{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:22:59Z","timestamp":1762431779710,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:00:00Z","timestamp":1573862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007948","name":"Deutscher Akademischer Austausch Dienst Kairo","doi-asserted-by":"publisher","award":["57147166"],"award-info":[{"award-number":["57147166"]}],"id":[{"id":"10.13039\/501100007948","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Look-up table (LUT)-based canopy reflectance models are considered robust methods to estimate vegetation attributes from remotely sensed data. However, the LUT inversion approach is sensitive to measurements and model uncertainties, which raise the ill-posed inverse problem. Therefore, regularization options are needed to mitigate this problem and reduce the uncertainties of estimates. In this study, we introduce a new method to regularize the LUT inversion approach to improve the accuracy of biophysical parameters (leaf area index (LAI) and fractional vegetation cover (fCover)). This was achieved by incorporating known variable correlations that existed at the test site into the LUT approach to correlate the model variables of the Soil\u2013Leaf\u2013Canopy (SLC) model using the Cholesky decomposition algorithm. The retrievals of 27 potato plots obtained from the regularized LUT (LUTreg) were compared with the standard LUT (LUTstd), which did not consider variable correlations. Different solutions from both types of LUTs (LUTreg and LUTstd) were utilized to improve the quality of the model outputs. Results indicate that the present method improved the accuracy of LAI estimation, with the coefficient of determination R2 = 0.74 and normalized root-mean-square error NRMSE = 24.45% in LUTreg, compared with R2 = 0.71 and NRMSE = 25.57% in LUTstd. In addition, the variability of LAI decreased in LUTreg (5.10) compared with that in LUTstd (12.10). Hence, our results give new insight into the impact of adding the correlation between variables to the LUT inversion approach to improve the accuracy of estimations. In this study, only two correlated variables (LAI and fCover) were examined; in subsequent studies, the full correlation matrix based on the Cholesky algorithm should be explored.<\/jats:p>","DOI":"10.3390\/rs11222681","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T04:31:10Z","timestamp":1574051470000},"page":"2681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3448-0415","authenticated-orcid":false,"given":"Asmaa","family":"Abdelbaki","sequence":"first","affiliation":[{"name":"Environmental Remote Sensing and Geoinformatics Department, Trier University, 54286 Trier, Germany"},{"name":"Soils and Water Science Department, Faculty of Agriculture, Fayoum University, Fayoum 63514, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Schlerf","sequence":"additional","affiliation":[{"name":"Luxembourg Institute of Science and Technology (LIST), Remote Sensing and Eco-Hydrological Modelling Group, L-4422 Belvaux, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4696-2144","authenticated-orcid":false,"given":"Wout","family":"Verhoef","sequence":"additional","affiliation":[{"name":"Water Resources Department, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Udelhoven","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Geoinformatics Department, Trier University, 54286 Trier, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.isprsjprs.2011.09.013","article-title":"Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models","volume":"66","author":"Darvishzadeh","year":"2011","journal-title":"ISPRS J. Photogramm. 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