Effects of image pansharpening on soil total nitrogen prediction models in South India

Xu, Y and Smith, S E and Grunwald, S and Abd-Elrahman, A and Wani, S P (2018) Effects of image pansharpening on soil total nitrogen prediction models in South India. Geoderma (TSI), 320. pp. 52-66. ISSN 00167061

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Image fusion is in its infancy in the application of Digital Soil Mapping, and the incorporation of the image pansharpened spectral indices into the soil prediction models has seldom been analyzed. This research performed image pansharpening of Landsat 8, WorldView-2, and Pleiades-1A in a smallholder village called Masuti in South India using three pansharpening techniques: Brovey, Gram-Schmit (GS), and Intensity-Hue-Saturation (IHS) methods. The research analyzed the relationships between multispectral (MS) and pansharpened (PAN) spectral indices and soil total nitrogen (TN), developed the soil TN prediction models using Random Forest methods, and explored the effects of different PAN spectral indices on soil TN prediction models. The results showed the spectral behavior of PAN spectral indices and MS spectral indices were similar. The results also demonstrated that soil TN models based on MS/PAN spectral indices have slightly higher model performance and more detailed characterization of TN spatial pattern compared with soil TN models based on MS spectral indices. Soil TN models based on the GS PAN and MS spectral indices attained slightly higher prediction accuracy compared with those based on other PAN and MS spectral indices. This research advocates the promotion of image pansharpening techniques in digital soil mapping and soil nutrient management research.

Item Type: Article
Divisions: Research Program : Asia
Uncontrolled Keywords: Image pansharpening, Digital soil models, Soil total nitrogen, Remote sensing, Smallholder farm settings, Digital Soil Mapping, Nutrient management, Soil, Karnataka, Bijarpur district, Masuti village
Subjects: Others > Digital Soil Mapping
Others > GIS Techniques/Remote Sensing
Others > Soil Fertility
Others > Semi-Arid Tropics
Others > Soil
Others > South Asia
Others > Smallholder Agriculture
Others > Karnataka
Others > Soil Science
Others > Digital Agriculture
Depositing User: Mr Ramesh K
Date Deposited: 05 Apr 2018 06:21
Last Modified: 05 Apr 2018 06:24
URI: http://oar.icrisat.org/id/eprint/10524
Official URL: http://dx.doi.org/10.1016/j.geoderma.2018.01.017
Acknowledgement: Funding for this project was provided by the grant award No. 1201943 “Development of a Geospatial Soil-Crop Inference Engine for Smallholder Farmers” EAGER National Science Foundation and Research Foundation for Youth Scholars of Beijing Technology and Business University. The soil analysis was performed in the soil laboratory at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Patancheru/Hyderabad, Telangana State, India. We thank Christopher M. Clingensmith at University of Florida, and other ICRISAT staff members and villagers of Kothapally for support with field sampling. We also thank Yiming Xu's PhD committee members Dr. Thomas K. Frazer and Dr. Vimala D. Nair for their guidance and commitment. A matching assistantship for Yiming Xu was provided by School of Natural Resources and Environment, University of Florida, and China Scholarship Council.
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