{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:04Z","timestamp":1780444804761,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tarim University President\u2019s Fund","award":["TDZKCX202205"],"award-info":[{"award-number":["TDZKCX202205"]}]},{"name":"Tarim University President\u2019s Fund","award":["TDZKSS202227"],"award-info":[{"award-number":["TDZKSS202227"]}]},{"name":"Tarim University President\u2019s Fund","award":["2018YFE0107000"],"award-info":[{"award-number":["2018YFE0107000"]}]},{"name":"Tarim University President\u2019s Fund","award":["42071068"],"award-info":[{"award-number":["42071068"]}]},{"name":"Tarim University President\u2019s Fund","award":["41061031"],"award-info":[{"award-number":["41061031"]}]},{"name":"Tarim University President\u2019s Fund","award":["42201073"],"award-info":[{"award-number":["42201073"]}]},{"DOI":"10.13039\/501100012166","name":"The National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["TDZKCX202205"],"award-info":[{"award-number":["TDZKCX202205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"The National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["TDZKSS202227"],"award-info":[{"award-number":["TDZKSS202227"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"The National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFE0107000"],"award-info":[{"award-number":["2018YFE0107000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"The National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42071068"],"award-info":[{"award-number":["42071068"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"The National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["41061031"],"award-info":[{"award-number":["41061031"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"The National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42201073"],"award-info":[{"award-number":["42201073"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation of China","award":["TDZKCX202205"],"award-info":[{"award-number":["TDZKCX202205"]}]},{"name":"National Science Foundation of China","award":["TDZKSS202227"],"award-info":[{"award-number":["TDZKSS202227"]}]},{"name":"National Science Foundation of China","award":["2018YFE0107000"],"award-info":[{"award-number":["2018YFE0107000"]}]},{"name":"National Science Foundation of China","award":["42071068"],"award-info":[{"award-number":["42071068"]}]},{"name":"National Science Foundation of China","award":["41061031"],"award-info":[{"award-number":["41061031"]}]},{"name":"National Science Foundation of China","award":["42201073"],"award-info":[{"award-number":["42201073"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic matter (SOM) is a key factor for evaluating soil fertility. Rapidly monitoring organic matter content in desert soil can provide a scientific basis for the rational development and utilization of reserve arable land resources. Although spectral inversion accuracy for SOM under laboratory-controlled conditions is high, it is time-consuming and costly compared to the in situ spectroscopic determination method. However, in situ spectroscopy causes losses in accuracy due to interference from external environmental factors (e.g., the surface roughness of soil, changes in weather conditions, atmospheric water vapor, etc.). Therefore, reducing or removing the interference of external environmental factors to improve the accuracy of in situ spectroscopy for estimating SOM is challenging. In this study, visible and near-infrared (Vis-NIR) in situ spectral data were collected from 135 topsoil (0\u201320 cm) samples in a desert area of northwestern China, and organic matter content was measured. Three spectral pre-processing methods\u2014the standard normal transform (SNV), reciprocal logarithm (log(1\/R)) and normalization (NOR)\u2014combined with three feature variable selection methods\u2014the particle swarm algorithm (PSO), ant colony algorithm (ACO) and simulated annealing (SA) algorithm\u2014were used to filter the spectral feature bands of SOM, and then partial least squares regression (PLSR), a back propagation neural network (BPNN) and a convolutional neural network (CNN) were used to construct the estimation models of SOM. The results indicated that the SNV could enhance the spectral information related to SOM and improve the accuracy of model estimation, and it was one of the most effective spectral pretreatment methods. Compared with the model constructed with the full-band spectroscopy method, the feature variable selection method could effectively improve the estimation accuracy of the Vis-NIR in situ spectroscopy model. The most obvious improvement was found with PSO, where R2 and RPD were improved by more than 0.34 and 0.16, respectively, and RMSE was reduced by more than 0.29 g kg\u22121. The accuracy of the CNN model was higher than that of the BPNN and PLSR models, both for the inversion model of SOM built from full-band spectral data and the bands selected by the characteristic variable selection method. SNV-PSO-CNN is the optimal hybrid model for in situ spectral measurement of SOM (R2 = 0.71, RPD = 1.88, RMSE = 1.67 g kg\u22121) and can realize the quantitative in situ spectral inversion of SOM in desert soils.<\/jats:p>","DOI":"10.3390\/rs14205221","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:58:51Z","timestamp":1666141131000},"page":"5221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Estimating Soil Organic Matter Content in Desert Areas Using In Situ Hyperspectral Data and Feature Variable Selection Algorithms in Southern Xinjiang, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Peimin","family":"Yang","sequence":"first","affiliation":[{"name":"College of Agriculture, Tarim University, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Soil Science, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9353-0307","authenticated-orcid":false,"given":"Bifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Defang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Agriculture, Tarim University, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Agriculture, Tarim University, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.microc.2018.12.027","article-title":"Removing the moisture effect in soil organic matter determination using NIR spectroscopy and PLSR with external parameter orthogonalization","volume":"145","author":"Poppi","year":"2019","journal-title":"Microchem. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.geoderma.2013.08.013","article-title":"The dimensions of soil security","volume":"213","author":"McBratney","year":"2014","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/nature16069","article-title":"The contentious nature of soil organic matter","volume":"528","author":"Lehmann","year":"2015","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00374-005-0068-z","article-title":"Decomposition of pea and maize straw in Pakistani soils along a gradient in salinity","volume":"43","author":"Muhammad","year":"2006","journal-title":"Biol. Fert. Soils"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.09.013","article-title":"Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis\u2013NIR spectroscopy","volume":"310","author":"Xu","year":"2018","journal-title":"Geoderma"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2016.10.033","article-title":"Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory","volume":"288","author":"Bao","year":"2017","journal-title":"Geoderma"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Chen, Y., Guo, L., Fei, T., and Qi, K. (2016). Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy. Remote Sens., 8.","DOI":"10.3390\/rs8090755"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.geoderma.2006.03.050","article-title":"High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures","volume":"136","author":"Selige","year":"2006","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.geoderma.2011.09.018","article-title":"Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa","volume":"167","author":"Nocita","year":"2011","journal-title":"Geoderma"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/S0169-7439(98)00055-0","article-title":"Improvement of PLS model transferability by robust wavelength selection","volume":"41","author":"Swierenga","year":"1998","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0003-2670(00)00718-2","article-title":"Development of robust calibration models in near infra-red spectrometric applications","volume":"411","author":"Swierenga","year":"2000","journal-title":"Anal. Chim. Acta."},{"key":"ref_14","first-page":"1580","article-title":"Cross-validation for the selection of spectral variables using the successive projections algorithm","volume":"18","author":"Silva","year":"2007","journal-title":"J. Am. Chem. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.aca.2010.03.048","article-title":"Variables selection methods in near-infrared spectroscopy","volume":"667","author":"Zou","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1366\/13-07294","article-title":"Soil organic carbon content estimation with laboratory-based visible\u2013near-infrared reflectance spectroscopy: Feature selection","volume":"68","author":"Shi","year":"2014","journal-title":"Appl. Spectrosc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.catena.2017.05.008","article-title":"Determination of rice root density from Vis\u2013NIR spectroscopy by support vector machine regression and spectral variable selection techniques","volume":"157","author":"Xu","year":"2017","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"120949","DOI":"10.1016\/j.saa.2022.120949","article-title":"Prediction of soil organic matter content based on characteristic band selection method","volume":"273","author":"Xie","year":"2022","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hong, Y., Chen, Y., Yu, L., Liu, Y., Liu, Y., Zhang, Y., Liu, Y., and Cheng, H. (2018). Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by VIS\u2013NIR spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030479"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"119963","DOI":"10.1016\/j.saa.2021.119963","article-title":"Research on estimation models of the spectral characteristics of soil organic matter based on the soil particle size","volume":"260","author":"Xie","year":"2021","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115653","DOI":"10.1016\/j.geoderma.2021.115653","article-title":"Estimation of soil organic matter content using selected spectral subset of hyperspectral data","volume":"409","author":"Sun","year":"2022","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bai, Z., Xie, M., Hu, B., Luo, D., Wan, C., Peng, J., and Shi, Z. (2022). Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China. Sensors, 22.","DOI":"10.3390\/s22166124"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s11947-014-1381-z","article-title":"Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk samples of Canadian wheat","volume":"8","author":"Mahesh","year":"2015","journal-title":"Food Bioprocess Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses","volume":"5","author":"Wold","year":"1984","journal-title":"SIAM. J. Sci Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.geoderma.2015.12.014","article-title":"Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization","volume":"267","author":"Wijewardane","year":"2016","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1080\/03650340.2020.1802013","article-title":"Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis\u2013NIR spectroscopy","volume":"67","author":"Cheng","year":"2021","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10661-008-0385-4","article-title":"Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China","volume":"154","author":"Liu","year":"2009","journal-title":"Environ. Monit. Assess."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.geoderma.2019.06.016","article-title":"Convolutional neural network for simultaneous prediction of several soil properties using visible\/near-infrared, mid-infrared, and their combined spectra","volume":"352","author":"Ng","year":"2019","journal-title":"Geoderma"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3563761","DOI":"10.1155\/2019\/3563761","article-title":"Deep learning application for predicting soil organic matter content by VIS-NIR spectroscopy","volume":"2019","author":"Xu","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_31","unstructured":"World Reference Base for Soil Resources (2014). International Soil Classification System For naming Soils and Creating Legends for Soil Maps, Food and Agriculture Organization of the United Nations."