Indian Journal of Radio & Space Physics Vol. 32. August 2003. pp. 1 93-197

Comparison of space-based microwave polarization difference index and normalized difference vegetation index for crop growth monitoring

R P Singh & V K Dadhwal

Crop Inventory and Modeling Division. Agricultural Resources Group. Space Applications Centre (lSRO). Ahmedabad 380 015

Received 21 May 2002; revised 28 October 2002; accepted 30 January 2003

This paper reports the passive microwave radiometry application for regional assessment of crop growth in India. Brightness temperature (TB) of various vegetation classes observed at 37 GHz using special sensor microwave imager (SSMI) data. during Kharif season in 1 999 in India are analyzed. Vertical as well as hQrizontal polarization brightness temperature is used to derive microwave polarization difference index (MPDI) for different crops/vegetation classes at various phenological stages. This MPDI index is related with the ten-day maximum value of composite of normalized difference vegetation index (NDVI) data of NOAA-A VHRR sensor. A non-linear inverse relationship has been developed between the MPDI and NDVI of different crops over various regions. It has been observed that the surface wetness/soil moisture in rice growing region influenced the MPDI-NDVI relationship.

Key words: Microwave. Radiometry. Crop growth. Remote sensing

1 Introduction The temporal assessment of crop and its growing

environment have paramount importance in crop yield prediction as well as studying the earth' s climate. It is useful for meteorological analysis and net primary producti vity of crops, because vegetation cover influences the exchange of heat, moisture and energy between land surfaces and atmosphere, which affect weather and climate. Observations from polar orbiting satellite in optical as well as microwave region provide quantitative global vegetation information. The normalized difference vegetation index (NDVI) deri ved using optical red and near infrared reflectance has been extensively used in estimating biophysical parameters including fractional cover, leaf area index, biomass and photosynthetic acti'Vity l . Observations in the optical region are affected by atmospheric parameters such as aerosol, water vapour and ozone and most severely by clouds. This creates hindrance in monitoring Kharif crops in monsoon season in India. The · only solution for regular assessment is either temporal composting of the optical data or use of microwave remote sensing. Among the widely used active microwave systems for agricultural applications are C-band (5.3 GHz) synthetic aperture radar (SAR) onboard ERS and Radarsat platform. Satellite based SAR has been widely used for crop area assessment as well as soil moisture estimation,

but its applications are area specific and limited by coarse revisit period.

Passive microwave radiometers, although limited by spatial resolution, have shown global applicability in assessment of land surface temperature, surface wetness and snow cover2. Choudhury and Tucker3

have shown the use of microwave polarization difference index (MPDI) for vegetation assessment. Lakshmi . et al.4 have also used special sensor microwave imager (SSMI) data for estimation of soil moisture and evapo-transpiration fluxes. In the past. efforts have been made by Indian scientists5.6 to use microwave passive remote sensing data recorded by SAMIR radiometer onboard Bhaskara satellite, multi­frequency scanning microwave radiometer (MSMR) onboard IRS-P4 satellite and DMSP-SSMI for land applications7•8• Recently, Rao et al.9 have related antecedent precipitation index (API) over India with brightness temperature observed from Nimbus­SMMR data. Many studies have been conducted using either optical data or microwave data. But much less attention has been given towards the combined use of both for vegetation assessment in India. This study explores the utility of SSMI radiometer data for crop growth assessment and relates vegetation indices such as NDVI and MPDI over Indian land mass. A non-linear inverse relationship is also developed between the SSM! sensor derived MPDI and NOVI

194 INDIAN J RADIO & SPACE PHYS, AUGUST 2003

estimated from NOAA-AVHRR sensor for different crops over various regions.

