ganges and indus river basin land use/land cover (lulc ... and indus river basin land use/land cover...
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Remote Sensing of Environm
Ganges and Indus river basin land use/land cover (LULC) and irrigated
area mapping using continuous streams of MODIS data
Prasad S. ThenkabailT, Mitchell Schull, Hugh Turral
International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lanka
Received 7 September 2004; received in revised form 8 December 2004; accepted 11 December 2004
Abstract
The overarching goal of this study was to map irrigated areas in the Ganges and Indus river basins using near-continuous time-series (8-
day), 500-m resolution, 7-band MODIS land data for 20012002. A multitemporal analysis was conducted, based on a mega file of 294
wavebands, made from 42 MODIS images each of 7 bands. Complementary field data were gathered from 196 locations. The study began
with the development of two cloud removal algorithms (CRAs) for MODIS 7-band reflectivity data, named: (a) blue-band minimum
reflectivity threshold and (b) visible-band minimum reflectivity threshold.
A series of innovative methods and approaches were introduced to analyze time-series MODIS data and consisted of: (a) brightness-
greenness-wetness (BGW) RED-NIR 2-dimensional feature space (2-d FS) plots for each of the 42 dates, (b) end-member (spectral angle)
analysis using RED-NIR single date (RN-SD) plots, (c) combining several RN-SDs in a single plot to develop RED-NIR multidate (RN-
MDs) plots in order to help track changes in magnitude and direction of spectral classes in 2-d FS, (d) introduction of a unique concept of
space-time spiral curves (ST-SCs) to continuously track class dynamics over time and space and to determine class separability at various
time periods within and across seasons, and (e) to establish unique class signatures based on NDVI (CS-NDVI) and/or multiband reflectivity
(CS-MBR), for each class, and demonstrate their intra- and inter-seasonal and intra- and inter-year characteristics. The results from these
techniques and methods enabled us to gather precise information on onset-peak-senescence-duration of each irrigated and rainfed classes.
The resulting 29 land use/land cover (LULC) map consisted of 6 unique irrigated area classes in the total study area of 133,021,156 ha
within the Ganges and Indus basins. Of this, the net irrigated area was estimated as 33.08 million hectares26.6% by canals and 73.4z5 by
groundwater. Of the 33.08 Mha, 98.4% of the area was irrigated during khariff (Southwest monsoonal rainy season during JuneOctober),
92.5% irrigated during Rabi (Northeast monsoonal rainy season during NovemberFebruary), and only 3.5% continuously through the year.
Quantitative Fuzzy Classification Accuracy Assessment (QFCAA) showed that the accuracies of the 29 classes varied from 56% to
100%with 17 classes above 80% accurate and 23 classes above 70% accurate.
The MODIS band 5 centered at 1240 nm provided the best separability in mapping irrigated area classes, followed by bands 2 (centered at
859 nm), 7 (2130 nm) and 6 (1640 nm).
D 2005 Elsevier Inc. All rights reserved.
Keywords: MODIS; Reflectance; Irrigated areas; Land use; Land cover (LULC); Ganges; RED-NIR; Change vector analysis; Spiral curve; Two-band
vegetation indices
1. Background and rationale
The World Summit on Sustainable Development
(WSSD) in Johannesburg (2002) declared water to be the
0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2004.12.018
T Corresponding author. Tel.: +94 1 2787404; fax: +94 1 2786854.E-mail address: [email protected] (P.S. Thenkabail).
most critical resource in the twenty-first centurywith
increasing demands and decreasing supplies. Irrigation is
estimated to consume about 60% of the worlds diverted
freshwater resources. In response to continued population
growth (projected to rise from 6 billion now to 8.3 billion
in 2030) and increased calorific intake of food (to 3000
calories per day per person from the current 2100; FAO,
2003), the demand for water for irrigation is forecast to
ent 95 (2005) 317341
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341318
grow. This is neither feasible, due to shortage of water
resources in many parts of the globe nor desirable because
of the negative environmental impacts of irrigation
schemes.
Improved water accounting is required to track
agricultural and nonagricultural water use, particularly in
irrigation. This will require mapping LULC and irrigated
area classes on a near-continuous (e.g., every 8-day)
basis. Most of the LULC classification efforts in the past
three decades used single or a selected few remote
sensing images (see Foody, 2002). Such classifications
provide little or no information on the temporal dynamics
of LULC classes, highly limiting their use in applications
such as hydrological modeling and evapotranspiration
estimations (DeFries & Los, 1999). In recent years,
AVHRR pathfinder time-series images (e.g., DeFries et
al., 1998; Loveland et al., 2000) have been used to
capture temporal dynamics of LULC at global level.
However, it is only recently that near-continuous (e.g., 8-
day composites) time series images from sensors such as
Moderate Imaging Spectrometer (MODIS) on board
NASAs Terra and Aqua satellites have allowed assess-
ment of LULC dynamics and quantitative landscape
characteristics (e.g., biomass, leaf area index) (Huete et
al., 2002) in near real time. For example, using these
datasets, vegetation in continuous streams are currently
produced (http://glcf.umiacs.umd.edu/data/modis/vcf/).
MODIS data are also known to provide a significant
improvement in terms of quality relative to the heritage
AVHRR data (Friedl et al., 2000). The advances in
spectral, spatial, radiometric, and temporal resolutions of
MODIS datasets () are further complimented by advances
in cloud/haze removal algorithms, time compositing, and
normalization of data into reflectance. It is well estab-
lished that LULC and irrigated area maps of the present
day require capturing quantitative dynamics over space
and time (DeFries & Los, 1999; Foody, 2002; Huete et
al., 2002) in order to enable them to be used more
productively in studies such as hydrological modeling
(Foody, 2002), drought assessments (Thenkabail et al.,
2004b), impact on biodiversity (Chapin et al., 2000),
human habitability and climate change (Skole et al.,
1994), global warming (Penner et al., 1992) and soil
erosion (Douglas, 1983).
The research described in this paper falls within the
framework of the Global Irrigated Area Mapping (GIAM)
project at IWMI (Droogers, 2002; Turral, 2002). The
principal objective of GIAM is to map irrigated areas at
different levels (global to local) and at different scales
using satellite sensor data from various eras. Global LULC
are essential to advancing most global change research
objectives (Loveland et al., 2000). Regional and local
LULC efforts must aim for a greater number of discrete
classes of relevance to a wide variety of users (Thenkabail,
1999; Thenkabail & Nolte, 2003). Irrespective of the level
at which the classes are mapped, it is essential to establish
acceptable levels of accuracy (Thenkabail et al., 2004a) to
avoid serious implications of land cover misclassification
on, for example, global land surface models (DeFries &
Los, 1999).
In order to achieve this goal, GIAM uses datasets that
include AVHRR (1 km to 10 km), SPOT Vegetation (1
km), MODIS (250500 m), ASTER (1590 m), ETM+
(1530 m), TM (30 m), and IRS (523.5 m). Irrigated
classes form part of some of LULC mapping efforts (e.g.,
Loveland et al., 2000), but no special focus or
importance was given to them, leading to a large
percentage of mixed classes with natural vegetation.
Primarily, there are non-remote sensing based studies on
irrigated areas (e.g., CBIP, 1989, 1994; Siebert, 1999).
The Food and Agriculture Organization (FAO, 2003;
Framji et al., 19811983; Siebert, 1999) of the United
Nations estimates that about 20% of the arable land is
irrigated at present with various scenarios of projected
increases in the future, but provides no spatial map of
where these areas are. Current estimated trends in
irrigation development are generally derived from
national agricultural statistics with many uncertainties
about their accuracy.
With the overall scope of the GIAM project as
discussed in the previous paragraph in mind, we focus
on mapping LULC with particular interest on irrigated
areas in the Ganges and Indus river basin using MODIS
data for year 20012002. The study will use multi-date,
near-continuous, MODIS data, and adopt a series of
innovative methods and proceduresthe N-dimensional
change vector analysis (CVA), new space-time spiral-curve
techniques to assess subtle and not-so subtle quantitative
changes over time and space, and evaluate the study using
fuzzy classification accuracy assessment. Through these
measures we plan to demonstrate a unique set of data,
methods, procedures, and protocols for mapping irrigated
areas. The Ganges and Indus basins (referred to as Indo-
Gangetic) was selected for this study because it is one of
the most densely populated and intensively cultivated areas
of the world with irrigation forming a key role in food
production.
2. Study area and the MODIS data
The study area (see non-hatched area within the basin
boundary in Fig. 1) covers 63% (133,071,400 ha) of the
Indo-Gangetic plain (total area=211,224,444 ha). The
study area was chosen based on the importance of the
area for agriculture and irrigation and a need to map this
area (Droogers, 2002; Turral, 2002). The Ganges river
basin originates in the Himalayan glaciers named Gang-
otri, about 4270 m above sea level. It has one of the most
fertile lands and has a very high population density of
about 530 persons per square kilometer. The river flows
through 29 cities each with a population of over 100,000,
http://glcf.umiacs.umd.edu/data/modis/vcf/
-
SNDVI
255
204
153
102
51
0
600
N0
N20
E80 E100
600 1200KilometersScale 1:40 000 000
0
Fig. 1. The study area within the Ganges and Indus basins. The un-hatched portion of the Ganges and Indus basins shown on an AVHRR image. The Z-scale
shows scaled normalized difference vegetation index (SNDVI) for October 1990.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 319
23 cities each with a population between 50,000 and
100,000, and about 48 towns (Aitken, 1992; Ilich, 1996).
