Third IDMP CEE workshop: Policy oriented study on remote sensing agricultural drought monitoring systems by János Tamás

1. Policy oriented study on remote sensing
agricultural drought monitoring methods
Activity 5.5.
Prof. János Tamás
3rd IDMP CEE Workshop
Budapest, 2 & 4 October 2014

2. 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
Objective :
formulate concrete practical agricultural drought monitoring method and
intervention levels with calibrating for the important crops and fruits (wheat, corn
and apple)
Generals about Activity 5.5.

3. At what stage of the final output(s) are you at the moment?
2. RS tools for
vegetation indices
3. Agricultural
drought decision
support parameters
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)
OUTPUT 2: Toolbox with
the concrete identification
of remote sensing and
GIS data tools for
agricultural drought
monitoring and forecast
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

4. What has been done since the 2nd IDMP CEE
workshop till now (April 2014 – October 2014)?
 Data acquisition and processing
 Identification and calibration of drought risk level
 Drought risk evaluation and mapping

5. Objectives of Output 2
• Allow the conversion of different purpose drought indices,
such as meteorological, agricultural and hydrological ones,
and more water-saving agricultural land use alternatives
• To develop a process, which can provide information for
estimating relevant drought indexes and drought related
agricultural yield losses more effectively from remote sensed
spectral data.
• New calculation method, which provides early information on
physical implementation of drought risk levels

6. five major steps were done in order to promote the MODIS NDVI
calibration:
1. Reprojection of MODIS satellite TSA data and masking region of
interest
2. Extraction of MODIS NDVI time series by masks
3. Normalization of extracted NDVI data matrix and yield data
4. Clustering and converting
5. Calculate actual or estimated yield loss
Data acquisition and processing

7. Data acquisition and processing
a certain crop-county mask
MODIS NDVI images of a certain year
extraction process
extracted MODIS NDVI image for a certain crop in arable lands
ArcGIS model for extraction process of a certain crop-county mask
from MODIS NDVI images

8. 6 Y LONG TIME SERIES of WHEAT NDVI
DROUGHT IMPACT
Modis Terra/Aqua
Ground res. From 250 m
36 band, Cycle: 1 d

9. Identification and calibration of drought risk level
MODIS NDVI time series dataset, yield data is also available from 2000 – 2012.
Geographical position
Time
Time voxel
N-dimensional datacube

10. Identification and calibration of drought risk level
IWA 13th International Specialised Conference on Watershed and River Basin Management - San Francisco, CA, USA , 9-12 Sept 2014. 11
05 05 050505
Dry
year
Wet
year
Modis NDVI values for arable land in Békés County, Hungary (June –
September)

11. Identification and calibration of drought risk level
IWA 13th International Specialised Conference on Watershed and River Basin Management - San Francisco, CA, USA , 9-12 Sept 2014. 12
Normalized yield changes of maize and wheat (2000-2012)
(Source: KSH and INSSE)

12. Identification and calibration of drought risk level
IWA 13th International Specialised Conference on Watershed and River Basin Management - San Francisco, CA, USA , 9-12 Sept 2014. 13
Yield changes of maize and wheat (2000-
2012)(Source: KSH and INSSE)
Green: optimal
(wet) years

13. Identification and calibration of drought risk level
IWA 13th International Specialised Conference on Watershed and River Basin Management - San Francisco, CA, USA , 9-12 Sept 2014. 14
Yield changes of maize and wheat (2000-
2012)(Source: KSH and INSSE)
Red: drought
affected
years

14. Identification and calibration of drought risk level
IWA 13th International Specialised Conference on Watershed and River Basin Management - San Francisco, CA, USA , 9-12 Sept 2014. 15
Yield changes of maize and wheat (2000-2012)
(Source: KSH and INSSE)
Blue: extreme
precipitation

15. Identification and calibration of drought risk level
IWA 13th International Specialised Conference on Watershed and River Basin Management - San Francisco, CA, USA , 9-12 Sept 2014. 16
Yield changes of maize and wheat (2000-2012)
(Source: KSH and INSSE)
Green: optimal
(wet) years
Red: drought
affected
years
Blue: extreme
precipitation

16. Reference spectral curves were generated in order to determine the Watch,
Early warning, Warning, Alert and Catastrophe levels of NDVI
Identification and calibration of drought risk level
Watch: When plant water stress is observed in sensitive phenological phases
Early Warning: When relevant plant water stress is observed. The available soil moisture is
close to critical, and it is suggested for farmers to start preparation of intervention.
Predicted potential yield loss is up to 10%.
Warning: When plant stress translates into significant biomass damage, and there is time to
start the intervention actions. Potential yield loss is up to 20%.
Alert: When farmers expect irreversible vegetation damage with real negative profit, and
they have to consider to give up additional cultivation actions in crop production in that actual
vegetation period. Potential yield loss is up to 30%.
Catastrophe: When serious damages and profit loss mitigation is necessary. Potential yield
loss is up to 40%.