2 Physical basis of microwave radiometry of vegetation

Passive microwave remote sensing involves the measurements of natural thermal emission from surface. Brightness (spectral exitance) of a black body radiator may be approximated by Rayleigh-Jeans formula as

Bbb = 2kT/}.} . . . ( 1 )

where, k i s Boltzmann's constant, T the physical temperature and A. the wavelength. Power available at the output of an ideal band-pass filter operating in frequency bandwidth of l!:.J, connected to a linearly polarized antenna for a blackbody, is given by

. . . (2)

Since a black body radiates more energy at a given temperature than any other material at the same temperature, the brightness B(S,ep) of a real material may be considered as the brightness of an equivalent black body at cooler temperature TB (S,ep), which is given by

B;(S,ep) = 2k TB;(S,ep )/A?; i = h or v . . . (3)

where TB (S,ep), called the brightness temperature of a material in the direction S,ep (incidence angle and azimuth angle, respectively), i refers to horizontal (h) or vertical (v) polarization. The polarized emissivity of the material is defined as

Ci (S,ep) = Bi (S,ep; nlBbb (n

= TBi (S,ep) / T; i =h or v . . . (4)

The emissivity of agricultural area depends both on sensor parameters such as viewing angular geometry, frequency and polarization and surface characteristics, viz., surface roughness, soil moisture and vegetation cover. Generally the emissivity of vertical polarization is greater than horizontal polarization 10. Dry soil has more emissivity as compared to wet soil lO and it also increases with surface roughness, thereby increasing TB• The soil moisture plays an important role in determining the soil emissivity as well as brightness temperature. It has · been shown

theoreticallyl l that NOVI and MPOI are related to leaf area index (LA I) and vegetation cover fraction. The main mechanism in optical region is the large difference of both the reflectivity and transmitivity of the plants in the visible and near-infrared due to

chlorophyll absorption. The NOVI is defined as

NDVI = (Pn - Pr ) / (Pn + Pr ) . . . (5)

where, Pn and Pr are near-infrared and red band reflectance, respectively.

The vegetation emits largely un-polarized radiation in microwave region and acts as a scattering/absorbing medium for polarized radiation emitted from the soil. The vegetation cover results in the reduction of the radiometric sensitivity to soil moisture. Emissivity increases with vegetation cover as compared to bare fielq emissivity. With the increase in vegetation cover, both vertical and horizontal emissivities would increase, but the relative difference between these polarized emissivities (represented by MPOI) decreases. The MPDI is defined as

. . . (6 )

where, TBv and TBh are brightness temperatures of vertical and horizontal polarizations of the given frequency. Scaling with multiplicative factor 1 00 was done to make MPOI comparable to NOV I values. Becker and Choudhury I I have modelled vegetation response in optical and microwave region expressed through MPOI and NOVI as

MPDl = (MPDlM - MPDI)/ NDVIM - NDVl) r l NDVIM - NDVlo ) ) . . . (7)

+MPDlo where, MPDIM and NDVIM are, respectively, the values of MPOI and NOVI for maximum vegetation cover, while MPDlo and NDVlo are the respective bare soil values. The unknown parameter y was obtained from the least square fit of the total data acquired in Kharif season.

3 Data used The vertical as well as horizontal polarized

brightness temperatures from SSMI radiometer during June 1999-November 1999 over India were used in the study. The descending passes of SSMI sensor onboard defence meteorological satellite program

SINGH & DADHW AL: COMPARISON OF SPACE-BASED MPDI AND NOVI FOR CROP GROWTH 1 95

(OMSP)-FI3 satellite over Indian subcontinent were analyzed. Initial ten-days pass (date: 1 - 1 0 of each month) of June, July, September, October and November including last ten-days passes (date: 2 1-30 of each month) of September and November were used in the analysis. The SSMI is a conical scanning microwave radiometer system flown on the OMSP satellites. The SSMI orbit is near-circular, sun­synchronous and near-polar, with an altitude of 860 km and an inclination of 98.8°. The orbital period is 102 min. This orbit provides a complete coverage of the earth, except for two small circular sectors of 2.4° centered on the north and south poles 12. The SSMI is a seven-channel, four-frequency, linearly polarized, passive microwave radiometric system, which measures atmospheric, ocean and terrain microwave brightness temperatures at 1 9.35, 22.235, 37.0 and 85.5 GHz at constant incident angle of about 53° (Table 1). The SSMI rotates continuously about an axis parallel to the local spacecraft vertical and measures the upwelling scene of brightness temperatures. The absolute brightness temperature of the scene, incident upon the antenna is received and spatially filtered to produce an effective input signal or antenna temperature at the input of the feed horn antenna.