The source of the Indus River is in Western Tibet in the
Mount Kailas region at an altitude of 5500 m above sea
level. The Indus basin comprises the Indus river, its five
major left bank tributariesthe Jhelum, Chenab, Ravi,
Beas and Sutlej riversand one major right bank
tributary, the Kabul (Khan, 1999). The catchments contain
some of the largest glaciers in the world outside the Polar
Regions (Meadows, 1999). The southwest monsoon or
khariff season (June to October) is followed by northeast
monsoon or Rabi season (November to February). The
mean annual rainfall is about 2000 mm, of which
approximately 70% occurs during the khariff season.
The dry season (MarchMay) the highest temperatures
vary between 40 and 45 8C.In order to enable the study of the characteristics of
land use and irrigation on a near- continuous basis, the 8-
day composite MODIS images of year 2001, a rainfall
normal year, and year 2002, which experienced rainfall
deficit in terms of amount and distribution, were selected).
One of the main goals of the study was to establish crop
calendar for irrigated area crops as precisely as possible.
The goal was to determine onset-duration-magnitude of the
peak-senescence for each irrigated area class. As a result
we need to use as frequent images as possible-leading us
to use 8-day composites and apply cloud removal
algorithm rather than use 32-day images with significantly
lesser cloud issues. About 95% of the Ganges basin (total
area 95,111,154 ha) and 37% of the Indus basin
(116,113,290 ha), were covered by 3 MODIS tiles
(h24v06, h25v06, and h26v06; each tile of 10001000km). The three tiles were mosaicked into a single
contiguous tile by running batch scripts in ERDAS
Imagine 8.6 from which the areas within the Ganges and
Indus basins were delineated (Fig. 1).
3. Methods and techniques
3.1. Mega file: multitemporal MODIS data for Ganges and
Indus river basins
In this study, we use the MOD09 product, with 7 of the
36 MODIS 500 m bands. The MOD09 is computed from
MODIS level 1B land bands 17 (centered at 648 nm, 858
nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm).
The product is an estimate of the surface reflectance for
each band as it would have been measured at ground level
if there was no atmospheric scattering or absorption
(Vermote et al., 2002). The original MODIS data are
acquired in 12-bit (04096 levels), and are stretched to 16-
bit (065,536 levels). Dividing these data by 100 will
make them comparable to laboratory spectra in the 0
100% range.
The long time series analysis of MODIS data requires
construction of mega datasets that involve hundreds of
bands. Altogether 294 bands (42 images7 bands) from21 images from year 2001 and 2002 were formulated
into a single mega file of approximately 7 GB. A
separate 42-band NDVI mega file (one NDVI band for
each date) was also created. The single mega file
facilitate (a) analyzing the time series in their entirety
(e.g., they perform unsupervised classification of 294-
band data and determine how classes change in
magnitude and direction over space and time) and (b)
tracking quantitative changes at any level in near-
continuous mode (e.g., NDVI variations at pixel or
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341320
entire study-area level in 8-day time interval). Performing
analysis on 10 s or 100 s of images of individual dates
is too cumbersome, leads to repetitive work, hard to
keep track of class number changes for a given pixel,
and just leads to chaos of handling too many files. In
comparison mega file offers a single file of data, a
single file of output, and provide temporal variations for
every pixel in quantitative terms (e.g., NDVI dynamics
over time).
3.2. Cloud removal algorithm
The Indo-Gangetic basin is subject to the effects of the
oscillating Sub-Tropical Convergence Zone (www.srh.
weather.gov). These effects include the monsoon (June
September), which brings extensive cloud cover and heavy
rains. During this season, there is a great change in the
vegetation cover, rapid change in its dynamics and biomass
accumulation. In order to retain the maximum number of
time series images during this period we (a) retained all
images with b5% cloud cover and (b) developed a cloud-
masking algorithm so as to eliminate areas of cloud cover
and retain the rest of the image as is. Of 42 images, 8 images
had 2540% cloud cover which also implies that 6075% of
133 million hectare study area is cloud-free. Our attempts to
use MODIS quality control layers and flags were not
successful and resulted in several difficulties. These include:
(a) cloud vs. snow vs. desert sand vs. aerosol confusion: as a
result of this often Himalayan seasonal snow was removed
as cloud; (b) over-correction issue: over correction by
quality control flags lead to significantly low reflectance
values which in turn effected temporal NDVI profiles; and
(c) bblockyQ effects: applying quality flags lead to bblockyQeffects in the images probably as a result of original quality
flags being performed at 1-km pixel size which seemed to
cause bblocky/noisyQ effects in 500-m pixels (four 500-mpixels in one 1-km pixels). In fact, we were able to establish
a more consistent, smooth, and stable NDVI profiles from
the MODIS cloud removal algorithm specially developed in
this study rather than use MOD09 QC layers.
3.2.1. Cloud algorithm: statistical characteristics
Clouds have unique spectral characteristics with consis-
tently high reflectivity in all visible and NIR wavebands, but
are quite often mixed with snow and desert backgrounds
the other two highly reflective classes. To establish clear
statistical characteristics for clouds, we obtained sample
spectra from 350 locations for clouds, 240 locations for
snow and 180 locations for deserts. When the means,
minima and maxima of spectra for the clouds, snow and
desert were plotted the results showed there were 2 excellent
possibilities for separating most of the clouds.
3.2.1.1. Blue band minimum reflectivity threshold for cloud.
When we use minimum blue band reflectivity of 21% or
above (a) all clouds get removed, (b) much of snow gets
removed and (c) none of the desert gets removed. A simple
algorithm for cloud removal in ERMapper (ERMapper,
2004) was:
If i3N21% then null else I 1
Where, i3 is MODIS band 3 (blue band). The algorithm
assigns null values to all cloud areas.
3.2.1.2. Visible band minimum reflectivity threshold for
cloud. The minimum reflectivity of clouds in the MODIS
visible bands (bands 3, 4, and 1), provide the best
separability in which almost all clouds gets removed.
The algorithm for cloud removal, using this approach
with MODIS visible bands 3 (blue), 4 (green), and 1
(red) was
If i1N22 and i3N21 and i4N23 then null else I 2
However, when using this approach much of snow and
a significant portion of the desert also get removed. This is
not a problem, since we have several other time series
images where snow and desert data exist in their entirety.
So clouds were removed using Eq. (2), but snow and desert
areas were retained in their entirety, based on non-cloudy
images.
The results of cloud removal have been illustrated before
and after images in Fig. 2.
3.3. Normalization of temporal variability
The MODIS reflectance product has gone through a
rigorous atmospheric correction scheme based on the 6S
radiative transfer code for normalizing for molecular
scattering, gaseous absorption and aerosols that affect
the top of the atmosphere (TOA) signal (see inter alia
Vermote et al., 2002). Aerosol effects are known to
remain uncorrected even after long compositing periods
(e.g., a month) (Vermote et al., 2002) so such effects in
8-day time intervals are significant. It would be desirable
to do further corrections for these effects, for which we
found a time-invariant location in the Rajasthan desert,
calculated mean values of each band for each of the 42
images for this time-invariant location, determined the
calibration coefficient of each band for each date by
dividing its reflectance by the mean, and then normalized
images of each date by multiplying using calibration
coefficients.
3.4. Image processing and interpretation
A summary of the image processing and interpretation
undertaken in this research is provided in Fig. 3. The
basis of the work stems from unsupervised classification
of all bands in the mega file, followed by various
innovative refinements in class membership using techni-
ques derived from RED-NIR and time series signatures,
which are discussed in more detail in the section on
http:www.srh.weather.gov
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Before Cloud Removal Algorithm Day 153 2001
After Cloud Removal Algorithm Day 153 2001
Before Cloud Removal Algorithm Day 185 2001
After Cloud Removal Algorithm Day 185 2001
E70
N25
500 0 500 1000Kilometers
RGB
TCC;RGB1,4,3648, 555, 470 nmDay 153 2001
Scale 1:16 500 000
E75 E80 E90 E95 E100E85 E70
N25
500 0 500 1000Kilometers
RGB
TCC;RGB1,4,3648, 555, 470 nmDay 185 2001
Scale 1:16 500 000
E75 E80 E90 E95 E100E85
E70
N25
500 0 500 1000Kilometers
RGB
TCC;RGB1,4,3648, 555, 470 nmDay 153 2001
Scale 1:16 500 000
E75 E80 E90 E95 E100E85 E70
N25
500 0 500 1000Kilometers
RGB
TCC;RGB1,4,3648, 555, 470 nmDay 185 2001
Scale 1:16 500 000
E75 E80 E90 E95 E100E85
Fig. 2. MODIS images before and after application on cloud removal algorithm. An algorithm was developed to remove cloud from MODIS data. The figures
above show the cloud removal capability of the algorithms.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 321
Results and discussion. Ground-truth data have therefore
been a crucial element in the process and are described in
the next section.
Fig. 3. Ground truth data point distributions in the study area. Precise location of t
MODIS RED-NIR image. Color key: red: dry, cyan/green/yellow: green, blue/li
legend, the reader is referred to the web version of this article.)
We adopted a hierarchical classification system based on
modified Anderson classification (Anderson et al., 1976).
For example, if a class does not belong to rice (class A) or
he 9090 m ground truth locations spread across the study area shown on aght blue: wet. (For interpretation of the references to colour in this figure
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341322
sugarcane (class B) it will fall into a higher category of
irrigated croplands (classes A and B).
4. Ground-truth data
Ground truthing was conducted during October 122,
2003 to coincide with the peak khariff (monsoonal rainy
season from June to October) conditions. For such a large
area as the Ganges and Indus basins, random or systematic
sampling is unrealistic and costly (Muchoney & Strahler,
2002). Therefore, the sampling was stratified by access
through roads and foot paths and randomized by locating
sites every few minutes of the drive.
