Identification of Arid and Drought Prone areas from Remote Sensing based Land
Surface Parameters – A study from a Tropical Region.
C.V.Srinias1, K.P.R.Vittal Murty2 and Y.V.N.Krishna Murty3
1. Radiological Safety Division, Radiological Safety & Environment Group, Indira
Gandhi Centre for Atomic Research, Kalpakkam, India
2. Department of Meteorology & Oceanography, Andhra University, Visakhapatnam
India.
3. Regional Centres, National Remote Sensing Centre (NRSC), Hyderabad, India
Email of corresponding author: cvsri@igcar.gov.in
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Abstract
In this study the Land Surface Parameters consisting Normalized Difference Vegetation Index
(NDVI), surface temperature, albedo, vegetation growing period and dryness quotient were
estimated using the NOAA-AVHRR data from a tropical region in central Maharasthra, India to
identify the arid and drought regions. The climatic conditions in the drought prone districts were
analyzed from the rainfall, water balance, growing period parameters determined from long term
mean meteorological data in the study region. A comparative analysis of remote sensing based
land surface paramerers with the climatic parameters revealed that the drought prone areas were
associated with typic arid to dry semi-arid climate, very low NDVI of 0.15 to 0.30, low span of
growing season of 60 to 120 days, a high surface albedo of 16.7 to 23.5 per cent, dryness
quotient 2.2 to 4.7 and high land surface temperature of 300 K in contrast to the high rainfall
zones in the state. The study revealed that the remote sensing based parameters were highly
useful in the detection of arid and drought prone regions and can be operationally used for the
purpose with a prior long term analysis of the data. The study also indicated the influence of the
the global ElNino / Southern Oscillation phenomena for the aridity and drought events in the
region apart from regional parameters.
[Keywords: Aridity, Droughts, NDVI, albedo, surface temperature, growing season.]
1. Introduction
Arid and semi-arid zones are important components of earth’s environment. They are fragile
ecosystems, where vegetation is absent or scarse due to insufficient moisture. They are
characterized with frequent droughts where moisture is a major limiting factor. Low rainfall,
sparse vegetation and high surface temperature are the prominent characteristics of an arid/ semiarid region. Aridity and droughts are possibly caused by positive feedback mechanisms
involving land surface factors (Charney, 1975). Increase in surface albedo due to altered land
use/ land cover or over grazing increases radiative losses over an area enhancing the negative net
radiation balance. This may affect the local Hadley circulation leading to reduction of rainfall
causing further aridity. Study of arid zones and drought prone areas is essential from scientific
as well as social and economic points of view. Drought can be defined according to
meteorological, hydrological and agricultural criteria. Aridity and drought events are generally
associated with anomalous weather patterns, which are reflected in poor quantities and
distribution of rainfall over an area. The meteorological causes of drought vary according to the
climate. In tropical regions droughts are associated with changes in summer monsoonal
circulation causing a delay or failure of the monsoon system leading to break monsoon
conditions and prolonged dry phase. In India, the southwest monsoon system describes the rainy
season, failure of which leads to aridity and droughts. Forecasting of the droughts in India is
linked with the forecasting of monsoon which is influenced by several global and regional
factors such as the El Nino and Southern Oscillation (ENSO) events (Ropelewski and Halpert,
1986, Nicholls, 1993). The affected areas are the arid and semi-arid regions where the annual
rainfall is not only less but its coefficient of variation is also very high. Droughts in these areas
are associated with the absence of significant rainfall for a longer period which is sufficient
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enough to cause moisture deficits in the soil due to evapotranspiration and decrease in stream
flow, disrupting the normal biological and human activities (McNab and Karl, 1991).
Several methods have been proposed for the analysis and identification of arid and
drought conditions. The conventional methods are based on analysis of rainfall, radiation,
temperature (Koppen, 1936), water balance / hydrological approach involving comparison of
mean annual precipitation with mean annual potential evapotranspiration and computation of
climatic indices (Thorthwaite, 1948; Palmer, 1965; Subramanian et al., 1965; Subrahmanyam,
1983; Rao et al., 1972; George, 1972; Appa Rao et al., 1979; Krishnan, 1988), and energy
balance approach involving comparison of mean annual net radiation with the heat flux required
to evaporate mean annual precipitation (Budyko, 1958; Lettau, 1969). Arid and semi-arid regions
are characterized by low mean annual precipitation of about <250 mm and <800 mm, high
surface temperature (Koppen, 1936) and moisture index (Im) <=-61 and -60 to -21 respectively
(Thornthwaite, 1948).
Conventional methods of climate studies suffer in delineation of spatial extent of aridity/
droughts, because they are based on point observations and analysis of discrete meteorological
data. More so it depends on the network of stations for collection of data which is often sparse.
