Indian Journal of Radio & Space Physics

Vol. 35, April 2006, pp. 116-121

Radiometric scene correction of temporal multi-spectral satellite data

for crop discrimination

R N Sahoo1, R K Tomar

1, C S Rao

1, V K Sehgal

1, Nirupa Charchi

2, I P Abrol

2, M K Tiwari

3 & M K Wadhawani

4

1Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012 2Centre for Advancement of Sustainable Agriculture, NASC Complex, Pusa, New Delhi 110 012

3National Physical Laboratory, New Delhi 110 012 4Bihar Agricultural College, Sabour, Bhagalpur 813 210, Bihar

Received 13 August 2004; revised 7 February 2006; accepted 27 February 2006

Multi-date satellite images under different conditions of the same area are difficult to compare because of change in

atmospheric propagation, sensor response and illuminations. To overcome this problem, a radiometric normalization tech-

nique, which is based on the statistical invariance of the reflectance of man-made in-scene elements (pseudo invariant fea-

tures) was attempted. The LISS-III data of IRS-1D of three dates were taken for discrimination of crops and retrieval of crop

statistics. To develop temporal NDVI profile of the various crop types, relative image-to-image radiometric scene normali-

zation of each band was done using linear transformation. Water body, orchard and other less dynamic features were ex-

cluded and multidate-NDVI image having only agricultural crops was obtained for identification and classification of vari-

ous crops. Nine classes were identified and discriminated as different crops by analyzing temporal NDVI profile pattern

based on ground truth, crop calendar and information on crop sowing and harvesting time. Spatial distribution of different

crops was analyzed and crop area statistics was computed.

Keywords: Radiometric normalization, Pseudo-invariant features, Cropping pattern analysis, NDVI,

Unsupervised classification

PACS No.: 95.40.+s

1 Introduction

The value of satellite remote sensing data for crop-

ping pattern analysis is well established from decadal

research in remote sensing application in Indian Agri-

culture. Cropping pattern of an area reflects the spatial

arrangement of crops and inter-crop variability that

exists. Multi-date data are essential to derive cropping

pattern as, in general, there are more than one crop

grown in a season with different growing calendar.

With repeated coverage, radiometric consistency is

hard to maintain between images obtained on differ-

ent dates due to varying atmospheric conditions,

variations in the solar illumination angles, sensors

calibration trends, etc. Without some form of nor-

malization, true changes in the scene between the two

acquisition dates are difficult to interpret, because

they are masked by non-scene-dependent changes.

Therefore, along with multi-temporal image registra-

tion, radiometric correction is an outstanding re-

quirement of image preprocessing for cropping pat-

tern analysis1.

There are two levels of radiometric corrections, i.e.

absolute and relative, developed for imagery used for

land cover change detection. Absolute radiometric

correction makes it possible to relate the digital

counts in satellite image data to radiance at the sur-

face of the earth, requiring sensor calibration coeffi-

cients, an atmospheric correction algorithm and re-

lated input data2,3

. A number of radiative transfer

codes (RTCs) based on radiative transfer theory have

been developed to correct for atmospheric effects in

satellite images4-6

. However, these corrections require

accurate measurements of atmospheric optical proper-

ties at the time of image acquisition. These measure-

ments are frequently unavailable or of questionable

quality, which makes routine atmospheric correction

of images difficult with RTCs. A variety of relative

radiometric correction techniques have been devel-

oped. Hall et al.7 developed radiometric correction

technique through the use of landscape elements,

whose reflectance values are nearly constant over

time. This technique yields radiometrically-

normalized data and does not require sensor calibra-

tion or atmospheric parameters. The shortcoming of

this approach is that the landscape elements are se-

lected by visual inspection, which could result in sub-

jective radiometric normalization.

