The Utility of IRS-1C LISS-Ill and PAN-Merged Data for

Mapping Salt-Affected Soils R.S. Dwivedi, K.V. Ramana, S.S. Thammappa, and A.N. Singh

Abstract Waterlogging and subsequent salinization andlor alkalization of soils is the major land degradation process operating in the irrigated areas of the arid and semi-arid regions of the world. Before taking up any prevention or reclamation measures for such lands, information on their nature, spatial extent, magni- tude, distribution, and temporal behavior is a prerequisite. In the study reported here, an attempt has been made to evaluate the potential of high spatial resolution (5.8 m) Panchromatic (PAN) sensor data and the Linear Imaging Self-scanning Sensor (LISS-III) data from the Indian Remote Sensing Satellite (1%- IG) for detection and delineation of salt-affected soils in a portion of the Indo-Gangetic alluvial plains of northern India. The approach involves the merging of ~ S S - I I I and PAN data through an Intensity, Hue, and Saturation ( I H ~ transforma- tion, and a subsequent supervised classification using a per- pixel Gaussian maximum-likelihood classification algorithm. Results indicate a deterioration in the overall accuracy of salt- afiected soils derived from LIS-111 data as compared to IRS-IB LISS-II data owing to an improvement in the spatial resolution (23.5 m for US-III versus 36.5 m for LISS-II), leading to enhanced intra-class spectral variability. The PAN and LISS-III hybrid data without any transformation ranked last in terms of ovemll accuracy. Overall accuracy figures for LISS-II, LISS-ID, and PAN and HSS-ID hybrid data with the nis transformation have been on the order of 89.6 percent, 85.9 percent, and 82.5 percent, respectively.

Introduction Soils are termed saline or salt-affected when the concentration of salt in the root zone exceeds 4dSlm (Richards, 1954). Salt- affected soils comprise 19 percent of the 2.8 billion hectares of arable land worldwide (Szabolcs, 1989). In India alone, an esti- mated 7.1 million ha of land are reported to be affected by salin- ity andlor alkalinity (Abrol and Bhumbla, 1971). Such soils are, generally, encountered in an arid or semi-arid climate and are derived from weathering of indigenous minerals (Tanzi, 1990) but also occur extensively in sub-humid and coastal zones. Salt-affected soils are, however, more important eco- nomically in the irrigated commands-the areas where irriga- tion is practiced to produce crops (Carter, 1975). In fact, non- saline irrigated soils can turn saline in time when leaching is inadequate to remove salt applied in the irrigation water, or drainage is insufficient to prevent a saline groundwater table from rising to within about 1.5 m of the soil surface (Wiegand

et al., 1994). The presence of sodium-bearing minerals in the parent material, alternate wet and dry seasons with evapo- transpiration exceeding precipitation, coupled with the intro- duction of canal irrigation have led to the development of soil salinity andlor alkalinity in the region through secondary sali- nization. Some of the most unfavorable properties of these soils include high salt content, poor structure, limited micro- bial activity, low percolation rates, and other characteristics which restrict plant growth and human settlement. Information on the nature, extent, and spatial distribution of salt-affected soils is a pre-requisite for planning and implementation of any reclamation andlor prevention measures. Once a reclamation program is underway, a mechanism is needed to monitor its success and progress.