Application of Remote sensing techniques in the delineation of vegetation and water on the
earth's surface and in determination of weather parameters in atmosphere is unique. Generally
the observed changes in vegetation lag in time relative to the conditions described by
meteorological indices (e.g., crop moisture index, Palmer drought index) based on observations
of rainfall and temperature. Droughts are associated with a decline in biological productivity,
increased surface albedo and increased surface temperatures. Remote Sensing techniques have
been widely employed for the delineation of vegetation covers. The deficient rainfall of a region
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would be reflected in the status of crops / vegetation and the surface effects as observed from the
vegetation index, surface temperature and albedo. These parameters, singly or combined also
provide vital information about the arid conditions and drought. Since remote sensing based
methods analyze the data over an extensive area they are more useful compared to classical
approaches. Vegetation is the only component known to strongly absorb visible light, and highly
reflect and transmit in the near infrared region, characteristics that help in monitoring
photosynthetically active vegetation. The Normalized Difference Vegetation Index (NDVI) is a
transformation of reflected radiation in the visible (0.58-0.68 m) and near IR (0.725-1.10 m)
bands of NOAA AVHRR data and is a function of green leaf area and biomass. Numerous
studies showed that NDVI was highly useful in monitoring vegetation and drought (Justice et al ,
1985; Kogan, 1995; Jeyseelan and Thiruvengadachari, 1986; Nicholson and Farrar, 1994). In
arid and semi-arid regions, the incidence of droughts can be monitored using NDVI which
correlates well with percent vegetation cover, biomass, and biological productivity (Nicholson,
1994) and hence can be used to identify vegetation stress zones. National Agricultural Drought
Assessment and Montioring System (NADMS) was initiated in India to monitor the drought
conditions periodically based on AVHRR NDVI and supplemented by the ground based rainfall
data (Jeyaseelan and Thiruvengadachari, 1986). Land surfaces are typically associated with
spatial homogeneity of surface cover. The seasonality characteristics of vegetation canopy can be
derived from vegetation indices. Several studies reveal the association of soil moisture and
vegetation activity (e.g., Gutman, 1990). NDVI is an integrated function of photosynthesis, leaf
area and evapotranspiration (Running and Ramakrishna, 1988). It has been found that NDVI of
active vegetation lies typically between 0.1 to 0.6, values at higher end indicating increased
photosynthetic activity and greater canopy density (Tarpley et al., 1984). Development of
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vegetation during the growing season can be monitored by detecting the changes in vegetation
indices. Critical thresholds of NDVI can be identified to distinguish vegetated land from nonvegetated land in the non-growing season (Loveland et al., 1991). Thus the land surface
parameters (vegetation index, surface temperature, albedo, growing period etc) derived from
remote sensing data can be used to detect the land surface effects and as potential parameters for
the identification of arid and drought regions in monsoon climatic environment.
Maharashtra state, in tropical India, experiences semi-arid climate over a major area.
Droughts are common phenomena in various parts of this state. The aridity and the droughts in
central Maharashtra are attributed to the rain-shadow effect of the long chain of mountain ranges
called Western Ghats (also called Sahyadri ranges), which run along the west coast of India.
Drought prone areas in Maharasthra were delineated following a soil climatic based approach
(Challa and Wadodkar, 1996) and were classified into slight, moderate and highly drought prone
considering the water retention characteristics of soils, rainfall distribution and its variability
(Anonymous, 1993). In the present study an attempt was made to examine the climatic
conditions in some of the drought prone districts of central Maharashtra using the remote sensing
derived land surface parameters of vegetation index, albedo, land surface temperature and crop
growing season derived from NOAA AVHRR environmental satellite data. The above
parameters were analysed in relation to the rainfall, water balance components, climatic indices
determined from conventional methods using climate / meteorological data. The intent of this
study was to examine how well the satellite derived land surface parameters correlate with the
climatic conditions determined from conventional data and to study how such parameters can be
used for identification of arid and drought prone regions in a tropical region.
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2. Description of Study Region
The study area, Maharashtra lies between 15 44’ and 21 40’ latitudes and 73 15’ and
80 33’ E longitudes and forms a major part of Indian peninsula with an area of about 304391 sq
km (Figure 1). It has diverse physiographic, soils, vegetation and climatic conditions. The
rainfall received during the south west monsoon from June to September varies from 450 mm in
rain shadow areas to 6000-7000 mm in Western Ghats (Raman, 1974) (Figure 1a). It increases
from 700 mm in central parts to 1500 mm in eastern parts. The state has four major climatic
zones (Figure 1b), i.e., Sub-humid to Semi-arid in the Eastern Maharashtra, Semi-arid in the
Central Maharashtra and Perhumid along the Konkan coast plains, Humid to Perhumid transition
zone along the Western Ghats (IMD, 1986) which is a long chain of highlands running along the
western edge of the Indian subcontinent. The land use of the state comprises forests, croplands,
pastures and scrub lands. The forests occupy about 17.3% of the total geographical area (TGA),
and are distributed in the western, northern and eastern parts associated with relatively high
rainfall. About 58.1 per cent of the land is cropland and the rest is either covered with forest or
scrub vegetation and pastures (Challa et al ., 1885).
3. Data and Methods
Analysis of climatic data was carried out for nine frequently drought prone areas in semiarid central Maharashtra covering the districts of Dule, Jalgeon, Nasik, Pune, Satara, Sangli,
Sholapur, Ahmednagar and Beed. Mean monthly rainfall of over 30 years (India meteorological
Department, 1973), mean weekly rainfall data of the years from 1946 to 1980 and
evapotranspiration of various locations in the study area (Rao et al., 1971) are used in the water
balance analysis (Thornthwaite and Mather, 1955; Subrahmanyam, 1983). The water balance
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equation (1) was solved in monthly and weekly time steps to evaluate the water deficit (WD) or
water surplus (WS) components. The water balance is given by
P  ET  S  R  0
(1)
where P is the rainfall, ET is the evapotranspiration, S is the change in the soil moisture due to
depletion by evapotranspiration or infiltration of water by precipitation and R is the runoff. The
release of soil moisture ‘S’ is an exponential function.
