Monitoring drought and its management became easier with the help of remote sensing..several drought monitoring indices can be used to monitor drought condition. this ppt consists of information regarding droughts in relation to agriculture and their monitoring with the help of remotely sense based indices.

1. Professor Jayashankar Telangana State Agricultural University
College of Agriculture, Rajendranagar, Hyderabad
M. VEERENDRA
RAM/18-07
Department of Agronomy
ROLE OF REMOTE SENSING AND GIS
IN
DROUGHT MONITORING AND MANAGEMENT
AGRON 512
Submitted bySubmitted to
Dr. P. Laxminarayana
Professor & Head
Department of Agronomy

2. INTRODUCTION
There was a saying ‘drought follows the plough’ a statement
which was proved true in the present changing climatic scenario.
Agriculture is the immediate victim of drought. In India 68% of net
sown area is drought prone and 50% of drought prone is severe in
nature.
Although the green revolution lead to an intensive increase in
agricultural production, frequent droughts offset the gains from
enormous technical efforts. In the last 10 years of 20th century,
widespread intensive droughts claimed around 50 million tons of
food grains.(FAO 2000).
Drought starts unnoticed and develops cumulatively. By the time
the results are evident, it is too late. So crisis lead response is of
no use, a proactive approach that reduces the impact builds
resilience and can cope up with drought is the need of the hour.
Therefore, understanding drought and monitoring have drawn
attention of hydrologists, meteorologists, and agricultural
scientists.

3. FAO, 2018
2006–2016

4. CRIDA, 2013

5. CRIDA, 2013

6. CRIDA, 2013

7. PRECIS model
CRIDA, 2013

8. PRECIS model
CRIDA, 2013

9. PRECIS model
CRIDA, 2013

10. PRECIS model
CRIDA, 2013

11. DROUGHT DEFINITIONS
A drought is a complex phenomenon that can be defined from
several perspectives
*Drought as a sustained period of time without significant
rainfall. Linsely et al. (1959)
*Drought as the smallest annual value of daily stream flow.
Gumbel (1963)
*Drought as a significant deviation from the normal hydrologic
conditions of an area Palmer (1965)
*FAO defines a drought hazard as “the percentage of years when
crops fail from the lack of moisture.” FAO (1983)
*Drought means a sustained, extended deficiency in
precipitation. WMO (1986)
*The definition of drought change with the user i.e for a farmer,
meteorologist, hydrologist etc…

12. Period No.of droughts
1801-1825 6
1826-1850 3
1851-1875 6
1876-1900 3
1901-1925 10
1926-1950 2
1951-1975 6
1976-2002 5
Table 1. History of droughts in india
Kulsreshta et al., 2003

13. Figure 1. Chronology of drought

15. Meteorology indices
*1) Aridity Anomaly Index (AAI)
*2) Standardized Precipitation Index (SPI)
*3) Aridity Index (AI)
*4) Crop Moisture Index (CMI)
*5) NOAA Drought Index (NDI)
*6) Palmer Drought Severity Index (PDSI)
*7) Standardized Anomaly Index (SAI)
*8) Agricultural Reference Index for Drought (ARID)
*9) Crop-specific Drought Index (CSDI)
*10) Reclamation Drought Index (RDI)

16. Soil moisture indices
*1)Soil Moisture Anomaly (SMA)
*2)Evapotranspiration Deficit Index (ETDI)
*3)Soil Moisture Deficit Index (SMDI)
* 4)Soil Water Storage (SWS)
Hydrology indices
*1)Palmer Hydrological Drought Severity Index (PHDI)
*2)Standardized Reservoir Supply Index (SRSI)
*3)Standardized Stream flow Index (SSFI)
*4)Standardized Water-level Index (SWI)
* 5) Stream flow Drought Index (SDI)

17. Limitations
Although meteorological information from ground stations has
good accuracy and is popular world wide, the distribution of
meteorological stations is insufficient for the spatial information
detection.
The spatial extent of drought can not be properly identified
unless there is a good distribution of meteorological stations
throughout the area. Even then requirement of time and cost of
data preparation, chances of occurring errors may hinder the
procedures of drought mitigation.
In this context, drought monitoring through satellite based
information has been popularly accepted in recent years for its
synoptic view.