Role of Remote Sensing Hitherto, the aforesaid information on salt-affected soils has been generated by conventional methods using cadastral maps or topographic maps as a database, which is tedious, time-con- suming, and cost-prohibitive. Aerial photographs (Myres et al., 1966) and Apollo-9 (Wiegand et al., 1971) and Skylab space photographs at 1:3,000,000 scale (Everitt et al., 1977) have been used for the delineation of salt-affected soils. Neverthe- less, the large gap in the desired information continued until the launch of the Earth Resources Technology Satellite-1 (ERTS- I), later named Landsat-1 during 1972. The four-channel Multispectral Scanner System (MSS) aboard Landsat-1, with spectral measurements made in the narrow and discrete bands of the electromagnetic spectrum, provided a synoptic view of the Earth's surface every 18 days. Such data enabled soil scien- tists to generate information on salt-affected soils (Venkatarat- nam and Rao, 1977; Singh et al., 1977; Venkataratnam, 1980; Budd and Milton, 1982; Sharma and Bhargawa, 1988; Singh and Dwivedi, 1989), which was adequate for planning reclama- tion programs. In addition, in making use of spaceborne tempo- ral multispectral data, the extent and spatial distribution of salt-affected soils have been monitored (Rao and Venkatarat- nam, 1991; Singh, 1994; Rao et al., 1996).

The follow-ups to the Landsat series of satellites with the additional Thematic Mapper (TM) sensor with its improved spa- tial, spectral, radiometric, and temporal resolution had, indeed, opened a new vista in mapping and monitoring of salt-affected soils, apart from other degraded lands (Rao et al., 1991; Dwivedi, 1992; Singh, 1994; Dwivedi and Sreenivas, 1998). In addition,

R.S. Dwivedi, K.V. Ramana, and S.S. Thammappa are with the National Remote Sensing Agency, Balanagar, Hyderabad-BOO 03 7, India (dwivedi-rs@msa.gov.in).

A.N. Singh is with the Uttar Bradesh Remote Sensing Applica- tion Centre, Lucknow, India.

Photogrammetric Engineering & Remote Sensing Vol. 67, No. 10, October 2001, pp. 1167-1175.

0099-1112/01/6710-ll67$3.00/0 O 2001 American Society for Photogrammetry

and Remote Sensing

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October 2001 1167

TABLE 1. LANDSAT-5 AND IRS1A/lB ~NSTRUMENTAT~ON CHARACTERISTICS

Band Width (m) Band Description Landsat-MSS * Landsat-TM* LISS-I* * LISS-II* *

band-1 0.50-0.60 0.45-0.52 0.45-0.52 0.45-0.52 band-2 0.60-0.70 0.52-0.60 0.52-0.59 0.52-0.59 band-3 0.70-0.80 0.63-0.69 0.62-0.68 0.62-0.68 band-4 0.80-1.10 0.76-0.90 0.77-0.86 0.77-0.86 band-5 1.55-1.75 band-6 10.45-12.50 band-7 2.08-2.35 Ground IFOV1 82 m 30 m band 1 to 5,7) 120 m (band 6) 72.5 m 36.25 m Data rate2 15 M bitsls 85 M bitsls 5.2 M bitls 10.4 X 2 M bitsls Quantization level3 64 256 128 128

*Salomanson et aL, 1980 **Joseph, 1992 Note: 'Ground instantaneous field of view or spatial resolution

aThe rate of data transmission to ground station 3Radiometric resolution

the Multispectral Linear Array (MLA) data from the SPOT series of satellites with a 20-m spatial resolution were analyzed to gener- ate detailed information on salt-affected soils which is useful for implementation of reclamation programs (Shanna and Bhar- gawa, 1987; Toth et al., 1991; Rao et al., 1993 ). Everitt et al. (1988) used narrow band videography to detect and estimate the areal extent of salt-affected soils in southern Texas. Wiegand et al. (1991) related three bands of video and the three HRV scanner bands of SPOT-1 to lint yield and percent ground cover of cotton in a single 15-ha salt-affected field. With the launch of the Indian Remote Sensing Satellite (IRS-IA) in 1988, spaceborne multispectral data with a spatial resolution comparable to the Landsat MSSITM (Table 1) and a push-broom scanning mode of data acquisition similar to SPOT, became available. The potential of the Linear Imaging Self-scanning Sensors (LISS-I and II) onboard the IRS series of satellites, i.e., IRS-IA, lB, and P2, for pro- viding the desired information on soil salinity and/or alkalinity was exploited in several studies (Dwivedi and Venkataratnam, 1992).

