Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery

Stevens, François; Bogaert, Pierre-Maurice; Wesemael, Bas van

doi:10.1016/j.geoderma.2015.05.008

Cited by 16 publications

(5 citation statements)

References 31 publications

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“…SOC patterns in fields covering the hill slope from the crest to the foot slope showed patterns related to erosion of C associated with the topsoil. Stevens et al (2015) further demonstrated the importance of land management on SOC content. Their residual maximum likelihood model (REML) applied to the SOC map produced from the AHS160 flight over Luxembourg predicted that a field effect accounted for 48 ± 8% of the variance in SOC content in a cluster of fields (Fig.…”

Section: Quality Of the Predicted Soil Property Mapsmentioning

confidence: 85%

“…This paper showed that these prediction uncertainty maps may be used to better Fig. 3 Predicted SOC maps (left) from the AHS 160 campaign in Luxembourg (Stevens et al 2010) and (right) using a residual maximum likelihood model (Stevens et al 2015) 1 3 characterize the quality of the soil properties mapping results, mask no-soil pixels and define the soil sampling and the calibration dataset.…”

Section: Quality Of the Predicted Soil Property Mapsmentioning

confidence: 99%

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Imaging Spectroscopy for Soil Mapping and Monitoring

et al. 2019

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There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy-and decision-makers. Soil and imaging spectroscopy, based on visible-near-infrared and shortwave infrared (400-2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from airand spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world's soil resources.

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Section: Quality Of the Predicted Soil Property Mapsmentioning

confidence: 85%

Section: Quality Of the Predicted Soil Property Mapsmentioning

confidence: 99%

Imaging Spectroscopy for Soil Mapping and Monitoring

et al. 2019

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“…In addition, we found that the largest difference in SOC between DL and PF was observed in regions with mean annual temperatures >10 C, mean annual precipitation in the range of 600-1200 mm, or elevation in the range of 0-500 m. This may be attributed to the greater differences in carbon input, decomposition rate of organic material, and field managements between DL and PF in the regions. Different SOC changes (Stevens et al, 2015). This study found that PF had higher SOC content than DL across different soil depths and cropping systems (Figure 4).…”

Section: Effects Of Different Farmland Utilization Types On Soc In Di...mentioning

confidence: 67%

“…In this study, farmland SOC was influenced not only by natural environmental factors but also by cropping systems. Cropping systems can directly influence the content and distribution of SOC, as well as indirectly affect its synthesis and decomposition by interacting with other factors (Stevens et al, 2015). This study found that PF had higher SOC content than DL across different soil depths and cropping systems (Figure 4).…”

Section: Discussionmentioning

confidence: 99%

Effects of different farmland utilization types on soil organic carbon in China: A meta‐analysis

Ouyang,

Zhu

2023

Land Degrad Dev

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Exploring the relationship between soil organic carbon (SOC) and different farmland utilization types can contribute to achieving carbon neutrality in agriculture. Over the past few decades, numerous researchers have conducted a multitude of studies on the effects of farmland utilization types on SOC in China, and these studies have yielded a wealth of data from field experiments. However, these experiments are limited in that they lack sufficient quantitative analyses of studies conducted on regional or national scales. In this study, we analyzed the three most common farmland utilization types (paddy field [PF], dry land [DL], and vegetable field [VF]) in China using a national‐scale meta‐analysis and included data from 108 observations from 28 studies in the field. In this meta‐analysis, we conducted three comparisons (control vs. treatment): (a) PF versus DL, (b) PF versus VF, and (c) DL versus VF. The results showed that in comparison (a), DL had a significantly lower SOC than PF (ln R++ = −0.463, 95% confidence intervals [CIs] = −0.580 ~ −0.345), with DL showing 37.1% lower SOC than PF. In comparison (b), although there was no statistically significant difference between PF and VF (ln R++ = −0.078, 95% CIs = −0.167 ~ 0.011), VF had 7.5% lower SOC than PF. In comparison (c), VF had significantly higher SOC than DL (ln R++ = 0.227, 95% CIs = 0.147 ~ 0.307), with VF showing 25.5% higher SOC than DL. It should be noted that the effects of farmland utilization types on SOC content observed in China varied depending on different factors such as the regions, climatic conditions, and cropping systems. In conclusion, SOC content could be affected by farmland utilization types. Therefore, in order to promote the development of low‐carbon agriculture in China, it is important to consider farmland utilization types in agricultural production, as well as to consider carbon budgets, food security, and field practices to maximize agronomic and environmental benefits.

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“…A feature of ecological integrity is self-organization capacity [21]. The greater the self-organization capacity of a system, the greater is its ability to maintain organization and resist perturbations.…”

Section: Framework For Evaluation Of Landscape Ecological Integritymentioning

confidence: 99%

Evaluation of Landscape Ecological Integrity in the Yulin Region, China

et al. 2018

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We developed a framework and an index to evaluate landscape ecological integrity. The framework was applied to the Yulin region (Shaanxi Province, China) to evaluate its overall ecological integrity and the effect of the Grain for Green Project on landscape ecological integrity. Landscape ecological integrity (LEI) is the ability of an ecosystem to maintain its self-organization capacity, stability, and diversity in structure and function. A landscape having high ecological integrity has three major characteristics: complex structure, high self-organization capacity, and a high level of stability. The LEI can be evaluated using five indicators: landscape fragmentation, connectance, ecological sensitivity, diversity, and vegetation productivity. The results indicate that the LEI in the Yulin region was relatively low during the period from 2000 to 2015. From 2000 to 2005, areas of very low and low LEI decreased, and areas of moderate and high LEI increased. From 2005 to 2010, areas of low and high LEI decreased, and areas of moderate LEI increased. Furthermore, from 2010 to 2015, areas of very low and low LEI increased, and areas of moderate and high LEI decreased. Overall, the LEI of the region was low, but increased between 2000 and 2010, and decreased between 2010 and 2015. On the basis of these findings, we conclude that the Grain for Green Project in the Yulin region has been successful in improving regional LEI.

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Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery

Cited by 16 publications

References 31 publications

Imaging Spectroscopy for Soil Mapping and Monitoring

Imaging Spectroscopy for Soil Mapping and Monitoring

Effects of different farmland utilization types on soil organic carbon in China: A meta‐analysis

Evaluation of Landscape Ecological Integrity in the Yulin Region, China

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