., Pham, T.D., Nguyen, C.T., Delfos, J., Archibald, R., Dang, K.B., Hoang, N.B., Guo, W., Ngo, H.H.: A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and sar data fusion. Sci. Total Environ. 804, 150187 (2022). https:\/\/doi.org\/10.1016\/j.scitotenv.2021.150187, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0048969721052645","DOI":"10.1016\/j.scitotenv.2021.150187"},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Odebiri, O., Odindi, J., Mutanga, O.: Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review. Int. J. Appl. Earth Obs. Geoinf. 102, 102389 (2021). https:\/\/doi.org\/10.1016\/j.jag.2021.102389, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0303243421000969","DOI":"10.1016\/j.jag.2021.102389"},{"key":"22_CR21","doi-asserted-by":"publisher","unstructured":"Padarian, J., Minasny, B., McBratney, A.B.: Using deep learning to predict soil properties from regional spectral data. Geoderma Reg. 16, e00198 (2019). https:\/\/doi.org\/10.1016\/j.geodrs.2018.e00198, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352009418302785","DOI":"10.1016\/j.geodrs.2018.e00198"},{"key":"22_CR22","doi-asserted-by":"publisher","unstructured":"Paul, C., Bartkowski, B., D\u00f6nmez, C., Don, A., Mayer, S., Steffens, M., Weigl, S., Wiesmeier, M., Wolf, A., Helming, K.: Carbon farming: Are soil carbon certificates a suitable tool for climate change mitigation? J. Environ. Manage. 330, 117142 (2023). https:\/\/doi.org\/10.1016\/j.jenvman.2022.117142, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0301479722027153","DOI":"10.1016\/j.jenvman.2022.117142"},{"key":"22_CR23","doi-asserted-by":"publisher","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019). https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0021999118307125","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"22_CR24","doi-asserted-by":"publisher","unstructured":"Shen, Z., Ramirez-Lopez, L., Behrens, T., Cui, L., Zhang, M., Walden, L., Wetterlind, J., Shi, Z., Sudduth, K.A., Baumann, P., Song, Y., Catambay, K., Viscarra Rossel, R.A.: Deep transfer learning of global spectra for local soil carbon monitoring. ISPRS J. Photogramm. Remote. Sens. 188, 190\u2013200 (2022). https:\/\/doi.org\/10.1016\/j.isprsjprs.2022.04.009, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S092427162200106X","DOI":"10.1016\/j.isprsjprs.2022.04.009"},{"key":"22_CR25","doi-asserted-by":"publisher","unstructured":"Shen, Z., Viscarra\u00a0Rossel, R.A.: Automated spectroscopic modelling with optimised convolutional neural networks. Scientific Reports 11(1) (2021). https:\/\/doi.org\/10.1038\/s41598-020-80486-9","DOI":"10.1038\/s41598-020-80486-9"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Tsakiridis, N.L., Keramaris, K.D., Theocharis, J.B., Zalidis, G.C.: Simultaneous prediction of soil properties from vnir-swir spectra using a localized multi-channel 1-d convolutional neural network. Geoderma 367, 114208 (2020). https:\/\/doi.org\/10.1016\/j.geoderma.2020.114208, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0016706119308870","DOI":"10.1016\/j.geoderma.2020.114208"},{"key":"22_CR27","doi-asserted-by":"publisher","unstructured":"Tziolas, N., Tsakiridis, N., Ogen, Y., Kalopesa, E., Ben-Dor, E., Theocharis, J., Zalidis, G.: An integrated methodology using open soil spectral libraries and earth observation data for soil organic carbon estimations in support of soil-related sdgs. Remote Sens. Environ. 244, 111793 (2020). https:\/\/doi.org\/10.1016\/j.rse.2020.111793, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0034425720301632","DOI":"10.1016\/j.rse.2020.111793"},{"key":"22_CR28","doi-asserted-by":"publisher","unstructured":"Vaudour, E., Gomez, C., Lagacherie, P., Loiseau, T., Baghdadi, N., Urbina-Salazar, D., Loubet, B., Arrouays, D.: Temporal mosaicking approaches of sentinel-2 images for extending topsoil organic carbon content mapping in croplands. Int. J. Appl. Earth Obs. Geoinf. 96, 102277 (2021). https:\/\/doi.org\/10.1016\/j.jag.2020.102277, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S030324342030920X","DOI":"10.1016\/j.jag.2020.102277"},{"key":"22_CR29","doi-asserted-by":"publisher","unstructured":"Viscarra Rossel, R.A., Behrens, T.: Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1), 46\u201354 (2010). https:\/\/doi.org\/10.1016\/j.geoderma.2009.12.025, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0016706109004315","DOI":"10.1016\/j.geoderma.2009.12.025"},{"key":"22_CR30","doi-asserted-by":"publisher","unstructured":"Viscarra\u00a0Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O.: Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1), 59\u201375 (2006). https:\/\/doi.org\/10.1016\/j.geoderma.2005.03.007, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0016706105000728","DOI":"10.1016\/j.geoderma.2005.03.007"},{"key":"22_CR31","doi-asserted-by":"publisher","unstructured":"Wadoux, A.M.J.C.: Using deep learning for multivariate mapping of soil with quantified uncertainty. Geoderma 351, 59\u201370 (2019). https:\/\/doi.org\/10.1016\/j.geoderma.2019.05.012","DOI":"10.1016\/j.geoderma.2019.05.012"},{"key":"22_CR32","unstructured":"Wimmera CMA: Wimmera cma (2024), https:\/\/wcma.vic.gov.au\/"},{"key":"22_CR33","doi-asserted-by":"publisher","unstructured":"Xu, X., Du, C., Ma, F., Qiu, Z., Zhou, J.: A framework for high-resolution mapping of soil organic matter (som) by the integration of fourier mid-infrared attenuation total reflectance spectroscopy (ftir-atr), sentinel-2 images, and dem derivatives. Remote Sensing 15(4) (2023). https:\/\/doi.org\/10.3390\/rs15041072, https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85149255932&doi=10.3390%2frs15041072 &partnerID=40 &md5=429e173fa9b005ae1285d5847b67434f, export Date: 01 August 2023; Cited By: 2","DOI":"10.3390\/rs15041072"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0963-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T08:41:28Z","timestamp":1733560888000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0963-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,8]]},"ISBN":["9789819609628","9789819609635"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0963-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,8]]},"assertion":[{"value":"8 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}