The MODIS data require a minimum sampling unit of
500 m500 m, which in itself is inadequate. A largersampling unit is desired, but was quite impractical in the
field. The approach we adopted was to look for contiguous
areas of homogeneous classes within which to sample (see
Thenkabail, 2003, for sampling LAI), taking a representa-
tive area of 90 m90 m. Class labels were assigned in thefield, using a system that allows merging to a higher class or
breakdown into a distinct class, based on the land cover
percentages taken at each location.
In all, about 6500 km were covered to gather data from
196 sample locations (Fig. 4). The precise locations of the
samples were recorded by GPS in the Universal Transverse
Mercator (UTM) and the latitude/longitude coordinate
system with a common datum of WGS84. The sample size
per class varied from 8 to 37 and the ideal target of 50
Cloud Removal Algorithms (CRAs): (A) Blue band minimum reflectivity threshold, (B) Visible band minimum reflectivity threshold
Class assignment
Mega-fof 294 b42 MOD
End Member Analysis (EMA) : Brightness-greenness-Wetness (BGW) 2-dimensional Feature Space (BGW
Class refinement
NIR-RED Single Dates (NR-SDs)
NIR-RED Multi Dates (NR-MDs)
Class simplification
Extraction of Irrigated pixels
Class Signatures Multi-band Reflectivity (CS-MBR)
Calculate Statistics
Space-Time Spiral-Curve (ST-SCs) from multi-date
tasseled cap
2-band spectral plots 2:1 & 6:7
Mega Classes
Fig. 4. Methods and techniques workflow diagram. Flow chart showing methods an
of MODIS data.
samples (Congalton, 1988) was infeasible due to limitations
in resources.
At each location (e.g., Fig. 5), the following data were
recorded:
1. LULC classes: levels I, II and III of the Anderson
approach.
2. Land cover types (percentage): trees, shrubs, grasses,
built-up area, water, fallow lands, weeds, different crops,
sand, snow, rock, and fallow farms.
3. Crop types, cropping pattern and cropping calendar: for
khariff, rabi (second main cropping period from Novem-
ber to March) and interim seasons.
4. Source of water: irrigated, rain-fed, supplemental
irrigation.
5. 311 digital photos hot linked @ 196 locations.
The data were organized in proprietary image processing
and GIS formats with accompanying metadata so that they
could be co-located with the unsupervised classification
(e.g., Fig. 4).
5. Results and discussion
5.1. Unsupervised classification
To begin with, unsupervised classification was per-
formed on the mega file (UC-MF) using an ISODATA
statistical clustering algorithm for multidimensional data
ile (MFC) for time-series analysis ands for 2001 and 2002 IS 500-m 7-band images
Net irrigated area
Sub-pixel
composition
Multidate-multiband unsupervised classification
(MD-MB UC)
Ground truth
Class Signature based on NDVI (CS-NDVI): time-series
Quantitative Fuzzy Classification Accuracy Assessment
(QFCAA)
Kharif, Rabi, and Continuous irrigated
areas
d techniques of LULC and irrigated area mapping using continuous streams
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Fig. 5. Photographs illustrating irrigated area classes and forest cover land use and land cover (LULC) classes. At each ground truth point, 2 photographs were
taken apart from other ground truth data. Illustrated here are representative photos (ae) of 6 unique irrigated area classes (classes 2126) and representative
photos (fh) of 3 forest classes (classes 2729).
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 323
(ERDAS, 2004). Initially, 100 classes were obtained as a
starting block for further refinement and analysis. The
UC-MF provides a substantial within-class variance
(Friedl et al., 2000; McIver & Friedl, 2002) that is
essential to map classes within a theme (e.g., different
types of irrigated-area classes). The sample size of the
field-plot data was insufficient for certain classes to make
the supervised classification robust. Hence unsupervised
approach backed by RED-NIR plots (Sections 5.2 and
5.3), ground truth data (Sections 4 and 5.4), temporal NDVI
plots (Section 5.9), and space-time spiral curves (Section
5.10) were used.
5.2. RED-NIR Plots for single dates (RN-SDs), class
identification and labeling
The spectral properties of the 100 classes obtained
through UC-MF were analyzed, based on their distribution
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341324
in brightnessgreennesswetness (BGW) RED-NIR feature
space. The distributions of a selection of the 100 unique
spectral classes for May 2001 are illustrated in Fig. 6.
All classes were identified and labeled, based on their
position in the BGW RED-NIR feature space, use of higher-
resolution images (Geocover Landsat TM MrSid images),
NDVI thresholds at different time periods and ground truth
information (e.g., Figs. 4 and 5).
All of the information was used in the hierarchical class
labeling process that led to the reduction of the 100 classes
to the final 29 classes. The pure pixels (the brightest, the
greenest and the wettest) are at the edges of the triangle
(spectral angle). Most pixels are some combination, linear or
nonlinear of these purest pixels. Brightness is represented by
albedo (approximately the mean of the red and NIR
reflectances) and the greenness by the difference between
NIR and red bands. The brightnessgreenness space is just a
458 clockwise rotation of the red and NIR space. Treecanopies and hills have deeper shadows compared with
crops making tree classes to cluster in the wetnessgreen-
Snow type 1
Snow type 2
Snow Type 3 Barren Type 8Very Bright Soils
Barren Type 7Moist Soils
Forest Type 6
Forest Type 1
Forest Type 2
Forest Type 3
Forest Type 4
Forest Type 5
Barren
Water Type 1
Water type 3
Wa
Mixed: irrigated crops
Mixed: Natural Veg. (open)/dry rain fed ag
CroCrop type 3
Mixed: grasslands (floodplain)/Irrigated crops (moist)
Crop type 7
Natural Vegetation (floodplain) Crop type 6
Mixed: Natural Veg. / cropsAgriculture (floodplains)
Mixed: open forest/ crops
Mixed: Forest/ sugarcane & rice
Crop type 1Wetlands
MODIS band 1 Vs. MODIS band
MODIS ban
MO
DIS
ban
d 2
ref
lect
ance
(%
)
0
0
15
25
35
5
10
20
40
30
10155
Fig. 6. RED-NIR single dates (RN-SDs) plot of 100 unsupervised classes. The 10
band 1 (red) and band 2 (NIR). The classes are shown in brightnessgreennessw
further investigations during ground truthing. Similar to figure shown above RN-
ness areas compared to the crop classes clustering in the
brightnessgreenness area (see Fig. 6).
5.3. RED-NIRs for multi-dates (RN-MD)
The 42 separate TC SDs, one for each MODIS image,
were plotted together to observe and interpret classes. We
found that it was more useful to juxtapose RED-NIR plots
of multiple dates (RN-MDs) in a single plot (e.g., Fig. 7) in
order to arrive at the final 29 classes (Table 1). The TC MDs
capture both the direction and magnitude of change in time
and space. The change angle (h) and change magnitude (M)were computed using equations (Zhan et al., 2002):
h arctan Dkred=DkNIR 3
M Sqrt Dkred 2 DkNIR 2h i
4
where h=change direction or angle; M=change magni-tude; Dkred=red reflectance at time 2-red reflectance at
Type 6
ter Type 2
Barren Type 1
Barren Type 2
Seasonal Snow Type 1
Barren Type 3
Barren Type 5
Barren Type 4
Mixed: Barren/Irrigated crop
Mixed: Barren/rain fed crop
/ riparian vegetation
Mixed: barren/ fallow crops
Mixed: Natural veg. (open)/ supplemental ag.Mixed: Riparian vegetation (moist), wetlands/built-up
.
Crop type 5p type 4
Mixed: Natural Veg. / Irrigated crops
Mixed: water / barren land
Mixed: Rangelands & open areas/ rain fed crops
Soil line2 mean reflectance values: May 9, 2001
d 1 reflectance (%)20 30 40
3525
0 unsupervised classes are plotted taking mean class reflectance in MODIS
etness (BGW) feature space and their preliminary class names identified for
SDs were plotted for each of the 42 dates.
-
0 10 20 30 40
MODIS Band 1 Reflectance (%)
0
10
20
30
40
MO
DIS
Ban
d 2
Ref
lect
ance
(%)
1
2
45
6
7
8
9
1011
12
13
14
1516
1718
19
20
21
22
2324
25
26
27
28
29
12
4
5
6
8
9
1011
12
13
14
15
16
17
18
1920
2122
232425 2627
28
29
1
4
5 6
8
9
1011
12
1314
15
16
17
181920
21
2223
24
25
26
27
2829
MODIS band 1 Vs. MODIS Band 2 mean refelectance values: Jan. 1, 2002(green);May 9, 2002 (red); Sept. 6, 2002 (blue)
Soil Line
Fig. 7. RED-NIR multi dates (RN-MDs) Change vector analysis of 29 unsupervised classes. First the 100 unsupervised classes shown in Fig. 6 are reduced to
29 classes after a rigorous analysis including RN-SDs, ground truth, vegetation index signatures, RN-MDs, and others (e.g., geo-cover TM images). Here, we
illustrate the magnitude and direction of change of each of the 29 LULC classes over time using RN-MDs taking a driest month (May), a wettest monsoon
month (September), and a second Rabi cropping month (January) during year 2002. RN-MDs were also initially plotted for all 100 classes. These plots are also
done for year 2001 and for other dates in both years.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 325
time 1; DkNIR=NIR reflectance at time 2-NIR reflectanceat time 1; arctan=arc tangent; Sqrt=square root.
We investigated the dynamics of the classes in three key
seasons: rabi peak in January, summer in May, and
monsoonal peak in September (Fig. 7). The connectivity
of the vectors of three distinct classes during the three
dates is illustrated in Fig. 7. Class 8 is barren land and
remains near the soil line during all three seasons (Fig. 7).
