Author
Listed:
- Giribabu Dandabathula
(Regional Remote Sensing Centre-West, India.)
- Sagar Subhashrao Salunkhe
(Regional Remote Sensing Centre-West, India.)
- Apurba Kumar Bera
(Regional Remote Sensing Centre-West, India.)
- Koushik Ghosh
(Regional Remote Sensing Centre-West, India.)
- Rohit Hari
(Regional Remote Sensing Centre-West, India.)
- Preeti Biradar
(Central University of Karnataka, India.)
- Karthik Reddy Chirala
(Central University of Karnataka, India.)
- Mahesh Kumar Gaur
(ICAR-Central Arid Zone Research Institute, India.)
Abstract
As one of the Earth’s most important natural resources, soil plays a prominent role in regulating ecosystem services, human food production systems and in facilitating a region’s sustainable development. Of late, due recognition has been given to soil sciences and soil information systems as they act as a core to achieve the targets of land degradation neutrality and help in fostering soil governance. In this regard, the availability of global soil databases paves the way for implementing successful soil information systems. Currently, harmonized world soil database from the Food and Agriculture Organization and SoilGrids from International Soil References and Information Centre serve various global soil data products in a geospatial-ready format for the scientific fraternity. In this study, SoilGrids 2.0 is validated with in situ measurements in the arid region of the Thar Desert. Soil fractions and pH at the top surface (0–5 cm) and subsurface (5–15 cm) were measured through soil sample analysis collected from the study area and compared with the values retrieved from SoilGrids 2.0 for the same location. This comparison shows that the SoilGrids 2.0 has underestimated the sand fragments up to ~28% and overestimated ~14% for silt and clay fragments. Deviation of pH in SoilGrids 2.0 was also observed with the root mean square error of one unit. However, in the comparison of soil texture classes from the field and the one given by SoilGrids 2.0, a systematic shift was found, indicating the robustness of SoilGrids prediction algorithm that can be fine-tuned by incorporating additional soil profiles (from contributing agencies) as the current snapshot of the soil database lacks dense and well-distributed soil profiles in this arid region.
Suggested Citation
Handle:
RePEc:epw:ejgeo0:v:3:y:2022:i:6:id:16356
DOI: 10.24018/ejgeo.2022.3.6.356
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