{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:40:04Z","timestamp":1760244004704,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2009,11,3]],"date-time":"2009-11-03T00:00:00Z","timestamp":1257206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper introduces derivations of soil line influences on two-band vegetation indices (VIs) and vegetation isolines in the red and near infra-red reflectance space. Soil line variations are described as changes in the soil line parameters (slope and offset) and the red reflectance of the soil surface. A general form of a VI model equation written as a ratio of two linear functions (e.g., NDVI and SAVI) was assumed. It was found that relative VI variations can be approximated by a linear combination of the three soil parameters. The derived expressions imply the possibility of estimating and correcting for soil-induced bias errors in VIs and their derived biophysical parameters, caused by the assumption of a general soil line, through the use of external data sources such as regional soil maps.<\/jats:p>","DOI":"10.3390\/rs1040842","type":"journal-article","created":{"date-parts":[[2009,11,4]],"date-time":"2009-11-04T02:31:33Z","timestamp":1257301893000},"page":"842-857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Derivation of Soil Line Influence on Two-Band Vegetation Indices and Vegetation Isolines"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0978-8610","authenticated-orcid":false,"given":"Hiroki","family":"Yoshioka","sequence":"first","affiliation":[{"name":"Department of Information Science and Technology, Aichi Prefectural University, 1522-3 Kumabari, Nagakute, Aichi 480-1198, Japan"}]},{"given":"Tomoaki","family":"Miura","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Management, College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, 1910 East-West Road, Honolulu, HI 96822, USA"}]},{"given":"Jos\u00e9 A. M.","family":"Dematt\u00ea","sequence":"additional","affiliation":[{"name":"Department of Soil Science, University of S\u00e3o Paulo, Av. P\u00e1dua Dias, 11, Piracicaba, S\u00e3o Paulo, Brazil"}]},{"given":"Karim","family":"Batchily","sequence":"additional","affiliation":[{"name":"Natural Resources Conservation Service, USDA, 355 East Hancock Avenue, Athens, GA 30601, USA"}]},{"given":"Alfredo R.","family":"Huete","sequence":"additional","affiliation":[{"name":"Department of Soil, Water and Environmental Science, University of Arizona, 1177 East Fourth Street, Tucson, AZ 85721, USA"}]}],"member":"1968","published-online":{"date-parts":[[2009,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.2747\/1548-1603.45.1.16","article-title":"The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation","volume":"45","author":"Brown","year":"2008","journal-title":"GIScience and Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.14358\/PERS.73.10.1149","article-title":"Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques","volume":"73","author":"Li","year":"2007","journal-title":"Photogrammetric Engineering and Remote Sens."},{"key":"ref_3","unstructured":"National Research Council (2000). 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