In contrast, class 17 is rain-fed agriculture with rangelands
and is close to soil line on the bright side of BGW feature
space between khariff and rabi seasons. During the khariff
peak (September) and rabi peak (January), class 17 is in
the greennessbrightness area. Class 22 is irrigated and has
high greenness during September, mid-way in the green-
nesswetness feature space in January, and only comes
anywhere nearer the soil line during the summer month of
May.
The availability of time series images has provided an
opportunity to define irrigated areas and other LULC
classes (e.g., rainfed agriculture) based on their seasonal
or multi-seasonal dynamics. The phenological informa-
tion contained in these multi-temporal images signifi-
cantly contributes to land cover classification, further
confirming the similar results by Dymond et al. (2002),
Jensen (2000) and Schriever and Congalton (1995).
5.4. LULC classes and their linkage with land cover (LC)
percentages: class labeling and area calculations
A total of 29 LULC classes (Table 1, Fig. 8) were mapped
which showed clear spectral separability on one or more
single dates (e.g., Fig. 6), and/or one or more multiple dates
(e.g., Fig. 7), and/or over a near-continuous time interval
(e.g., Fig. 9a and b). The total study area within the Ganges
and Indus basins was 133,021,156 ha (Table 1) where there
was a high degree of irrigation (e.g., see classes 2126 in Fig.
8 and Table 1). Class 30 was data noise that amounted to
0.5% of the total study area and, hence, was negligible.
The LULC name is based on predominance of a particular
land cover. For example, the name for class 27 is bForests(Himalayan): Mature.Q The land cover (LC) of this class isdominated by mature forests (31.7%, see Table 1), which
occur along the Himalayan mountains. The trees were 20+
years and hence classified as mature. Similarly, class 18 was
labeled brain-fed cropsQ since this was an intensely croppedarea class that is heavily dependent on seasonal rains.
-
Table 1
LULC and irrigation area the study area in Ganges and Indus from MODIS time series images of 2001 and 2002
Class
(#)
Class name (name) MODIS
LULC
area (ha)
MODIS
LULC
percent (%)
Watering method
(irrigation
type/rainfed)
Ground
truth LC %
of tree
Ground
truth LC %
of shrubs
Ground
truth LC %
of grass
Ground
truth LC %
of cultivated
all the LCs within a LULC class (%)
1 Water: Lakes and Rivers 133883 0.1 NA NA NA NA NA
2 Water: Marshland or
estuary
36449 0.0 NA NA NA NA NA
3 Water: Glacial Lakes 23570 0.0 NA NA NA NA NA
Water Total 193901 0.1
4 Wetlands: Natural
vegetation
86615 0.1 NA NA NA NA NA
5 Wetlands: Agriculture 1059235 0.8 NA NA NA NA NA
Wetlands Total 1145850 0.9
6 Snow: Seasonal 2830150 2.1 NA NA NA NA NA
7 Snow: Year round 1507185 1.1 NA NA NA NA NA
Snow Total 4337335 3.3
8 Barren lands: Himalayas
with bright tones, river
beds and built-up
1649611 1.2 NA
9 Barren lands: Himalayas
with bright tones
859473 0.6 NA NA NA NA NA
Barren lands Total 2509085 1.9
10 Desert lands: Lower
NDVI
7779006 5.8 NA NA NA NA NA
11 Desert lands: Higher
NDVI
9752495 7.3 NA NA NA NA NA
Desert lands Total 17531501 13.2 NA NA NA NA NA
12 Mixed: Marshlands and
Himalayan barren lands
with dark tones
625817 0.5 NA
13 Mixed: Rice, other crops,
and wetlands
2731665 2.1 wetlands 1.0 0.3 20.0 75.3
14 Mixed: Rice, other crops,
shrubs, and young
secondary forest
22823167 17.2 rainfed+
supplemental
2.9 12.7 11.9 54.1
Mixed classes and crops
rice dominant Total
26180649 19.7
15 Mixed: Rangelands,
open areas, rainfed
crops, and sub-urban
built-up
4158052 3.1 rainfed 6.1 7.0 41.1 25.1
16 Mixed: Shrublands, fallow
lands, built-up, and others
3411039 2.6 rainfed 1.0 20.4 7.7 33.7
Rangelands and
shrublands Total
7569091 5.7
17 Rainfed Crops and
Rangelands
7584546 5.7 rainfed 1.4 5.3 29.5 43.5
18 Rainfed Crops 5347864 4.0 rainfed 0.0 5.0 0.0 95.0
Rainfed Total 12932411 9.7
19 Forests (open): mix with
rice and other crops
1822605 1.4 rainfed 1.5 0.0 13.8 66.8
20 Forests (open): mix with
rice and natural vegetation
2719730 2.0 rainfed 5.3 6.7 20.0 61.3
Forests (open) Total 4542335 3.4
21 Irrigated: Rice, sugarcane,
other crops
3150636 2.4 Canal+tube
well
3.8 0.5 2.0 91.3
22 Irrigated: Rice,
sugarcane, agroforests,
other crops
6046429 4.5 Canal+tube
well
11.2 8.4 7.5 61.6
23 Irrigated: Other crops,
fallow farms, rice
16212207 12.2 tube well 1.4 1.5 1.8 90.8
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341326
-
Ground
truth LC %
of others
Ground truth
LC % of rice
only LC %
Actual tree area
of class (LULC
area of classtree
Actual shrub
area of class
(LULC area of
Actual grass area
of class (LULC
area of classgrass
Actual cultivated
area of class
(LULC area of
Actual other
areas of class
(LULC area of
Actual rice
area within
cultivated area
cover % of class)
(ha)
classshrubcover % of
class) (ha)
cover % of class)
(ha)
classcultivatedcover % of class)
(ha)
classothercover % of
class) (ha)
(LULC area
of classricecover % of
class) (ha)
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA NA NA NA NA NA
NA NA NA
NA NA NA NA NA NA
3.5 70.0 28000 6829 546333 2055578 94925 1912165
18.3 28.6 667578 2906784 2724833 12345432 4178542 6526792
20.6 1.4 255483 291064 1710741 1045453 855311 59401
37.1 12.1 34110 696827 263137 1150007 1266957 414198
20.3 0.0 103426 400602 2240958 3302656 1536905 0
0.0 0.0 267 267126 0 5080471 0 0
18.0 45.5 27795 0 250608 1216589 327613 829285
6.7 46.7 145052 181315 543946 1668101 181315 1269207
2.5 39.3 118149 15753 63013 2874955 78766 1236625
11.4 31.6 674781 504877 450459 3727321 688991 1910369
4.6 25.2 227480 241377 287188 14714662 741500 4083160
(continued on next page)
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 327
-
Table 1 (continued)
Class
(#)
Class name (name) MODIS
LULC
area (ha)
MODIS
LULC
percent (%)
Watering method
(irrigation
type/rainfed)
Ground
truth LC %
of tree
Ground
truth LC %
of shrubs
Ground
truth LC %
of grass
Ground
truth LC %
of cultivated
all the LCs within
a LULC class (%)
24 Irrigated: Water logged
crops (Indus), rice, shrubs
7623035 5.7 Canal+tube
well
0.5 16.3 3.3 28.8
25 Irrigated: Rice with
wetlands
5607387 4.2 tube well 0.9 0.2 7.8 65.3
26 Irrigated: Rice and
other crops
6762875 5.1 tube well 1.6 0.3 5.5 87.4
Irrigated Total 45402568 34.1
27 Forests (Himalayan):
Mature
2412553 1.8 NA 31.7 15.3 33.3 0.0
28 Forests (Himalayan):
Young and wetlands
6010237 4.5 floodplain/
tube well
19.2 8.0 10.0 16.5
29 Forests (Himalayan):
Young
1635143 1.2 NA 25.0 1.0 60.0 0.0
Forests Total 10057933 7.6
30 Striping: Noise 618497 0.5 noise noise noise noise noise
Total Area from all
classes (ha)
133021156 100.0
Total area of particular LC from all LULC classes
Total % area of particular LC from all LULC classes
26.6
73.4
A total of 62.9% of the Ganges Indus basins is covered in this study. The actual LULC class areas are determined by multiplying LULC areas obtained from
MODIS images with LC percentages of each class determined during ground-truthing.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341328
The ability to map a large number (29) of classes
(Fig. 8) even at 500 m spatial resolution, demonstrates
the strength of the 7-band near continuous MODIS data
and attests to the improved sensitivity of this instru-
ment compared to earlier sensors. Even within an
irrigated area class, 6 distinct classes (classes 2126 in
Table 1) were spectrally and temporally differentiated
(Fig. 9b).
