Comparing geospatial techniques to predict SOC stocks

Liu, Yanfang; Guo, Long; Jiang, Qinghu; Zhang, Haitao; Chen, Yiyun

doi:10.1016/j.still.2014.12.002

Cited by 71 publications

(35 citation statements)

References 43 publications

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“…In these areas, the distance to residential area is the main influential environmental factor. Areas near the residential area contain low SOM content, which is also consistent with the study [9]. Liu, Guo, Jiang, Zhang and Chen [9] indicated that the soil …”

Section: Vnir Model Based On the Calibration Set Selected Using The Msupporting

confidence: 85%

“…Areas near the residential area contain low SOM content, which is also consistent with the study [9]. Liu, Guo, Jiang, Zhang and Chen [9] indicated that the soil near residential areas is seriously affected by frequent human activities, leading to a low SOM content. In addition to the distance to the residential area, another main influential environmental variable is soil humidity quantified by the NDMI.…”

Section: Vnir Model Based On the Calibration Set Selected Using The Msupporting

confidence: 85%

“…SOM content is considerably influenced by the surrounding environment, and presents spatial heterogeneity [8,9,25]. According to Liu, Guo, Jiang, Zhang and Chen [9], the main environmental variables that affect SOM in the study area include the following three types: (1) soil humidity quantified by the Normalized Difference Moisture Index (NDMI); (2) slope; and (3) distance to residential area.…”

Section: Environmental Variablesmentioning

confidence: 99%

“…Details of data processing and calculation of environmental variables are reported in previous studies [9,26].…”

Section: Environmental Variablesmentioning

confidence: 99%

“…In most cases, field samplings are not designed to collect specific samples that are representative of the relationships between VNIR spectra and component concentrations. In addition, the design is difficult to establish because the relationships may vary due to the locations and surrounding environments [8,9]. Therefore, we assume that "component concentration representation", "spectrum representation", and "environment representation" may be equivalent to "relationship representation".…”

Section: Introductionmentioning

confidence: 99%

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Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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Abstract:The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank-Kennard-Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

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Section: Vnir Model Based On the Calibration Set Selected Using The Msupporting

confidence: 85%

Section: Vnir Model Based On the Calibration Set Selected Using The Msupporting

confidence: 85%

Section: Environmental Variablesmentioning

confidence: 99%

“…Details of data processing and calculation of environmental variables are reported in previous studies [9,26].…”

Section: Environmental Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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Soil organic carbon estimation along an altitudinal gradient of chir pine forests in the Garhwal Himalaya, India: A field inventory to remote sensing approach

Kumar

Thakur

et al. 2022

Land Degrad Dev

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Chir pine (Pinus roxburghii, Sarg.) forests are dominant in the Indian Himalayan region and act as a huge carbon (C) sink. However, measuring the C sink in soil is complex and time-intensive, and therefore the present study attempts to estimate the soil organic carbon (SOC) through a remote sensing (RS) approach. We estimated SOC stock of chir pine forests along an altitudinal gradient at three soil depths (0-30, 30-60 and 60-100 cm) in the Garhwal Himalaya, Uttarakhand. Fourteen forest stands at four altitudes, viz., <1000 m above sea level (m asl), 1001-1400 m asl, 1401-1800 m asl and >1801 m asl were surveyed and served for data collection. A model for predicting SOC was developed through stepwise regression analysis based on vegetation information and altitude as independent variables with the field data on SOC.For vegetation information, we used the normalized difference vegetation index (NDVI) measured through remote sensing (RS). The mean SOC stock up to 100 cm depth was increased with increasing altitude and were in the order of 69.

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Understanding drivers of the spatial variability of soil organic carbon in China's terrestrial ecosystems

Li,

Yang,

Zhang

et al. 2023

Land Degrad Dev

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The carbon balance of the global ecosystems is significantly influenced by the soil organic carbon (SOC) pool in China's terrestrial ecosystems. Nevertheless, the current understanding of the main controlling factors of SOC in different ecosystems and their discrepancies is limited. The goal of this research was to better understand the human and environmental variables affecting SOC in China's terrestrial ecosystems. We designed a conceptual framework using 2674 samples collected from four ecosystems (grasslands, shrublands, wetlands, and croplands) in China during the 2000–2014 period, combining geodetector and multiple regression (MR) approaches to investigate the effects of environmental conditions, human activity, and their interplay on surface SOC (0–20 cm). Results showed that there were large discrepancies in the strength of influencing factors among different ecosystems. Total nitrogen (TN), mean annual temperature (MAT), and bulk density (BD) were the major factors influencing SOC in grasslands. BD, TN, and pH dominated in shrublands. For wetlands, SOC stocks were primarily attributed to maximum temperature (TMMX), MAT, and potential evapotranspiration (PET). Croplands are predominantly controlled by minimum temperature (TMMN), MAT, and TN. These results highlight that natural factors, particularly climatic and soil characteristics, were the dominant factors controlling SOC stocks in China's terrestrial ecosystems. This work also highlights that the interaction of two influencing factors, especially, pairs of soil characteristics factors, pairs of climate and soil characteristics factors, can well explain the drivers of SOC on the surface soil in China. Our study emphasizes the spatial heterogeneity of the factors that influence SOC in terrestrial ecosystems, enhancing knowledge of SOC at the national level, and providing the guideline for devising better policy to improve C sequestration and mitigate climate changes.

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Comparing geospatial techniques to predict SOC stocks

Cited by 71 publications

References 43 publications

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Soil organic carbon estimation along an altitudinal gradient of chir pine forests in the Garhwal Himalaya, India: A field inventory to remote sensing approach

Understanding drivers of the spatial variability of soil organic carbon in China's terrestrial ecosystems

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