{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T05:13:46Z","timestamp":1736140426785,"version":"3.32.0"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T00:00:00Z","timestamp":1663372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Jilin Province Science and Technology Development Plan","award":["20210101101JC"]},{"name":"Foundation of the Education Department of Jilin Province","award":["JJKH20211288KJ"]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFC1801203"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["41301364"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Soil salinization has become one of the major environmental problems threatening food security. The identification and knowledge of the spatial distributions of soil salinization are key in addressing this problem. This study assumes that a good saline land identification effect can be obtained with the help of deep learning methods. Therefore, this study used the OLI sensor data from the Landsat-8, based on the U2-Network, and proposes a method to extract saline land from remote sensing images. The study also adds different salinity indices (SI, SI1, and SI2) to explore its impact on classification accuracy. Through our method, accurate saline soil distribution information were obtained, and several verification indicators (the Intersection-over-Union (IoU), recall, precision, and F1-score) were all measured above 0.8. In addition, compared with the multi-spectral training results, the classification accuracy increased after adding a specific salinity index, and most of the accuracy indices increased by about 2% (the IoU increased by 3.70%, recall increased by 1.50%, precision increased by 2.81%, and F1-score increased by 2.13%). In addition, we also included a case study based on our methodology to analyze the distribution characteristics and changes of saline soil in the Zhenlai area of Northeast China from 2016 to 2020. We found that the area of saline land in the Zhenlai area has reduced, which shows that the extraction method proposed in this study is feasible. Overall, this paper indicates that deep learning-based methods can efficiently extract the salinity of soil and enhance the mapping of its spatial distribution. The study has the broad impact of supplementing satellite imagery for salinity modeling and helping to guide agricultural land management practices for northeastern China and other salinized regions.<\/jats:p>","DOI":"10.3390\/rs14184647","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T08:49:22Z","timestamp":1663577362000},"page":"4647","source":"Crossref","is-referenced-by-count":7,"title":["Extraction of Saline Soil Distributions Using Different Salinity Indices and Deep Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Qianyi","family":"Gu","sequence":"first","affiliation":[{"name":"Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5552-7972","authenticated-orcid":false,"given":"Yang","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1178-2776","authenticated-orcid":false,"given":"Yaping","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6688-9031","authenticated-orcid":false,"given":"Huitian","family":"Ge","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1796-5266","authenticated-orcid":false,"given":"Xiaojie","family":"Li","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33017","DOI":"10.1073\/pnas.2013771117","article-title":"Predicting long-term dynamics of soil salinity and sodicity on a global scale","volume":"117","author":"Hassani","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hasanuzzaman, M., Nahar, K., and Fujita, M. (2013). Plant response to salt stress and role of exogenous protectants to mitigate salt-induced damages. Ecophysiology and Responses of Plants under Salt Stress, Springer.","DOI":"10.1007\/978-1-4614-4747-4_2"},{"key":"ref_3","first-page":"357","article-title":"Polarized Reflectance Characteristics of Some Soils","volume":"24","author":"Song","year":"2004","journal-title":"Sci. Geogr. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134718","DOI":"10.1016\/j.scitotenv.2019.134718","article-title":"Desiccation crisis of saline lakes: A new decision-support framework for building resilience to climate change","volume":"703","author":"Hassani","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1111\/j.1475-2743.2009.00251.x","article-title":"Soil carbon dynamics in saline and sodic soils: A review","volume":"26","author":"Wong","year":"2010","journal-title":"Soil Use Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1002\/ldr.1148","article-title":"Physicochemical properties of saline soils and aeolian dust","volume":"24","author":"Lanhai","year":"2013","journal-title":"Land Degrad. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1002\/ldr.2385","article-title":"Microbial and enzyme activities of saline and sodic soils","volume":"27","author":"Singh","year":"2016","journal-title":"Land Degrad. Dev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.soilbio.2014.11.001","article-title":"Salt effects on the soil microbial decomposer community and their role in organic carbon cycling: A review","volume":"81","author":"Rath","year":"2015","journal-title":"Soil Biol. Biochem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4056","DOI":"10.1007\/s11356-014-3739-1","article-title":"Effect of salinity stress on plants and its tolerance strategies: A review","volume":"22","author":"Parihar","year":"2015","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17635","DOI":"10.1073\/pnas.2005925117","article-title":"River basin salinization as a form of aridity","volume":"117","author":"Perri","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.geoderma.2018.01.033","article-title":"Extensive reclamation of saline-sodic soils with flue gas desulfurization gypsum on the Songnen Plain, Northeast China","volume":"321","author":"Zhao","year":"2018","journal-title":"Geoderma"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105272","DOI":"10.1016\/j.catena.2021.105272","article-title":"Study on a fast EC measurement method of soda saline-alkali soil based on wavelet decomposition texture feature","volume":"203","author":"Ren","year":"2021","journal-title":"Catena"},{"key":"ref_13","first-page":"139","article-title":"Changes of soil microbial characteristics in saline-sodic soils under drip irrigation","volume":"14","author":"Liu","year":"2014","journal-title":"J. Soil Sci. Plant Nutr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2014.03.025","article-title":"Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region","volume":"230","author":"Allbed","year":"2014","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khorram, S., Koch, F.H., van der Wiele, C.F., and Nelson, S.A. (2012). Remote Sensing, Springer Science & Business Media.","DOI":"10.1007\/978-1-4614-3103-9"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Metternicht, G., and Zinck, A. (2008). Remote Sensing of Soil Salinization: Impact on Land Management, CRC Press.","DOI":"10.1201\/9781420065039"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1016\/j.ecolind.2011.03.025","article-title":"Using hyperspectral vegetation indices as a proxy to monitor soil salinity","volume":"11","author":"Zhang","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, Y., Shrestha, V., Piasecki, C., Wolfe, B., Hamilton, L., Millwood, R.J., Mazarei, M., and Stewart, C.N. (2021). Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery. Plants, 10.","DOI":"10.3390\/plants10122726"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kumar, L., Schmidt, K., Dury, S., and Skidmore, A. (2002). Imaging spectrometry and vegetation science. Imaging Spectrometry, Springer.","DOI":"10.1007\/978-0-306-47578-8_5"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1080\/01431161.2011.599346","article-title":"Detecting and distinguishing moisture-and salinity-induced stress in wheat and maize through in situ spectroradiometry measurements","volume":"3","author":"Elmetwalli","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111741","DOI":"10.1016\/j.rse.2020.111741","article-title":"Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network","volume":"245","author":"Waldner","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112757","DOI":"10.1016\/j.rse.2021.112757","article-title":"Utilizing unsupervised learning, multi-view imaging, and CNN-based attention facilitates cost-effective wetland mapping","volume":"267","author":"Hu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17656","DOI":"10.1038\/s41598-019-53797-9","article-title":"Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2013.10.020","article-title":"Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data","volume":"141","author":"Huang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"23","article-title":"Free water table area monitoring on wetlands using satellite and UAV orthophotomaps-Kampinos National Park case study","volume":"7","year":"2019","journal-title":"Meteorol. Hydrol. Water Manag. Res. Oper. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"146253","DOI":"10.1016\/j.scitotenv.2021.146253","article-title":"An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran","volume":"778","author":"Garajeh","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"022204","DOI":"10.1117\/1.JRS.12.022204","article-title":"Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data","volume":"12","author":"Zeng","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"125321","DOI":"10.1016\/j.jhydrol.2020.125321","article-title":"A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer","volume":"591","author":"Sahour","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_31","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical image computing and computer-assisted intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.agee.2008.10.009","article-title":"Shrinkage and fragmentation of grasslands in the West Songnen Plain, China","volume":"129","author":"Wang","year":"2009","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, J., Gao, C., Lin, Q., Zhang, S., Zhao, W., Lu, X., and Wang, G. (2015). Temporal and spatial changes in black carbon sedimentary processes in wetlands of Songnen Plain, Northeast of China. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140834"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s11769-015-0779-5","article-title":"Quantitative analysis of relationships between crack characteristics and properties of soda-saline soils in Songnen Plain, China","volume":"25","author":"Ren","year":"2015","journal-title":"Chin. Geogr. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s12665-009-0079-4","article-title":"Dynamics of the soil water and solute in the sodic saline soil in the Songnen Plain, China","volume":"59","author":"Liu","year":"2009","journal-title":"Environ. Earth Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1109\/TGRS.2016.2646420","article-title":"Estimating soil salinity under various moisture conditions: An experimental study","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","first-page":"9","article-title":"Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection","volume":"12","author":"Gao","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","unstructured":"Chinese Academy of Sciences (2022, July 28). Geospatial Data Cloud. Available online: http:\/\/www.gscloud.cn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Allbed, A., and Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: A review. Adv. Remote Sens., 2013.","DOI":"10.4236\/ars.2013.24040"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.scitotenv.2016.08.177","article-title":"The threat of soil salinity: A European scale review","volume":"573","author":"Daliakopoulos","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_44","unstructured":"Tripathi, N., Rai, B.K., and Dwivedi, P. (1997, January 20\u201325). Spatial modeling of soil alkalinity in GIS environment using IRS data. Proceedings of the 18th Asian conference on remote sensing, Kualalampur, Malaysia."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.agwat.2004.09.038","article-title":"Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators","volume":"77","author":"Khan","year":"2005","journal-title":"Agric. Water Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.geoderma.2005.10.009","article-title":"Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data","volume":"134","author":"Douaoui","year":"2006","journal-title":"Geoderma"},{"key":"ref_47","unstructured":"Khan, S., and Abbas, A. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. International Congress on Modelling and Simulation (MODSIM 2007), Modelling and Simulation Society of Australia and New Zealand."},{"key":"ref_48","unstructured":"Yishan, S. (2020). Remote Sensing Retrieval of Electrical Conductivity of Saline Soil in West Jilin Province and Research on Temporal Variation in Thirty Years, University of Chinese Academy of Sciences (Northeast Institute of Geography and Agroecology)."},{"key":"ref_49","first-page":"100420","article-title":"Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions","volume":"20","author":"Lhissou","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Kokkinos, I., and Savalle, P.-A. (2015, January 7\u201312). Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298636"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ye, Z., Wei, J., Lin, Y., Guo, Q., Zhang, J., Zhang, H., Deng, H., and Yang, K. (2022). Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model. Remote Sens., 14.","DOI":"10.3390\/rs14061523"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wei, X., Li, X., Liu, W., Zhang, L., Cheng, D., Ji, H., Zhang, W., and Yuan, K. (2021). Building outline extraction directly using the u2-net semantic segmentation model from high-resolution aerial images and a comparison study. Remote Sens., 13.","DOI":"10.3390\/rs13163187"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11530","DOI":"10.1109\/JSTARS.2021.3123398","article-title":"A Large-Scale Mapping Scheme for Urban Building From Gaofen-2 Images Using Deep Learning and Hierarchical Approach","volume":"14","author":"Zhou","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1002\/mp.15335","article-title":"Automatic quadriceps and patellae segmentation of MRI with cascaded U2-Net and SASSNet deep learning model","volume":"49","author":"Cheng","year":"2022","journal-title":"Med. Phys."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"103813","DOI":"10.1016\/j.bspc.2022.103813","article-title":"A bone segmentation method based on Multi-scale features fuse U2Net and improved dice loss in CT image process","volume":"77","author":"Liu","year":"2022","journal-title":"Biomed. Signal Processing Control"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ple\u0219oianu, A.-I., Stupariu, M.-S., \u0218andric, I., P\u0103tru-Stupariu, I., and Dr\u0103gu\u021b, L. (2020). Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model. Remote Sens., 12.","DOI":"10.3390\/rs12152426"},{"key":"ref_57","first-page":"102091","article-title":"Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns","volume":"89","author":"Wu","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","first-page":"532","article-title":"Ecological effects and pattern optimization of the development and utilization of saline-alkali land in western Jilin Province: Taking Zhenlai County, Baicheng City, Jilin Province as an example","volume":"37","author":"Wang","year":"2021","journal-title":"Resour. Dev. Mark."},{"key":"ref_59","first-page":"760","article-title":"The change of marsh landscape pattern in Zhenlai county during 1980 to 2018 and the effects due to human disturbance","volume":"48","author":"Ji","year":"2021","journal-title":"J. ZheJiang Univ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"155543","DOI":"10.1016\/j.scitotenv.2022.155543","article-title":"Role of rice cultivation on fluorine distribution behavior in soda saline-alkali land","volume":"835","author":"Wang","year":"2022","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T02:18:43Z","timestamp":1736129923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,17]]},"references-count":60,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184647"],"URL":"https:\/\/doi.org\/10.3390\/rs14184647","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,9,17]]}}}