Crop yield prediction under soil salinity using satellite derived vegetation indices | Land Portal

Resource information

Date of publication: 
December 2016
Resource Language: 
ISBN / Resource ID: 

Monitoring the crop yield is one of the key factors to define agricultural land management strategies. Recent developments in spatial information technologies enabled cost and energy saving in crop yield prediction. The aim of this paper was to predict yield of the three major crops and yield loss under soil salinity effect which is one of the most important limitation in many Mediterranean countries. Crop yields were estimated using vegetation indices and Stepwise Linear Regression (SLR) derived from Landsat (Thematic Mapper and Enhanced Thematic Mapper) TM/ETM satellite images. Additionally, related crop pattern of the area was mapped using multi-temporal Landsat data set using object based classification. Soil salinity was mapped using radial basis function and field measurements with a Root Mean Square Error (RMSE) accuracy of 0.96dSm−1. The predictions were validated using real-time field measurements. Mean percent error (MPE) for wheat, corn and cotton were 7.9%, 8.8% and 6.3% respectively. Crop yield estimates were incorporated with various degrees of soil salinity. Soil salinity ranging between 8 and 10dSm−1 resulted yield loss of 55%, 28%, and 15% in corn, wheat and cotton respectively. The highest soil salinity resistance was observed only at cotton in 18dSm−1 with 55% yield loss.

Authors and Publishers

Author(s), editor(s), contributor(s): 

Satir, Onur
Suha Berberoglu


Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals.

All knowledge begins as uncommon—unrecognized, undervalued, and sometimes unaccepted. But with the right perspective, the uncommon can become the exceptional.

Data provider

Related categories

Related content: 

Share this page