Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data - Southwest ...
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Remote Sensing of Environment 95 (2005) 317 – 341
www.elsevier.com/locate/rse
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 2001–2002. 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 hectares—26.6% 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 June–October),
92.5% irrigated during Rabi (Northeast monsoonal rainy season during November–February), 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 most critical resource in the twenty-first century—with
increasing demands and decreasing supplies. Irrigation is
The World Summit on Sustainable Development estimated to consume about 60% of the world’s diverted
(WSSD) in Johannesburg (2002) declared water to be the 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
T Corresponding author. Tel.: +94 1 2787404; fax: +94 1 2786854. calories per day per person from the current 2100; FAO,
E-mail address: p.thenkabail@cgiar.org (P.S. Thenkabail). 2003), the demand for water for irrigation is forecast to
0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2004.12.018
318 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
grow. This is neither feasible, due to shortage of water acceptable levels of accuracy (Thenkabail et al., 2004a) to
resources in many parts of the globe nor desirable because avoid serious implications of land cover misclassification
of the negative environmental impacts of irrigation on, for example, global land surface models (DeFries &
schemes. Los, 1999).
Improved water accounting is required to track In order to achieve this goal, GIAM uses datasets that
agricultural and nonagricultural water use, particularly in include AVHRR (1 km to 10 km), SPOT Vegetation (1
irrigation. This will require mapping LULC and irrigated km), MODIS (250–500 m), ASTER (15–90 m), ETM+
area classes on a near-continuous (e.g., every 8-day) (15–30 m), TM (30 m), and IRS (5–23.5 m). Irrigated
basis. Most of the LULC classification efforts in the past classes form part of some of LULC mapping efforts (e.g.,
three decades used single or a selected few remote Loveland et al., 2000), but no special focus or
sensing images (see Foody, 2002). Such classifications importance was given to them, leading to a large
provide little or no information on the temporal dynamics percentage of mixed classes with natural vegetation.
of LULC classes, highly limiting their use in applications Primarily, there are non-remote sensing based studies on
such as hydrological modeling and evapotranspiration irrigated areas (e.g., CBIP, 1989, 1994; Siebert, 1999).
estimations (DeFries & Los, 1999). In recent years, The Food and Agriculture Organization (FAO, 2003;
AVHRR pathfinder time-series images (e.g., DeFries et Framji et al., 1981–1983; Siebert, 1999) of the United
al., 1998; Loveland et al., 2000) have been used to Nations estimates that about 20% of the arable land is
capture temporal dynamics of LULC at global level. irrigated at present with various scenarios of projected
However, it is only recently that near-continuous (e.g., 8- increases in the future, but provides no spatial map of
day composites) time series images from sensors such as where these areas are. Current estimated trends in
Moderate Imaging Spectrometer (MODIS) on board irrigation development are generally derived from
NASA’s Terra and Aqua satellites have allowed assess- national agricultural statistics with many uncertainties
ment of LULC dynamics and quantitative landscape about their accuracy.
characteristics (e.g., biomass, leaf area index) (Huete et With the overall scope of the GIAM project as
al., 2002) in near real time. For example, using these discussed in the previous paragraph in mind, we focus
datasets, vegetation in continuous streams are currently on mapping LULC with particular interest on irrigated
produced (http://glcf.umiacs.umd.edu/data/modis/vcf/). areas in the Ganges and Indus river basin using MODIS
MODIS data are also known to provide a significant data for year 2001–2002. The study will use multi-date,
improvement in terms of quality relative to the heritage near-continuous, MODIS data, and adopt a series of
AVHRR data (Friedl et al., 2000). The advances in innovative methods and procedures—the N-dimensional
spectral, spatial, radiometric, and temporal resolutions of change vector analysis (CVA), new space-time spiral-curve
MODIS datasets () are further complimented by advances techniques to assess subtle and not-so subtle quantitative
in cloud/haze removal algorithms, time compositing, and changes over time and space, and evaluate the study using
normalization of data into reflectance. It is well estab- fuzzy classification accuracy assessment. Through these
lished that LULC and irrigated area maps of the present measures we plan to demonstrate a unique set of data,
day require capturing quantitative dynamics over space methods, procedures, and protocols for mapping irrigated
and time (DeFries & Los, 1999; Foody, 2002; Huete et areas. The Ganges and Indus basins (referred to as Indo-
al., 2002) in order to enable them to be used more Gangetic) was selected for this study because it is one of
productively in studies such as hydrological modeling the most densely populated and intensively cultivated areas
(Foody, 2002), drought assessments (Thenkabail et al., of the world with irrigation forming a key role in food
2004b), impact on biodiversity (Chapin et al., 2000), production.
human habitability and climate change (Skole et al.,
1994), global warming (Penner et al., 1992) and soil
erosion (Douglas, 1983). 2. Study area and the MODIS data
The research described in this paper falls within the
framework of the Global Irrigated Area Mapping (GIAM) The study area (see non-hatched area within the basin
project at IWMI (Droogers, 2002; Turral, 2002). The boundary in Fig. 1) covers 63% (133,071,400 ha) of the
principal objective of GIAM is to map irrigated areas at Indo-Gangetic plain (total area=211,224,444 ha). The
different levels (global to local) and at different scales study area was chosen based on the importance of the
using satellite sensor data from various eras. Global LULC area for agriculture and irrigation and a need to map this
are essential to advancing most global change research area (Droogers, 2002; Turral, 2002). The Ganges river
objectives (Loveland et al., 2000). Regional and local basin originates in the Himalayan glaciers named Gang-
LULC efforts must aim for a greater number of discrete otri, about 4270 m above sea level. It has one of the most
classes of relevance to a wide variety of users (Thenkabail, fertile lands and has a very high population density of
1999; Thenkabail & Nolte, 2003). Irrespective of the level about 530 persons per square kilometer. The river flows
at which the classes are mapped, it is essential to establish through 29 cities each with a population of over 100,000,
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 319
E80° E100°
SNDVI
255
204
N20°
153
102
51
N0°
0
600 0 600 1200
Scale 1:40 000 000 Kilometers
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.
