Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis

Yang, Jixiang; Li, Xinguo; Ma, Xiaofei

doi:10.3390/rs15225294

Cited by 3 publications

(3 citation statements)

References 64 publications

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“…Similarly, Ye et al demonstrated the superiority of GBRT and XGBoost over RF in SOC estimation using GF-6 hyperspectral satellite data [61]. Furthermore, Yang Li et al showcased the superiority of XGBoost over the RF algorithm in SOC estimation based on spectrometer observations [56]. In the future, although the denoising methods employed in this study proved to be highly effective, they are regarded as fundamental techniques in the field of noise removal.…”

Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 97%

“…Previous studies have also reported similar improvements regarding the effectiveness of the SG filter in enhancing estimation accuracy. Yang et al utilized log(1/R) + SG + FOD with PRISMA satellite imagery, resulting in a remarkable 25% improvement compared to solely using reflectance values (R) for organic carbon estimation [56]. Xu et al used SG + log(1/R) as a preprocessing method for predicting SOM content based on laboratory spectrometer observations.…”

Section: Discussionmentioning

confidence: 99%

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Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Golkar Amoli,

Hasanlou,

Taghizadeh Mehrjardi

et al. 2024

Remote Sensing

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Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies.

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Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

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Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Golkar Amoli,

Hasanlou,

Taghizadeh Mehrjardi

et al. 2024

Remote Sensing

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Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Lin,

Ma,

Jiang

et al. 2024

Agriculture

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Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

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Handheld In Situ Methods for Soil Organic Carbon Assessment

Loria,

Lal,

Chandra

2024

Sustainability

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Soil organic carbon (SOC) assessment is crucial for evaluating soil health and supporting carbon sequestration efforts. Traditional methods like wet digestion and dry combustion are time-consuming and labor-intensive, necessitating the development of non-destructive, cost-efficient, and real-time in situ measurements. This review focuses on handheld in situ methodologies for SOC estimation, underscoring their practicality and reasonable accuracy. Spectroscopic techniques, like visible and near-infrared, mid-infrared, laser-induced breakdown spectroscopy, and inelastic neutron scattering each offer unique advantages. Preprocessing techniques, such as external parameter orthogonalization and standard normal variate, are employed to eliminate soil moisture content and particle size effects on SOC estimation. Calibration methods, like partial least squares regression and support vector machine, establish relationships between spectral reflectance, soil properties, and SOC. Among the 32 studies selected in this review, 14 exhibited a coefficient of determination (R2) of 0.80 or higher, indicating the potential for accurate SOC content estimation using in situ approaches. Each study meticulously adjusted factors such as spectral range, pretreatment method, and calibration model to improve the accuracy of SOC content, highlighting both the methodological diversity and a continuous pursuit of precision in direct field measurements. Continued research and validation are imperative to ensure accurate in situ SOC assessment across diverse environments. Thus, this review underscores the potential of handheld devices for in situ SOC estimation with good accuracy and leveraging factors that influence its precision. Crucial for optimizing carbon farming, these devices offer real-time soil measurements, empowering land managers to enhance carbon sequestration and promote sustainable land management across diverse agricultural landscapes.

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Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis

Cited by 3 publications

References 64 publications

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Handheld In Situ Methods for Soil Organic Carbon Assessment

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