{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:57:39Z","timestamp":1775667459979,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T00:00:00Z","timestamp":1681084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The water cycle around the globe is significantly impacted by the moisture in the soil. However, finding a quick and practical model to cope with the enormous amount of data is a difficult issue for remote sensing practitioners. The traditional methods of measuring soil moisture are inefficient at large sizes, which can be replaced by remote sensing techniques for obtaining soil moisture. While determining the soil moisture, the low return frequency of satellites and the lack of images pose a severe challenge to the current remote sensing techniques. Therefore, this paper suggested a novel technique for Soil Moisture Retrieval. In the initial phase, image acquisition is made. Then, VI indexes (NDVI, GLAI, Green NDVI (GNDVI), and WDRVI features) are derived. Further, an improved Water Cloud Model (WCM) is deployed as a vegetation impact rectification scheme. Finally, soil moisture retrieval is determined by the hybrid model combining Deep Max Out Network (DMN) and Bidirectional Gated Recurrent Unit (Bi-GRU) schemes, whose outputs are then passed on to enhanced score level fusion that offers final results. According to the results, the RMSE of the Hybrid Classifier (Bi-GRU and DMN) method was lower (0.9565) than the RMSE of the Hybrid Classifier methods. The ME values of the HC (Bi-GRU and DMN) were also lower (0.728697) than those of the HC methods without the vegetation index, the HC methods without the presence of water clouds, and the HC methods with traditional water clouds. In comparison to HC (Bi-GRU and DMN), the HC method without vegetation index has a lower error of 0.8219 than the HC method with standard water cloud and the HC method without water cloud.<\/jats:p>","DOI":"10.3390\/rs15082005","type":"journal-article","created":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T01:33:03Z","timestamp":1681176783000},"page":"2005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9899-2161","authenticated-orcid":false,"given":"G. S.","family":"Nijaguna","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning, S.E.A. College of Engineering and Technology, Bangalore 560049, India"}]},{"given":"D. R.","family":"Manjunath","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, B.M.S. College of Engineering, Bangalore 560019, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-4707","authenticated-orcid":false,"given":"Mohamed","family":"Abouhawwash","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI 48824, USA"},{"name":"Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1167-2430","authenticated-orcid":false,"given":"S. S.","family":"Askar","sequence":"additional","affiliation":[{"name":"Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}]},{"given":"D. Khalandar","family":"Basha","sequence":"additional","affiliation":[{"name":"Department ECE, Institute of Aeronautical Engineering, Dundigal, Hyderabad 500049, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2325-855X","authenticated-orcid":false,"given":"Jewel","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Kaunas University of Technology, K. Donelai\u010dio g. 73, 44249 Kaunas, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1007\/s12524-021-01480-w","article-title":"Retrieval of Surface Roughness Over Cropped Area using Modified Water Cloud Model (MWCM), Oh Model and SAR Data","volume":"50","author":"Rawat","year":"2022","journal-title":"J. Indian Soc. 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