Subsequently, spaceborne spectral measurements from three payloads, i.e., the Panchromatic (PAN) camera, the Linear Imaging Self-scanning Sensor (LISS-m), and the Wide Field Sen- sor (w~FS) representing state-of-the-art sensors with respect to spatial, spectral, and temporal resolution (Table 2), became available with the launch of the Indian Remote Sensing Satel- lite IRS-1C in late 1995 and IRs-ID in 1997. A pilot study was con- ducted using IRS-lc LIss-m data for the delineation of salt- affected soils (Dwivedi and Sreenivas, 1998). In this article, we

report the results of a pilot study which was taken up to evalu- ate the potential of IRS-IC LISS-m and PAN data for the delinea- tion of salt-affected soils in parts of the Lucknow and Unnao districts, Uttar Pradesh, northern India.

The Study Area The study area covered 103.7 sq km was located between 26'46' to 26'52'N and 80'40' to 80°45'E in parts of the Malihabad block of Lucknow district and Hasanganj district of Umao dis- trict, Uttar Pradesh (Figure 1). Lithologically, the area is part of the vast Indo-Gangetic alluvium of Pleistocene age. Physio- graphically, it is an alluvial plain, which is dotted with palaeo- channels, oxbow lakes, back swamps, and local depressions. The area is drained by Nagwa stream. The area is irrigated by the Sharda canal system. Owing to agentle slope and the lack of a well-defined drainage network, the surface drainage is mod- erate to poor. The climate of the area is semi-arid and sub-tropi- cal with a mean monthly temperature ranging from 15.6'C during January to 33°C during June. The mean annual precipita- tion in the area is 966.6 mm. Soil moisture and soil tempera- ture regimes of the area qualify as ustic and hyperthermic, respectively (U.S. Department of Agriculture, 1975).

The Data Set We have used the Indian Remote Sensing Satellite (IRS-ic) Lin- ear Imaging Self-scanning Sensor (LISS-IU) and Panchromatic sensor (PAN) data acquired on 22 March 1998. In order to draw a comparison with contemporary sensor data, IRS-IB LI~s-II data

TABLE 2. SPOT AND I R S l C INSTRUMENTATION CHARACTERISTICS

Band Width (pm) Band Width (pm)

SPOT IRS-IC

Band Description MLA PLA LISS-111 PAN WiFS

Band 1 Single band 0.50-0.73 - Single band 0.50-0.75 - Band 2 0.50-0.59 - 0.52-0.59 - Band 3 0.61-0.68 - 0.62-0.68 - 0.62-0.68 Band 4 0.79-0.89 - 0.77-0.86 - 0.77-0.86 Band 5 - - 1.55-1.75 Ground IFOV(m) 20 m 10 m 23.5 m for bands 2,3, and 4

70.5 m for band 5 Data rate (Mbps) 25 25 35.794 (VNIR) 84.903 2.061ti

1.3906 (SWIR) Quantization level 256 256 128 64 128

VNIR = Visible and near-infrared bands, (SWIR = Short-wave infrared band) Source: Joseph et a]., 1996

U68 October 2001 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

N

UlTAR PRADESH

Figure 1. Location map of the study area.

acquired on 23 March 1998 were used. In addition, Survey of India topographic maps at 1:50,000 and 1:25,000 scales and published soil survey maps and reports were also used as col- lateral information.