18. Role of remote sensing and GIS in drought
studies
Remote sensing is the acquisition of information about an
object or phenomenon without making physical contact
with the object.
Geographic Information System is a system designed to
capture, store, manipulate, analyse, manage and present
spatial or geographic data.
*Remote sensing tools and techniques make it possible to
obtain and distribute continuous information rapidly over
large areas by means of sensors operating in several
spectral bands, mounted on aircraft or satellites.
*A satellite, which orbits the Earth, is able to explore the
whole surface in a few days and repeat the survey of the
same area at regular intervals
*A meteorological station can connect to GIS and keep
receiving meteorological information directly entered into
GIS and then these data will managed and analysed
uniformly by the system database.

19. Figure 2. The process of remote sensing

20. How the Object is Identified by Sensor?
The Basic principle of Remote Sensing is that each object reflect and emit
energy of particular part of EMR in a unique way. Therefore, the signatures
received from different objects is always different. This is called its
Spectral signature. This is the key for interpretation in RS.
Figure 3. Spectrum of reflectance for some components on earth

21. NADAMS
National Agricultural Drought Assessment and Monitoring
System
*In India NADAMS was initiated towards the end of 1986 with
the participation of NRSC as a nodal agency of execution
with the support of IMD. NADAMS was made operational in
1990 and has been providing agricultural drought information
in terms of prevalence , severity and persistence at state ,
district and sub district level.
SNO SATELLITE/SENSOR SPATIAL
RESOLUTION
TEMPORAL
RESOLUTION
1 NOAA-AVHRR ( swath -2700km) 1100 Twice a day
2 IRS 1C/1D WiFS(810 km) 188 5 days
3 IRS P3 WiFS(810km) 188 5 days
4 RESOURCESAT 1 AWiFS(740km) 56 5 days
Table 2. Satellites and Sensors being used for drought monitoring in NADAMS project

22. Figure 4. different sensors used in remote sensing
Sensors
• It refers to the device that record the electromagnetic radiation
reflected from the object .
• The detection of electromagnetic energy can be performed either
photographically or electromagnetically.

23. *1.Normalized Difference Vegetation Index
*2.Vegetation Condition Index
*3.Temperature condition index
*4.Vegetation health index
*5. Enhanced Vegetation Index
*6.Vegetation drought response index
*7.Water requirement satisfaction index
*8.Normalised difference water index
*9.Soil adjusted vegetation index
*10.Evaporative stress index
REMOTE SENSING INDICES

24. *The NDVI is a measure of the greenness or vigor of
vegetation. The basic concept of NDVI is that the healthy
vegetation reflects NIR radiation and absorbs RED radiation.
This becomes reverse in case of unhealthy or stressed
vegetation.
NDVI is computed by the formula as:
NDVI= (NIR-RED) /(NIR+RED)
Figure 5. Absorbance and reflectance of radiation in a healthy and unhealthy plant
NORMALIZED DIFFERENCE VEGETATION INDEX

25. Figure 6. NDVI map covering different continents during 1st week of 2012
NDVI values range from −1to+1
No green vegetation = nearly 0
Highest possible density of vegetation = nearly 1
Areas of barren rock, sand and snow= <0.1
Shrub and grassland = 0.2–0.3
Temperate and tropical rainforests = 0.6-0.8

26. Figure 7. NDVI map of Rajasthan during August 2002(drought year) and
2003(normal year)
Identification of differences in the vegetative health during a drought
year and normal year in Rajasthan of India using NDVI data derived from
NOAA-AVHRR.
Visible differences were observed between western and eastern
parts of Rajasthan. This kind of spatial variability is because of uneven
distribution of rainfall in different parts of the state. The NDVI of 2002
is very less than 2003 due to vegetation stress condition.
Dipanwita et al., 2014

27. Significant correlations have been found between NDVI values and
precipitation data. Therefore NDVI can be used for monitoring drought
in this area.
Bajgiran et al., 2008
Figure 8. Average NDVI vs average three month precipitation (mm) in
north west of Iran and correlation between them

28. LIMITATIONS
The utility of NDVI for studying vegetation and related
issues is constrained by several errors that usually occurs
due to atmospheric noises and other reasons like satellite
orbital drift, sensor degrading etc…
It is often fall short in real time drought monitoring due to
lagged vegetation response to drought.
With NDVI we can not compare drought for different crops
in different regions as some crops are sensitive to moisture
stress than the others.