SAHOO et al.: RADIOMETRIC CORRECTION OF TEMPORAL MULTI-SPECTRAL SATELLITE DATA

117

Mathematical model that best describes the

normalization of two images of the same area

acquired at two different times involves the use of

regression. The algorithm assumes that the pixel

samples at day 1 are linearly related to the pixels for

the same locations at day 2. This implies that the

spectral reflectance properties of the sample pixels

have not changed during the time interval. These

sample pixels are known as pseudo-invariant features

(PIFs) and key to the image regression method of

radiometric normalization8-10

. The values PIFs are in-

scene man-made elements (e.g. urban areas in this

study). The PIF technique is designed to permit scene-

to-scene normalization by implementing a set of

radiometric transformations. Each spectral channel is

made to appear, as though imaged through the same

response function and same atmosphere, as a second

multi-spectral image of the same area. The technique

does not require sensor calibration or atmospheric

parameters. A problem with this PIF approach is the

difficulty to automate, because pseudo-invariant

objects have to be identified manually. Besides, the

results of this approach can be biased when the

selected pseudo-invariant objects are not rigorously

invariant.

In the present study an attempt has been made to

use PIF technique for radiometric normalization of

multi-spectral temporal IRS satellite data for cropping

pattern analysis during rabi season and also to

analyze spatial distribution of the crops grown in river

bank ecosystem.

2 Study area

The study area is a part of Gangetic active flood

plain of district Bhagalpur, Bihar, extending from

25º12' 50" to 25º 24' 31" N latitude and 86°43'37" to

87º13'57" E longitude. The area is highly fertile, but

its productivity is low as it is affected by frequent

floods in the river Ganga. The area is mostly mono-

cropped and rabi season (October to April) is the po-

tential crop growing period. Kharif crops are always

under risk and uncertainty due to flood.

3 Materials

3.1 Satellite data

The LISS-III data of IRS-1D of three dates (07

Dec. 2002, 26 Jan. 2003 and 11 Apr. 2003) were used

for cropping pattern analysis during rabi season and

PAN (7 Mar. 2002) of IRS 1D was used for image-to-

image registration and urban and built-up area extrac-

tion. Except these three dates, no other date with

cloud free data was available for the period (October-

April) under study.

3.2 Collateral data

Along with satellite data products, other collateral

data used were Survey of India toposheet Nos. in

1:50,000 scale.

4 Methodology

The details of approach and methodology adopted

are described below (Fig. 1).

4.1 Ground truth collection using GPS

Ground truth was collected during the month of

March 2003 with GPS Leica GS-5 and Survey of

India toposheets. The Compaq-iPAQ pocket PC run-

ning at 200 MHz was used during field visit, which

can run Arc-pad (ver. 6.0) and is able to connect GPS

under MS-windows-CE ver. 3.09. Before taking

actual observations, shape files were created in Arc-

pad for point, line and polygon and with the name of a

Fig. 1—Flow diagram of methodology

INDIAN J RADIO & SPACE PHYS, A P W 2006

probable place to be visited with date. Observations on point, line and polygon were .made and for each observation, an attributes library was created. This spatial information along with attributes was saved in respective shape files. The projection system used is WGS 84 and zone 44 N for overlaying the GPS point on the satellite image. The ground control points (GCPs) collected, using GPS, are overlaid on FCC as shown in Fig. 2.

4.2 Geometric correction

In geometric correction, a standard geographic coordinate system is selected for all images of interest. The selection of GCPs is very important for geometric correction. Different images could be projected onto the same geographic coordinate system, so that spatial and temporal changes can be detected and analyzed. The IRS-1D LISS-III data of 26 Jan. 2003 were geometrically registered with PAN (7 Mar. 2002) which were corrected with GCPs collected through GPS using second polynomial order and nearest neighbourhood sampling method using data preparation module of ERDAS imaging (ver. 8.6). The resulting error due to misregistration is approximately 0.5 pixel. The iemaining LISS-111 images were rectified using the corrected image of 26 Jan. 2003 and the same re-sampling method.

