Spatial Prediction of Calcium Carbonate and Clay Content in Soils using Airborne Hyperspectral Data
| العنوان: | Spatial Prediction of Calcium Carbonate and Clay Content in Soils using Airborne Hyperspectral Data |
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| المؤلفون: | T. Ravisankar, G. Sujatha, Tarik Mitran, K. G. Janakirama Suresh, K. Sreenivas |
| المصدر: | Journal of the Indian Society of Remote Sensing. 49:2611-2622 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Topsoil, Spectroradiometer, Soil test, Geography, Planning and Development, Linear regression, Soil water, Earth and Planetary Sciences (miscellaneous), Hyperspectral imaging, Environmental science, Soil science, Regression analysis, Clay minerals |
| الوصف: | Reflectance spectroscopy can provide an alternate approach to traditional method for estimation of a large scale of major soil parameter. In the present study, an airborne high-resolution hyperspectral data Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) was used to spatially predict topsoil calcium carbonate (CaCO3) and clay content in parts of Karnataka, India. A total of 24 locations were selected over agricultural and waste land, and soil samples were collected, and surface reflectance was measured using an ASD Field Spec Pro Spectroradiometer in the laboratory. Continuum Removal (CR) method was used to normalize the reflectance spectra. Continuum Removal Absorption Depth (CRAD) of bands 2205 and 2340 nm was used to predict clay and CaCO3 content in soils through linear regression. The mean clay and CaCO3 content of 35.6% (95%CI 28.07–43.16%) and 4.16% (95%CI 3.61–4.71%) for surface soils were predicted by continuum removal linear regression (CRLR) method with estimation errors of RMSE = 11.0 and 2.19, R2 = 0.51 and 0.58, respectively. The 95% confidence intervals were used to calculate the uncertainty of the prediction which showed 42.3% and 26.4% for clay and CaCO3 prediction, respectively. The uncertainty assessment shows that CRLR approach is not a very promising tool for quantitative spatial prediction of soil clay; however, it can be used fairly for CaCO3 prediction from airborne hyperspectral data. The higher uncertainties in the clay estimates may be due to the nature and types of various clay minerals present in the studied soils. The regression models developed may or may not be utilized for other regions with similar variability. |
| تدمد: | 0974-3006 0255-660X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::00b7692e4997920ec0aee68f8b288fc7 https://doi.org/10.1007/s12524-021-01415-5 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........00b7692e4997920ec0aee68f8b288fc7 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 09743006 0255660X |
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