23 cities each with a population between 50,000 and Imagine 8.6 from which the areas within the Ganges and
100,000, and about 48 towns (Aitken, 1992; Ilich, 1996). Indus basins were delineated (Fig. 1).
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 3. Methods and techniques
major left bank tributaries—the Jhelum, Chenab, Ravi,
Beas and Sutlej rivers—and one major right bank 3.1. Mega file: multitemporal MODIS data for Ganges and
tributary, the Kabul (Khan, 1999). The catchments contain Indus river basins
some of the largest glaciers in the world outside the Polar
Regions (Meadows, 1999). The southwest monsoon or In this study, we use the MOD09 product, with 7 of the
khariff season (June to October) is followed by northeast 36 MODIS 500 m bands. The MOD09 is computed from
monsoon or Rabi season (November to February). The MODIS level 1B land bands 1–7 (centered at 648 nm, 858
mean annual rainfall is about 2000 mm, of which nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm).
approximately 70% occurs during the khariff season. The product is an estimate of the surface reflectance for
The dry season (March–May) the highest temperatures each band as it would have been measured at ground level
vary between 40 and 45 8C. if there was no atmospheric scattering or absorption
In order to enable the study of the characteristics of (Vermote et al., 2002). The original MODIS data are
land use and irrigation on a near- continuous basis, the 8- acquired in 12-bit (0–4096 levels), and are stretched to 16-
day composite MODIS images of year 2001, a rainfall bit (0–65,536 levels). Dividing these data by 100 will
normal year, and year 2002, which experienced rainfall make them comparable to laboratory spectra in the 0–
deficit in terms of amount and distribution, were selected). 100% range.
One of the main goals of the study was to establish crop The long time series analysis of MODIS data requires
calendar for irrigated area crops as precisely as possible. construction of mega datasets that involve hundreds of
The goal was to determine onset-duration-magnitude of the bands. Altogether 294 bands (42 images7 bands) from
peak-senescence for each irrigated area class. As a result 21 images from year 2001 and 2002 were formulated
we need to use as frequent images as possible-leading us into a single mega file of approximately 7 GB. A
to use 8-day composites and apply cloud removal separate 42-band NDVI mega file (one NDVI band for
algorithm rather than use 32-day images with significantly each date) was also created. The single mega file
lesser cloud issues. About 95% of the Ganges basin (total facilitate (a) analyzing the time series in their entirety
area 95,111,154 ha) and 37% of the Indus basin (e.g., they perform unsupervised classification of 294-
(116,113,290 ha), were covered by 3 MODIS tiles band data and determine how classes change in
(h24v06, h25v06, and h26v06; each tile of 10001000 magnitude and direction over space and time) and (b)
km). The three tiles were mosaicked into a single tracking quantitative changes at any level in near-
contiguous tile by running batch scripts in ERDAS continuous mode (e.g., NDVI variations at pixel or
320 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
entire study-area level in 8-day time interval). Performing removed and (c) none of the desert gets removed. A simple
analysis on 10 s or 100 s of images of individual dates algorithm for cloud removal in ERMapper (ERMapper,
is too cumbersome, leads to repetitive work, hard to 2004) was:
keep track of class number changes for a given pixel,
If ði3N21%Þ then null else I ð1Þ
and just leads to chaos of handling too many files. In
comparison mega file offers a single file of data, a Where, i3 is MODIS band 3 (blue band). The algorithm
single file of output, and provide temporal variations for assigns null values to all cloud areas.
every pixel in quantitative terms (e.g., NDVI dynamics
over time). 3.2.1.2. Visible band minimum reflectivity threshold for
cloud. The minimum reflectivity of clouds in the MODIS
3.2. Cloud removal algorithm visible bands (bands 3, 4, and 1), provide the best
separability in which almost all clouds gets removed.
The Indo-Gangetic basin is subject to the effects of the The algorithm for cloud removal, using this approach
oscillating Sub-Tropical Convergence Zone (www.srh. with MODIS visible bands 3 (blue), 4 (green), and 1
weather.gov). These effects include the monsoon (June– (red) was
September), which brings extensive cloud cover and heavy
If ði1N22 and i3N21 and i4N23Þ then null else I ð2Þ
rains. During this season, there is a great change in the
vegetation cover, rapid change in its dynamics and biomass However, when using this approach much of snow and
accumulation. In order to retain the maximum number of a significant portion of the desert also get removed. This is
time series images during this period we (a) retained all not a problem, since we have several other time series
images with b5% cloud cover and (b) developed a cloud- images where snow and desert data exist in their entirety.
masking algorithm so as to eliminate areas of cloud cover So clouds were removed using Eq. (2), but snow and desert
and retain the rest of the image as is. Of 42 images, 8 images areas were retained in their entirety, based on non-cloudy
had 25–40% cloud cover which also implies that 60–75% of images.