17. Reference spectral curves
Drought risk and signalling NDVI levels for maize and wheat

18. 2003 (drought affected year) 2008 (average year)
Risk levels
Tisza
catchment
(T.c.)
Hungarian
part of T.c.
Jász-
Nagykun-
Szolnok
Hajdú-
Bihar
Tisza
catchment
(T.c.)
Hungarian
part of T.c.
Jász-
Nagykun-
Szolnok
Hajdú-
Bihar
Wheat (area %)
Catastrophe
38.44 45.38 59.08 34.80 11.99 13.42 15.73 4.77
Alert 8.25 8.27 7.84 7.70 5.05 5.23 5.40 3.30
Warning 8.29 7.96 6.99 8.03 0.45 4.41 4.62 3.04
Early
Warning 10.59 9.75 7.50 10.69 8.30 8.09 8.75 6.13
Watch 7.18 6.42 4.51 7.82 10.14 9.69 10.57 9.06
No yield
loss 27.25 22.22 14.07 30.96 64.07 59.15 54.92 73.70
Maize (area %)
Catastrophe
51.77 51.86 66.06 20.92 24.54 19.85 21.24 10.74
Alert 2.94 8.06 2.11 1.80 2.78 1.90 2.15 1.27
Warning 8.93 8.11 6.62 7.03 9.92 6.87 6.92 4.97
Early
Warning 8.93 7.83 6.46 8.92 11.90 8.83 9.73 6.77
Watch 8.38 2.51 5.99 10.18 12.46 10.61 11.52 9.54
No yield
loss 19.05 21.63 12.76 51.16 38.39 51.94 48.44 66.70
Percentage of affected areas with different drought risks
for wheat and maize (100% is the total area of investigated region)
MAIZE
significant correlation were
found between normalized
NDVI values and maize yield
from the middle of June, to the
end of August, including the
most drought sensitive
blooming period (July)
WHEAT
From May to mid June is
found to be reliable for yield
prediction and forecasting .
Wheat is more drought
tolerable than maize.

19. Drought risk evaluation and mapping
2008

20. Our plans for the final period (till 31st of January 2015.):
Yield - Profit estimation (2008 normal year/2012 drought year
0
100
200
300
400
500
600
Dry/wet cereal prise %
Maize (T) price index (dry/wet year) Maize(T) price index (dry/wet year)
Catastrophe: When serious damages and
profit loss mitigation is necessary.
Potential yield loss is up to 40% but prise up to 300%
Market can not compensate yield loss,
but give extra large profit on irrigated areas
Source: Stock market price, AKI
Wheat

21. • Agricultural drought characteristics were identified
• Monitoring and drought index conversion method was
elaborated based on remote sensing data of Tisza
watershed
• Decision support system will be developed assisting
farmers in reducing drought risks
Summary

22. • Activity 2.1: Guideline for Drought Management Plan - MODIS NDVI based
agricultural drought mapping method can be one of the support guideline n
preparation of the Drought maps.
• Activity 5.1: Experimental field research on increasing of soil-water
holding capacity in agriculture and Activity 5.6: Upgrading agricultural
drought monitoring and forecasting: the case of Ukraine and Moldova -
Soil water capacity mapping, described in this Output can support the
identification of soil water holding capacity in regional scale.
• Activity 5.4: Drought Risk Management Scheme: a decision support
system - Yield calibrated drought risk levels can increase the feasibility of
other recently available of decision support system by the visualization and
communication the probability of occurrence of different phases of droughts.
• Activity 6.2: Capacity building trainings - Participation on capacity building
trainings with interpreting how to implement NDVI based yield calibrated
imagery in drought mapping and forecast.
• Activity 7.1: Development of the Compendium of Good Practices - Review
of remote sensing in agricultural drought monitoring and forecast for decision
support system.
Possible links for other activities

23. • Why fAPAR is not used?
– NDVI is worldwide available for free (http://earthexplorer.usgs.gov/) and
the most studied index on the field of vegetation analysis
• Area specific yield forecast?
– Forecasting of yield(loss) of a ROI (region of interest) sites with local
calibration data. Risk maps shows, how much the yieldloss (t/ha) could
be in the harvest period, if current conditions remains.
• Is the magnitude of potential yield losses applicable to other CEE countries?
– Yes, since NDVI is strongly correlates to biomass, and biomass with
with yield; thus low NDVI means low biomass, resulting low yield.
• CDI and threshold methods are missing
– Accepted, amended
• Two references without figures
– cleared
Referring to PRG comments

24. Thank you for your attention