Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants |
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| Institution: | 1. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen, Guangdong, China;2. College of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China;3. The Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;4. State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China;1. Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China;2. College of Information Engineering, Shenzhen University, Shenzhen 518060, China;3. College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China;4. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;5. School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China;6. School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523419, China |
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| Abstract: | Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg?1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg?1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg?1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = ?0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra. |
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| Keywords: | Visible and near-infrared reflectance spectroscopy Genetic algorithm Partial least squares regression Soil organic matter Leaf chlorophyll Paddy soil |
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