This document outlines a policy-oriented study on remote sensing methods for agricultural drought monitoring. It describes the partners and their qualifications, defines the main objective as formulating concrete drought monitoring methods and intervention levels for key crops, and lays out the process flow and activities. Progress so far includes analysis of soil water status, time series spectral data collection, yield data assessment, and creating masks and extracting arable land from MODIS images. The outputs will be reports on soil water content, remote sensing and GIS tools for monitoring, and integrating tools and levels into monitoring systems. Reflections were provided on capacity building, report comments, and linking with other activities.

1. Policy oriented study on remote sensing
agricultural drought monitoring methods
Activity 5.5.
Prof. János Tamás
2nd IDMP CEE Workshop
Ljubljana, 8 – 9 April 2014

2. Partners & Outputs
Key qualifications of partners
• Hungary (University of Debrecen and GWP HU):
– Applied hydrological remote sensing and GIS;
– Spatial Decision Supporting Systems
• Romania (University of Oradea):
- Geography and Integrated watershed management
• Slovakia (Institute of Hydrology of the Slovak Academy of Sciences):
- Agricultural water management, Soil hydrology

3. Task definition
• The main objective of this case study is to formulate
concrete practical agricultural drought monitoring method
and intervention levels with calibrating for the important
crops and fruits (wheat, corn and apple)

4. 2. RS tools for
vegetation indices
3. Agricultural
drought decision
support parameters
Finalize OUTPUT 1:
An analysis report on
the role of soil and
crop water content
status in
waterbalance within
different agricultural,
landuse and water
management
practices at rain fed
and irrigated systems
for the most important
crops and fruit (wheat,
corn and apple)
Finalize OUTPUT 2:
Toolbox with the concrete
identification of remote
sensing and GIS data
tools for agricultural
drought monitoring and
forecast
Finalize OUTPUT 3:
Report on integration of
RS and GIS tools and
intervention levels into
drought monitoring
system
June 2013-Dec 2013
Sept 2013 – Jun 2014
May 2014 – Jan 2015
2. RS tools for
vegetation
indices
1. Analysis of
green and
brown water
status
3. Agricultural
drought decision
support
parameters
No changes in sceduling of the case study

5. Process flow of RS agricultural drought monitoring
methods
NDVI
Time
Series
Land
use
mask
Calibration
with Yield
statistical
data
Meteorological
Data
Calibration
with Drought
Index
Soil Physical
Data
Calibration
with available
water content
SDSS
Classification
Watch Varning Alert
Plant Specific
Drought Risk
Evaluation

6. What has been done from the 1st IDMP
CEE workshop till now (October 2013 –
April 2014)?
Progress Report
 Analysis of brown water status
 Analysis of green water content
 Spectral time series data sources
 Data integration and processing
 Yield data assessment

7. What has been done from the 1st IDMP CEE workshop till now
(October 2013 – April 2014)? - Brown water status
Progress Report
Problems with characterization of brown water:
1. National soil mapping methods are partly different from World
Reference Base for Soil Resources (WRB).
2. To calculate brown water capacity of watersheds need to select critical
parameter list: physical characteristics of soils, TWC, FC, TAW, WP,
k.
3. A horizon is under continuous periodic cultivation, therefore the actual
porosity is changing continuously in soil. Compaction and sealing are
also problem.

8. MODIS NDVI dataset
Progress Report
What has been done from the 1st IDMP CEE workshop till now
(October 2013 – April 2014)? - Spectral time series

9. Model for creating mask and extraction of arable land of a ROI out of a MODIS image
What has been done from the 1st IDMP CEE workshop till now
(October 2013 – April 2014)? - Data integration and processing
Progress Report
Regions of Interest
(ROI)
Shape of a certain
ROI
Final mask:
Arable lands of a
certain ROI
NDVI of a certain
ROI on arable
lands

10. Model for extraction process of a final ROI mask from MODIS NDVI images
What has been done from the 1st IDMP CEE workshop till now
(October 2013 – April 2014)? - Data integration and processing
Progress Report

11. Reference years for drought
affected and average years
based on yields
• Green: optimal (wet) years
• Red: drought affected years
• Blue: extreme precipitation
What has been done from the 1st IDMP CEE workshop till now
(October 2013 – April 2014)? - Yield data assessment
Progress Report

12. At what stage of the final output(s) are you now? Progress Report
05 05 050505
Wet
year
Dry
year

13. At what stage of the final output(s) are you now? Progress ReportAt what stage of the final output(s) are you now?

14. Concerning Activity list:
1. What does it mean: “to predict area-specific yield forecasts?”
 Means forecast of yields in the case of extreme drought situation at a
certain place
2. The name and type of output 3 are inconsistent. We also suggest to use in
the report the same titles which are used in the Activity list.
 Type of output three is a guideline for national governments and local
stakeholders to integrate RS and GIS tools and intervention levels into
drought monitoring system.
3. How 5.5 case study could lead to short-term policy design?
 Policy makers can prepare for the effects of a severe drought on the food
supply of the society in the impacted regions. It also help proactive
redesigning of regional water governance.
Milestone 1 was an internal report, which focused on technical problems. Policy
orientation will be strongly considered in the final report.
Reflection for PRG report:

15. Capacity building “Training for trainers” meeting in Szarvas,
Hungary
- Consultancy
with Ministry,
Academic
sector,
- Engineers Ass.
- Farmers Ass.
Problem is the
permanent
reorganization
of the water
management
sectors in
Hungary
Reflection for PRG report:

16. Concerning the report (milestone 1)
1. Hydrological drought is as complex as agricultural drought
 We absolutely agree with that statement.
2. It would be interesting to know how this output will be linked with Act. 5.4 and
Act. 1.3.
 The possible contributions of the 5.5. case study to other Acts. Will be
considered in the 2nd phase of the project.
3. Descriptions of brown and green water are also provided without any
reference
 Green and brown water were defined in 11th Alps-Adria Scientific
Workshop, which was a joint conference of the Slovak Academy of
Sciences, Institute of Hydrology and the Hungarian Academy of Sciences,
Committees of Crop Production, Plant Protection, Soil Science and
Agricultural Chemistry. 26-31 March, 2012 Smolenice, Slovakia
Reflection for PRG report:

17. Concerning the 6 specific comments
1. The average annual daily temperature is 100 C; Clear mistyping.
 Cleared and corrected
2. The minimum and maximum annual precipitations between years 1900 and 1950 were 342
mm and 874 mm, respectively.’
 Mistyping corrected , it supposed to be 1950-2000.
3. The yield gains produced by irrigation were statistically significant every year.’ Too strong
statement.
 After consultation with the Romanian partner we cleared this statement.
4. Information have to be mentioned with references only (for example figures 4 and 5).
 These figures were self edited based on yield data.
5. NDVI has Agricultural drought means critical decrease in water content of plants, therefore
the identification of this critical water loss is essential.’ It is for the soil water content.
 Corrected: „plants which strongly correlates the available water content of soil,”
6. In Hungary information is given with reference to biomass and not to the water content. How
was it calculated? (for example fig 4 of the report)
 It was calculated based on international patents and references. We calculated by 15 %
watercontent in yield which is the harvest ripe status of the grain (Farrer et al. 2006,
USDA-ARS 2004)
Reflection for PRG report:

18. Thank you for your attention