29. It is an NDVI derived index.
*It uses the NOAA-AVHRR NDVI data ,normalises the
geographical differences and creates a possible
comparision between different regions (it filters out the
contribution of local geographic resources to the spatial
variability of NDVI).
*It can estimate the status of vegetation according to the
best and worst vegetation vigour over a particular period
in different years that gives more accurate results as
compared to NDVI while monitoring the drought at
regional scale.
VEGETATION CONDITION INDEX

30. VCI= (NDVI j – NDVI min)/(NDVI max- NDVI min)
Where, NDVIj = NDVI of date j
..it separates the long term ecological signal from the short
term climate signal.
*It is expressed in % ranging from 1 to 100.
50 to 100 = above normal vegetation
50 to 35 = drought condition
below 35= severe drought condition.

31. *It was found that severe drought condition prevailed during kharif
season of the year 2002 over a large area of Rajasthan. The onset and
extent of drought can be clearly observed from the VCI maps.
*The average VCI values were found to be < 50 in all the districts and
<35 in most of the districts indicating severe drought.
Diapanwita et al., 2014
Figure 9. VCI map for different fortnights of kharif crops of drought(2002) and
normal (2003) years

32. *Doesn’t provide information regarding the factor causing the
stress i.e drought or excess moisture which is a major limitation.
Figure 10. VCI vs yield maps for Sorghum, Pearl millet, Maize
Dipanwita et al., 2014

33. *During the rainy season in general, it is common for
overcast conditions to prevail for up to three weeks. When
conditions last longer than this, the weekly NDVI values
tend to be depressed, giving the false impression of water
stress or drought conditions.
*To remove the effects of contamination in satellite
assessment of vegetation conditions, Kogan (1995, 1997)
suggested the use of a Temperature Condition Index (TCI).
*The TCI is calculated much in the same way as the VCI, but
its formulation is modified to reflect the vegetation’s
response to temperature (i.e. the higher the temperature
the more extreme the drought).
*Slight changes in vegetation health due to thermal stress
could be monitored using the analysis of TCI data.
TEMPERATURE CONDITION INDEX

34. TCI=100( BT max – BT)/(BT max-BT min)
*where BT, BTmax and BTmin are the smoothed weekly
brightness temperature, multi-year maximum and multi-
year minimum, respectively, for each grid cell.
Figure 11.TCI map of India during September month in the years
2009 (drought), 2010(wet), 2013(normal)
Arnab et al., 2014

35. VEGETATION HEALTH INDEX
VHI= 0.5 (TCI) + 0.5(VCI)
It is the combination of TCI and VCI
The vegetation health index (VHI) has been developed using the
VCI and TCI and is found to be more effective compared to other
indices in monitoring vegetative drought (Kogan 1990, 2001; Singh
et al. 2003).

36. Figure 12.Comparision of VHI and SPI maps of Uttar pradesh during a drought year ,
wet year and normal year in August and September months
Arnab et al., 2014

37. Table 3. The correlation coefficient results between RSIDS and SPEI for
different seasons in different subzones of Yellow river basin.
* Significant at 5% ** significant at 1%
By investigating the capability of RSDIs under different spatio–temporal
patterns, the optimal RSDIs in spring, summer, autumn, and winter were
found to be the VHI, TCI, MTVDI, and VCI, respectively, and the average
correlation coefficient between the RSDIs and the SPEI was 0.577
RSDIs should be adopted to monitor drought conditions in the YRB,
which can provide a reasonable scientific basis for relevant departments
to plan and make decisions relating to drought.
Wang et al., 2018

