Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China

Zhang, Liyuan; Wu, Zhen; Sun, Xiaoqiang; Yan, Junying; Sun, Yueqi; Liu, Peijia; Chen, Jie

doi:10.3390/plants12071464

Cited by 2 publications

(1 citation statement)

References 64 publications

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“…Compared to the traditional multiple regression model, the RF has a faster training speed, can process large-scale and complex geographic data, and can estimate the relative importance of each feature 19 , 24 . Currently, this model is utilized in simulation research on the spatial distribution of population, crops, livestock, and soil organic matter 25 , 26 .…”

Section: Introductionmentioning

confidence: 99%

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Liu,

Wang,

Yang

et al. 2024

Sci Rep

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Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0–125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0–25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

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Section: Introductionmentioning

confidence: 99%

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Liu,

Wang,

Yang

et al. 2024

Sci Rep

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Estimating and mapping the soil total nitrogen contents in black soil region using hyperspectral images towards environmental heterogeneity

Lin,

Mei,

et al. 2024

Front. Environ. Sci.

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Introduction: Fast and accurate estimation and spatial mapping of soil total nitrogen (TN) content is important for the development of modern precision agriculture, such as soil fertility monitoring and land reclamation decision-making. Hyperspectral remote sensing has been demonstrated to be an accurate real-time technique for rapid estimation and mapping of soil TN content.Methods: To solve the problem of poor accuracy and generalization of estimation models caused by soil environmental heterogeneity in estimating and mapping soil TN content using hyperspectral images, 502 soil samples were collected from a typical black soil area in Yushu City, Jilin Province, China, as a test area, and three sample grouping strategies were established by soil environmental variables (soil type, thickness of the black soil layer, and topographic factors), and Pearson correlation coefficient and competitive adaptive reweighted sampling algorithm were used to determine the TN characteristic bands of each sample set under different strategies. Based on the data characteristics of the sub-sample set, the local regression estimation model based on sample grouping was constructed using the CatBoost algorithm, and the estimation and distribution mapping of soil TN content was carried out.Results and Discussion: The results showed that after dividing the samples according to the differences in soil environmental factors, the characteristic information of the samples is more targeted, with more abundant numbers and distribution ranges of TN characteristic bands. Compared to the global regression estimation with all samples, the local regression based on the grouping of soil environment differences showed improved accuracy, with the local regression estimation model constructed with the ST-G strategy exhibiting the highest estimation accuracy (Rp2 = 0.839). The results can provide a reference for large-area soil properties mapping, and technical support for soil quality digitization and precision fertilization.

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Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China

Cited by 2 publications

References 64 publications

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Estimating and mapping the soil total nitrogen contents in black soil region using hyperspectral images towards environmental heterogeneity

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