S  AWCe  ( P  ET ) / AW C
(2)
AWC is the storage capacity of the soil moisture, (P-ET) are accumulated values of (P-ET)<0
up to the month or week considered, which is always negative. The actual evapotranspiration
(AET) is estimated as S+P where S is positive and ET when S is negative. The water deficit
is treated as ET-AET. The water surplus is treated as (P-ET)-S which is the water available
above the storage capacity of soil that percolates to the water table. The climatological indices
viz., humidity index (Ih), ariditiy index (Ia) and the moisture index (Im) on annual basis were
computed as
Ih 
WS
x100
ET
(3)
Ia 
WD
x100
ET
(4)
Im  Ih  Ia
(5)
The data on water holding capacity of the soils in the study area were obtained from the Soil
Resource Inventory of Maharashtra (Challa et al., 1995). Analysis of water balance was done
using the mean climatic data as well as with the yearly data. The dry years were identified based
on the annual aridity index, its mean and departure from standard deviation (Subrahmanyam,
1983). Vegetation Growing period (GP) was also considered as an indicator of the prevailing
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climatic conditions as the vegetation would reflect the availability of soil moisture, the
antecedent rainfall and temperature regimes (Mandal et al., 1996). The growing period was
computed from the mean meteorological data using the FAO method (Higgins and Kassam,
1981; FAO, 1983) based on comparison of precipitation (P) and potential evapotranspiration
(PE) taking into consideration of the soil water storage. It was estimated as the sum of climatic
moist period (CMP) and Soil moisture supported moist period (SMP) from the mean climatic
data. The climatic moist period was calculated as the period during which precipitation was
greater than or equal to 0.5 PE called vegetation moist phase plus a period in which precipitation
was greater than evapotranspiration called the humid phase. The CMP was calculated for 240
stations in the state using data on mean monthly rainfall data and potential evapotranspiraton
obtained from IMD scientific reports (IMD, 1973; Rao et al., 1971). The spatial analysis of point
data for various parameters was made using SPANS (Spatial Analysis System) Geographical
Information System (GIS) (PCI, 1998). Weather stations with their location coordinates
(longitude, latitude) were digitized and attributed with the CMP values which were then
interpolated using spherical Krigging in GIS.
The dryness factor (Lettau, 1969) was calculated according to the relation
D  Q * /( LxP)
(6)
where Q* is the net radiation, L is the latent heat of vaporization of water and P is the
precipitation. The net radiation term in (6) was evaluated from the procedure outlined by
(Budyko, 1958)
Q*  ( R  q)(1   )  ( LWu  LWd )  H  LE  G
(7)
where LWu and LWd are upward and downward longwave emissions by the earth-atmosphere
system, ‘R’ and ‘q’ are the direct and diffuse solar radiation received at the earth’s surface, ‘’ is
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the albedo of the land surface. The net radiation Q* is estimated from sensible heat flux (H),
latent heat flux (LE) and Soil Heat flux (H) terms from the surface energy balance procedure
(SEB) (Tarpley, 1994). Surface meteorological data, albedo, surface temperature and roughness
coefficient derived from NDVI were used to evaluate the net shortwave radiation, net longwave
radiation and soil heat flux components of the SEB.
The land surface parameters NDVI, albedo and surface temperature (Ts) and vegetation
growing period were derived from NOAA-AVHRR 1 km Global land 10-day composites
(USGS, 1993) for the visible, near infra-red and thermal infrared bands. This is a preprocessed
data for cloud screening, atmospheric corrections and is available as composite over 10 days
periods for perceivable changes in biophysical quantifies. The AVHRR imagery were
georeferenced and processed for the estimation of various land surface parameters using the
EASI/PACE image processing software (PCI, 1988). The vegetation index NDVI was quantified
using a normalized difference of the atmospheric corrected reflectance R1 and R2 of the visible
and infrared bands of AVHRR. NDVI was estimated at 10 day intervals at a spatial resolution of
1km. The albedo () was estimated from the spectral radiance (Li) summed over reflective bands
(visible, near IR), exo-atmosphere solar irradiance of spectral bands (Ei), solar zenith angle ()
and distance correction factor (D) for instantaneous sun-earth distance using the procedure
outlined by Robinove,(1981) and Saunders (1990). Ts was derived using an empirical
relationship between the emissivity and vegetation cover (Van de Griend and Owe, 1993). The
roughness coefficient and turbulent transfer coefficient were determined from the NDVI and
surface meteorological data (Srinivas et al., 2001a) using aerodynamic turbulent transfer theory.
The GP was also estimated from NDVI time series (Loveland 1981; Running et al, 1995) as a
period of greenness in which NDVI is above a threshold value of 0.1 to distinguish vegetation
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from non-vegetated land in the non-growing season. The NDVI time series was compared with
the pattern of growing period at each district to find their relative variation and correlation trends.
Comparative analysis was made between GP estimated using water balance principle and that
obtained from NDVI to determine how the GP obtained from NDVI time series correlates with
that obtained from climatic data. The analysis of remote sensing data was selectively made for
the years 1992 and 1995 which have different rainfall characteristics. The year 1995 was a La
Nina year with above normal rainfall while 1992 has relatively low rainfall compared to 1995.
Interpretation of remote sensing derived land surface parameters over these years with
contrasting monsoon rainfall characteristics would enable to assess the utility of remote sensing
data for climate studies in the study region.
4. Results
As per the objectives of the study the climate of the drought prone region was first examined by
making long term rainfall data analysis, water balance computation and by vegetation growing
period analysis. Subsequently the remote sensing based parameters of vegetation index, NDVI
based growing period, albedo, surface temperature, and dryness quotient were analysed to
examine their association with the climatic parameters.
