{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:11:13Z","timestamp":1760364673821,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871413","61801015"],"award-info":[{"award-number":["61871413","61801015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of polarimetric synthetic aperture radar (PolSAR), quantitative parameter inversion has been seen great progress, especially in the field of soil parameter inversion, which has achieved good results for applications. However, PolSAR data is also often many terabytes large. This huge amount of data also directly affects the efficiency of the inversion. Therefore, the efficiency of soil moisture and roughness inversion has become a problem in the application of this PolSAR technique. A parallel realization based on a graphics processing unit (GPU) for multiple inversion models of PolSAR data is proposed in this paper. This method utilizes the high-performance parallel computing capability of a GPU to optimize the realization of the surface inversion models for polarimetric SAR data. Three classical forward scattering models and their corresponding inversion algorithms are analyzed. They are different in terms of polarimetric data requirements, application situation, as well as inversion performance. Specifically, the inversion process of PolSAR data is mainly improved by the use of the high concurrent threads of GPU. According to the inversion process, various optimization strategies are applied, such as the parallel task allocation, and optimizations of instruction level, data storage, data transmission between CPU and GPU. The advantages of a GPU in processing computationally-intensive data are shown in the data experiments, where the efficiency of soil roughness and moisture inversion is increased by one or two orders of magnitude.<\/jats:p>","DOI":"10.3390\/rs12030415","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T10:51:07Z","timestamp":1580295067000},"page":"415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["GPU-Based Soil Parameter Parallel Inversion for PolSAR Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8413-4756","authenticated-orcid":false,"given":"Qiang","family":"Yin","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-2373","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7261-7606","authenticated-orcid":false,"given":"Yongsheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Du, C., Qin, Q., Liu, M., Feng, H., Dong, H., and Wang, N. (2013, January 21). Soil moisture inversion and validation based on new remote sensing platform. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723387"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4056","DOI":"10.1109\/TGRS.2013.2279183","article-title":"Estimation of soil moisture and surface roughness from single-polarized radar data for bare soil surface and comparison with dual-and quad-polarization cases","volume":"52","author":"Kweon","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/s8010070","article-title":"Assessment of evapotranspiration and soil moisture content across different scales of observation","volume":"8","author":"Verstraeten","year":"2008","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yin, Q., Cao, F., and Hong, W. (2008, January 7\u201311). Analysis of Valid Ranges in Soil Inversion Models Based on the Cloude-Pottier Decomposition. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779120"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jhydrol.2012.06.021","article-title":"A review of the methods available for estimating soil moisture and its implications for water resource management","volume":"458","author":"Dobriyal","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TGRS.2015.2496348","article-title":"Unsupervised Learning of Generalized Gamma Mixture Model with Application in Statistical Modeling of High- Resolution SAR Images","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Transac. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, F., Tang, B., Yin, Q., and Sun, X. (2018). Slim and efficient neural network design for resource-constrained SAR target recognition. Remote Sens., 10.","DOI":"10.3390\/rs10101618"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1109\/TGRS.2015.2510161","article-title":"A SAR image despeckling method based on two-dimensional S-transform shrinkage","volume":"54","author":"Gao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gao, F., Yang, Y., Wang, J., Sun, J., Yang, E., and Zhou, H. (2018). A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images. Remote Sens., 10.","DOI":"10.3390\/rs10060846"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Srivastava, P.K., Pandey, P.C., Petropoulos, G.P., Kourgialas, N.N., Pandey, V., and Singh, U. (2019). GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques. Resources, 8.","DOI":"10.3390\/resources8020070"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2016.02.048","article-title":"Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations","volume":"180","author":"Piles","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1109\/JSTARS.2019.2940973","article-title":"Optimal Combination of Polarimetric Features for Vegetation Classification in PolSAR Image","volume":"12","author":"Yin","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3956","DOI":"10.1109\/JSTARS.2014.2330333","article-title":"Accelerating Time-Domain SAR Raw Data Simulation for Large Areas Using Multi-GPUs","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jung, S.G., Hong, J.Y., and Oh, Y. (2007, January 11). Verification of Surface Scattering Models and Inversion Algorithms with the Polarimetric Backscatter Measurements of a Bare Soil Surface. Proceedings of the Asia-Pacific Microwave Conference, Bangkok, Thailand.","DOI":"10.1109\/APMC.2007.4554789"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, C., Chen, Y., Tong, L., Jia, M., and Pang, S. (2011, January 24). The method for soil moisture inversion based on ground-based scattering measurement. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049870"},{"key":"ref_17","unstructured":"Kweon, S.K., Park, S.M., and Oh, Y. (2014, January 13). Improvement of soil moisture inversion for single-polarized SAR data of bare soil surfaces using DInSAR technique. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xue, Q., Sheng, Q., and Ma, S. (2009, January 7). Application of Optimum Perturbation Algorithm for Parameter Inversion Identification of Soil Moisture Model under Ecological Slope Protection. Proceedings of the IEEE International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China.","DOI":"10.1109\/AICI.2009.105"},{"key":"ref_19","unstructured":"Wang, Y., Li, Y., Sun, S., Zhou, Q., and Han, N. (2011, January 27). Research on inversion of soil water dynamic parameters by field soil moisture content. Proceedings of the IEEE International Conference on New Technology of Agricultural, Zibo, China."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Oh, Y., and Jung, S.G. (2008, January 7). Inversion Algorithm for Soil Moisture Retrieval from Polarimetric Backscattering Coefficients of Vegetation Canopies. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779013"},{"key":"ref_21","unstructured":"Oh, Y. (2004, January 20). Comparison of two inversion methods for retrieval of soil moisture and surface roughness from polarimetric radar observation of soil surfaces. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AL, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Geddert, N., and Jeffrey, I. (2018, January 19). A Dynamically Balanced OpenMP-CUDA Implementation of PDE-Based Contrast Source Inversion for Microwave Imaging. Proceedings of the International Symposium on Antenna Technology and Applied Electromagnetics, Waterloo, ON, Canada.","DOI":"10.1109\/ANTEM.2018.8572994"},{"key":"ref_23","unstructured":"Li, M., Wang, X.Y., and Abubakar, A. (2016, January 24). Accelerating nonlinear inversion algorithms on GPU platform for electromagnetic data. Proceedings of the International Symposium on Antennas and Propagation, Okinawa, Japan."},{"key":"ref_24","unstructured":"Wang, X.Y., Li, M., and Abubakar, A. (2016, January 8). Acceleration of multiplicative regularized contrast source inversion algorithm using paralleled computing architecture. Proceedings of the IEEE Progress in Electromagnetic Research Symposium, Shanghai, China."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ries, F., De Marco, T., Zivieri, M., and Guerrieri, R. (2009, January 14). Triangular matrix inversion on graphics processing unit. Proceedings of the ACM Conference on High Performance Computing Networking, Storage and Analysis, Portland, OR, USA.","DOI":"10.1145\/1654059.1654069"},{"key":"ref_26","unstructured":"Gao, C., Li, L., Zhao, Z., and Huang, H. (2008, January 28). Parallel computation of the large grounding grids in multi-layer soil using moment method. Proceedings of the IEEE World Automation Congress, Hawaii, HI, USA."},{"key":"ref_27","unstructured":"Gomez-Calvino, J., Colominas, I., Navarrina, F., Casteleiro, M., and Cela, J. (2000, January 21). Parallel computing aided design of earthing systems for electrical substations in non-homogeneous soil models. Proceedings of the IEEE International Workshop on Parallel Processing, Toronto, ON, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.compeleceng.2015.05.018","article-title":"Accelerating aerial image simulation using improved CPU\/GPU collaborative computing","volume":"46","author":"Zhang","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","first-page":"434","article-title":"Computation Reduction Oriented Circular Scanning SAR Raw Data Simulation on Multi-GPUs","volume":"5","author":"Hu","year":"2016","journal-title":"J. Radars"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Z., Su, D., Zhu, H., Li, W., Zhang, F., and Li, R. (2017). A Fast Synthetic Aperture Radar Raw Data Simulation using Cloud Computing. Sensors, 17.","DOI":"10.3390\/s17010113"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, H., Li, G., Zhang, F., Hu, W., and Li, W. (2016, January 10). A spaceborne SAR on-board processing simulator using mobile GPU. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729303"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/JSTARS.2016.2594272","article-title":"A deep collaborative computing based SAR raw data simulation on multiple CPU\/GPU platform","volume":"10","author":"Zhang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/JSTARS.2017.2787728","article-title":"Multiple mode SAR raw data simulation and parallel acceleration for gaofen-3 mission","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, G., Zhang, F., Ma, L., Hu, W., and Li, W. (2015, January 26). Accelerating SAR imaging using vector extension on multi-core SIMD CPU. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325819"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, C., Zhang, F., Ma, L., Li, G., Hu, W., and Li, W. (2015, January 26). Efficient SAR raw data parallel simulation based on multicore vector extension. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326883"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/36.134086","article-title":"An empirical model and an inversion technique for radar scattering from bare soil surfaces","volume":"30","author":"Oh","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","unstructured":"Smith, J.R., and Mirotznik, M.S. (2004, January 20). Rough surface scattering models. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AL, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/TGRS.2003.810702","article-title":"Inversion of surface parameters from polarimetric SAR","volume":"41","author":"Hajnsek","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:30:36Z","timestamp":1760362236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,28]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030415"],"URL":"https:\/\/doi.org\/10.3390\/rs12030415","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,1,28]]}}}