eprintid: 10191
rev_number: 17
eprint_status: archive
userid: 1305
dir: disk0/00/01/01/91
datestamp: 2017-09-25 09:42:26
lastmod: 2017-10-30 06:45:15
status_changed: 2017-09-25 09:42:26
type: article
metadata_visibility: show
contact_email: Library-ICRISAT@CGIAR.ORG
creators_name: Xu, Y
creators_name: Smith, S E
creators_name: Grunwald, S
creators_name: Abd-Elrahman, A
creators_name: Wani, S P
creators_name: Nair, V D
icrisatcreators_name: Wani, S P
affiliation: Department of Environmental Science and EngineeringBeijing Technology and Business University (Beijing)
affiliation: School of Natural Resource and Environment, University of Florida (Gainesville)
affiliation: School of Forest Resources and Conservation – Geomatics Program, University of Florida (Gainesville)
affiliation: Pedometrics, Landscape Analysis and GIS Laboratory, Soil and Water Sciences Department, University of Florida (Gainesville)
affiliation: Gulf Coast REC/School of Forest Resources and Conservation – Geomatics Program, University of Florida (Plant City)
affiliation: ICRISAT (Patancheru)
affiliation: Soil and Water Sciences Department, University of Florida (Gainesville)
country: China
country: USA
country: India
title: Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings
ispublished: pub
subjects: S100
subjects: S40006
subjects: SM1
subjects: s2.11
subjects: s72
divisions: CRPS2
full_text_status: restricted
keywords: Spatial downscaling, Soil nutrients, Digital soil mapping, Generalized additive models, Remote sensing, Geographic information system, Smallholder farms, Digital soil mapping, Kothapally village, GIS, Soil prediction models, Digital soil mapping information, Masuti village
note: This work is supported by the grant award
no. 1201943 BDevelopment 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,
India. We thank Christopher M. Clingensmith at the
University of Florida, and ICRISAT staff members and villagers
of Kothapally and Masuti for the support with field sampling. We
also thank Yiming Xu’s PhD committee member Dr. Thomas K.
Frazer for his commitment and guidance.A matching assistantship
for Yiming Xu was provided by the School of Natural Resources
and Environment, University of Florida, and China Scholarship
Council.
abstract: 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.
date: 2017-09-11
date_type: published
publication: Environmental Monitoring and Assessment
volume: 189
number: 10
publisher: Springer International Publishing
pagerange: 1-16
id_number: 10.1007/s10661-017-6212-z
refereed: TRUE
issn: 0167-6369
official_url: http://dx.doi.org/10.1007/s10661-017-6212-z
related_url_url: https://scholar.google.co.in/scholar?hl=en&as_sdt=0%2C5&q=Spatial+downscaling+of+soil+prediction+models+based+on+weighted+generalized+additive+models+in+smallholder+farm+settings&btnG=
related_url_type: pub
citation:   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     
document_url: http://oar.icrisat.org/10191/1/10.1007%252Fs10661-017-6212-z.pdf