4.1 Rainfall in central Maharashtra and ENSO events
The drought years and the degree of drought identified from the analysis of rainfall are
presented in Table. 1. The annual rainfall fluctuations of the region were expressed as percent
standard departure from the long-term climatic mean (Figure 2a). Positive and negative
departures indicate excess and deficit rainfall conditions respectively. The rainfall followed a
cycle of decreasing and increasing phases with a period ranging from 4 to 7 years. Rainfall
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averaged across the drought prone areas in Maharashtra was in association with El Nino events
for some years. Most of these years were identified to be El Nino years (Quinn et.al., 1987;
Rasmusson and Carpenter,1983; Khan,1994) and significant departures of rainfall below normal
are observed (Table 1). Mean annual rainfall, aridity index in the study region were plotted with
the Southern Oscillation Index (SOI) to examine their relative variation. Rainfall had similar
trend as hat of SOI with alternating increasing and decreasing magnitudes but with slight lead in
time. It was noted that the rainfall followed the same periodicity as with SOI, except for few
deviations, thus indicating the influence of the El Nino phenomena on regional rainfall (Figures
2b). The aridity index determined from climatic water balance procedure and averaged for these
areas had an opposite trend with SOI (Figure 2c) i.e, higher aridity index corresponded with
lower SOI and vice versa. Years of extreme drought coincided with years when the Southern
Oscillation Index (SOI) was strongly to moderately negative. Conversely major floods occurred
when the SOI was strongly positive. The years marked with El Nino were characterized by
higher aridity index values ranging from 52 to 81 and lower SOI values ranging from -0.15 to 1.8. The rainfall departures from normal in El Nino years ranged between -40% and +10.1%.
The La Nina years were characterized with aridity index ranging between 59.3 to 71.8 % and
SOI ranging from -1.0 to 1.0. The analysis indicated the possible existence of a teleconnection
between the drought events in Maharashtra and the ElNino-Southern Oscillation phenomena, in
general, which is well known from several studies on Indian monsoon rainfall and other studies
on major drought and flood episodes in the dry land areas of the world (Williams and Balling,
1993). However, the present analysis showed that some normal years were also marked with SOI
anomalies, therby indicating that ENSO events might be one among other factors responsible for
regional rainfall variation in drought prone areas of Maharashtra..
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4.2 Drought Climatology and Water Balance
The drought frequency expressed as a percentage of the drought years for a few drought
prone districts was presented in Table 2. The annual average rainfall during the period of study
(1946 to 1988) was found to range from 532 mm at Satara district to 780 mm at Jalgaon district.
Considering aridity anomaly as an indicator for incidence of drought, the districts Dhule and
Pune have 31%, Jalgaon has 21%, Parbani has 20%, Sholapur has 16%, Satara has 17% and
Nasik has 12% of years of drought occurrence between 1946 and 1988. The periodicity of
drought cycle varies from 3 yrs at Dhule and Pune districts to 8 years at Nasik district. The
principal drought class was moderate in Sholapur, Nasik, moderate to severe in Parbhani, severe
in Dhule, Jalgaon and Pune and large in Satara district. The coefficient of variation of rainfall in
these districts was found to range from 21.6 % to 31.0%.
Water balance analysis of the mean climatic data of 45 locations in the drought prone
areas in the semiarid central Maharashtra (Table 2) indicated that there was no seasonal water
surplus throughout the year. The districts Jalgaon, Sholarpur, Sangli, Satara and parts of
Aurangabad, Pune were characterized with severe arid conditions. It was noted that the annual
water deficit in various parts of central Maharastra was of the order of 950 mm in moderate
drought prone districts, 1100 mm in severe drought prone districts, and 1200 mm in large
drought prone districts. The aridity index was about 60.0 in moderate drought districts, 65.0 in
severe drought districts and about 70.0 in large drought districts. The climate was dry semi-arid
in a major area and typic arid in some parts in these districts. The moisture index (Im) was
between -83.3 and -66.7 in parts of Satara, Sholapur and Sangli districts and between -66.7 and 50.0 in major areas in the districts of Dhule, Jalgaon, Nasik, Ahmednagar, Pune, Aurangabad,
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Sangli and Sholapur (Figure.1b). As per water balance calculation, the soil remained dry in most
part of the year and recharge/storage of a very meager amount occured during the rainy season
between June to September. The mean soil moisture recharge estimated from soil water
budgeting was found to be of the order of 56.1 mm in Aurangabad, 81.2 mm in Dhule, 111.1 in
Jalgaon, 18.1 mm in Nasik, 30 mm in Pune, 44.4 mm in Sholapur, 15 mm in Sangli and 16.1 mm
in Satara districts. As already noted the annual rainfall in these districts had large increasing and
decreasing variations with periodicity of about 4 or 5 years (Figure.2a). These districts were
classified into different degrees of drought proneness based on the moisture retention
characteristics of soils in earlier studies (e.g, Anonymous, 1993; Challa and Wadodkar, 1996).
4.3 Land Use/ Land cover and NDVI
The land use/ land cover of the study area was delineated from field observations
collected during the soil resource mapping of Maharastra (Challa et al, 1995) (Figure 1c). This
data was spatially mapped using GIS and used in interpretation of prevailing vegetation. The
principal crops cultivated in the arid to semi-arid drought prone districts were Jowar, Bajra,
Redgram, Gram, Cotton, groundnut, Safflower, Soybean and fodder which are basically dry land
crops. Rice and Sugarcane are grown in parts of Kolhapur district, the western parts of which
were received high rainfall. Fallow and scrublands were noticed in the districts of Satara, Sangli,
Beed, Ahmednagar and parts of Pune where fodder, maize, Jowar, cotton and other commercial
crops were grown. The average annual rainfall of the study area was low in 1992 relative to that
in 1995. Figure 3 gives the trends of the average NDVI and the percent crop cover during the
growing season in the above districts for the year 1992. The NDVI was noted to be low in the
districts Solapur, Pune, Jalgaon, Satara and Sangli which had scrublands and fallow lands and
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very low percent cropped area. A similar trend between NDVI and the percent cropped area was
also noted for the year 1995.
4.4 NDVI and Growing period
Figure 4 gives the monthly variation of NDVI and rainfall at four stations Satara, Pune,
Dhule and Jalgaon in the drought prone region in 1992, which indicated NDVI had positive trend
with rainfall at the respective locations. A time lag of about 30 days was observed in the trends
of NDVI with rainfall indicating gradual response of vegetation to rainfall. Analysis of seasonal
NDVI with mean annual rainfall showed that the NDVI followed a similar trend as that of
rainfall in both 1992 and 1995 at various stations in Maharasthra (Figure 5). The NDVI values
were relatively low in 1992 compared to 1995 possibly due to the reduction of rainfall in 1992.
