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Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings

OAR@ICRISAT

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Relation http://oar.icrisat.org/10119/
http://dx.doi.org/10.1016/j.jenvman.2017.06.017
10.1016/j.jenvman.2017.06.017
 
Title Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings
 
Creator Xu, Y
Smith, S E
Grunwald, S
Abd-Elrahman, A
Wani, S P
 
Subject Remote Sensing
Soil
Smallholder Agriculture
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
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights —
 
Identifier http://oar.icrisat.org/10119/1/Journal%20of%20Environmental%20Management.pdf
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