Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning

Yiping Peng, Ting Wang, Shujuan Xie, Zhenhua Liu, Chenjie Lin, Yueming Hu, Jianfang Wang and Xiaoyun Mao ()
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Yiping Peng: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Ting Wang: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Shujuan Xie: Guangdong Academy of Social Sciences, Guangzhou 510635, China
Zhenhua Liu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Chenjie Lin: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Yueming Hu: College of Tropical Crops, Hainan University, Haikou 570228, China
Jianfang Wang: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Xiaoyun Mao: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2023, vol. 13, issue 6, 1-12

Abstract: Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca 2+ , K + , Mg 2+ , and Na + ) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg 2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca 2+ and Na + , and SPA was the optimal algorithm for determining the characteristic bands of soil K + and Mg 2+ . The most accurate estimation models for soil Ca 2+ , K + , Mg 2+ , and Na + contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg 2+ (R 2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca 2+ : R 2 = 0.83, RPIQ = 2.47; K + : R 2 = 0.83, RPIQ = 2.58; Na + : R 2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg 2+ content with an R 2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg 2+ content at the regional scale.

Keywords: soil cations; VIS-NIR spectroscopy; feature screening; machine learn algorithm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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