NDVI was relatively low in semi-arid rain-shadow central region as compared to the humid/
perhumid Western Ghat region and the sub-humid eastern Maharasthra, indicating a low
percentage of surface vegetation cover in semi-arid rein in both years.
The climatic moist period (Higgins and Kassam, 1981; Srinivas et al., 2001b) in
Maharasthra varied from 59 to 182 days (Figure 6d) and followed a similar spatial pattern as that
of spatial rainfall distribution in the study region (Figure 1a). The lowest growing period of 60 to
135 was observed in eastern parts of Satara, south eastern part of Pune, northern part of Sangli
and western parts of Sholapur. The rain-shadow dry region was found to have a growing period
of 135 to 165 days with some pockets having 120-135 days and 150-165 days. Central
Maharashtra region had a growing season of 135-180 days. Konkan coast covering the districts
of Sindhudurg, Ratnagiri, Raigad and Thane districts was found to have a growing period of 165
to 227 days, the highest was noted in Ratnagiri and coastal strip of Thane and Sindhudurg. A
high growing period of 180 to 227 days was also noted in eastern Maharashtra in part of
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Chandrapur, Gadchchiroli, Bhandara and Nagpur districts, which receive a high annual rainfall
of about 1100 to 1500 mm.
The NDVI integrated from June to October was considered as an indicator of biomass during the
growing season in the study region. To examine the spatio-temporal variation of NDVI and to
assess the growing season from its time series data, five monthly data sets were considered over
September, October, November, December and February. The average NDVI in the middle 10
day period (11-20th) of each month was considered representative of NDVI for each month for
this purpose. The NDVI values were considered in the range from 0.1 to >0.5 at 0.05 intervals.
NDVI values higher than 0.5 were considered to characterize the perennial vegetation such as
forests, values lower than 0.15 as scrublands and the intermediate values with vegetation from
various crops. It was noted from the NDVI map of the study area for February (non-growing
season) (Figure 6a), September and October (growing season) (Figures 6b,c) that the districts of
Dhule (part), Jalgaon (part), Ahmednagar, Aurangabad, Beed, Pune, Sholapur and Sangli were
characterized by relatively low NDVI (<0.1 to 0.25) in the peak of the growing season which
indicated the prevalence of unfavorable growing conditions in these areas. The growing period of
these districts estimated from NDVI time series was found to be of the order of 75 to 125 days
(Srinivas et al., 2001b). During September and October many districts had NDVI in the range of
0.2 to 0.5 (Figures 6b,c) except the eastern parts of Satara, Sangli, western parts of Sholapur,
Pune, central parts of Dhule, Nasik and Beed. This indicated the senescent stage of vegetation in
those areas and a growing period of 60 to 120 days. NDVI in September in other areas was
typically between 0.3 to 0.5 representing vigorous vegetation. NDVI progressively decreased in
November, December and February in all districts, the area with NDVI < 0.25 increased initially
up to September, decreased gradually thereupon till November and increased again thereafter
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(Table 2). The area under the NDVI range 0.25-0.35 increased from August to December and
thereafter a decreasing trend was observed. The area with NDVI range 0.35-0.40 increased from
August to November and decreased subsequently. The NDVI classes 0.40-0.45, 0.45-0.50 and
>0.5 are found to occupy a greater percent of the area in August, and reduced abruptly from
September onwards.
In November NDVI in the districts Dhule, eastern Nasik, Ahmednagar, Pune, Sholapur,
eastern portion of Osmanabad, Beed, Aurangabad was noted to be in the range of <0.1 to 0.25
indicating the withered stage of vegetation, limiting the growing season to 135 to 150 days. The
area with dormant vegetation was further increased in December as observed from NDVI map.
The districts of Buldhana, Akola, Parbhani and parts of Nanded, Latur were characterized with
NDVI below 0.20 with a growing period of 165-180 days. The areas occupied by forests in the
districts of Chandrapur, Gadchchiroli, parts of Bhandara, Western Ghats region in Sindhudurg,
Ratnagiri, Raigad were in the NDVI range of 0.45 - >0.50 even in February (Fig.7), while a
major area of the state had NDVI below 0.3. This indicated a growing season of 220 days or
more in areas having perennial vegetation. These areas were associated with dense forests and
heavy precipitation in the range of 1300 mm to 6000 mm (Challa et al., 1995). The results agree
with the FAO model output and a correlation factor of r=0.78 was obtained between the two
estimates of growing period from climatic data and NDVI (Table 3, Figure 7). However,
deviation in some cases might have been due to using fewer NDVI data sets. Multi temporal
NDVI data sets would be helpful in detecting the duration of growing season more accurately.
4.5 Surface albedo
Figure 8 shows the surface albedo map of Maharasthra for February during non-growing season
and November during growing season. The surface albedo was found to be high in the pre-
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monsoon season i.e., April and May, decreased to low values in the rainy season (June to
September) and increased to moderate to high values in the post monsoon and winter season
(November to December). The albedo at various locations was observed to be characteristic of
the local NDVI pattern. Both albedo and NDVI patterns significantly varied spatially depending
upon the rainfall and vegetation pattern at each location.