-
Ground
truth LC %
of others
Ground truth
LC % of rice
only LC %
Actual tree area
of class (LULC
area of classtree
Actual shrub
area of class
(LULC area of
Actual grass area
of class (LULC
area of classgrass
Actual cultivated
area of class
(LULC area of
Actual other
areas of class
(LULC area of
Actual rice
area within
cultivated area
cover % of class)
(ha)
classshrubcover % of
class) (ha)
cover % of class)
(ha)
classcultivatedcover % of class)
(ha)
classothercover % of
class) (ha)
(LULC area
of classricecover % of
class) (ha)
51.3 22.5 38115 1238743 247749 2191623 3906805 1715183
25.8 53.8 50778 9657 438934 3662870 1445148 3018332
5.3 60.2 107618 17348 369018 5913105 355786 4072427
19.7 0.0 763975 369925 804184 0 474469 0
46.3 15.8 1151962 480819 602025 991689 2783742 951621
14.0 0.0 408786 16351 981086 0 228920 0
noise noise
ha. 4803355 7645397 12524211 61940511 19145695 27998764
% 3.6 5.7 9.4 46.6 19145695.4 21.0
Irrigated: canal ha 8793899 classes 21, 22,
and 24 in area
irrigated: tube
wells
ha 24290637 classes 23, 25,
and 26 in area
Irrigated (Total:
canal+tube well)
ha 33084536 classes 21 to
26 in area
% 24.9 classes 21 to
26 in %
Irrigated (Khariff
Total: canal+tube
well)
32555183 98.4 % of NET
Irrigated (Rabi
Total: canal+tube
well)
30603195 92.5% of
NET
Irrigated (Continuous Khariff-summer-Rabi Total:
canal+tube well)
1157959 3.5% of
NET
Irrigated (Gross from kariff, Rabi, continuous Total: canal+tube well) 64316337
Rainfed: Total ha 13463277 classes 15 to
20 in area
% 10.1 classes 15 to
20 in %
Rainfed+supplemental: Total ha 12345432 class 14 in area
% 9.3 class 14 in %
Wetland cultivation: Total ha 3047267 classes 13 and
28 in area
% 2.3 classes 13 and
28 in %
Cultivated: Total from all classes ha 61940511 Classes 13
to 29 in ha.
% 46.6 Classes 13 to
29 in %
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 329
5.5. Irrigated area, rice area and cropped area estimates
Each LULC class is a composite of several LC types
(see Table 1). For example, in class 22, cultivated areas
(61.6%) dominate but there are significant other LC types
that include other land cover (11.4%), trees (11.2%),
shrubs (8.4%) and grasses (7.5%) (Table 1). Of the
cultivated areas, 31.6% is rice cropthe single major
-
Fig. 8. The 29 LULC and irrigated area classes in the study area. Final 29 classes were mapped using 294 band MODIS data (42 MODIS images, each of 7
bands, during 2001 and 2002). The study area covers 63% of the Ganges and the Indus basins.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341330
crop of the class. Sugarcane was the next major crop
although statistics of sugarcane are not shown in Table 1.
Precise estimates of various thematic areas within classes
were calculated as follows (see Table 1):
Tree area in class 22
LULC class area for class 22 LC percentage of tree for class 22
6; 046; 429 11:2=100 674; 781 ha 11:2%
Using the same approach, there were 504,877 ha (8.4%)
of shrubs, 450,489 ha (7.5%) of grasses, 372,7321 ha
(61.6%) of cultivated areas and 688,991 ha (11.4%) of other
areas. The rice crop alone totaled 1,910,369 ha (31.6%).
We propose the above approach to area calculations since
it takes into account sub-pixel composition of the pixels. Let
us take the example of irrigated area class 21 which has a
total area of 3,150,616 ha (Table 1). Every pixel of this class
is irrigated, but at different degreesome pixels are 100%
irrigated and some 50% and some others a different pro-
portion. In order to calculate exact area under irrigation for
this class, we will need to perform sub-pixel decomposition.
We adopt a fairly straightforward approach based on land
cover (LC) composition for the class based on ground truth
data. The accuracy of this approach increases with sample
size for the class. Since we have fairly large sample size
locations for each class we feel confident that our area
estimates are reasonable. Normally, most studies take non-
decomposed pixel areas as actual areas of a particular land
use class.
Field data on bwatering sourceQ (column 5 of Table 1)was used to define classes as irrigated, rain-fed, rain-fed
with supplemental irrigation and flooded or wetland
cultivated. Classes 21, 22 and 24 were canal irrigated and
classes 23, 25 and 27 were tube-well irrigated. The same
approach described in the previous paragraph was used to
estimate the irrigated areas in each class wherein the total
area is multiplied by LC percent for crops in class 21
through 27 (since these classes are exclusive irrigated
agriculture). For example, the irrigated area resulting in a
total irrigated area of 33,084,536 ha (24.9% of the total
study area). Of this, canal irrigated area was 8,793,899 ha
(6.6% of the total area of the study of 133,021,156 ha)
compared the tube-well supplied area of 24,290,637 ha
(18.3% of the total area). The cropland LCs of classes 23,
25, and 27 were exclusively tube-well irrigated. The
cropland LCs of classes 21, 22, and 24 were overwhelm-
ingly canal irrigated, but has some very minor tube well
irrigated mix that we ignore.
There were 12,345,432 ha (9.3% of the total area of the
study) of rain-fed areas with substantial supplemental
irrigation of one sort or another. A significant portion,
3,047,267 ha (2.3%) incorporated wetland cultivation.
-
All Class type biomass fluctuation for 2001 and 2002
-0.2
0
0.2
0.4
0.6
0.8
1
1 41 57 73 89 113
129
185
249
345
361 33 49 65 81 10
512
115
320
931
335
3
Julian Date
ND
VI V
alu
e
class 18-Rainfed Crops class 21- Irrigated: Rice, sugarcane, other crops
class 27- Forests (Himalayan): Mature class 5- Wetlands: Agriculture
class 7- Snow: Year round class 8- Barren lands: Himalayas with bright tones, river beds and built-up
class 10 Desert lands: Lower NDVI class 15- Mixed : Rangelands, open areas, rainfed crops, and sub-urban built-up
Irrigated crop biomass fluctuation for 2001 and 2002
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 33 41 49 57 65 73 81 89 105
113
121
129
153
185
209
249
313
345
353
361 1 33 41 49 57 65 73 81 89 105
113
121
129
153
185
209
249
313
345
353
361
Julian Date
ND
VI V
alu
e
class 21- Irrigated: Rice, sugarcane, other crops class 22- Irrigated: Rice, sugarcane, agroforests, other crops
class 23- Irrigated: Other crops, fallow farms, rice class 24- Irrigated: Water logged crops (Indus), rice, shrubs
class 25- Irrigated: Rice with wetlands class 26- Irrigated: Rice and other crops
a
b
Fig. 9. MODIS NDVI signatures over time. With the availability of near-real-time MODIS data it is possible to develop LULC spectral signatures over time. (a)
Illustrates MODIS NDVI signatures for 8 spectrally distinct classes over 2 years. The classes are spectrally separable, distinctly, from each other at one time or
the other. (b) Illustrates MODIS NDVI signatures for 6 spectrally close irrigated area classes over 2 years. Time series MODIS data enables separability even
within close classes at one time or the other.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 331
Purely rain-fed dryland cropping was estimated at
13,463,277 ha (10.1%).
Rice is grown not only in the 6 irrigated classes 2126,
but also in other classes albeit only to some extent. Specific
LC percentages of rice crop within LULC classes 1329
were used to estimate the total rice area of 27,998,763 ha
(21% of the total study area) (Table 1).
The total cultivated land area in the study region is
61,940,511 ha (46.6% of the total area). Classes 112 have
almost no area under cultivation or irrigation and occupy
18.6% of the total area. LC percentages were not measured on
the ground, as these classes are relatively pure (e.g., LULC
classes composed one predominant LC type) and also rather
inaccessible. These classes were identified, based on their
spectral characteristics, GeoCover Landsat TM images, their
geographic location and from numerous other sources of data
(e.g., USGS LULC classification, Loveland et al., 2000).
It is important to note that the precise class areas pre-
sented in Table 1 were applicable to the khariff season only,
as LC percentage data were not available for other seasons.
We adopted a unique strategy to determine intensity of
irrigation in different seasons. The maximum monthly
NDVI composite images for different seasons were masked
out taking the spatial extent of irrigated areas of classes 21
-
Table 2
Cropping pattern for different classes in the study area in Ganges and Indus river basins
MODIS LULC class # Khariff crops Summer crops Rabi crops
crop-1 crop-2 crop-3 crop-4 crop-1 crop-2 crop-3 crop-4 crop-1 crop-2 crop-3 crop-4
1 NA NA NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA NA NA
7 NA NA NA NA NA NA NA NA NA NA NA NA
8 NA NA NA NA NA NA NA NA NA NA NA NA
9 NA NA NA NA NA NA NA NA NA NA NA NA
10 NA NA NA NA NA NA NA NA NA NA NA NA
11 NA NA NA NA NA NA NA NA NA NA NA NA
12 NA NA NA NA NA NA NA NA NA NA NA NA
13 Rice maize Moong arhar Rice maize moong arhar Wheat Gram Barley Mustard
14 Rice Jawar Basra Moong Rice Jawar Basra Moong Wheat Barley Gram Mustard
15 Jawar Soybean Basra Rice Jawar Soybean Basra Rice Wheat Barley Sugarcane Mustard
16 Jawar Urd Moong Rice Jawar Urd Moong Rice Wheat Barley Gram Mustard
17 Jawar Basra Gowar Jawar Basra Gowar Wheat Barley Gram Mustard
18 Jawar Basra Arhar Jawar Basra Arhar Wheat Barley Gram Mustard
19 Jawar Rice Basra Maize Jawar Rice Basra Maize Wheat Barley Mustard Maize
20 Rice Jawar Basra Vegetables Rice Jawar Basra Vegetables Sugarcane Wheat Barley Potato
21 Rice Jawar Sugarcane Vegetables Rice Jawar Sugarcane Vegetables Wheat Barley Sugarcane Berseem
22 Jawar Rice Mango Vegetables Jawar Rice Mango Vegetables Wheat Sugarcane Barley Mustard
23 Jawar Basra Arhar Rice Jawar Basra Arhar Rice Wheat Barley Gram Mustard
24 Jawar Rice Vegetables Jawar Rice Vegetables Wheat Sugarcane Vegetables
25 Jawar Rice Arhar Maize Jawar Rice Arhar Maize Wheat Sugarcane Mustard Vegetables
26 Rice Jawar Basra Maize Rice Jawar Basra Maize Wheat Barley Gram Mustard
27 NA NA NA NA NA NA NA NA Wheat Mustard Maize Sugarcane
28 Jawar Rice Basra arhar Jawar Rice Basra arhar NA NA NA NA
29 NA NA NA NA NA NA NA NA NA NA NA NA
30 noise noise noise noise noise noise noise noise noise noise noise noise
The cropping pattern are given for different seasons.