4.3 Radiometric normalization

Radiometric normalization was done based on pseudo-invariant features (PIFs) in the images, which are objects spatially well defined and spectrally and radiometrically stable. The ideal PIFs are those that meet the following criteria: (i) they are approximately at the same elevation as the rest of the scene (for a

better representation of the atmospheric conditions across the scene), (ii) they are in a relatively flat area (to minimize the effects of solar azimuth differences), and (iii) should have a minimal amount of vegetation (as vegetation readily changes in response to seasonal changes and environmental stresses)' ' . The assumptions considered while selecting PIFs of the study area were such that (i) the DN values of the PIF pixels involved in correction procedure are invariant during the period under study, (ii) the variation in the image digital number (DN) during the period are linear and spatially homogenous for all PIFs involved in the normalization procedure and (iii) linear effects are much greater than non-linear effects. The image having highest dynamic DN range, i.e. April 2003, was selected as the reference (or base) image for radiometric normalization of other two images (i.e. December 2002 and January 2003).

The PIFs such as urban area, helipad, playground etc. from the study area were segmented from images of different dates. If these PlFs can be isolated on day 1 and day 2, then their reflectivity distribution in each spectral channel should represent samples from the same reflectance population. These reflectance distributions should be linearly related if the samples indeed represent a statistically invariant population. For objects with the same reflectivity on both the dates, the digital counts on each day are simple linear functions of the same variables and, therefore, are linear functions of each other.

Thus, according to Schott et al. I'

where

DNli = Pixel value of day 1 * DNzi = Pixel value of day 2** oli= Standard deviation (SD) of PIF of day I* ozi= Standard deviation (SD) of PIF of day 2** pli = Mean of PIF of day l* p2i = Mean of PIF of day 2** i = band number (i=1,2,3,4) day l * = April (reference image) or its equivalent day 2** = Either December or January

1 The mean and standard deviation of PIFs of each 1' band of images of three dates were retrieved and

given in Table 1. Using these values in above formula band-to-band radiometric normalization was done.

Fig. 2-GCP points overlaid on FCC of study area Table 1 also shows a comparison between the mean

SAHOO er al.: RADIOMETRIC CORRECTION OF TEMPORAL MULTI-SPECTRAL SATELLITE DATA

Table I-Mean and standard deviation of PIFs of three dates be- fore and after normalization

Before normalization After normalization Date-Band Mean SD Mean SD

4.5 Preparation of multi-date normalized difference vegeta- tion index (NDVI) image

The normalized difference vegetation index (NDVI), being a potential indicator for crop growth and vigour, was used in the study, which is expressed as

NDVI = (NIR - Red) - ( ~ 3 - J32)

- (NIR + Red) (B3 + B2)

The NDVI image was prepared from images of three dates and scaled to unsigned 8 bit (0-255). The spectral profile of different crop classes were ana- lyzed using temporal NDVI images. All three NDVI images were stacked together as multi-date NDVI image (Fig. 3) having December NDVI as band 1, January NDVI as band 2 and April NDVI as band 3.

~ p r . - 4 234.97 19.93 . 234.97 19.93 The multi-date NDVI image was clustered into nine classes and temporal NDVI profile was plotted for

and standard deviation values of December and Janu- each class (Fig. 4). The features, which were not dy- ary images with April as reference image at respective namic (i.e. does not change NDVI value significantly) band levels. The normalized bands of images of re- found to have NDVI spectral almost as a s~ective dates were stacked get the FCC and for straight line. Those classes were found to be barren further analysis. lands, orchards, etc. and removed from the image. All

4.4 Elimination of water body remaining features in multi-date NDVI image were

~h~ surface water features of the study were agricultural crops, which are of our interest to identify

identified using normalized difference water index them.

(NDWI) given by ~ c f e e t e r s ' ~ . It is expressed as 5 Results and discussion (Green-NIR) ( ~ 1 - B 3 ) The multi-date NDVI image having only agricul-

NDWI = - - (Green + NIR) (B 1 + B3)

tural crop features was classified into nine classes using ISODATA cluster analysis. Temporal NDVI

Water bodies including river were identified as- profiles Fig. 4(a) and (b)] for classes without and signing the value at higher range of 8 bit NDWI im- with radiometric correction were plotted. Ground age using density-slicing technique and eliminated truth information helped in matching NDVI profile to from all three images. . crop growth pattern and assigned it to a particular