The surface albedo was high in central Maharashtra relative to the western and eastern
Maharashtra regions (Table 4). Albedo was found to range between 17.6 to 20.3 with an average
value of 19.1 in the semi-arid region, and between 15.2 and 19.6 with an average value of 17.3 in
the subhumid eastern Maharasthra region in 1992. In 1995, albedo ranged between 18.7 to 20.9
with an average value of 19.6 in humid/ perhumid region, between 17.4 to 20.6 with an average
of 18.8 in semi-arid region, and between 16.5 to 22.4 with an average value of 20.4 in sub-humid
eastern Maharashtra region. Albedo was not only higher in drought prone areas, but its
interannual variation was also lower. The variation of surface albedo during 1992 and 1995 was
between 0.3 and 0.4 in arid and semi-arid areas and about 2.0 in humid/ perhumid high rainfall
regions. The seasonal average surface albedo from April to December was very low in the subhumid eastern region, and relatively high in the semi-arid dry land areas.
4.6 Land Surface Temperature
The land surface temperatures in Maharasthra in the pre-monsoon summer time in April, 1995
and post monsoon time in November 1995 were shown in Figure 9. Genearally Land surface
temperature was noted to be high in the central Maharashtra and decreased towards the eastern
Maharasthra and Western Ghats region. Seasonal average land surface temperature in the study
area (Table 5) ranged between 298.7 °K and 301.3 °K with an average value of 299.8 °K in
semi-arid region, and between 294.8 °K and 302.0 °K with an average value of 297.8 °K in sub-
18
humid eastern region respectively in 1992. In 1995, the surface temperature was noted to vary
between 295.4 °K and 301.8 °K with an average value of 298.9 °K in humid/perhumid western
Maharashtra, between 298.1 °K and 303.8 °K with an average value of 300.9 °K in semi-arid
region, and between 294.3 °K and 301.2 °K with an average value of 297.8 °K in sub-humid
eastern region.. The semi-arid central region identified with the drought prone districts was
found to experience higher surface temperatures both in 1992 and 1995. The above analysis
reveals that humid/ perhumid and subhumid regions had relatively low surface temperatures in
both years of observation.
4.7 Dryness Quotient
The dryness quotient represents the net radiation available for evaporation from a wet
surface to the heat required to evaporate the mean annual precipitation. It is less than unity in
humid areas and greater than unity in dry areas. Increasing values represent the trend towards an
arid climate. The dryness quotient evaluated from the surface energy balance was greater than
2.0 for the districts of Jalgaon, Dhule, Nasik, Ahmednagar, Parbhani, Pune, Beed, Satara and
Sholapur in both 1992 and 1995 (Table 6). The dryness quotient for these districts ranged
between 2.2 and 4.7, the districts with high values being Nasik, Ahmednagar, Dhule, Pune,
Satara and Jalgaon. The dryness quotient for the subhumid eastern Maharasthra region ranged
between 1.55 to 2.56, and for the humid/ perhumid western ghat region between 0.31 to 1.55
(Figures 10 a, b).
5. Conclusions
In this study the satellite remote sensing derived NDVI, albedo, surface temperature, dryness
ratio and vegetation growing period from NOAA-AVHRR data over the tropical region,
19
Maharashtra, in India were analysed in association with the climatic data and the water balance
components estimated from climatic data in an attempt to determine the utility of remote sensing
based land surface parameters in climate analysis and to delineate the arid / drought affected
areas in the region. The analysis was made over the arid to semiarid central Maharahstra. The
analysis of rainfall and water balance components in the semi-arid central Maharashtra showed
that the districts of Sholapur, Parbhani, Dhule, Jalgaon, Nasik, Pune, Ahmednagar, Beed, Sangli
and parts of Aurangabad experience low mean annual rainfall with a coefficient of variation of
20 to 31% and high aridity index (between 51% to 74%). The drought cycle had a frequency
between 3 to 8 years in these parts of the state. The vegetation in these districts consisted mainly
of dry land crops with fallow and scrublands.
The rain shadow region was characterized with typic arid to semi-arid conditions with a
low vegetation index (0.15 to 0.30), short growing season (60 to 120 days), high surface albedo
(16.7 to 23.5 per cent), high dryness quotient (2.0 to 0.47) and high surface temperatures
(>299.8 °K). NDVI analysis revealed the presence of dense vegetative cover in areas with high
amount of mean annual rainfall, particularly in humid/ perhumid Western Ghats region and the
subhumid eastern Maharasthra region, and very low NDVI values in the rain shadow semi-arid
area in central Maharasthra. The growing season mean NDVI varied between 0.09 to 0.28 with a
mean of 0.17 in 1992 and from 0.23 to 0.35 with a mean of 0.28 in 1995 in the above districts.
The NDVI pattern reflected the span of growing periods and the prevailing land cover in the
above districts. The relatively low NDVI during the growing season reflects the typically poor
vegetation conditions and the prevailing aridity in these parts. A long term NDVI time series
analysis of the study area would help in the spatial delineation of arid/ drought prone areas by
assessing the deviation of NDVI from its long term normal value (Nicholson et al, 1988). The
20
present study showed NDVI was low in sparsely vegetated areas with low rainfall and high in
regions with perennial vegetation and high rainfall. A threshold value of NDVI during the
growing season can be used to distinguish the semi-arid and arid regions.
The study reveals that the land surface parameters viz., NDVI, albedo, land surface
temperature, dryness quotient and vegetation growing period derived from remote sensing data
correlate well with the climatic data (rainfall, water balance components etc) and that they can be
used in the identification and delineation of aird/ semi-arid and drought prone areas. This method
based on analysis of satellite imagery has the unique advantages of obtaining more accurate
spatial estimation of aridity and drought over the conventional methods based on traditional
meteorological point information. However, a time series analysis of parameters NDVI, albedo,
surface temperature, crop growing season over climatically significant long period over a given
region would be necessary to determine the threshold limits of each parameter for their
application in the detection of arid and drought conditions.
6. Acknowledgements
Authors are thankful to the India Meteorological Department, Pune for providing the
meteorological data used in the study. The NOAA AVHRR data was obtained from the USGS
EROS data center from their online achieves. Sri S. Rajasekhar, RRSSC, Nagpur is
acknowledged for his help in the processing of NOAA AVHRR data.