P.S.Thenkabailet
al./Rem
ote
Sensin
gofEnviro
nment95(2005)317341
332
-
Table
3
Irrigated
area
comparisonsbetweendifferentstudiesforstudyarea
inGanges
andIndus
Studyarea
Totalarea
ofthis
studyin
hectares
relative
tototal
basin
area
(ha)
Totalarea
ofthis
study
inpercent
relative
tototal
basin
area
(%)
Irrigated
area
USGSusing
AVHRR1000
m19921993
36im
ages
each
of1NDVI
band(ha)
Irrigated
area
USGSusing
AVHRR1000
m19921993
36im
ages
each
of1NDVI
band(%
)
Irrigated
area
GLC2000
usingSPOT
1000m
2000
36each
of1
NDVIband
(ha)
Irrigated
area
GLC2000
usingSPOT
1000m
2000
36each
of1
NDVIband
(%)
Irrigated
area
thisstudyusing
MODIS
500-m
20012002
42
images
each
of
7-bands(ha)
Irrigated
area
thisstudyusing
MODIS
500-m
20012002
42
images
each
of
7-bands(%
)
Irrigated
area
withsupplemental
thisstudyusing
MODIS
500-m
2001200242
images
each
of
7-bands(ha)
Irrigated
area
with
supplementalthis
studyusing
MODIS
500-m
2001200242
images
each
of
7-bands(%
)
Ganges
andIndusbasins
133021156
63
40046229
30.1
72614135
54.6
33084536
24.9
45429968
34.2
Ganges
basin
90221264
95
32255630
35.8
56466954
62.6
26873934
29.8
37602567
41.7
Irrigated
area
inthisstudybased
onMODIS
dataof20012002relativeto
irrigated
area
byUSGS1993:studyarea
inGanges
andIndus
(%)
13.4
increase
Irrigated
area
inthisstudybased
onMODIS
dataof20012002relativeto
irrigated
area
byUSGS1993:Ganges
basin
(%)
16.6
Irrigated
area
inthisstudybased
onMODIS
dataof20012002relativeto
irrigated
area
byGLC2000:studyarea
inGanges
andIndus
(%)
37.4
decrease
Irrigated
area
inthisstudybased
onMODIS
dataof20012002relativeto
irrigated
area
byGLC2000:Ganges
basin
(%)
33.4
Irrigated
areasmapped
usingdifferentstudiesiscompared
withthisstudy.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 333
through 26. The masked areas for different months in the
seasons were then classified and areas irrigated and fallow
were determined based on their seasonal NDVI dynamics.
The classes will cluster based on NDVI dynamics. A class
with high degree of irrigation (say 90100% of pixel areas
are irrigated) will have a higher NDVI threshold over 24
months of a growing season relative to a class with a low
degree of irrigation (say 2030% of pixel areas are
irrigated). Based on this approach, we determined 98.4%
of this area during Khariff (JuneOctober), 92.5% during
Rabi (NovemberFebruary), but only 3.5% all through the
year or continuous (apart from Khariff and Rabi, also in
MarchMay) cropping.
5.6. Cropping pattern
The cropping pattern of classes 1329 are given in Table
2 for khariff and rabi. In some cases, there is a short interim
season between rabi and khariff when summer crops are
grown if water is available, and according to ground survey
these are the same combinations as for khariff. The irrigated
area classes, 2126, have either rice or sorghum as main
crops during khariff and, where applicable, in summer. The
cropping mix in rabi is generally wheat-sugarcane or wheat-
barley. The main rain-fed crops, classes 17 and 18, have
sorghum-millet in khariff, but change to wheat-barley in rabi
(Table 2). During the field work, the authors were
accompanied by highly knowledgeable local agricultural
experts from the Indian National systems (see Acknowl-
edgements) who were instrumental in determining rabi and
summer crops at each field plot location, at times involving
interview with local farmers.
5.7. Irrigated area comparison with other studies
The results of this study were compared with: (a) USGS
study using monthly AVHRR 1-km NDVI time series from
April 1992 to March 1993 (Loveland et al., 2002), and (b)
global land cover (GLC) for year 2000 using monthly SPOT
1-km data (Belward et al., 2003). In the GLC2000 study,
data from the 4 spectral bands of the SPOT sensor were
used: blue (0.430.47 Am), red (0.610.68 Am), infrared(0.780.89 Am) and shortwave infrared (1.581.75 Am).
In the entire study area, the combined irrigated and
supplemental irrigated areas mapped using 20012002
MODIS data in this study showed an increase of 13.4%
to 45,429,968 ha, compared with the USGS figure of
40,046,229 ha (Table 3). The GLC2000 irrigated areas
(72,614,135 ha) did not tally with our study. This is
because GLC has 2 irrigated area classes (class 32 and 33)
with a contrasting definition. Class 32 is irrigated with
intensive agriculture, which is similar to our irrigated area
classes. Almost all of the spatial distribution of this class
fell within our irrigated-area classes. However, the
GLC2000 class number 33 (irrigated agriculture) with
38.4 million hectares is a predominantly rain-fed with some
-
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341334
irrigated. Almost all of our rain-fed classes, 17 and 18, fall
within this class 33. In addition, classes we identified as
rangelands and some of the forests are also labeled irrigated
agriculture by GLC200.
The comparisons are only indicative to show how the
irrigated areas are estimated for Ganges river basin by
various studies using remote sensing datasets of wide
range of characteristics. The results of this study
performed at 500-m were compared with AVHRR and
SPOT classifications performed at 1-km scale. The USGS
AVHRR study use 2 broad bands (band 1 and 2), The
GLC SPOT study used 4 broad bands (2 visible and 2
SWIR). This study used 7 narrow bands (4 visible, 2
NIR and 2 SWIR). Thereby, differences in LULC or
irrigated areas are often as a result of factors such as
data types, class definitions, level at which the classes
are mapped, analysis methods and techniques, and
resources spent on analysis and classification schemes
rather than change per se.
5.8. Tree cover, shrub and grass cover in the basin
The tree shrubs and grasses are predominant in classes 27
to 29. But often, other classes have a significant percentage
of one of these cover types. For example, 11.16% of the
land cover in irrigated-area class 22 was trees as a result of
agroforests forming part of the cropping system. Using the
tree, shrub and grass percentages of all classes we found
there was 4,803,355 ha (or 3.6% of the total area) of trees,
7,645,397 ha (5.7%) of shrubs, and 12,524,211 ha (9.4% of
grasses) in the study area.
5.9. Class signatures, NDVI-reflectivity thresholds, and
onset-peak-senescence-duration of crops
The class signatures of NDVI (CS-NDVI) are unique
time series of a class using NDVI or spectral reflectivity
in individual wavebands. It is not possible to have
temporal signatures when single date or a few date
images are used as is often the case with most LULC
studies. The set of NDVI class signatures is shown in Fig.
9a and b for classes mapped in Fig. 8. Threshold NDVIs
and NDVI signatures over time help us determine the
onset and duration of cropping seasons (rabi and khariff),
the intensity of cropping in drought and normal years and
the end of a cropping season.
MODIS CS-NDVI signatures are presented and dis-
cussed for a set of distinct classes (Fig. 9a) and
thematically similar classes (Fig. 9b). The NDVI of forest
class 27 never falls below 0.5 on any date throughout a
year and across years (Fig. 9a). The agricultural lands in
wetlands (class 5) have a moderately high NDVI
throughout the year as a result of continuous soil moisture
availability. The rainfed agriculture (class 18 in Fig. 9a)
shows the dramatic differences in NDVI dynamics during
the normal year (2001) vs. drought year (2002). During
the normal year, the NDVI for Khariff season steeply
raises from Julian day 160, reaches peak NDVI of 0.25
and then starts falling reaching low values again around
Julian day 300. In contrast, during 2002 the NDVI never
rose above 0.2 and near complete crop failure is obvious
relative to NDVI dynamics of 2001. Rangeland class 15
has a sharp NDVI increase from about 0.25 during the
driest period to little over 0.6 during the monsoon from
June to October. During khariff, this is a class with rise in
NDVI almost similar to that of irrigated-area class 21.
However, the 2 classes are distinctly separate during other
periods. As expected, the desert class has a near-flat
NDVI across the year.
The temporal signatures of the six irrigated classes are
plotted in Fig. 9b. Irrigated class 21 peaks on day 49 (rabi
crop peak), reaches the lowest biomass around day 129 (dry
season, low), and reaches peak again around day 249
(khariff, crop peak). The cycle is remarkably similar for
both 2001 and 2002 (see Fig. 9a). For example, the rabi crop
peak green period or critical growth phase was around
Julian day 57 during 2001 and day 49 during 2002.
Senescence begins around day 89 during year 2001 and
day 81 during year 2002. Based on these results, the
nominal crop duration from sowing to harvest during khariff
is (Fig. 9a) 180 days (Julian day 153333 days), rabi is 142
days (from day 333 of 1 year to the next year Julian day
110), and a short dry season of no cropping for 43 days
(days 110153).
