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The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400\u20131000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 \u00d7 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 \u00d7 0.05 m. The extracted spectra within every 1 \u00d7 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky\u2013Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450\u2013600 and 750\u2013900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg\u22121. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg\u22121; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 \u00d7 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg\u22121 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg\u22121 and 1.48 g kg\u22121 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.<\/jats:p>","DOI":"10.3390\/rs15051433","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset"],"prefix":"10.3390","volume":"15","author":[{"given":"Yang","family":"Yan","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}]},{"given":"Jiajie","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China"}]},{"given":"Baoguo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5910-9807","authenticated-orcid":false,"given":"Chengzhi","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4996-3177","authenticated-orcid":false,"given":"Wenjun","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6706-0361","authenticated-orcid":false,"given":"Yuanfang","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.envsci.2010.07.004","article-title":"Soil loss and conservation in the black soil region of Northeast China: A retrospective study","volume":"13","author":"Xu","year":"2010","journal-title":"Environ. Sci. Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1071\/EA97158","article-title":"Soil chemical analytical accuracy and costs: Implications from precision agriculture","volume":"38","author":"Rossel","year":"1998","journal-title":"Aust. J. Exp. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1111\/j.1365-2486.2010.02341.x","article-title":"Effects of land use change and management on the European cropland carbon balance","volume":"17","author":"Ciais","year":"2011","journal-title":"Glob. Change Biol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, Y., Kayem, K., Hao, Y., Shi, Z., Zhang, C., Peng, J., Liu, W., Zuo, Q., Ji, W., and Li, B. (2022). Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors. Remote Sens., 14.","DOI":"10.3390\/rs14133020"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.rse.2018.08.024","article-title":"Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images","volume":"217","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1080\/01431160802282854","article-title":"Use of HyMap imaging spectrometer data to map mineralogy in the Rodalquilar caldera, southeast Spain","volume":"30","author":"Bedini","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A review on UAV-based applications for precision agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.geoderma.2018.09.049","article-title":"Soil fertility assessment by Vis-NIR spectroscopy: Predicting soil functioning rather than availability indices","volume":"337","author":"Recena","year":"2019","journal-title":"Geoderma"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, W., Rezaei, E.E., Nouri, H., Yang, T., Li, B., Gong, H., Lyu, Y., Peng, J., and Sun, Z. (2021). Quick detection of field-scale soil comprehensive attributes via the integration of UAV and sentinel-2B remote sensing data. Remote Sens., 13.","DOI":"10.3390\/rs13224716"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., Fu, T., Wang, F., and Shi, Z. (2019). Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens., 11.","DOI":"10.3390\/rs11070736"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.geoderma.2018.09.046","article-title":"UAV based soil salinity assessment of cropland","volume":"338","author":"Ivushkin","year":"2019","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fang, Y., Hu, Z., Xu, L., Wong, A., and Clausi, D.A. (2019, January 24\u201326). Estimation of iron concentration in soil of a mining area from UAV-based hyperspectral imagery. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8920973"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108384","DOI":"10.1016\/j.ecolind.2021.108384","article-title":"Estimating the spatial distribution of soil total arsenic in the suspected contaminated area using UAV-Borne hyperspectral imagery and deep learning","volume":"133","author":"Wei","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1366\/0003702854248656","article-title":"Linearization and scatter-correction for near-infrared reflectance spectra of meat","volume":"39","author":"Geladi","year":"1985","journal-title":"Appl. Spectrosc."