Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Space-time mapping of soil salinity using probabilistic bayesian maximum entropy
Stochastic Environmental Research and Risk Assessment, Volume 18, No. 4, Year 2004
Notification
URL copied to clipboard!
Description
The mapping of saline soils is the first task before any reclamation effort. Reclamation is based on the knowledge of soil salinity in space and how it evolves with time. Soil salinity is traditionally determined by soil sampling and laboratory analysis. Recently, it became possible to complement these hard data with soft secondary data made available using field sensors like electrode probes. In this study, we had two data sets. The first includes measurements of field salinity (ECa) at 413 locations and 19 time instants. The second, which is a subset of the first (13 to 20 locations), contains, in addition to ECa, salinity determined in the laboratory (EC2.5). Based on a procedure of cross-validation, we compared the prediction performance in the space-time domain of 3 methods: kriging using either only hard data (HK) or hard and mid interval soft data (HMIK), and Bayesian maximum entropy (BME) using probabilistic soft data. We found that BME was less biased, more accurate and giving estimates, which were better correlated with the observed values than the two kriging techniques. In addition, BME allowed one to delineate with better detail saline from non-saline areas. © Springer-Verlag 2004.
Authors & Co-Authors
Douaik, Ahmed
Belgium, Ghent
Universiteit Gent
Morocco, Rabat
Institut National de la Recherche Agronomique, Morocco
van Meirvenne, Marc
Belgium, Ghent
Universiteit Gent
Tóth, Tibor
Hungary, Budapest
Magyar Tudomanyos Akademia
Serre, Marc L.
United States, Chapel Hill
The University of North Carolina at Chapel Hill
Statistics
Citations: 62
Authors: 4
Affiliations: 4
Identifiers
Doi:
10.1007/s00477-004-0177-5