Record Details

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings

OAR@ICRISAT

View Archive Info
 
 
Field Value
 
Relation http://oar.icrisat.org/10191/
http://dx.doi.org/10.1007/s10661-017-6212-z
10.1007/s10661-017-6212-z
 
Title Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings
 
Creator Xu, Y
Smith, S E
Grunwald, S
Abd-Elrahman, A
Wani, S P
Nair, V D
 
Subject GIS Techniques/Remote Sensing
Soil
Smallholder Agriculture
Soil Science
Digital Agriculture
 
Description Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (Kex) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.
 
Publisher Springer International Publishing
 
Date 2017-09-11
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights —
 
Identifier http://oar.icrisat.org/10191/1/10.1007%252Fs10661-017-6212-z.pdf
Xu, Y and Smith, S E and Grunwald, S and Abd-Elrahman, A and Wani, S P and Nair, V D (2017) Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings. Environmental Monitoring and Assessment, 189 (10). pp. 1-16. ISSN 0167-6369