38. * VCI showed higher areal extent than TCI and VHI in January to May
and December. In contrary, drought areal extent of TCI was tend to
high in August to November (dry season). While, drought area extent
of VHI was range between drought areal extent TCI and VCI, caused by
the equal weight of TCI and VCI in VHI calculation.
* TCI proved to be detected drought sensitively in dry months when
high temperature occurred.
*While VCI detected drought more sensitive in wet season as well than
TCI and VHI.
*Meanwhile, VHI provide better comprehension about drought
occurrence. Wang et al., 2018
Figure 13. Drought areal extent of TCI , VCI and VHI during 2015 in East Java

41. *The NADAMS report is useful for the decision makers for
the management of agricultural drought. This report
provides a comprehensive picture of the drought situation
which acts as a complementary information along with
their ground based information.
MONTH ASSESSMENT IMPLICATIONS
June Normal Agricultural situation is normal
July
August
Watch Progress of agricultural situation is slow
Ample scope of recovery
No external intervention is needed
Alert Very slow progress of agricultural situation
Need for intervention
Develop and implement contingency plans to minimize loss
Sept
Oct
Mild. Drought
Mod. Drought
Severe
Crops have suffered stress slightly
Considerable loss in production
Take measures to alleviate
High risk significant reduction in crop yield
Management measures to provide relief
Table 3.Agricultural drought warning and declaration in NADAMS project

42. Limitations for Remote sensing in Indian conditions
*• Small size of plots.
*• Diversity of crops sown in a particular area.
*• Variability of sowing and harvesting dates in different
fields.
*• Inter cropping and mixed cropping practices.
*• Extensive cloud cover during the rainy season.

43. NDVI is the simplest and mostly used index but is sensitive to
background and atmospheric noises and saturates in high
biomass regions.
VCI is an NDVI derived index which gives accurate results at
regional scale, normalises NDVI and allows comparison of
different ecosystems but doesn’t give information whether the
stress is due to drought or excess moisture.
TCI gives the information regarding the stress faced by the
vegetation due to high temperatures. It can distinguish the
stress occurred due to drought or excess moisture.
VHI is the combination of VCI and TCI which provide better
comprehension about drought occurrence.
Conclusions

44. These indices have their own applicability. Examination of
adaptation associated with limitations in drought indices would
be useful for index selection and improvement.
Selection of an index is crucial to monitor the drought, an
index should be chosen for a particular region which shows
good correlation with other indices in that region.
As there was good correlation between ground based and RS
based indices in monitoring the drought situations , RS based
indices can be used effectively for monitoring drought.

45. Arnab, K., Suneet, D. and Dipanwita, D. 2016: Monitoring the vegetation
health over India during contrasting monsoon years using satellite remote sensing
indices. Saudi Society for Geosciences, 9:144.
Bajgiran, P.R., Darvishsefat, A.A., Khalili, A. and Makhdoum, F. 2008: Using
AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran.
Journal of Arid Environments ,72 : 1086–1096.
Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J. and Reed, B.C. 2008: The
Vegetation Drought Response Index (VegDRI): a new integrated approach for
monitoring drought stress in vegetation. GIScience & Remote Sensing, 45:16–46.
Chandrasekar, K., Sesha Sai, M.V.R., Roy, P.S. and Dwevedi, R.S. 2010: Land
Surface Water index (LSWI) response to rainfall and NDVI using the MODIS
vegetation index product. International Journal of Remote Sensing, 31:3987–4005.
Dipanwita, D., Arnab, K., Patel, N.R., Saha, S.K. and Siddiqui, A.R. 2015:
Assessment of agricultural drought in Rajasthan(India) using remote sensing
derived Vegetation Condition Index (VCI) and Standardized Precipitation Index
(SPI). The Egyptian Journal of Remote Sensing and Space Sciences,18: 53–63.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G.
2002: Overview of the radiometric and biophysical performance of the MODIS
vegetation indices. Remote Sensing of Environment, 83(1):195–213.
References