(1 (4)

Fig. 3-FCCs of (g) Decembel: 2002 (b) January, 2003 (c) April 2003 and (d) multidate NDVI image

INDIAN J RADIO & SPACE PHYS, APRIL 2006

easy to identify the crop. Even a crop like wheat hav- ing sown early and late could be identified due to a significant difference in its NDVI profile pattern. The growth of kans grass and vegetables has good association with river banks and chickpeallentil to low

4 ref'

class. Since the temporal pattern that is representative of the crop dynamics is specific to a crop, it facilitates crop identification. There is significant difference between the NDVI profile pattern of the corrected and uncorrected ones. It is clearly revealed from Fig. 4 that no interpretation can be drawn for identifying NDVI profile of uncorrected image into different crop classes.

Information on growing period of different crops obtained during ground truth collection is shown in Fig. 5 as crop calendar. Based on crop calendar and ground truth (GPS data), the temporal NDVI profile of each class was identified as different crop [Fig. 4 (b)] and their spatial distributions without and with radiometric correction are shown in Fig. 6 [(a) and (b)] respectively.

Matching unique temporal NDVI profile with growth conditions of a crop at different date makes it

bras [kans] egetable crops

ate sown wheat

- --

~an,& Dee. 02 ~prTO3

Fig. 4--Temporal NDVI profile of different classes (a) without radiometric correction and (b) with radiometric correction

I

Oct. Nov. I Dec. I Jan. I Feb. I Mar. I Apr. May. <-------------- winter maize-------->

: .L <---------- vegetables ------------------- ---- I I . I I I 1 ---black gram-> 1 Fig. 6-Classified image showing spatial distribution-of classes

(a) without radiometric correction and (b) with radiometric Fig. 5-Crop calendar of study area during rabi season correction

SAHOO et al.: RADIOMETRIC CORRECTION OF TEMPORAL MULTI-SPECTRAL SATELLITE DATA

121

Table 2—Computed area statistics of different crops grown in

district Bhagalpur, Bihar

Class No of pixels Area, ha Area,%

Grass (kans) 93191 5720.07 3.38

Vegetable crops 31613 1940.41 1.15

Maize 310959 19086.68 11.26

Late sown wheat 231879 14232.75 8.40

Chickpea/lentil 72380 4442.69 2.62

Black Gram 213981 13134.17 7.75

Arhar 155194 9525.82 5.62

Early sown wheat 243299 14933.71 8.81

Mustard 147722 9067.19 5.35

Others 1260550 77372.64 45.66

Total 2760768 169456.11 100.00

lying areas called tal lands. The specific association

of crop to growing areas helped, to a great extent, in

identifying it. The area statistics of different crops is

computed and its proportion to total area is given in

Table 2. It was found that out of approx 170 thousand

ha of study area, 54% is covered by various field

crops including grass. The wheat crop was found to

be the dominating one followed by maize and black

gram. Winter maize and wheat are grown mostly in

flood plains close to river bank, locally known as di-

ara land; pulses are common in tal lands. Arhar crop

sown during May/June, if not damaged by flood, re-

mains in the field up to late March/early April. Other

46% includes built-up area, orchards/plantation areas,

barren lands, river, water bodies, etc.

6 Conclusions

The results demonstrate the potential of radiometric

normalization using PIF technique to quantitatively

transform one multi-spectral image to a second mul-

tispectral image of the same area. The transformed

images were demonstrated to be comparable with an

average error of approximately 1 DN value. Limita-

tion of the present approach is that some of the fea-

tures, like soil with moist surface near river banks and

tal lands and barren land with shrubs which change

their reflectance with time, have been assumed as in-

variant features and variant features, respectively,

adding error to classification. However, the technique

is found to be very useful for cropping pattern analy-

sis using temporal multi-spectral satellite remote sens-

ing data through temporal NDVI profile analysis.

Acknowledgement This study is the result of a project funded by Na-

tional Agricultural Technology Project, Indian Coun-

cil of Agricultural Research (ICAR), which is duly

acknowledged.

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