relation: http://oar.icrisat.org/10119/
title: Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings
creator: Xu, Y
creator: Smith, S E
creator: Grunwald, S
creator: Abd-Elrahman, A
creator: Wani, S P
subject: Remote Sensing
subject: Soil
subject: Smallholder Agriculture
subject: Digital Agriculture
description: Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers  are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze  the spatial resolution effects of different remote sensing images on soil prediction models in two  smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State),  and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian  kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (Kex) in the  topsoil (0e15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m),  RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as  band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple  images showed relatively strong correlations with soil Kex in two study areas. The research also  suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based  soil prediction models would not necessarily have higher prediction performance than coarse spatial  resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings  need select the appropriate spectral indices and consider different factors such as the spatial resolution,  band width, spectral resolution, temporal frequency, cost, and processing time of different remote  sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to  smallholder farm settings all over the world and help smallholder farmers implement sustainable and  field-specific soil nutrient management scheme.
publisher: Elsevier
date: 2017-09-15
type: Article
type: PeerReviewed
format: application/pdf
language: en
identifier: http://oar.icrisat.org/10119/1/Journal%20of%20Environmental%20Management.pdf
identifier:   Xu, Y and Smith, S E and Grunwald, S and Abd-Elrahman, A and Wani, S P  (2017) Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings.  Journal of Environmental Management, 200.  pp. 423-433.  ISSN 03014797     
relation: http://dx.doi.org/10.1016/j.jenvman.2017.06.017
relation: 10.1016/j.jenvman.2017.06.017