133 million hectare study area is cloud-free. Our attempts to The results of cloud removal have been illustrated before
use MODIS quality control layers and flags were not and after images in Fig. 2.
successful and resulted in several difficulties. These include:
(a) cloud vs. snow vs. desert sand vs. aerosol confusion: as a 3.3. Normalization of temporal variability
result of this often Himalayan seasonal snow was removed
as cloud; (b) over-correction issue: over correction by The MODIS reflectance product has gone through a
quality control flags lead to significantly low reflectance rigorous atmospheric correction scheme based on the 6S
values which in turn effected temporal NDVI profiles; and radiative transfer code for normalizing for molecular
(c) bblockyQ effects: applying quality flags lead to bblockyQ scattering, gaseous absorption and aerosols that affect
effects in the images probably as a result of original quality the top of the atmosphere (TOA) signal (see inter alia
flags being performed at 1-km pixel size which seemed to Vermote et al., 2002). Aerosol effects are known to
cause bblocky/noisyQ effects in 500-m pixels (four 500-m remain uncorrected even after long compositing periods
pixels in one 1-km pixels). In fact, we were able to establish (e.g., a month) (Vermote et al., 2002) so such effects in
a more consistent, smooth, and stable NDVI profiles from 8-day time intervals are significant. It would be desirable
the MODIS cloud removal algorithm specially developed in to do further corrections for these effects, for which we
this study rather than use MOD09 QC layers. found a time-invariant location in the Rajasthan desert,
calculated mean values of each band for each of the 42
3.2.1. Cloud algorithm: statistical characteristics images for this time-invariant location, determined the
Clouds have unique spectral characteristics with consis- calibration coefficient of each band for each date by
tently high reflectivity in all visible and NIR wavebands, but dividing its reflectance by the mean, and then normalized
are quite often mixed with snow and desert backgrounds— images of each date by multiplying using calibration
the other two highly reflective classes. To establish clear coefficients.
statistical characteristics for clouds, we obtained sample
spectra from 350 locations for clouds, 240 locations for 3.4. Image processing and interpretation
snow and 180 locations for deserts. When the means,
minima and maxima of spectra for the clouds, snow and A summary of the image processing and interpretation
desert were plotted the results showed there were 2 excellent undertaken in this research is provided in Fig. 3. The
possibilities for separating most of the clouds. basis of the work stems from unsupervised classification
of all bands in the mega file, followed by various
3.2.1.1. Blue band minimum reflectivity threshold for cloud. innovative refinements in class membership using techni-
When we use minimum blue band reflectivity of 21% or ques derived from RED-NIR and time series signatures,
above (a) all clouds get removed, (b) much of snow gets which are discussed in more detail in the section onP.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 321
Before Cloud Removal Algorithm Day 153 2001 Before Cloud Removal Algorithm Day 185 2001
E70Ê E75Ê E80Ê E85Ê E90Ê E95Ê E100Ê E70Ê E75Ê E80Ê E85Ê E90Ê E95Ê E100Ê
N25Ê
N25Ê
TCC;RGB TCC;RGB
1,4,3 1,4,3
648, 555, 470 nm 648, 555, 470 nm
500 0 500 1000 Day 153 2001 500 0 500 1000 Day 185 2001
Kilometers Kilometers
Scale 1:16 500 000 Scale 1:16 500 000
RGB RGB
After Cloud Removal Algorithm Day 153 2001 After Cloud Removal Algorithm Day 185 2001
E70Ê E75Ê E80Ê E85Ê E90Ê E95Ê E100Ê E70Ê E75Ê E80Ê E85Ê E90Ê E95Ê E100Ê
N25Ê
N25Ê
TCC;RGB TCC;RGB
1,4,3 1,4,3
648, 555, 470 nm 648, 555, 470 nm
500 0 500 1000 Day 153 2001 500 0 500 1000 Day 185 2001
Kilometers Kilometers
Scale 1:16 500 000 Scale 1:16 500 000
RGB RGB
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.
Results and discussion. Ground-truth data have therefore We adopted a hierarchical classification system based on
been a crucial element in the process and are described in modified Anderson classification (Anderson et al., 1976).
the next section. For example, if a class does not belong to rice (class A) or
Fig. 3. Ground truth data point distributions in the study area. Precise location of the 9090 m ground truth locations spread across the study area shown on a
MODIS RED-NIR image. Color key: red: dry, cyan/green/yellow: green, blue/light blue: wet. (For interpretation of the references to colour in this figure
legend, the reader is referred to the web version of this article.)322 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
sugarcane (class B) it will fall into a higher category of samples (Congalton, 1988) was infeasible due to limitations
irrigated croplands (classes A and B). in resources.
At each location (e.g., Fig. 5), the following data were
recorded:
4. Ground-truth data
1. LULC classes: levels I, II and III of the Anderson
Ground truthing was conducted during October 1–22, approach.
2003 to coincide with the peak khariff (monsoonal rainy 2. Land cover types (percentage): trees, shrubs, grasses,
season from June to October) conditions. For such a large built-up area, water, fallow lands, weeds, different crops,
area as the Ganges and Indus basins, random or systematic sand, snow, rock, and fallow farms.
sampling is unrealistic and costly (Muchoney & Strahler, 3. Crop types, cropping pattern and cropping calendar: for
2002). Therefore, the sampling was stratified by access khariff, rabi (second main cropping period from Novem-
through roads and foot paths and randomized by locating ber to March) and interim seasons.
sites every few minutes of the drive. 4. Source of water: irrigated, rain-fed, supplemental
The MODIS data require a minimum sampling unit of irrigation.
500 m500 m, which in itself is inadequate. A larger 5. 311 digital photos hot linked @ 196 locations.
sampling unit is desired, but was quite impractical in the
field. The approach we adopted was to look for contiguous The data were organized in proprietary image processing
areas of homogeneous classes within which to sample (see and GIS formats with accompanying metadata so that they
Thenkabail, 2003, for sampling LAI), taking a representa- could be co-located with the unsupervised classification
tive area of 90 m90 m. Class labels were assigned in the (e.g., Fig. 4).
