Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data

Tan, Kun; Ma, Weibo; Wu, Fuyu; Du, Qian

doi:10.1007/s10661-019-7510-4

Cited by 63 publications

(34 citation statements)

References 48 publications

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“…The results (Figures 16-18) obtained from the latest published Random Forest method [66] were similar to the case involving XGBoost. They also showed that SNR was the most sensitive variable among the input variables.…”

Section: Discussionsupporting

confidence: 55%

“…In this case, the input is sample data, and the output will be the expected result. Some existing and proven machine learning and neural networks methods have emerged to establish the estimation model based on the correlation selected features and retrieve soil moisture from SMOS data [66,67]. Both machine learning and neural networks are of artificial intelligence.…”

Section: Discussionmentioning

confidence: 99%

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GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

et al. 2019

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Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.

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Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

et al. 2019

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“…Thus, identifying effective wavelengths for rapidly estimating crop leaf N concentration has become an extremely important topic in canopy spectral studies. Several canopy spectral transformation techniques, such as first-derivative reflectance (FDR) (Ihuoma and Madramootoo, 2019;Wen et al, 2019) and continuum removal (CR) (Tian et al, 2017;Tan et al, 2019), have been used to improve the signal-to-noise ratio, minimize the impact of atmospheric noise, and enhance weak spectral information of remote monitoring of leaf N concentration in crops. Experimental investigations have shown that the FDR technique can resolve overlapping absorption phenomena and can minimize the influences of soil or atmospheric background noise (Hruschka, 1987;Miphokasap et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis Models Based on Full Spectra Range and Effective Wavelengths Using Different Transformation Techniques for Rapid Estimation of Leaf Nitrogen Concentration in Winter Wheat

Dong

Wang

et al. 2020

Front. Plant Sci.

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To develop a stable estimation model and identify effective wavelengths that could explain the variations in leaf nitrogen (N) concentration with different N supplies, growing seasons, ecological locations, growth stages, and wheat cultivars. Four field experiments were performed during two consecutive years (2017-2019) at three sites (Yuanyang, Hebi, and Wenxian) in Henan, China. In situ canopy spectral reflectance data under the aforementioned N supply conditions were obtained over a range of 400-950 nm (visible and near-infrared region). On the basis of the canopy raw spectral reflectance data and their subsequent transformation by two different techniques, firstderivative reflectance (FDR) and continuum removal (CR), four multivariate regression methods were comparatively analyzed and used to develop predictive models for estimating leaf N concentration: multiple linear regression (MLR), principal component regression (PCR), partial least square (PLS), and support vector machine (SVM). Results showed that leaf N concentration and canopy reflectance significantly varied with the levels of N fertilization, and a good correlation was observed for all the spectral techniques. Seven wavelengths with relatively higher r values than the bands of the raw spectra centered at 508, 525, 572, 709, 780, 876, and 925 nm were specified using the FDR technique. Based on the full wavelengths, the FDR-SVM model exhibited a good performance for leaf N concentration estimation, with coefficients of determination (r 2 val) for the validation datasets and corresponding relative percent deviations (RPD val) values of 0.842 and 2.383, respectively. However, the FDR-PLS yielded a more accurate assessment of the leaf N concentration than did the other methods, with r 2 val and RPD val values of 0.857 and 2.535, respectively. The variable importance in projection (VIP) scores from the FDR-PLS with the all canopy spectral region were used to screen the effective wavelengths of the spectral data. Therefore, six effective wavelengths centered at 525, 573, 710, 780, 875, and 924 nm were identified for leaf N concentration estimation. The SVM regression method with the effective wavelengths

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“…can select characteristics bands with strong adaptability and remove incoherent bands, but do not consider the increase in model complexity caused by the collinearity problem, which affected the prediction accuracy [35]. Before the feature selection, CR can effectively improve the accuracy of subsequent feature band selection [36,37]. It can be seen from this research that combining the SPA method with sCARS can solve the problem of data redundancy and simplify the model, thereby improving the generalization ability and prediction accuracy of the model.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Wei

Wang

et al. 2020

Sensors

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With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

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Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data

Cited by 63 publications

References 48 publications

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

Multivariate Analysis Models Based on Full Spectra Range and Effective Wavelengths Using Different Transformation Techniques for Rapid Estimation of Leaf Nitrogen Concentration in Winter Wheat

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

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