{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:03:12Z","timestamp":1790391792578,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,9]],"date-time":"2018-02-09T00:00:00Z","timestamp":1518134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National keypoint research and invention program of the thirteenth","award":["2016YFD0700304"],"award-info":[{"award-number":["2016YFD0700304"]}]},{"name":"Natural Science Foundations of China","award":["61405175"],"award-info":[{"award-number":["61405175"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Compared with the chemical analytical technique, the soil nitrogen acquisition method based on near infrared (NIR) sensors shows significant advantages, being rapid, nondestructive, and convenient. Providing an accurate grasp of different soil types, sensitive wavebands could enhance the nitrogen estimation efficiency to a large extent. In this paper, loess, calcium soil, black soil, and red soil were used as experimental samples. The prediction models between soil nitrogen and NIR spectral reflectance were established based on three chemometric methods, that is, partial least squares (PLS), backward interval partial least squares (BIPLS), and back propagation neural network (BPNN). In addition, the sensitive wavebands of four kinds of soils were selected by competitive adaptive reweighted sampling (CARS) and BIPLS. The predictive ability was assessed by the coefficient of determination R2 and the root mean square error (RMSE). As a result, loess ( 0.93 &lt; R p 2 &lt; 0.95 , 0.066 g \/ kg &lt; RMSE p &lt; 0.075 g \/ kg ) and calcium soil ( 0.95 &lt; R p 2 &lt; 0.96 , 0.080 g \/ kg &lt; RMSE p &lt; 0.102 g \/ kg ) achieved a high prediction accuracy regardless of which algorithm was used, while black soil ( 0.79 &lt; R p 2 &lt; 0.86 , 0.232 g \/ kg &lt; RMSE p &lt; 0.325 g \/ kg ) obtained a relatively lower prediction accuracy caused by the interference of high humus content and strong absorption. The prediction accuracy of red soil ( 0.86 &lt; R p 2 &lt; 0.87 , 0.231 g \/ kg &lt; RMSE p &lt; 0.236 g \/ kg ) was similar to black soil, partly due to the high content of iron\u2013aluminum oxide. Compared with PLS and BPNN, BIPLS performed well in removing noise and enhancing the prediction effect. In addition, the determined sensitive wavebands were 1152 nm\u20131162 nm and 1296 nm\u20131309 nm (loess), 1036 nm\u20131055 nm and 1129 nm\u20131156 nm (calcium soil), 1055 nm, 1281 nm, 1414 nm\u20131428 nm and 1472 nm\u20131493 nm (black soil), 1250 nm, 1480 nm and 1680 nm (red soil). It is of great value to investigate the differences among the NIR spectral characteristics of different soil types and determine sensitive wavebands for the more efficient and portable NIR sensors in practical application.<\/jats:p>","DOI":"10.3390\/s18020523","type":"journal-article","created":{"date-parts":[[2018,2,9]],"date-time":"2018-02-09T12:46:27Z","timestamp":1518180387000},"page":"523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors"],"prefix":"10.3390","volume":"18","author":[{"given":"Shupei","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Nie","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China"},{"name":"State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/S0034-4257(01)00347-9","article-title":"Relating soil surface moisture to reflectance","volume":"81","author":"Liu","year":"2002","journal-title":"Remote Sens. 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