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.geoderma.2018.08.006","article-title":"Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China","volume":"337","author":"Peng","year":"2019","journal-title":"Geoderma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2018.08.011","article-title":"National digital soil map of organic matter in topsoil and its associated uncertainty in 1980\u2019s China","volume":"335","author":"Liang","year":"2019","journal-title":"Geoderma"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.still.2015.06.004","article-title":"Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions","volume":"155","author":"Ji","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ejor.2017.11.017","article-title":"An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses","volume":"267","author":"Montanari","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100819","DOI":"10.1016\/j.swevo.2020.100819","article-title":"Random convergence analysis of particle swarm optimization algorithm with time-varying attractor","volume":"61","author":"Liu","year":"2021","journal-title":"Swarm. Evol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1145\/264029.264043","article-title":"Enhanced simulated annealing for globally minimizing functions of many-continuous variables","volume":"23","author":"Siarry","year":"1997","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(95)00163-T","article-title":"Further investigation on a comparative study of simulated annealing and genetic algorithm for wavelength selection","volume":"311","author":"Kalivas","year":"1995","journal-title":"Anal. Chim. Acta"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1111\/ejss.12165","article-title":"Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques","volume":"65","author":"Wetterlind","year":"2014","journal-title":"Eur. J. Soil Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.biosystemseng.2016.04.018","article-title":"Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy","volume":"152","author":"Morellos","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.eswa.2014.08.018","article-title":"Back propagation neural network with adaptive differential evolution algorithm for time series forecasting","volume":"42","author":"Wang","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.1080\/0143116031000114851","article-title":"The use of backpropagating artificial neural networks in land cover classification","volume":"24","author":"Kavzoglu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_45","first-page":"134","article-title":"Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network","volume":"39","author":"Qu","year":"2018","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2018.09.015","article-title":"Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging","volume":"218","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/00401706.1969.10490666","article-title":"Computer aided design of experiments","volume":"11","author":"Kennard","year":"1969","journal-title":"Technometrics"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Behrens","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1180\/claymin.2008.043.1.03","article-title":"Reflectance and emission spectroscopy study of four groups of phyllosilicates: Smectites, kaolinite-serpentines, chlorites and micas","volume":"43","author":"Bishop","year":"2008","journal-title":"Clay Miner."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compag.2007.02.010","article-title":"Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy","volume":"61","author":"Christy","year":"2008","journal-title":"Comp. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"Trends Anal. Chem."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"389","DOI":"10.2136\/sssaj2006.0211","article-title":"In situ characterization of soil clay content with visible near-infrared diffuse reflectance spectroscopy","volume":"71","author":"Waiser","year":"2007","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"113900","DOI":"10.1016\/j.geoderma.2019.113900","article-title":"In situ and laboratory soil spectroscopy with portable visible-to-near-infrared and mid-infrared instruments for the assessment of organic carbon in soils","volume":"355","author":"Hutengs","year":"2019","journal-title":"Geoderma"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.geoderma.2009.06.002","article-title":"Accounting for surface roughness effects in the near-infrared reflectance sensing of soils","volume":"152","author":"Wu","year":"2009","journal-title":"Geoderma"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2007.12.009","article-title":"Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils","volume":"144","author":"Stevens","year":"2008","journal-title":"Geoderma"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106031","DOI":"10.1016\/j.compag.2021.106031","article-title":"Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection","volume":"182","author":"Lao","year":"2021","journal-title":"Comp. Electron. Agric."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.aca.2009.06.046","article-title":"Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration","volume":"648","author":"Li","year":"2009","journal-title":"Anal. Chim. Acta"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"918","DOI":"10.2136\/sssaj2006.0285","article-title":"A mechanism study of reflectance spectroscopy for investigating heavy metals in soils","volume":"71","author":"Wu","year":"2007","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"108533","DOI":"10.1016\/j.buildenv.2021.108533","article-title":"A coupled computational fluid dynamics and back-propagation neural network-based particle swarm optimizer algorithm for predicting and optimizing indoor air quality","volume":"207","author":"Li","year":"2022","journal-title":"Build. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, J., Liu, G., Yang, Y., Liu, Z., and Deng, H. (2019). Hyperspectral estimation model of forest soil organic matter in northwest Yunnan Province, China. Forests, 10.","DOI":"10.3390\/f10030217"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.12.025","article-title":"Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods","volume":"318","author":"Khosravi","year":"2018","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.aca.2009.01.017","article-title":"Variable selection in visible-near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer","volume":"635","author":"Liu","year":"2009","journal-title":"Anal. Chim. Acta"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_64","first-page":"2512","article-title":"Near Infrared Spectral Analysis Modeling Method Based on Deep Belief Network","volume":"40","author":"Zhang","year":"2020","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"114729","DOI":"10.1016\/j.geoderma.2020.114729","article-title":"Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: Optimal band combination algorithm and spectral degradation","volume":"382","author":"Zhang","year":"2021","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:56:54Z","timestamp":1760144214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,18]]},"references-count":65,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205221"],"URL":"https:\/\/doi.org\/10.3390\/rs14205221","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,18]]}}}