{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:55:04Z","timestamp":1760144104095,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Major Project of China","award":["04-H30G01-9001-20\/22","42222107","D040102"],"award-info":[{"award-number":["04-H30G01-9001-20\/22","42222107","D040102"]}]},{"name":"Outstanding Youth Program of the National Natural Science Foundation of China","award":["04-H30G01-9001-20\/22","42222107","D040102"],"award-info":[{"award-number":["04-H30G01-9001-20\/22","42222107","D040102"]}]},{"name":"National Civil Aerospace Project of China","award":["04-H30G01-9001-20\/22","42222107","D040102"],"award-info":[{"award-number":["04-H30G01-9001-20\/22","42222107","D040102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs\u2019 concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg\/kg, respectively). The highest R2 values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R2 = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg\/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively.<\/jats:p>","DOI":"10.3390\/rs16071129","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:28:06Z","timestamp":1711369686000},"page":"1129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimation of Soil-Related Parameters Using Airborne-Based Hyperspectral Imagery and Ground Data in the Fenwei Plain, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Chenchen","family":"Jiang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-308X","authenticated-orcid":false,"given":"Huazhong","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"},{"name":"United Remote Sensing Application Research Center of Chinese Universities, Ministry of Education, Beijing 100871, China"}]},{"given":"Zian","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]},{"given":"Hui","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]},{"given":"Yuanjian","family":"Teng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]},{"given":"Hongqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]},{"given":"Xixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Dingjian","family":"Jin","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Mengran","family":"Wang","sequence":"additional","affiliation":[{"name":"Hebei Provincial Prospecting Institute of Hydrogeology and Engineering Geology (Hebei Remote Sensing Center), Shijiazhuang 050021, China"}]},{"given":"Rongyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Baozhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2302-3056","authenticated-orcid":false,"given":"Jinshun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Beijing Key Lab of Spatial Information Integration and Its Application, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1111\/gcb.12159","article-title":"An estimate of potential 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