Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm

Zhang, Zipeng; Jianli, 丁建丽 Ding; Jingzhe, 王敬哲 Wang

doi:10.3788/aos201939.0228003

Cited by 3 publications

(1 citation statement)

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“…The findings of their study provide a scientific basis for the rapid monitoring of the soil organic carbon content in high-altitude regions with cold climates. Similar related studies can be found in citations [8][9][10].…”

Section: Introductionsupporting

confidence: 82%

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: Introductionsupporting

confidence: 82%

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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An experimental study on the influence of copper and lead concentration on the spectral reflectance of maize leaves

Zhang

Yang

Gao

2020

Remote Sensing Letters

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Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

Zheng

Ding

Zhang

2022

Sensors

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The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

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Spectral Characteristics of Oasis Soil in Arid Area Based on Harmonic Analysis Algorithm

Cited by 3 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

An experimental study on the influence of copper and lead concentration on the spectral reflectance of maize leaves

Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

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