Spatial distribution dependency of soil organic carbon content to important environmental variables

Mirchooli, Fahimeh; Kiani-Harchegani, Mahboobeh; Darvishan, Abdulvahed Khaledi; Falahatkar, Samereh; Sadeghi, Seyed Hamidreza

doi:10.1016/j.ecolind.2020.106473

Cited by 56 publications

(16 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RF algorithm determines the importance of each predictor variable by assessing the increase in prediction error when the OOB of that variable changes while the other variables remain constant [ 58 , 59 ]. The advantage of the RF algorithm is that it is highly robust in terms of predicting the noise in it, while reducing overfitting of the model [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

“…The regression results will also be used as a reference for the GWR model to exclude landscape indicators that contribute weakly to the LST in UFZ. The RF regression is a machine learning model that is generally considered effective in reducing overfitting in high-dimensional data processing and is able to tap into the potential impact patterns of the phenomenon [ 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

Gao

Zhan

Yang

et al. 2020

IJERPH

View full text Add to dashboard Cite

Local warming induced by rapid urbanization has been threatening residents’ health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

Gao

Zhan

Yang

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Many studies have demonstrated the effectiveness of this estimation method (Yang et al, 2016;Wang et al, 2020). Although different researches used different modeling methods such as linear models (Razakamanarivo et al, 2011;Xie et al, 2021), nonlinear models (Mondal et al, 2017), and machine learning (Lin et al, 2020;Zhou et al, 2020), the selection of predictor variables was the common foundation of all such methods (Mirchooli et al, 2020). The selection of these predictors can be roughly divided into terrain-related predictors and vegetation-related predictors (Mirchooli et al, 2020;Wang et al, 2020).…”

Section: Choices Of Environmental Variables Of Remote Sensing For Soc and Stn Stocks Estimationsmentioning

confidence: 99%

“…Although different researches used different modeling methods such as linear models (Razakamanarivo et al, 2011;Xie et al, 2021), nonlinear models (Mondal et al, 2017), and machine learning (Lin et al, 2020;Zhou et al, 2020), the selection of predictor variables was the common foundation of all such methods (Mirchooli et al, 2020). The selection of these predictors can be roughly divided into terrain-related predictors and vegetation-related predictors (Mirchooli et al, 2020;Wang et al, 2020). The terrain-related variables generally affect the SOC and STN stocks by changing the spatial combination of water and heat conditions and the accumulation and transport of soil materials (Keskin et al, 2019).…”

Section: Choices Of Environmental Variables Of Remote Sensing For Soc and Stn Stocks Estimationsmentioning

confidence: 99%

“…The spatial distribution of soil organic carbon and soil total nitrogen stocks is affected by soil-forming factors such as topography, vegetation, soil temperature, and soil parent material (Wang et al, 2012). However, in different regions, the factors affecting soil organic carbon and total nitrogen stocks are not consistent (Ruiz Sinoga et al, 2012;Mirchooli et al, 2020). The traditional methods of predicting soil organic carbon and total nitrogen stocks involve using a certain sampling method to investigate a large number of soil samples in the region, before using statistical analysis to predict the soil organic carbon and soil total nitrogen stocks in the entire region (Scull et al, 2003;Brevik et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the Spatial Distribution of Soil Organic Carbon and Soil Total Nitrogen Stocks by Remote Sensing Methods: A Case Study in a Forest-dominated Alpine Watershed, Western China

wang

Peng

et al. 2021

Preprint

View full text Add to dashboard Cite

Soil organic carbon and soil total nitrogen stocks are important indicators for evaluating soil health and stability. Accurately predicting the spatial distribution of soil organic carbon and total nitrogen stocks is an important basis for mitigating global warming, ensuring regional food security, and maintaining the sustainable development of ecologically fragile areas. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0–30 cm) into three soil layers of 0–10 cm, 10–20 cm, and 20–30 cm to carry out soil organic carbon and soil total nitrogen stocks estimation experiments in the Qilian Mountains in western China. A multiple linear regression model and a stepwise multiple linear regression model were used to estimate soil organic carbon and soil total nitrogen stocks. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results indicated that these two linear regression models showed good performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen stocks. In topsoil, remote sensing technology could be used to predict the soil properties in layers; however, as the soil depth increased, the performance of the linear regression models gradually decreased.

show abstract

Using a variety of machine learning approaches to predict and map topsoil pH of arable land on a regional scale

Sun

et al. 2023

Soil Science Soc of Amer J

View full text Add to dashboard Cite

In order to accurately predict soil properties, various machine learning (ML) approaches and hybrid models constructed by integrating ML into regression kriging framework were used to predict and map arable land topsoil pH in Henan province, central China. Random forest (RF), cubist (Cu), support vector machine, artificial neural network, multiple linear regression, classification and regression trees (CART) and their hybrid models were compared for pH accuracy prediction. Among all standalone ML models, RF had the best predictive performance, in terms of the metrics employed in this study, followed by Cu, and CART was the worst. Compared with their ML counterparts, hybrid models could improve the accuracy of topsoil pH prediction to various extents. The accuracy improvement of the hybrid models constructed based on the simple ML was much greater than that based on the complex ensemble ML. Except for artificial neural network kriging , there was no significant difference between different hybrid models in the predicted results of topsoil pH. The outputs from the best predictive models showed that weak acidic soils and weak alkaline soils were the dominant arable soils in the study region, accounting for more than 30% and more than 50% of the total arable land area respectively, the topsoil pH of arable land in the north of the study area is generally higher than that in the south. Boruta variable selection revealed that altitude, climatic covariates closely related to soil moisture availability and some soil properties were the most critical factors affecting and controlling the topsoil pH of arable land.

show abstract

Spatial distribution dependency of soil organic carbon content to important environmental variables

Cited by 56 publications

References 71 publications

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

Estimating the Spatial Distribution of Soil Organic Carbon and Soil Total Nitrogen Stocks by Remote Sensing Methods: A Case Study in a Forest-dominated Alpine Watershed, Western China

Using a variety of machine learning approaches to predict and map topsoil pH of arable land on a regional scale

Contact Info

Product

Resources

About