The six irrigated-area classes are identified by subtle
differences between these classes. Most of these classes
were dominated by rice and other irrigated crops during
khariff. Crop vigor, biomass levels and percent area
cultivated are comparable at certain times of the year,
but not at other times (Fig. 9b). In spite of many
similarities, the classes often provide significantly different
NDVI signatures (Fig. 9b) at one time or another during a
year. There are several reasons for this. The first is the
type of land cover within and between these classes. Class
22, for example, is found mainly along the Indus river
basin, is heavily irrigated and flooded (31% water) or
moist throughout the year, suppressing NDVI substan-
tially. The presence of flooding or wet soils may result in
substantial absorption in near-infrared leading to low
NDVI throughout the season. Irrigated land accounts for
85% of all cereal grain production (mainly rice and
wheat), all sugar production and most of the cotton
production (Khan, 1999). Class 21, for example, is
basically dryland that is irrigated whereas other classes
like 22 exhibit higher moisture levels. The NIR reflec-
tance in drier lands with vigorous vegetation is substan-
tially higher than the NIR reflectance in irrigated areas
with substantial moisture or water logging. The second is
that differences occur in LC percentages within and
between classes. Class 26, for example, has about 20%
more rice than class 21 (Table 1). Class 21 has greater
percentage of other crops including sugarcane. The third is
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 335
that the differences in irrigated classes 2126 can also be
attributed to differences in the cropping calendar between
these classes.
The threshold MODIS NDVI limits of the class are
time-dependent (e.g., Fig. 9a and b). For example, in
class 21, the threshold NDVI range during Julian days 1
to 81 was (a) 0.62 to 0.75 during 2001 and (b) 0.53 to
0.77 during 2002. No other class has such a high
threshold MODIS NDVI. But these fall drastically in
summer when the NDVI thresholds for Julian days of 113
to 153 were (a) 0.23 to 0.27 during 2001 and (b) 0.2 to
0.24 during 2002. The pattern of very high NDVI during
crop growing seasons and low NDVIs between crop
seasons is a characteristic of the irrigation classes in the
Ganges and Indus basins. Classes 22 and 25 are the only
classes with relatively high NDVI during summer as a
result of the presence of significant agroforests (11.2%,
listed under forests in Table 1) in class 22 and significant
summer cropping (17.5%, listed under other land covers
Table 4
Fuzzy classification accuracy assessment (FCAA)
MODIS LULC
class (#)
Sample
size
Fuzzy
classification
accuracy
TOTAL
Correct
(%)
Fuzzy
classification
accuracy
TOTAL
Incorrect
(%)
Fuzzy
classification
accuracy
(absolutely
correct) (100 %
correct) (%)
Fuzzy
classification
accuracy (mo
correct) (75 %
and above
correct) (%)
1 10 70 30 30 40
2 10 70 30 30 20
3 10 100 0 70 30
4 8 63 38 13 13
5 10 100 0 50 40
6 10 90 10 70 20
7 10 100 0 100 0
8 10 90 10 40 30
9 10 100 0 50 50
10 10 80 20 60 20
11 10 100 0 80 10
12 8 75 25 38 25
13 10 100 0 50 20
14 40 85 15 48 25
15 10 80 20 60 0
16 10 60 40 30 20
17 13 69 31 46 15
18 8 88 13 63 25
19 8 88 13 25 25
20 8 88 13 0 50
21 8 100 0 50 38
22 20 75 25 30 15
23 37 84 16 49 24
24 9 56 44 44 11
25 19 79 21 37 11
26 23 100 0 52 30
27 8 75 25 13 50
28 8 63 38 25 0
29 8 63 38 38 25
30 NA NA NA NA NA
Total (%) 82.3 17.7 44.4 23.5
The quantitative FCAAwas performed on all MODIS derive LULC classes were d
images during field visit, geo-cover Landsat TM images, and, rarely, land use ma
in Table 1) in class 25 as a result of the availability of
water or moisture. All classes have a distinct cropping
calendar, onset-peak-senescence cycle and the biomass
magnitudes.
In stark contrast to irrigated-area classes, the rain-fed
class 18 (Fig. 9a), has a MODIS NDVI around 0.15
throughout the year 2001 with an NDVI between 0.2 and
0.28 during days 185249 with a peak around day 209
(Tables 3 and 4). During 2001, NDVI of rain-fed class 17
rises to a peak of 0.46 on day 209 with values of 0.35 on
day 185 and 0.39 on day 249. During the rest of the year,
the NDVI of class 17 is between 0.2 and 0.3. During
2002, the rains failed and as a result the NDVI of classes
17 and 18 never rose above 0.33 and 0.17, respectively,
indicating a severe drought situation in rain-fed areas. The
irrigated classes were not affected by the drought of 2002,
hence a similar pattern of MODIS NDVI magnitudes,
durations, and onset-peak-senescence cycles was main-
tained as in 2001 (Fig. 9b).
stly
Fuzzy
classification
accuracy
(correct) (51 %
and above
correct) (%)
Fuzzy
classification
accuracy
(incorrect)
(51 % and above
incorrect) (%)
Fuzzy
classification
accuracy
(mostly incorrect)
(75 % and above
incorrect) (%)
Fuzzy
classification
accuracy
(absolutely
incorrect) (100 %
incorrect) (%)
0 20 10 0
20 20 10 0
0 0 0 0
38 38 0 0
10 0 0 0
0 0 10 0
0 0 0 0
20 10 0 0
0 0 0 0
0 10 10 0
10 0 0 0
13 13 13 0
30 0 0 0
13 10 5 0
20 10 10 0
10 30 10 0
8 23 0 8
0 0 0 13
38 0 0 13
38 0 13 0
13 0 0 0
30 25 0 0
11 3 8 5
0 11 22 11
32 16 5 0
17 0 0 0
13 13 0 13
38 13 13 13
0 25 0 13
NA NA NA NA
14.4 9.9 4.8 3.0
etermined using ground truth point data, observations marked on maps and
ps from other sources.
-
Soil Line
0
10
20
30
40
1
33
41
4957
6573 81
89
105113121
129
153185
209
249
313
345
353361
133
4149
57 6573
8189
105
113
121
129
153
185
209249
313
345
353
361
133
41
49
576573 81
89
105
113 121
129
153
185
209
249
313345
353361
1
33
41
49
57
657381
89105
113121
129153
185
209
249
313
345353
361
1
33
4149
57
65
73 8189105
113121
129153
185 209
249
313345
353361
LULC ClassesWater: Lakes and Rivers (class 1)-2001Mixed: Marshlands and Himalayan barren lands with dark tones (class 12)-2001Rainfed Crops (class 18)-2001Irrigated: Rice and other crops (class 26)-2001Forests (Himalayan): Mature (class 27)-2001
Soil Line
LULC Classes
Irrigated: Rice, sugarcane, agroforests, other crops (class22)-2001
Irrigated: Water logged crops (Indus), rice, shrubs (class 24)-2001
Irrigated: Rice with wetlands (class 25)-2001
0 10 20 30 40
Mean Reflectance (%) MODIS Band 1
0 10 20 30 40
Mean Reflectance (%) MODIS Band 1
0
10
20
30
40
Mea
n R
efle
ctan
ce (%
) MO
DIS
Ban
d 2
Mea
n R
efle
ctan
ce (%
) MO
DIS
Ban
d 2
1
49
73105
129209
345
1
49
73
105
129
209
3451
4973 105
129
209
345
a
b
Fig. 10. Space-time spiral curves (ST-SCs) to study subtle and not-so-subtle changes in LULC spectral separability. The ST-SCs are a unique and powerful
representation of observing subtle and not so subtle changes over time mapped in 2-dimensional feature space. MODIS band reflectance in band 1 (red) and
band 2 (NIR) are used to plot ST-SCs for: (a) 5 spectrally distinct LULC classes and (b) 6 spectrally similar irrigated area classes. As the spectral properties of
classes change over time, we can observe dates on which 2 or more classes spectral intersect (no spectral separability) or stay spectrally separate highlighting
the near-continuous interval multi-temporal data in LULC studies.
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341336
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 337
5.10. Space time spiral curves (SC-STs)
The space-time spiral curves (ST-SCs) (Fig. 10a and b)
are introduced as an innovative approach to represent and
track near continuous changes in class behavior over time
and space. The dynamics of five classes are shown in a 2-
dimentional feature space using MODIS class reflectivity in
Fig. 10a. In most cases, classes have their own bterritoryQand mostly move around within it. In Fig. 10a the rain-fed
class is in bbrightness territory,Q the irrigated class inbgreenness territoryQ and the water class in bwetnessterritory.Q ST-SCs depict change over time and capture themagnitudes of change no matter how subtle (e.g., an
incremental increase in leaf area) or dramatic (e.g., clear-
cut forests), temporary (e.g., senescing crop) or more
permanent (e.g., built-up areas in place of agricultural
lands). The classes shown in Fig. 10a rarely overlap one
another, providing an excellent opportunity to separate
classes on most dates. SC-STs tell us when two classes
have similar spectra and when they are most separable. In
contrast, the irrigated-area classes significantly overlap one
another on most dates (Fig. 10b) since these classes are
spectrally close to one another. However, there are one or
more dates when a given class is separable spectrally from
other classes. For example, on day 209 all six classes have a
unique place in the 2-d FS as shown in SC-ST (Fig. 10b)
and, similarly, on day 153.