field, 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. 5. Results and discussion
In all, about 6500 km were covered to gather data from
196 sample locations (Fig. 4). The precise locations of the 5.1. Unsupervised classification
samples were recorded by GPS in the Universal Transverse
Mercator (UTM) and the latitude/longitude coordinate To begin with, unsupervised classification was per-
system with a common datum of WGS84. The sample size formed on the mega file (UC-MF) using an ISODATA
per class varied from 8 to 37 and the ideal target of 50 statistical clustering algorithm for multidimensional data
Cloud Removal Algorithms (CRAs): Mega-file (MFC) for time-series analysis
(A) Blue band minimum reflectivity threshold, of 294 bands for 2001 and 2002
(B) Visible band minimum reflectivity 42 MODIS 500-m 7-band images
threshold
End Member Analysis (EMA) : Multidate-multiband
Brightness-greenness-Wetness (BGW)
Calculate Statistics unsupervised classification
2-dimensional Feature Space (BGW (MD-MB UC)
NIR-RED Single Dates Class
assignment
(NR-SDs) Class Signature based on NDVI
(CS-NDVI): time-series
NIR-RED Multi Dates Mega Classes
(NR-MDs)
Class refinement Ground truth
Space-Time Spiral-Curve
(ST-SCs) from multi-date
Sub-pixel
tasseled cap composition
Class
simplification Net irrigated
Class Signatures area
Multi-band Reflectivity (CS-MBR)
Extraction of
2-band spectral plots Irrigated pixels Kharif, Rabi, and Quantitative Fuzzy Classification
2:1 & 6:7 Continuous irrigated Accuracy Assessment
areas (QFCAA)
Fig. 4. Methods and techniques workflow diagram. Flow chart showing methods and techniques of LULC and irrigated area mapping using continuous streams
of MODIS data.P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 323 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 (a–e) of 6 unique irrigated area classes (classes 21–26) and representative photos (f–h) of 3 forest classes (classes 27–29). (ERDAS, 2004). Initially, 100 classes were obtained as a 5.3), ground truth data (Sections 4 and 5.4), temporal NDVI starting block for further refinement and analysis. The plots (Section 5.9), and space-time spiral curves (Section UC-MF provides a substantial within-class variance 5.10) were used. (Friedl et al., 2000; McIver & Friedl, 2002) that is essential to map classes within a theme (e.g., different 5.2. RED-NIR Plots for single dates (RN-SDs), class types of irrigated-area classes). The sample size of the identification and labeling field-plot data was insufficient for certain classes to make the supervised classification robust. Hence unsupervised The spectral properties of the 100 classes obtained approach backed by RED-NIR plots (Sections 5.2 and through UC-MF were analyzed, based on their distribution
324 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
in brightness–greenness–wetness (BGW) RED-NIR feature ness areas compared to the crop classes clustering in the
space. The distributions of a selection of the 100 unique brightness–greenness area (see Fig. 6).
spectral classes for May 2001 are illustrated in Fig. 6.
All classes were identified and labeled, based on their 5.3. RED-NIRs for multi-dates (RN-MD)
position in the BGW RED-NIR feature space, use of higher-
resolution images (Geocover Landsat TM MrSid images), The 42 separate TC SDs, one for each MODIS image,
NDVI thresholds at different time periods and ground truth were plotted together to observe and interpret classes. We
information (e.g., Figs. 4 and 5). found that it was more useful to juxtapose RED-NIR plots
All of the information was used in the hierarchical class of multiple dates (RN-MDs) in a single plot (e.g., Fig. 7) in
labeling process that led to the reduction of the 100 classes order to arrive at the final 29 classes (Table 1). The TC MDs
to the final 29 classes. The pure pixels (the brightest, the capture both the direction and magnitude of change in time
greenest and the wettest) are at the edges of the triangle and space. The change angle (h) and change magnitude (M)
(spectral angle). Most pixels are some combination, linear or were computed using equations (Zhan et al., 2002):
nonlinear of these purest pixels. Brightness is represented by
albedo (approximately the mean of the red and NIR h ¼ arctanðDkred =DkNIR Þ ð3Þ
reflectances) and the greenness by the difference between h i
NIR and red bands. The brightness–greenness space is just a M ¼ Sqrt ðDkred Þ2 þ ðDkNIR Þ2 ð4Þ
458 clockwise rotation of the red and NIR space. Tree
canopies and hills have deeper shadows compared with where h=change direction or angle; M=change magni-
crops making tree classes to cluster in the wetness–green- tude; Dk red=red reflectance at time 2-red reflectance at
Barren Type 4
MODIS band 1 Vs. MODIS band 2 mean reflectance values: May 9, 2001
Mixed: grasslands (floodplain)/
Irrigated crops (moist)
Mixed: irrigated crops/ riparian vegetation
Soil line
40 Crop type 3 Crop type 4 Crop type 5
Mixed: Natural Veg. (open)/dry rain fed ag. Barren Type 5
Mixed: Barren/rain fed crop
35 Crop type 7
Natural Vegetation (floodplain) Barren Type 3
Crop type 6
Mixed: Barren/Irrigated crop
Mixed: Natural Veg. / crops
Agriculture
30 (floodplains) Barren Type 2
Mixed: open forest/ crops Mixed: barren/ fallow crops
MODIS band 2 reflectance (%)
Mixed: Forest/ sugarcane & rice Mixed: Natural veg. (open)/ supplemental ag.
