Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning

Ge, Xiangyu; Jianli, 丁建丽 Ding; Jingzhe, 王敬哲 Wang; Fei, 王飞 Wang; Lianghong, 蔡亮红 Cai; Huilan, 孙慧兰 Sun

doi:10.3788/aos201838.1030001

Cited by 4 publications

(3 citation statements)

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“…Numerous studies have demonstrated that spectral transformations can reduce noise in the original spectral data, improve the discriminability of spectral data, and enhance data interpretability [25,26]. In order to improve the predictive accuracy of the model, this study applied various forms of differential transformations to the original spectral data of the study area.…”

Section: Discussionmentioning

confidence: 99%

Estimating Organic Matter Content in Hyperspectral Wetland Soil Using Marine-Predators-Algorithm-Based Random Forest and Multiple Differential Transformations

Jia,

Zu,

Yang

et al. 2023

Applied Sciences

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To achieve a rapid and accurate estimation of the soil organic matter (SOM) content in wetland soil, we focused on surface soil samples from the Xianshan Lake wetland area in Zhejiang Province and proposed a novel method called Marine-Predators-Algorithm-Based Random Forest (MPARF) to establish a fast detection model for the SOM content. This study analyzed 85 soil samples from the study area with the aim of assessing the performance of various combinations of ten differential transformation methods and five regression algorithms in predicting the SOM content. Our research findings demonstrate that the combination of second-order differentiation (SD) and MPARF yields the best results, with the highest R2 value (0.92) and the lowest RMSE (1.32 g/kg). Furthermore, we determined that the average SOM content in the study area’s soil is 9.77 g/kg. Additionally, we confirmed that different differential transformation methods contribute to improving the correlation between spectral data and the SOM content, thereby enhancing the development of predictive models. This study provides a robust methodology and foundation for future soil organic matter monitoring in the region.

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Section: Discussionmentioning

confidence: 99%

Estimating Organic Matter Content in Hyperspectral Wetland Soil Using Marine-Predators-Algorithm-Based Random Forest and Multiple Differential Transformations

Jia,

Zu,

Yang

et al. 2023

Applied Sciences

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“…Random Forest (RF) is an ensemble learning classifier, which is a machine learning algorithm formed by the combination of many CART decision trees and voting mechanisms [32]. Its greatest advantage lies in the importance of measurement variables [33]. The random forest algorithm uses a collection of classification trees to generate highly unbiased and accurate predictions based on voting across adaptive repetitions, which largely avoids overfitting [34].…”

Section: Classification Methodsmentioning

confidence: 99%

Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm

et al. 2021

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Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.

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“…PLS models can effectively catch the unique contributions of each independent variable to overcome multicollinearity. PLS models were constructed by the pls package in R software (Ge et al, 2018;Tan et al, 2020).…”

Section: Linear and Non-linear Predictive Modelsmentioning

confidence: 99%

Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

et al. 2023

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The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function. In temperate desert ecosystems, which functional trait to use to predict ecosystem function is an important scientific question. In this study, the minimum data sets of functional traits of woody (wMDS) and herbaceous (hMDS) plants were constructed and used to predict the spatial distribution of C, N, and P cycling in ecosystems. The results showed that the wMDS included plant height, specific leaf area, leaf dry weight, leaf water content, diameter at breast height (DBH), leaf width, and leaf thickness, and the hMDS included plant height, specific leaf area, leaf fresh weight, leaf length, and leaf width. The linear regression results based on the cross-validations (FTEIW - L, FTEIA - L, FTEIW - NL, and FTEIA - NL) for the MDS and TDS (total data set) showed that the R2 (coefficients of determination) for wMDS were 0.29, 0.34, 0.75, and 0.57, respectively, and those for hMDS were 0.82, 0.75, 0.76, and 0.68, respectively, proving that the MDSs can replace the TDS in predicting ecosystem function. Then, the MDSs were used to predict the C, N, and P cycling in the ecosystem. The results showed that non-linear models RF and BPNN were able to predict the spatial distributions of C, N and P cycling, and the distributions showed inconsistent patterns between different life forms under moisture restrictions. The C, N, and P cycling showed strong spatial autocorrelation and were mainly influenced by structural factors. Based on the non-linear models, the MDSs can be used to accurately predict the C, N, and P cycling, and the predicted values of woody plant functional traits visualized by regression kriging were closer to the kriging results based on raw values. This study provides a new perspective for exploring the relationship between biodiversity and ecosystem function.

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Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning

Cited by 4 publications

References 0 publications

Estimating Organic Matter Content in Hyperspectral Wetland Soil Using Marine-Predators-Algorithm-Based Random Forest and Multiple Differential Transformations

Estimating Organic Matter Content in Hyperspectral Wetland Soil Using Marine-Predators-Algorithm-Based Random Forest and Multiple Differential Transformations

Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm

Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

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