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, Y., Chen, Y., Zhang, Y., Shi, T., Wang, J., Hong, Y., Fei, T., and Zhang, Y. (2019). The influence of spectral pretreatment on the selection of representative calibration samples for soil organic matter estimation using Vis-NIR reflectance spectroscopy. Remote Sens., 11.","DOI":"10.3390\/rs11040450"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.vibspec.2018.05.002","article-title":"Robust generalized multiplicative scatter correction algorithm on pretreatment of near infrared spectral data","volume":"97","author":"Silalahi","year":"2018","journal-title":"Vib. Spectrosc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2018.08.010","article-title":"Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy","volume":"336","author":"Cheng","year":"2019","journal-title":"Geoderma"},{"key":"ref_20","first-page":"6","article-title":"Grid soil sampling","volume":"78","author":"Wollenhaupt","year":"1994","journal-title":"Better Crops"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"539","DOI":"10.2134\/agronmonogr9.2.2ed.c29","article-title":"Total carbon, organic carbon, and organic matter","volume":"Volume 9","author":"Nelson","year":"1983","journal-title":"Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1038\/s41598-020-57750-z","article-title":"Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law","volume":"10","author":"Tan","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1080\/01431160600639743","article-title":"Assessing spatio-temporal variations in plant phenology using Fourier analysis on NDVI time series: Results from a dry savannah environment in Namibia","volume":"27","author":"Wagenseil","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, X., Bao, N., Li, W., Liu, S., Fu, Y., and Mao, Y. (2021). Soil nutrient estimation and mapping in farmland based on uav imaging spectrometry. Sensors, 21.","DOI":"10.3390\/s21113919"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.2136\/sssaj1994.03615995005800050033x","article-title":"Field-scale variability of soil properties in central Iowa soils","volume":"58","author":"Cambardella","year":"1994","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-infrared reflectance spectroscopy\u2013principal components regression analyses of soil properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.geoderma.2019.01.006","article-title":"Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study","volume":"341","author":"Ji","year":"2019","journal-title":"Geoderma"},{"key":"ref_29","unstructured":"Hillel, D. (2012). Applications of Soil Physics, Elsevier."},{"key":"ref_30","unstructured":"Stenberg, B., Rossel, R.V., Mouazen, A.M., and Wetterlind, J. (2010). Advances in Agronomy, Academic Press."},{"key":"ref_31","first-page":"1989","article-title":"Prediction and validation of soil organic matter content based on hyperspectrum","volume":"40","author":"Lu","year":"2007","journal-title":"Sci. Agric. Sin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104703","DOI":"10.1016\/j.catena.2020.104703","article-title":"Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies","volume":"195","author":"Bao","year":"2020","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Rossel","year":"2006","journal-title":"Geoderma"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"586","DOI":"10.2136\/sssaj2011.0053","article-title":"Determination of soil organic matter and carbon fractions in forest top soils using spectral data acquired from visible\u2013near infrared hyperspectral images","volume":"76","author":"Holden","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3724\/SP.J.1010.2012.00277","article-title":"VIS-NIR reflectance spectroscopy of the organic matter in several types of soils","volume":"31","author":"Ji","year":"2012","journal-title":"J. Infrared Millim. Wave"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.biosystemseng.2013.02.004","article-title":"Soil organic matter sensing with an on-the-go optical sensor","volume":"115","author":"Kweon","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s12665-014-3580-3","article-title":"Priority selection rating of sampling density and interpolation method for detecting the spatial variability of soil organic carbon in China","volume":"73","author":"Zhang","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_38","first-page":"100092","article-title":"The effects of station density in geostatistical prediction of air temperatures in Sweden: A comparison of two interpolation techniques","volume":"11","author":"Njoku","year":"2023","journal-title":"Resour. Environ. Sustain."},{"key":"ref_39","unstructured":"Tsui, C., Liu, X., Guo, H., and Chen, Z. (2016). Geospatial Technology-Environmental and Social Applications, InTech."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1002\/jpln.201100181","article-title":"Effect of sampling density on regional soil organic carbon estimation for cultivated soils","volume":"175","author":"Sun","year":"2012","journal-title":"J. Plant Nutr. Soil Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1433\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:12Z","timestamp":1760122032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,3]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051433"],"URL":"https:\/\/doi.org\/10.3390\/rs15051433","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,3]]}}}