Mixed: Riparian vegetation (moist), wetlands/built-up
25 Wetlands
Crop type 1 Barren Type 1
Mixed: Natural Veg. / Irrigated crops
Forest Type 5
20 Seasonal Snow Type 1
Forest Type 4 Mixed: Rangelands & open areas/ rain fed crops
15
Forest Type 3 Mixed: water / barren land
Barren Type 6
Forest Type 2
10
Forest Type 1 Water Type 2
Water Type 1
Forest Type 6
5
Barren Type 7 Water type 3
Moist Soils
Snow Type 3 Barren Type 8
0 Very Bright Soils
5 15 25 35
0 10 20 30 40
Snow type 1 MODIS band 1 reflectance (%)
Snow type 2
Fig. 6. RED-NIR single dates (RN-SDs) plot of 100 unsupervised classes. The 100 unsupervised classes are plotted taking mean class reflectance in MODIS
band 1 (red) and band 2 (NIR). The classes are shown in brightness–greenness–wetness (BGW) feature space and their preliminary class names identified for
further investigations during ground truthing. Similar to figure shown above RN-SDs were plotted for each of the 42 dates.P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 325
MODIS band 1 Vs. MODIS Band 2 mean refelectance values: Jan. 1, 2002(green);
May 9, 2002 (red); Sept. 6, 2002 (blue)
40
11
10
21 7
10
11
26 29
24 18
23 29 18 10
5
22 617
20
282519
13 13 11
14 17
5 2324
30
2715 2
28
27
194
25
26
18
Soil Line
9 9
MODIS Band 2 Reflectance (%)
16 20 17 1421 6
23 22 9
21 24 16
4
26 14
1516 8
8
19 13
25 12
22 5 15
20 8
28 4
20
12
6
27
12
29
11
2
10 1
0
0 10 20 30 40
MODIS Band 1 Reflectance (%)
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.
time 1; Dk NIR=NIR reflectance at time 2-NIR reflectance confirming the similar results by Dymond et al. (2002),
at time 1; arctan=arc tangent; Sqrt=square root. Jensen (2000) and Schriever and Congalton (1995).
We investigated the dynamics of the classes in three key
seasons: rabi peak in January, summer in May, and 5.4. LULC classes and their linkage with land cover (LC)
monsoonal peak in September (Fig. 7). The connectivity percentages: class labeling and area calculations
of the vectors of three distinct classes during the three
dates is illustrated in Fig. 7. Class 8 is barren land and A total of 29 LULC classes (Table 1, Fig. 8) were mapped
remains near the soil line during all three seasons (Fig. 7). which showed clear spectral separability on one or more
In contrast, class 17 is rain-fed agriculture with rangelands single dates (e.g., Fig. 6), and/or one or more multiple dates
and is close to soil line on the bright side of BGW feature (e.g., Fig. 7), and/or over a near-continuous time interval
space between khariff and rabi seasons. During the khariff (e.g., Fig. 9a and b). The total study area within the Ganges
peak (September) and rabi peak (January), class 17 is in and Indus basins was 133,021,156 ha (Table 1) where there
the greenness–brightness area. Class 22 is irrigated and has was a high degree of irrigation (e.g., see classes 21–26 in Fig.
high greenness during September, mid-way in the green- 8 and Table 1). Class 30 was data noise that amounted to
ness–wetness feature space in January, and only comes 0.5% of the total study area and, hence, was negligible.
anywhere nearer the soil line during the summer month of The LULC name is based on predominance of a particular
May. land cover. For example, the name for class 27 is bForests
The availability of time series images has provided an (Himalayan): Mature.Q The land cover (LC) of this class is
opportunity to define irrigated areas and other LULC dominated by mature forests (31.7%, see Table 1), which
classes (e.g., rainfed agriculture) based on their seasonal occur along the Himalayan mountains. The trees were 20+
or multi-seasonal dynamics. The phenological informa- years and hence classified as mature. Similarly, class 18 was
tion contained in these multi-temporal images signifi- labeled brain-fed cropsQ since this was an intensely cropped
cantly contributes to land cover classification, further area class that is heavily dependent on seasonal rains.326 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
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 MODIS Watering method Ground Ground Ground Ground
(#) LULC LULC (irrigation truth LC % truth LC % truth LC % truth LC %
area (ha) percent (%) type/rainfed) of tree of shrubs of grass 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 36449 0.0 NA NA NA NA NA
estuary
3 Water: Glacial Lakes 23570 0.0 NA NA NA NA NA
Water Total 193901 0.1
4 Wetlands: Natural 86615 0.1 NA NA NA NA NA
vegetation
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 1649611 1.2 NA
with bright tones, river
beds and built-up
9 Barren lands: Himalayas 859473 0.6 NA NA NA NA NA
with bright tones
Barren lands Total 2509085 1.9
10 Desert lands: Lower 7779006 5.8 NA NA NA NA NA
NDVI
11 Desert lands: Higher 9752495 7.3 NA NA NA NA NA
NDVI
Desert lands Total 17531501 13.2 NA NA NA NA NA
12 Mixed: Marshlands and 625817 0.5 NA
Himalayan barren lands
with dark tones
13 Mixed: Rice, other crops, 2731665 2.1 wetlands 1.0 0.3 20.0 75.3
and wetlands
14 Mixed: Rice, other crops, 22823167 17.2 rainfed+ 2.9 12.7 11.9 54.1
shrubs, and young supplemental
secondary forest
Mixed classes and crops 26180649 19.7
rice dominant Total
15 Mixed: Rangelands, 4158052 3.1 rainfed 6.1 7.0 41.1 25.1
open areas, rainfed
crops, and sub-urban
built-up
16 Mixed: Shrublands, fallow 3411039 2.6 rainfed 1.0 20.4 7.7 33.7
lands, built-up, and others
Rangelands and 7569091 5.7
shrublands Total
17 Rainfed Crops and 7584546 5.7 rainfed 1.4 5.3 29.5 43.5
Rangelands
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 1822605 1.4 rainfed 1.5 0.0 13.8 66.8
rice and other crops
20 Forests (open): mix with 2719730 2.0 rainfed 5.3 6.7 20.0 61.3
rice and natural vegetation
Forests (open) Total 4542335 3.4
21 Irrigated: Rice, sugarcane, 3150636 2.4 Canal+tube 3.8 0.5 2.0 91.3
other crops well
22 Irrigated: Rice, 6046429 4.5 Canal+tube 11.2 8.4 7.5 61.6
sugarcane, agroforests, well
other crops
23 Irrigated: Other crops, 16212207 12.2 tube well 1.4 1.5 1.8 90.8
fallow farms, riceP.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 327
Ground Ground truth Actual tree area Actual shrub Actual grass area Actual cultivated Actual other Actual rice
truth LC % LC % of rice of class (LULC area of class of class (LULC area of class areas of class area within
of others only LC % area of classtree (LULC area of area of classgrass (LULC area of (LULC area of cultivated area
cover % of class) classshrub cover % of class) classcultivated classother (LULC area
(ha) cover % of (ha) cover % of class) cover % of of classrice
class) (ha) (ha) class) (ha) cover % 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)328 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
Table 1 (continued)
Class Class name (name) MODIS MODIS Watering method Ground Ground Ground Ground
(#) LULC LULC (irrigation truth LC % truth LC % truth LC % truth LC %
area (ha) percent (%) type/rainfed) of tree of shrubs of grass of cultivated
all the LCs within
a LULC class (%)
24 Irrigated: Water logged 7623035 5.7 Canal+tube 0.5 16.3 3.3 28.8
crops (Indus), rice, shrubs well
25 Irrigated: Rice with 5607387 4.2 tube well 0.9 0.2 7.8 65.3
wetlands
26 Irrigated: Rice and 6762875 5.1 tube well 1.6 0.3 5.5 87.4
other crops
Irrigated Total 45402568 34.1
27 Forests (Himalayan): 2412553 1.8 NA 31.7 15.3 33.3 0.0
Mature
28 Forests (Himalayan): 6010237 4.5 floodplain/ 19.2 8.0 10.0 16.5
Young and wetlands tube well
29 Forests (Himalayan): 1635143 1.2 NA 25.0 1.0 60.0 0.0
Young
Forests Total 10057933 7.6
30 Striping: Noise 618497 0.5 noise noise noise noise noise
Total Area from all 133021156 100.0
classes (ha)
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.
