{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:52:09Z","timestamp":1775875929606,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009037","name":"Science and Technology Research Partnership for Sustainable Development","doi-asserted-by":"publisher","award":["JPMJSA1608"],"award-info":[{"award-number":["JPMJSA1608"]}],"id":[{"id":"10.13039\/501100009037","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.<\/jats:p>","DOI":"10.3390\/rs13081519","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T23:35:12Z","timestamp":1618443312000},"page":"1519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-1266","authenticated-orcid":false,"given":"Kensuke","family":"Kawamura","sequence":"first","affiliation":[{"name":"Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6669-803X","authenticated-orcid":false,"given":"Tomohiro","family":"Nishigaki","sequence":"additional","affiliation":[{"name":"Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5372-7359","authenticated-orcid":false,"given":"Andry","family":"Andriamananjara","sequence":"additional","affiliation":[{"name":"Laboratoire des Radio-Isotopes, Universit\u00e9 d\u2019Antananarivo, BP 3383, Route d\u2019Andraisoro,  Antananarivo 101, Madagascar"}]},{"given":"Hobimiarantsoa","family":"Rakotonindrina","sequence":"additional","affiliation":[{"name":"Laboratoire des Radio-Isotopes, Universit\u00e9 d\u2019Antananarivo, BP 3383, Route d\u2019Andraisoro,  Antananarivo 101, Madagascar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7738-9913","authenticated-orcid":false,"given":"Yasuhiro","family":"Tsujimoto","sequence":"additional","affiliation":[{"name":"Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6473-6389","authenticated-orcid":false,"given":"Naoki","family":"Moritsuka","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture and Marine Science, Kochi University, Nankoku, Kochi 783-8502, Japan"}]},{"given":"Michel","family":"Rabenarivo","sequence":"additional","affiliation":[{"name":"Laboratoire des Radio-Isotopes, Universit\u00e9 d\u2019Antananarivo, BP 3383, Route d\u2019Andraisoro,  Antananarivo 101, Madagascar"}]},{"given":"Tantely","family":"Razafimbelo","sequence":"additional","affiliation":[{"name":"Laboratoire des Radio-Isotopes, Universit\u00e9 d\u2019Antananarivo, BP 3383, Route d\u2019Andraisoro,  Antananarivo 101, Madagascar"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1080\/03650340.2010.489554","article-title":"Nutrient constraint and yield potential of rice on upland soil in the south of the Dahoumey gap of West Africa","volume":"57","author":"Amadji","year":"2011","journal-title":"Arch. 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