A virtual hands-on training program on developing geospatial maps for supporting insurance products using Google Earth Engine and semi-automatic techniques was conducted for participants in Pakistan as part of the project “Strengthening Post-COVID-19 Food Security and Locust Attacks”. The nine participants were from the PARC Agrotech company (PATCO) technical team and crop reporting service teams from Punjab and Sindh in Pakistan. They were introduced to remote sensing and its applications in agriculture. Hands-on training using Google Earth Engine (GEE), Image Processing Software – ERDAS 2015 and various automatic classification techniques was provided along with several applications for using these modern tools.

1. Newsletter
Happenings
In-house version 6 Aug 2021, No.1918
Avirtual hands-on training program on developing
geospatial maps for supporting insurance products
using Google Earth Engine and semi-automatic
techniques was conducted for participants in Pakistan as
part of the project “Strengthening Post-COVID-19 Food
Security and Locust Attacks”.
The nine participants were from the PARC Agrotech
company (PATCO) technical team and crop reporting
service teams from Punjab and Sindh in Pakistan. They
were introduced to remote sensing and its applications
in agriculture. Hands-on training using Google Earth
Engine (GEE), Image Processing Software - ERDAS 2015
and various automatic classification techniques was
provided along with several applications for using these
modern tools.
With the demand for crop insurance increasing in
developing countries, it has become vital for stakeholders/
policy makers to gain access to spatial information on crop
type, crop health and stress at field level.
South-South collaboration
Virtual training in the use of remote sensing for the
agriculture sector in Pakistan
Screenshot of the virtual training for researchers in Pakistan.
Crop insurance is an area that extensively uses crop type
maps at various levels. Crop type and crop health maps
generated using remote sensing technology are used to
optimize the sampling of crop cutting experiment (CCE)
locations. CCEs are the basis for crop yield estimation to
determine crop loss. Crop acreage estimation at village/
sub-district level helps in making informed decisions on
claims of failed sowing and helps prevent ‘false insuring’
of more land than what is planted under a particular
crop. The satellite-derived crop area statistics provide
the basis for assessing national statistics and augment
the decision-making and planning process by providing
accurate information of even inaccessible areas. In
addition to national-level assessments, the crop area
and extent maps obtained from this classification
This work contributes to UN
Sustainable Development Goal