The ability to map a large number (29) of classes ment compared to earlier sensors. Even within an
(Fig. 8) even at 500 m spatial resolution, demonstrates irrigated area class, 6 distinct classes (classes 21–26 in
the strength of the 7-band near continuous MODIS data Table 1) were spectrally and temporally differentiated
and attests to the improved sensitivity of this instru- (Fig. 9b).P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 329
Ground Ground truth Actual tree area Actual shrub Actual grass area Actual cultivated Actual other Actual rice
truth LC % LC % of rice of class (LULC area of class of class (LULC area of class areas of class area within
of others only LC % area of classtree (LULC area of area of classgrass (LULC area of (LULC area of cultivated area
cover % of class) classshrub cover % of class) classcultivated classother (LULC area
(ha) cover % of (ha) cover % of class) cover % of of classrice
class) (ha) (ha) class) (ha) cover % 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 ha 24290637 classes 23, 25,
wells and 26 in area
Irrigated (Total: ha 33084536 classes 21 to
canal+tube well) 26 in area
% 24.9 classes 21 to
26 in %
Irrigated (Khariff 32555183 98.4 % of NET
Total: canal+tube
well)
Irrigated (Rabi 30603195 92.5% of
Total: canal+tube NET
well)
Irrigated (Continuous Khariff-summer-Rabi Total: 1157959 3.5% of
canal+tube well) 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 %
5.5. Irrigated area, rice area and cropped area estimates (61.6%) dominate but there are significant other LC types
that include other land cover (11.4%), trees (11.2%),
Each LULC class is a composite of several LC types shrubs (8.4%) and grasses (7.5%) (Table 1). Of the
(see Table 1). For example, in class 22, cultivated areas cultivated areas, 31.6% is rice crop—the single major330 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
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.
crop of the class. Sugarcane was the next major crop locations for each class we feel confident that our area
although statistics of sugarcane are not shown in Table 1. estimates are reasonable. Normally, most studies take non-
Precise estimates of various thematic areas within classes decomposed pixel areas as actual areas of a particular land
were calculated as follows (see Table 1): use class.
Field data on bwatering sourceQ (column 5 of Table 1)
Tree area in class 22 was used to define classes as irrigated, rain-fed, rain-fed
¼ LULC class area for class 22 with supplemental irrigation and flooded or wetland
cultivated. Classes 21, 22 and 24 were canal irrigated and
LC percentage of tree for class 22
classes 23, 25 and 27 were tube-well irrigated. The same
¼ 6; 046; 429 ð11:2=100Þ ¼ 674; 781 ha ð11:2%Þ approach described in the previous paragraph was used to
estimate the irrigated areas in each class wherein the total
Using the same approach, there were 504,877 ha (8.4%) area is multiplied by LC percent for crops in class 21
of shrubs, 450,489 ha (7.5%) of grasses, 372,7321 ha through 27 (since these classes are exclusive irrigated
(61.6%) of cultivated areas and 688,991 ha (11.4%) of other agriculture). For example, the irrigated area resulting in a
areas. The rice crop alone totaled 1,910,369 ha (31.6%). total irrigated area of 33,084,536 ha (24.9% of the total
We propose the above approach to area calculations since study area). Of this, canal irrigated area was 8,793,899 ha
it takes into account sub-pixel composition of the pixels. Let (6.6% of the total area of the study of 133,021,156 ha)
us take the example of irrigated area class 21 which has a compared the tube-well supplied area of 24,290,637 ha
total area of 3,150,616 ha (Table 1). Every pixel of this class (18.3% of the total area). The cropland LCs of classes 23,
is irrigated, but at different degree—some pixels are 100% 25, and 27 were exclusively tube-well irrigated. The
irrigated and some 50% and some others a different pro- cropland LCs of classes 21, 22, and 24 were overwhelm-
portion. In order to calculate exact area under irrigation for ingly canal irrigated, but has some very minor tube well
this class, we will need to perform sub-pixel decomposition. irrigated mix that we ignore.