The SSMI data are available at two spatial resolutions, viz. low spatial resolution (25 km) brightness temperature data of 1 9, 22, 37 GHz and high spatial resolution ( 12.5 Ian) data of 85 GHz along with their respective geo-Iocation information (latitude and longitude). The 37 GHz channel was used for analysis of seven representative land cover classes (sites), viz. (i) Forest (Bastar, M.P.), (ii) Grassland (Jaisalmer, Rajasthan), (iii) Rice (Punjab), (iv) Rice (Haryana), (v) Cotton (Ganganagar, Rajasthan), (vi) Soyabean (M.P.) and (vii) Sugarcane (Western UP). The duration of the analysis covered

Table I-Specification of the SSMI microwave radiometer data used in analysis

Frequency Polarization GHz

19.35 Vertical

19.35 Horizontal

22.235 Vertical

37.0 Vertical

37.0 Horizontal

85.5 Vertical

85.5 Horizontal

Integration -,-:

-=3:....:dB=-:�:..::oo.=..t:.<:p�rin:::t�s=iz:::.e

� period Along track Cross track ms km km

7.95 69 43

7.95 69 43

7.95 50 40

7.95 37 28

7.95 37 29

3.89 1 5 1 3

3.89 1 5 13

total Kharif season associated with the different crop growth stages as well as growing environment in terms of soil wetness.

4 Analysis of SSMI data The passive microwave SSMI radiometer data over

India was analyzed in combination with the optical NOAA-A VHRR NOVI data during Kharif season. Raw image files in hdf format were read to create 37 GHz brightness temperature and sets of geo-Iocation information files. The images were geo-referenced and re-sampled over the Indian region (5-40° N and 65- 100° E) at a cell resolution of 0. 1 degree. The MPOI was computed using vertical and horizontal polarization brightness temperatures. The NOAA­A VHRR 8 km Pathfinder1 3 geo-,eferenced data were used to locate different land cover classes based on ground information and NOVI temporal profile. The NOVI and corresponding MPOI values . were extracted for each land cover classes. The scaled NDVI values of the NOAA-A VHRR data in digital number (ON) were converteCi into crop NOVI ) 3 as:

NDVI = (NDVIDN - 128.0 ) x 0.008 . . . (8)

A non-linear regression fit was developed between MPOI and NDVI estimated for rice, forest and other crops such as soyabean, cotton and sugarcane. The analysis was carried out using ENVI software (Research Systems, Inc, USA).

5 Results and discussion The temporal profiles of both MPOI and NOV I for

different land cover classes were developed. The characteristic growth curves of different crops and vegetation classes as observed from NOAA-A VHRR data are shown in Fig. 1 along with the corresponding MPOI values. It can be seen that the rice in Punjab has a pattern, which is 30 days early as compared to rice in Haryana. The field preparation/water impounding has been detected for rice in Haryana as MPOI increased from 2 to 6 between June and July. In September, rice in Punjab showed minimum value of MPOI as compared to rice in Haryana and soyabean in MP [Fig. l (a)]. This is due to relatively maximum vigor (NOV!) of rice crop in Punjab. The cotton and grassland in Rajasthan showed very low NOVI values throughout the season and high MPDI in mid and late Kharif seasons as compared to other vegetation classes. Forest consistently showed higher NOVI and lower MPOI values.

1 96 INDIAN J RADIO & SPACE PHYS, AUGUST 2003

8 (a) CO

N � � S 5 � to-!:!o 4 � 3 � CO :E /t 0

+� .. ..

120 150 180 210

(b)

0

\ � 0

� �:'l 2

f!I • 120 150 180 210

( ) -..0- Rice (Punjab) 0.9 C __ Soyabean ( MP)

0:8 --+- Cotton (Rajasthan) __ Sugarcane (UP)

0.7 0.6

� 0.5 · Z 0.4

0.3 0.2 0.1

0 120 150 180 210

240

o Rice (Punjab)

+ Soyabean (MP) x Rice (Haryana)

270 300 330

X Sugarcane (UP) X Grassland (Rajasthan) o Cotton (Rajasthan) c Forest (MP)

t� � � 0" 0 0 . J - N � '11 240 270 300 330

........ Rice �aryana) -M- Grass nd (Rajasthan) ........ Forest (MP)

240 270 300 330 JULIAN DAYS

380

360

360

Fig. I-Temporal pattern of MPDI estimated from SSMI data [(a) and (b)] and NDVI from NOAA-AVHRR (c) during June­November 1999 over seven sites covering natural and agricultural vegetation in India

Relationship between MPOI and NOVI . was developed for three major vegetation categories such as (i) Rice, (ii) Soyabean, cotton and sugarcane and (iii) Natural vegetation and forest (Fig. 2). It was found that at lower NOVI, the MPOI values were high with large variation due to variation in soil moisture. The sensitivity of the MPOI at low NOVI varied for different crops as well as regions. It can be seen from Fig. 2 that rice have relatively high MPOI values as compared to other crops. As the NOVI of the crop increases, there is a decrease in the MPOI values, which shows relatively less variation.

