<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Y</mods:namePart><mods:namePart type="family">Xu</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">S E</mods:namePart><mods:namePart type="family">Smith</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">S</mods:namePart><mods:namePart type="family">Grunwald</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">A</mods:namePart><mods:namePart type="family">Abd-Elrahman</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">S P</mods:namePart><mods:namePart type="family">Wani</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers&#13;
are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze&#13;
the spatial resolution effects of different remote sensing images on soil prediction models in two&#13;
smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State),&#13;
and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian&#13;
kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (Kex) in the&#13;
topsoil (0e15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m),&#13;
RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as&#13;
band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple&#13;
images showed relatively strong correlations with soil Kex in two study areas. The research also&#13;
suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based&#13;
soil prediction models would not necessarily have higher prediction performance than coarse spatial&#13;
resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings&#13;
need select the appropriate spectral indices and consider different factors such as the spatial resolution,&#13;
band width, spectral resolution, temporal frequency, cost, and processing time of different remote&#13;
sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to&#13;
smallholder farm settings all over the world and help smallholder farmers implement sustainable and&#13;
field-specific soil nutrient management scheme.</mods:abstract><mods:classification authority="lcc">Remote Sensing</mods:classification><mods:classification authority="lcc">Soil</mods:classification><mods:classification authority="lcc">Smallholder Agriculture</mods:classification><mods:classification authority="lcc">Digital Agriculture</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2017-09-15</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Elsevier</mods:publisher></mods:originInfo><mods:genre>Article</mods:genre></mods:mods>