We adopt a fairly straightforward approach based on land There were 12,345,432 ha (9.3% of the total area of the
cover (LC) composition for the class based on ground truth study) of rain-fed areas with substantial supplemental
data. The accuracy of this approach increases with sample irrigation of one sort or another. A significant portion,
size for the class. Since we have fairly large sample size 3,047,267 ha (2.3%) incorporated wetland cultivation.P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 331
a All Class type biomass fluctuation for 2001 and 2002
1
0.8
0.6
NDVI Value
0.4
0.2
0
1
41
57
73
89
3
9
5
9
5
1
33
49
65
81
5
1
3
9
3
3
11
12
18
24
34
36
10
12
15
20
31
35
-0.2 Julian Date
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
b Irrigated crop biomass fluctuation for 2001 and 2002
1
0.9
0.8
0.7
NDVI Value
0.6
0.5
0.4
0.3
0.2
0.1
0
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
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
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.
Purely rain-fed dryland cropping was estimated at classes composed one predominant LC type) and also rather
13,463,277 ha (10.1%). inaccessible. These classes were identified, based on their
Rice is grown not only in the 6 irrigated classes 21–26, spectral characteristics, GeoCover Landsat TM images, their
but also in other classes albeit only to some extent. Specific geographic location and from numerous other sources of data
LC percentages of rice crop within LULC classes 13–29 (e.g., USGS LULC classification, Loveland et al., 2000).
were used to estimate the total rice area of 27,998,763 ha It is important to note that the precise class areas pre-
(21% of the total study area) (Table 1). sented in Table 1 were applicable to the khariff season only,
The total cultivated land area in the study region is as LC percentage data were not available for other seasons.
61,940,511 ha (46.6% of the total area). Classes 1–12 have We adopted a unique strategy to determine intensity of
almost no area under cultivation or irrigation and occupy irrigation in different seasons. The maximum monthly
18.6% of the total area. LC percentages were not measured on NDVI composite images for different seasons were masked
the ground, as these classes are relatively pure (e.g., LULC out taking the spatial extent of irrigated areas of classes 21332
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
P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
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. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341 333
Irrigated area with
supplemental this
through 26. The masked areas for different months in the
MODIS 500-m
images each of
2001–2002 42
seasons were then classified and areas irrigated and fallow
7-bands (%)
study using
were determined based on their seasonal NDVI dynamics.
decrease
increase
The classes will cluster based on NDVI dynamics. A class
34.2
41.7
with high degree of irrigation (say 90–100% of pixel areas
are irrigated) will have a higher NDVI threshold over 2–4
with supplemental
this study using
months of a growing season relative to a class with a low
MODIS 500-m
images each of
2001–2002 42
Irrigated area
7-bands (ha)
degree of irrigation (say 20–30% of pixel areas are
45429968
37602567
irrigated). Based on this approach, we determined 98.4%
–37.4
–33.4
13.4
16.6
of this area during Khariff (June–October), 92.5% during
Rabi (November–February), but only 3.5% all through the
this study using
Irrigated area
MODIS 500-m
images each of
2001–2002 42
year or continuous (apart from Khariff and Rabi, also in
7-bands (%)
March–May) cropping.
24.9
29.8
(%)
(%)
(%)
(%)
5.6. Cropping pattern
MODIS 500-m
this study using
Irrigated area
images each of
2001–2002 42
The cropping pattern of classes 13–29 are given in Table
7-bands (ha)
33084536
26873934
2 for khariff and rabi. In some cases, there is a short interim
season between rabi and khariff when summer crops are
USGS1993: study area in Ganges and Indus
GLC2000: study area in Ganges and Indus
grown if water is available, and according to ground survey
these are the same combinations as for khariff. The irrigated
1000 m 2000
Irrigated area
36 each of 1
using SPOT
NDVI band
area classes, 21–26, have either rice or sorghum as main
GLC2000
crops during khariff and, where applicable, in summer. The
54.6
62.6
(%)
cropping mix in rabi is generally wheat-sugarcane or wheat-
USGS1993: Ganges basin
GLC2000: Ganges basin
barley. The main rain-fed crops, classes 17 and 18, have Irrigated area
1000 m 2000
36 each of 1
using SPOT
NDVI band
sorghum-millet in khariff, but change to wheat-barley in rabi
72614135
56466954
GLC2000
(Table 2). During the field work, the authors were
accompanied by highly knowledgeable local agricultural
(ha)
experts from the Indian National systems (see Acknowl-
36 images each
AVHRR 1000
edgements) who were instrumental in determining rabi and
m 1992–1993
Irrigated area
USGS using
of 1 NDVI
summer crops at each field plot location, at times involving
band (%)
by
by
by
by
interview with local farmers.
relative to irrigated area
relative to irrigated area
relative to irrigated area
relative to irrigated area
30.1
35.8
Irrigated area comparisons between different studies for study area in Ganges and Indus
5.7. Irrigated area comparison with other studies
Irrigated area
36 images each
AVHRR 1000
m 1992–1993
USGS using
of 1 NDVI
40046229
32255630
band (ha)
The results of this study were compared with: (a) USGS Irrigated areas mapped using different studies is compared with this study.
study using monthly AVHRR 1-km NDVI time series from
April 1992 to March 1993 (Loveland et al., 2002), and (b)
Total area of this
study in percent
relative to total
global land cover (GLC) for year 2000 using monthly SPOT
2001–2002
2001–2002
2001–2002
2001–2002
basin area (%)
1-km data (Belward et al., 2003). In the GLC2000 study,
data from the 4 spectral bands of the SPOT sensor were
used: blue (0.43–0.47 Am), red (0.61–0.68 Am), infrared
data of
data of
data of
data of
63
95
(0.78–0.89 Am) and shortwave infrared (1.58–1.75 Am).