Overall, a non-linear inverse relationship was found between the MPOI and NOVI for different crops over various regions. Quantification of this behaviour was

8 ,----------------------------------7 0 6

3 2

o

o

/ o Rice l:J. Soya,Cotton,Scane X N.Veg, Forest

o 0

o +-------�------�--------�------� o 0.2 0.4

NOV! 0.6 0.8

Fig. 2-MPDI and NDVI derived for crops/ vegetation using SSMI at 37 GHz and NOAA-AVHRR data, respectively, during June- November 1 999 in India

carried out using Becker and Choudhury I I model and the results of the non-linear fit relating MPDI and NOVI are show:1 in Table 2. It was observed that y values ranged from 0.57 for rice to 0.67 for other crops to 0.69 for natural vegetation and forest. The rice showed higher MPOI range and lowel> y value as compared to other crops. It is due to the background surface wetness/soil moisture influences on the MPOI-NOVI relationshipl4. Two distinct MPDI­NOVI behaviours were seen for irrigated and unirrigated rice conditions due to which standard error of estimate (SEE) for rice was more (SEE = 1 .5 1 ) as compared to other classes. Unirrigated rice behaved in a manner similar to other classes, while irrigated rice showed linear decline of MPOI as NOVI increased (Fig. 2).

This study along with other global investigations has demonstrated the potential of space-borne microwave radiometry for agricultural application. The most important objective to be achieved from sp,ace-bome radiometers is to obtain images with the spatial resolution compared to the size of geophysical phenomenon. The present capabilities of available microwave radiometers in terms of poor spatia) resolution and high frequency channels are not optimal for overall agricultural applications. With advanced future microwave radiometers 1 5 with synthetic antenna technology, it would also be possible to assess crops at better spatial resolution.

6 Summary and conclusion The vertical as well as horizontal polarization

brightness temperature data of 37 GHz were analyzed for the period June-November 1 999 along with the

SINGH & DADHW AL: COMPARISON OF SPACE-BASED MPDI AND NDVI FOR CROP GROWTH 197

Table 2-Results of the non-linear fit relating MPDI and NDVI showing number of observation (n), correlation (r) and standard error of estimate (SEE) for different land cover classes

Class MPDlM MPDlo NDVIM

Rice 6.98 0.70 0.64

Other crops 4.44 0.74 0.58

Forest 2.99 0.56 0.74

Combined 6.98 0.56 0.74

NOAA-A VHRR NDVI data. It was observed that Punjab area showed very high value of MPDI in June as compared to other agricultural areas in India, while NDVI was low during this period. This was due to early sowing of rice, which is associated with good irrigation in Punjab. Temporal growth of rice, soybean, cotton and sugarcane was monitored with a characteristic increase in NOVI as well as a decrease in MPDI. Overall, a non-linear inverse relationship was found between the MPDI and NDVI of different crops over various regions. The study finds the usefulness of microwave radiometer data for broad characterization of the vegetation status. It is inferred that both NDVI and MPDI values provide complementary information at early as well as peak vegetative stage of crop and their integrated use can provide better understanding of crop and its environment

Acknowledgements The authors are thankful to Shri J S Parihar, Group

Director, Agricultural Resources Group and Mission Director, Remote Sensing Application Mission (SAC) for encouragement and guidance. Help in image analysis from Dr V K Sehgal and Shri S R Oza are thankfully acknowledged. The authors are also grateful to Dr A K Verma and Global Hydrology and Resource Centre (GHRC) for providing the relevant data.

NDVlo n y r SEE

0.05 40 0.57 0.55 1 . 5 1

0.04 42 0.67 0.70 0.56

0.07 4 1 0.69 0.41 0.56

0.04 123 0.40 0. 14 1.01

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