Different Approaches on Digital Mapping of Soil-Landscape Parameters

Garg, Pradeep; Garg, Rahul; Shukla, Gaurav; Srivastava, H. S.

doi:10.1007/978-981-15-3238-2_2

Cited by 3 publications

(1 citation statement)

References 169 publications

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“…Additionally, elevation provides information about the altitude and climate patterns of the area, which affect soil-forming factors such as temperature, precipitation, vegetation, and ultimately SOC sequestration [92]. Landform classifications are also essential for digital SOC mapping as they provide a higher-level understanding of the terrain features and their spatial distribution, allowing for more accurate modeling and mapping of SOC stocks [93]. Additionally, hydrological variables can be derived from DEM data using methods such as flow accumulation, flow direction, stream network delineation, watershed delineation, and computation of hydrological metrics like drainage density, stream order, and watershed characteristics.…”

Section: Topography Covariatesmentioning

confidence: 99%

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Radočaj,

Gašparović,

Jurišić

2024

Agriculture

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This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations’ Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping.

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Section: Topography Covariatesmentioning

confidence: 99%

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Radočaj,

Gašparović,

Jurišić

2024

Agriculture

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Climate-sensitive spatial variability of soil organic carbon in multiple forests, Central China

Shen

Xiao

Chen

et al. 2023

Global Ecology and Conservation

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Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability

Díaz-Guadarrama,

Varón-Ramírez,

Lizarazo

et al. 2024

Earth Syst. Sci. Data

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Abstract. Spatial soil databases can help model complex phenomena in which soils are a decisive factor – for example, evaluating agricultural potential or estimating carbon storage capacity. The Latin America and Caribbean Soil Information System, SISLAC, is a regional initiative promoted by the Food and Agriculture Organization's (FAO) Latin America and the Caribbean Soil Partnership to contribute to sustainable management of soil. SISLAC includes data from 49 084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. In addition, there are other soil databases in the region with about 40 000 soil profiles that can be integrated into SISLAC and improve it. However, some problems hinder its usages, such as the quality of the data and their high dimensionality. The objective of this research is evaluate the quality of the SISLAC data and the other available soil databases to generate a new improved version that meets the minimum quality requirements to be used for different purposes or practical applications. The results show that 15 % of the existing soil profiles had an inaccurate description of the diagnostic horizons and 17 % of the additional profiles already existed in SISLAC; therefore, a total of 32 % of profiles were excluded for these two reasons. Further correction of an additional 4.5 % of existing inconsistencies improved overall data quality. The improved database consists of 66 746 profiles and is available for public use at https://doi.org/10.5281/zenodo.7876731 (Díaz-Guadarrama and Guevara, 2023). This revised version of SISLAC data offers the opportunity to generate information that helps decision-making on issues in which soils are a decisive factor. It can also be used to plan future soil surveys in areas with low density or where updated information is required.

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Different Approaches on Digital Mapping of Soil-Landscape Parameters

Cited by 3 publications

References 169 publications

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Climate-sensitive spatial variability of soil organic carbon in multiple forests, Central China

Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability

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