21
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29
Table 1 Rainfall, aridity index, southern oscillation index and rainfall departures
in the drought prone area
Year Rainfall
Ia
SOI
Rainfall
El Nino / La Nina
Drought
(mm)
departure
Year
status
1946
762.2
59.3
0.16
6.0
Normal
N
1947
646.9
64.2
0.06
-10.0
Normal
N
1948
830.4
57.3
0.04
15.5
Normal
N
1949
802.7
60.3
-0.02
11.6
La Nina
N
1950
642.9
65.1
1.85
-10.6
Normal
M
1951
592.2
65.7
-1.10
-17.6
El Nino (Q)
S
1952
461.0
73.4
0.20
-35.9
Normal
S
1953
616.9
67.8
-1.10
-14.2
El Nino (Q&R)
S
1954
719.9
61.9
-0.15
0.1
La Nina
N
1955
848.3
59.2
1.75
18.0
Normal
N
1956
1000.4
52.0
1.20
39.1
Normal
N
1957
791.8
61.9
-0.75
10.1
El Nino (Q&R)
S
1958
743.4
61.9
-0.75
3.4
Normal
N
1959
864.1
55.7
-0.20
20.2
Normal
N
1960
690.8
62.5
0.25
-3.9
Normal
N
1961
843.5
60.2
-0.20
17.3
Normal
N
1962
698.9
60.6
0.25
-2.8
Normal
N
1963
736.5
63.2
-1.10
2.4
Normal
M
1964
716.4
61.2
1.00
-0.4
La Nina
N
1965
568.1
70.0
-1.15
-21.0
El Nino (Q&R)
S
1966
615.0
71.8
-1.00
-14.5
La Nina
M
1967
683.0
61.7
0.10
-5.0
Normal
N
1968
560.5
69.9
0.20
-22.0
Normal
M
1969
724.3
62.8
-0.80
0.7
El Nino (R )
L
1970
708.9
63.6
0.00
-1.4
La Nina
N
1971
506.6
70.9
1.00
-29.5
Normal
M
1972
424.0
81.4
-1.50
-41.0
El Nino (Q&R)
D
1973
723.1
60.2
0.15
0.6
La Nina
N
1974
732.1
63.1
0.80
1.8
Normal
N
1975
918.8
59.3
1.00
27.8
La Nina
N
1976
680.9
64.8
-0.70
-5.3
El Nino (Q&R)
L
1977
720.1
63.5
-1.20
0.2
Normal
N
1978
716.4
62.9
0.20
-0.4
La Nina
N
1979
745.6
61.6
0.10
3.7
Normal
N
1980
652.9
66.2
-0.50
-9.2
Normal
N
1987
882.4
56.0
-1.80
22.7
Normal
N
1988
1031.5
64.8
0.00
43.5
La Nina
N
N- No drought, M - Moderate drought, L- Large drought, S - Severe drought,
D - Disastrous drought. Q - El Nino (Quinn et al., 1987), R - El Nino (Rasmusson &
Carpenter, 1983)
30
Table 2 Rainfall characteristics and drought periodicity in the drought affected districts
District
Annual Monsoon
rainfall rainfall
(mm) (mm)
Solapur
750.6
558.6
Parbhani
920
780.4
Dhule
622.5
537.3
Jalgaon
780.6
663.7
Nasik
591.6
466.3
Satara
532.8
335.3
Pune
691.8
510.9
Aurangabad 615.4
500.0
Kolhapur
670.0
515.0
Sangli
555.0
485.0
Coeff.
% of
Variation drought
years
31
16
28.4
20
25
31
21.6
21
26.3
12
24.2
17
23.9
31
24.5
30
23.7
22
25.6
25
Water
deficit
Aridity
index
1161.0
1050.0
890.7
1175.0
1064.5
1190.5
972.5
1004.3
989.5
1135.6
63.2
56
68.3
63.5
64.1
71
59.1
62.2
59.0
67.1
31
Climate
Typic arid
Dry Sei-arid
Dry Semi-arid
Dry Semi-arid
Dry Semi-arid
Typic arid
Dry semi-arid
Dry Semi-arid
Typic arid
Typic arid
Drought
periodici
ty
5
3
6
8
6
5
3
5
5
5
Drought
class
Severe
Moderate
Severe
Severe
Moderate
Large
Severe
Severe
Moderate
Severe
Table 2 Area (in %) in each NDVI range in different months during growing season
NDVI Range
1 < 0.10
2 0.10 - 0.15
3 0.15 - 0.20
4 0.20 - 0.25
5 0.25 - 0.30
6 0.30 - 0.35
7 0.35 - 0.40
8 0.40 - 0.45
9 0.45 - 0.50
10 > 0.50
% of Area
August
September October
November
December
February
4.28
17.71
15.3
0.09
0.23
1.8
3.98
11.06
5.96
0.54
2.4
6.74
5.91
12.16
7.73
3.46
8.48
14.51
8.14
12.92
12.2
9.49
19.08
18.88
10.29
12.54
14.87
17.83
23.58
19.71
12.47
11.48
13.4
20.58
20.36
16.06
13.31
9.33
10.51
18.98
12.12
9.75
12.82
5.94
7.88
13.55
6.1
5.2
10.52
3.74
6.21
6.8
3.59
3.01
18.28
3.13
5.94
8.69
4.06
2.48
Table. 3. Growing period estimated using FAO method and form NDVI data in Mahrashtra
Location
Growing period from NDVI
1992
Akola
Chandrapur
Jalgaon
Dhule
Nasik
Ahmednagar
Parbhani
Raigarh
Pune
Beed
Satara
Ratnagiri
Sindhudurg
Sholapur
Thane
Nagpur
Kolhapur
1995
150
215
180
120
150
180
180
180
150
120
90
180
180
180
180
180
180
180
220
180
120
180
180
180
210
180
180
150
210
220
90
190
190
210
32
Growing period by FAO
method
1992
1995
176
160
196
203
180
164
130
132
147
125
150
171
180
165
--230
160
150
120
175
90
145
198
225
173
232
133
150
180
174
169
164
196
203
Table.4 Seasonal Surface Albedo in different regions of Maharashtra in 1992 and 1995
Region
Eastern
Maharashtra
(R1)
Semi-arid
rainshadow
central
(R2)
Western Ghats
& Konkan coast
(R3)
Location
Nagpur
Akola
Chandrapur
Parbhani
Jalgaon
Dhule
Rahuri
Pune
Ashti
Satara
Sholapur
Nasik
Karjat
Dapoli
Wakawali
Borgaon
Kolhapur
Shirala
Kundewadi
Kalyani
Alebdo
1992
Regional
average
19.19
20.32
19.81
17.63
17.90
16.65
19.80
18.38
23.46
17.42
20.03
15.16
19.46
15.77
16.53
18.52
19.62
18.69
16.18
15.74
19.24
19.09
17.30
33
Albedo
1995
Regional
average
18.92
20.93
19.96
18.69
18.53
19.81
17.53
20.55
17.44
19.34
18.38
16.53
22.36
21.67
22.31
17.78
21.31
21.58
19.95
20.29
19.63
18.80
20.42
Table 5.Seasonal Land Surface Temperature in different regions of Maharashtra in 1992 and 1995.