In the entire study area, the combined irrigated and
Total area of this
study in hectares
relative to total
based on MODIS
based on MODIS
based on MODIS
based on MODIS
basin area (ha)
supplemental irrigated areas mapped using 2001–2002
133021156
MODIS data in this study showed an increase of 13.4%
90221264
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
Irrigated area in this study
Irrigated area in this study
Irrigated area in this study
Irrigated area in this study
Ganges and Indus basins
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
Ganges basin
classes. Almost all of the spatial distribution of this class
Study area
fell within our irrigated-area classes. However, the
Table 3
GLC2000 class number 33 (irrigated agriculture) with
38.4 million hectares is a predominantly rain-fed with some334 P.S. Thenkabail et al. / Remote Sensing of Environment 95 (2005) 317–341
irrigated. Almost all of our rain-fed classes, 17 and 18, fall the normal year, the NDVI for Khariff season steeply
within this class 33. In addition, classes we identified as raises from Julian day 160, reaches peak NDVI of 0.25
rangelands and some of the forests are also labeled irrigated and then starts falling reaching low values again around
agriculture by GLC200. Julian day 300. In contrast, during 2002 the NDVI never
The comparisons are only indicative to show how the rose above 0.2 and near complete crop failure is obvious
irrigated areas are estimated for Ganges river basin by relative to NDVI dynamics of 2001. Rangeland class 15
various studies using remote sensing datasets of wide has a sharp NDVI increase from about 0.25 during the
range of characteristics. The results of this study driest period to little over 0.6 during the monsoon from
performed at 500-m were compared with AVHRR and June to October. During khariff, this is a class with rise in
SPOT classifications performed at 1-km scale. The USGS NDVI almost similar to that of irrigated-area class 21.
AVHRR study use 2 broad bands (band 1 and 2), The However, the 2 classes are distinctly separate during other
GLC SPOT study used 4 broad bands (2 visible and 2 periods. As expected, the desert class has a near-flat
SWIR). This study used 7 narrow bands (4 visible, 2 NDVI across the year.
NIR and 2 SWIR). Thereby, differences in LULC or The temporal signatures of the six irrigated classes are
irrigated areas are often as a result of factors such as plotted in Fig. 9b. Irrigated class 21 peaks on day 49 (rabi
data types, class definitions, level at which the classes crop peak), reaches the lowest biomass around day 129 (dry
are mapped, analysis methods and techniques, and season, low), and reaches peak again around day 249
resources spent on analysis and classification schemes (khariff, crop peak). The cycle is remarkably similar for
rather than change per se. both 2001 and 2002 (see Fig. 9a). For example, the rabi crop
peak green period or critical growth phase was around
5.8. Tree cover, shrub and grass cover in the basin Julian day 57 during 2001 and day 49 during 2002.
Senescence begins around day 89 during year 2001 and
The tree shrubs and grasses are predominant in classes 27 day 81 during year 2002. Based on these results, the
to 29. But often, other classes have a significant percentage nominal crop duration from sowing to harvest during khariff
of one of these cover types. For example, 11.16% of the is (Fig. 9a) 180 days (Julian day 153–333 days), rabi is 142
land cover in irrigated-area class 22 was trees as a result of days (from day 333 of 1 year to the next year Julian day
agroforests forming part of the cropping system. Using the 110), and a short dry season of no cropping for 43 days
tree, shrub and grass percentages of all classes we found (days 110–153).
there was 4,803,355 ha (or 3.6% of the total area) of trees, The six irrigated-area classes are identified by subtle
7,645,397 ha (5.7%) of shrubs, and 12,524,211 ha (9.4% of differences between these classes. Most of these classes
grasses) in the study area. were dominated by rice and other irrigated crops during
khariff. Crop vigor, biomass levels and percent area
5.9. Class signatures, NDVI-reflectivity thresholds, and cultivated are comparable at certain times of the year,
onset-peak-senescence-duration of crops but not at other times (Fig. 9b). In spite of many
similarities, the classes often provide significantly different
The class signatures of NDVI (CS-NDVI) are unique NDVI signatures (Fig. 9b) at one time or another during a
time series of a class using NDVI or spectral reflectivity year. There are several reasons for this. The first is the
in individual wavebands. It is not possible to have type of land cover within and between these classes. Class
temporal signatures when single date or a few date 22, for example, is found mainly along the Indus river
images are used as is often the case with most LULC basin, is heavily irrigated and flooded (31% water) or
studies. The set of NDVI class signatures is shown in Fig. moist throughout the year, suppressing NDVI substan-
9a and b for classes mapped in Fig. 8. Threshold NDVIs tially. The presence of flooding or wet soils may result in
and NDVI signatures over time help us determine the substantial absorption in near-infrared leading to low
onset and duration of cropping seasons (rabi and khariff), NDVI throughout the season. Irrigated land accounts for
the intensity of cropping in drought and normal years and 85% of all cereal grain production (mainly rice and
the end of a cropping season. wheat), all sugar production and most of the cotton
MODIS CS-NDVI signatures are presented and dis- production (Khan, 1999). Class 21, for example, is
cussed for a set of distinct classes (Fig. 9a) and basically dryland that is irrigated whereas other classes
thematically similar classes (Fig. 9b). The NDVI of forest like 22 exhibit higher moisture levels. The NIR reflec-
class 27 never falls below 0.5 on any date throughout a tance in drier lands with vigorous vegetation is substan-
year and across years (Fig. 9a). The agricultural lands in tially higher than the NIR reflectance in irrigated areas
wetlands (class 5) have a moderately high NDVI with substantial moisture or water logging. The second is
throughout the year as a result of continuous soil moisture that differences occur in LC percentages within and
availability. The rainfed agriculture (class 18 in Fig. 9a) between classes. Class 26, for example, has about 20%
shows the dramatic differences in NDVI dynamics during more rice than class 21 (Table 1). Class 21 has greater
the normal year (2001) vs. drought year (2002). During percentage of other crops including sugarcane. The third isYou can also read