5.11. Performance of MODIS spectral wavebands in
irrigated area mapping
The best wavebands for irrigated area mapping were
determined using 3 distinct methods. First, the class
7-band mean reflectance values for
0
5
10
15
20
25
30
35
40
45
MODIS band/ date
Mea
n R
efle
ctan
ce (%
)
class 21 Irrigated: Rice, sugarcane, other crops class 22 Irrigated: Rice, sug
class 24 Irrigated: Water logged crops (Indus), rice, shrubs class 25 Irrigated: Rice with
3 4 1 2
129
5 6 7 3 4 1 2
153
5 6 7 3 4 1 2
185
5 6 7
Fig. 11. Multi-band reflectivity signatures (MB-RS) is separating closely related
irrigated area classes we use the spectral strength of 7 MODIS bands. For example
sensor and this provides maximum separability in 6 closely related irrigated area
signatures based on multiple band reflectivity (CS-MBR)
of 7 MODIS bands were used to see spectral differ-
entiation of close classes such as irrigated area classes 21
26 (see Fig. 11). The greatest difference in reflectivity
between the six irrigated classes was found in MODIS
band 5. The results were consistent across dates,
confirming the utility of this band in separating spectrally
close irrigated-area classes. This band centered on 1240
nm is a unique 20 nm-wide narrow-band not found in
satellite sensors currently orbiting the globe except
MODIS and Hyperion, aboard the Earth Observing-1
(EO-1) satellite.
Second, multiband feature space plots (MB-FSP; Fig.
12ae) were used to determine class separability. Closely
related irrigated-area classes 2126 were used in the test in
order to determine whether the use of multiple wavebands
help separate them. There is good evidence that classes 23
and 25 (which were close to each other in band 1 vs. band
2 (Fig. 12a) prove to be distinct when band 2 and band 7
(Fig. 12b), band 6 vs. band 7 (Fig. 12c), and band 2 vs.
band 3 (Fig. 12d) are plotted. The results generally
support the use of multiple wavebands in irrigated-area
mapping and highlight the importance of bands 2, 7, 6 and
3 in that order.
Third, we determined the frequency of occurrence of
least redundant bands (FO-LRB) in irrigated area map-
ping. Correlations for 7 band (k1) by the 7 band (k2)matrix (see Thenkabail et al., 2004a, 2002) were
established for each class taking all 42 dates into account
and it was found that the higher the correlation between
bands greater the redundancy and vice versa. In each
correlation matrix, we plotted and looked for the lowest
R2 values (least redundancy) between two bands. Thus,
Irrigated classes: May 9-Nov. 9, 2001
arcane, agroforests, other crops class 23 Irrigated: Other crops, fallow farms, rice
wetlands class 26 Irrigated: Rice and other crops
3 4 1 2
209
5 6 7 3 4 1 2
249
5 6 7 3 4 1 2
313
5 6 7
irrigated area classes. In order to separate closely related classes such as 6
, the MODIS band 5 (centered at 1240 nm) is a unique band in any satellite
classes.
-
21
2223
24
25
26
21
22
23
24
2526
0 10 20 30 40
Mean Reflectance (%) MODIS Band 6
0 10 20 30 40
Mean Reflectance (%) MODIS Band 3
0 10 20 30 40
Mean Reflectance (%) MODIS Band 1
0 10 20 30 40
Mean Reflectance (%) MODIS Band 7
0
10
20
30
40
Mea
n R
efle
ctan
ce (%
) MO
DIS
Ban
d 2
0
10
20
30
40
Mea
n R
efle
ctan
ce (%
) MO
DIS
Ban
d 2
0
10
20
30
40
Mea
n R
efle
ctan
ce (%
) MO
DIS
Ban
d 2
0
10
20
30
40
Mea
n R
efle
ctan
ce (%
) MO
DIS
Ban
d 6
21
2223
24
25
26
Irrigated classes for Sept. 6, 2001 MODIS Bands 2 and 6
Irrigated classes for Sept. 6, 2001 MODIS Bands 3 and 2
Irrigated classes for Sept. 6, 2001 MODIS Bands 1 and 2
Irrigated classes for Sept. 6, 2001 MODIS Bands 6 and 7
21
2223
24
25
26
a b
c d
Fig. 12. Multi-band bispectral plots (MB-BP) for separating closely related irrigated area classes. Spectra of different combinations of MODIS bands were
plotted in 2-dimensional feature space to evaluate class separability of 6 irrigated area classes. There is remarkable improvement in class separability of 2
classes. Some classes like 23 and 25 that were close to each other when band 1 (red) vs. band 2 (NIR) are plotted (a) show distinct separability when 2 mid-
infrared bands, band 6 vs. band 7, are used (c).
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341338
the two-band combinations providing the three lowest R2
values were recorded for all classes. Similar plots and
selections of bands for each of the 42 classes were carried
out. The NIR band 2 occurred most frequently (over 25% of
the time), followed by the two shortwave infrared (SWIR)
bands 7 and 6. This means that MODIS band 2 is most useful
in LULC classifications followed by SWIR bands 7 and 6,
further supporting the results from the previous section. The
SWIR bands are subjected to less attenuation due to
atmospheric water (Kerber & Schutt, 1986) and more
strongly correlated with biophysical properties of vegetation
than the visible and NIR wavelengths (Foody et al., 1996).
6. Fuzzy classification accuracy assessment (FCAA)
Relative classification accuracies (Table 4) were eval-
uated using a fuzzy approach (Mickelson et al., 1998;
Woodcock & Gopal, 2000). Error matrix accuracies are
deterministic (correct or wrong; yes or no) whereas the
accuracy in this study is based on a fuzzy (absolutely
correct, mostly correct, correct, incorrect, mostly incorrect,
and absolutely incorrect) approach. When there is no simple
byes/noQ answer, but a scaled approach to error/accuracyassessments, the method is referred to as bfuzzyQ.
FCAA procedure begins with overlaying the 300
independent ground-truth data points (Fig. 4) on the 29
classes (Fig. 8). Great care was taken to avoid misregistra-
tion problems between field plot data, Landsat high
resolution imagery, and coarse resolution MODIS imagery
when performing fuzzy classification approach. This was
done by collecting field plot data from highly representative
locations for each of the 29 classes. All 300 independent
points were selected with care to avoid uncertainties and
ambiguities. The sites were selected by driving in the
vehicle; marking coordinates using a GPS, and recording
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P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317341 339
distances in vehicle odometer. The 300 locations were very
large homogeneous areas of at least 33 pixel areasrepresenting one of the 29 classes.
Actual LULC data available from the 33 pixel areagathered during ground truthing were compared with
classes mapped. A quantitative approach to FCAA was
adopted. At each point, if 9 pixels out of 9 pixels in the
LULC map matched exactly with ground-truth data then we
called it babsolutely correct.Q Similarly, the criterion adoptedwas: mostly correct (7599% correct), correct (5174%
correct), incorrect (2450% correct), mostly incorrect (1
24% correct) and absolutely incorrect (0% correct). Using
this approach we established a fuzzy classification criterion
for all points within each class with sample sizes for each
class varying between 8 and 40. These results were used to
derive a final single fuzzy classification criterion for each
class (Table 4). For example, in class 26 there were 23
ground-truth locations, each of 3 by 3 pixel area for a total
of 207 pixels. Of these, 108 pixels (52%) were absolutely
correct, 63 pixels (32%) were mostly correct and 36 (17%)
correct.
Overall, the FCAA established that the 29 classes were
accurate from 56 to 100%17 classes from 80% to 100%, 6
classes from 70% to 80%, and 6 other classes from 56% to
70% accurate. A high degree of classification accuracy was
observed when a large number of classes are mapped and
this highlights the value of using time-series multiple band
MODIS data in contrast to low levels of accuracy, 1654%,
reported for AVHRR data (e.g., Friedl et al., 2000; Strahler
et al., 1999).
Fuzzy logic also allows a qualitative understanding of the
impact of misclassification (Muchoney & Strahler, 2002). A
misclassification of classes other than irrigated areas is more
acceptable in this study, since we wish to determine irrigated
area and intensity. Intermixing between forest classes or an
irrigated-area class intermixing with another irrigated-area
class is more acceptable than an irrigated-area class getting
mixed with forests. The six irrigated-area classes, 2126, had
an accuracy (in %) of 100, 75, 84, 56, 79 and 100. Only class
24 had a low accuracy of 56%, but it mixed almost only with
the other five irrigated classes. The forest classes 28 and 29
had a low accuracy, also, mainly due to intermixing between
the two classes. In the International Geosphere Biosphere
Programmes Data and Information System (IGBP-DIS)
global land cover data, nearly 60% of the problems
addressed in the post-classification process for the set arose
from confusion between natural vegetation and agriculture
(Loveland et al., 1999). Similar issues arose with MODIS
data (Friedl et al., 2000).
7. Conclusions
This study proposed and implemented a suite of
techniques and methods for mapping irrigated-area and
other LULC classes at river-basin level using near-
continuous time-series (8-day) MODIS 7-band reflectance
data. The study espoused a new unique approach to time-
series MODIS data analysis that begins with a single 7-GB
mega file dataset of 294 bands (42 images of 7-bands
each) for Ganges and Indus basins during year 2001 and
2002. The study resulted in mapping a large number (29)
of classes (Fig. 8, Table 1), yet maintaining high levels of
accuracy (56100% with most classes accurate between
80% and 100%; Table 4). The study highlighted the use of
FCAA technique in coarse resolution data accuracy
assessments.
A new powerful concept of space-time spiral curves (ST-
SCs), which quantitatively tracks subtle and not-so-subtle
changes of spectral reflectivity of irrigated and LULC
classes in the 2-dimensional feature space (2-d FS) near-
continuously over time was introduced. The ST-SCs
establish the space-time domain of each class, demarcate
the precise bterritoryQ in which a particular class roams overtime in a 2-d FS, and identify the date/s on which a class is
best separable from other classes. A s