Land Surface Temperature LST (K) in
Region
Location
1992
1995
LST
Regional
LST
Regional
average
average
Eastern
Nagpur
298.92
299.44
297.05
298.93
Maharashtra
Akola
298.70
301.39
(R1)
Chandrapur
298.81
295.44
Parbhani
301.34
301.82
Semi-arid
Jalgaon
303.45
299.77
303.75
300.95
rainshadow
Dhule
298.93
300.64
central
Rahuri
295.91
298.08
(R2)
Pune
302.54
301.70
Ashti
298.77
299.78
Satara
298.94
299.14
Sholapur
299.87
303.55
Western Ghats
Nasik
300.04
297.83
301.17
297.83
& Konkan coast
Karjat
296.89
295.92
(R3)
Dapoli
297.17
297.59
Wakawali
298.08
298.45
Borgaon
294.75
294.34
Kolhapur
297.17
298.59
Shirala
295.20
296.74
Kundewadi
299.22
296.85
Kalyani
301.96
300.84
34
Table.6 Net radiation, Rainfall and Dryness Quotient in 1992 & 1995 from selected
locations in Maharashtra.
Station/
Year
Nagpur
Akola
Chandrapur
Jalgaon
Dhule
Nasik
Ahmednagar
Parbhani
Raigad
Pune
Beed
Satara
Ratnagiri
Wakawli
Borgaon
Kolhapur
Shirla
Kundewadi
Kalyan
Sholapur
Q*
1424.70
1530.30
1422.10
1561.40
1487.55
1631.30
1458.30
1597.80
1498.20
1419.80
1591.10
1546.40
1552.80
1517.90
1535.60
1564.75
1598.62
1482.80
1992
R
716.40
843.80
1181.70
814.40
728.60
537.80
411.20
805.92
668.80
610.60
414.20
3001.10
3692.20
735.00
1273.80
6435.40
273.30
751.90
Q*/L.R
2.56
2.33
1.55
2.47
2.63
3.90
4.57
2.55
2.88
2.99
4.94
0.66
0.54
2.66
1.55
0.31
7.53
2.54
Q*
1300.10
1499.40
1414.70
1530.20
1566.40
1584.10
1470.80
1531.10
1336.60
1443.40
1518.63
1523.58
1350.10
1326.90
1508.70
1431.10
1361.65
1448.30
1450.20
1442.10
1995
R
946.40
574.00
1036.00
556.70
479.20
436.70
587.30
789.50
3984.20
618.30
868.00
541.50
2956.70
3046.70
887.80
926.30
4387.30
926.30
795.60
843.80
Q*/L.R
1.77
3.36
1.76
3.54
4.20
4.67
3.22
2.50
0.43
3.00
2.25
3.62
0.59
0.56
2.19
1.99
0.40
2.01
2.35
2.20
Legend : Q*  Net radiation (Wm-2), R  Rainfall (mm), Q/L.R  Dryness ratio.
35
Figure 1. Rainfall (a), climatic zones (b) and land use / land cover of Maharashtra
Figure 2. Departure of annual rainfall from the climatic mean (a), variation of rainfall with
southern oscillation index (SOI) (b), and variation of aridity index (Ia) with SOI in Maharashtra
36
Figure 3. Seasonal mean NDVI and per cent under dry land crops grown in the drought prone
districts.
37
Figure 4. Trends of NDVI with rainfall at four stations Satara, Pune, Dhule and Jalgaon in
central Maharasthra
Figure 5. Seasonal integrated NDVI and Rainfall in a few stations in Maharasthra in a) 1992 and
b) 1995
38
Figure 6. Monthly trends of NDVI in 1995 for a) February , b) September c) October and d)
spatial pattern of climatic moist period in Maharasthra.
39
Figure 7. Trend of Growing period estimated from NDVI with the Growing period estimated
using FAO method in the study region.
40
Figure 8. Surface albedo in Maharasthra in 1995 for a) April and b) November months.
Figure 9. Land surface temperatures in Maharasthra in 1995 for a) April and b) November
months.
41
Figure 10. Dryness Quotient in different districts in Maharasthra for a) 1992 and b) 1995.
42