Methodology The methodology was comprised of (1) image processing, (2) image analysis, and (3) accuracy estimation.

image The first step in generating multisensor data sets is the georefer- encing of the image to a common map grid. When merging higher resolution data with lower resolution images, usually a high resolution image (here PAN data with a 5.8-m spatial reso- lution) is used as a reference for enhancement of the lower reso- lution data (uss-m data with a 23.5-m spatial resolution) (Haydn et al., 1982; Cliche et al., 1985). For multisensor data fusion, three main approaches, namely, statistical methods (Wu, 1985; Welch and Ehlers, 1987; Welch and Ehlers, 1988; Benediktsson and Swain, 1989), the Dempster-Shafer theory (Lehrer et al., 1987; Lee et al., 1987; Kim and Swain, 1990), and neural networks (Benediktsson and Swain, 1989; Jakubowicz, 1988), are used. Sensor fusion techniques, in general, could be divided into three categories according to the stage at which the fusion is performed: pixel-, feature-, and decision-level-based fusion. In pixel-based fusion, the sensor measurements are merged on a pixel-by-pixel basis (Baninger, 1990; Ringot and Kwok, 1990). Feature-based fusion techniques merge the dif- ferent data sources at an intermediate level. Image segmenta- tion is often performed for the different sources, and then the segmented images are fused together (Kropatsch, 1990; Rob- inson and Tilton, 1990). In decision-level fusion, the outputs

of each of the single-source interpretation modules are inte- grated to create a consensus interpretation (Kim and Swain, 1990; Perlant and McKeown, 1990; Benediktsson and Swain, 1992).

We have used the intensity, hue, and saturation (MS) trans- formation which refers to the parameters of human color per- ception (Lillesend and Kiefer, 1987). "Intensity" (Munsells' value: Munsell, 1950) refers to the total brightness of a color; "Hue," generally, refers to the dominant or average wavelength of light contributing to a color; and "Saturation" specifies the purity of a color relative to gray. To begin with, the digital LISS- m and PAN data covering a common area were digitally regis- tered on an IBM RS 6000 system using an image-to-image regis- tration algorithm taking 20 tie points (ground control points), and using a third-order polynomial transformation. Subse- quently, LISS-m data were resampled to a 6-m pixel dimension using nearest-neighborhood algorithms for further processing. Later, two sets of hybrid products were generated. In the first set, band-2 (0.52 to 0.59 pm) and band3 (0.62 to 0.68 pm) of LISS-ID data were replaced by PAN data. In the second set, the threebands0.52 to0 .59p,0 .62 to0.68pm,and0.77to0.86 pm of LISS-III data were transformed into intensity, hue, and saturation (MS), and the intensity (I) and saturation (s) compo- nents were replaced by PAN data during back transformation of MS data to RGB components.

Image Analysls Image analysis involves the following steps:

Preliminary Digital Analysis

Based on our experience, initially, broad categories of salt- affected soils were identified after displaying the digital multi- spectral data to the color monitor of the IBM RS 6000 system. A tentative legend in terms of image elements as seen on the color monitor and corresponding salt-affected soils was developed. Doubtful areas were located on 1:25,000- and 1:50,000-scale topographic maps of for further verification in the field.

Ground i h t h Collection

Field visits were made to establish the relationship between image elements of LISS-III and synthetic LISS-III and PAN data, and salt-affected soils. Initially, a reconnaissance traverse of the area was made to assess the trafficability of the terrain and to locate the sample points precisely. Subsequently, the points where observations were to be made were marked precisely on the topographic maps, and the observations with respect to land uselland cover, salt encrustation, surface drainage, imiga- tion source and depth and quality of groundwater, crop condi- tion-vigor and density and local relief were made. Later, in typical areas, soil profiles were excavated, morphological fea- tures were studied, and soil samples from typical profiles were collected for analysis in the laboratory. In addition, auger bore and surface observations were made outside the sample strip to account for variations in salt-affected soils therein. Detailed physical and chemical analysis of soil samples collected dur- ing field visits was carried out. Based on morphological charac- teristics and chemical analysis data, soils were classified according to soil taxonomy, (U.S. Department of Agriculture, 1998). The tentative legend, developed earlier, was modified vis-d-vis field observations. Salt-affected soils were subse- quently grouped into various categories and the magnitude of the problem was assessed based on pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP) values (Tables 3 and 4).

Final Digital Analysis

After displaying the digital LISS-III data on the color monitor of the EMRS 6000 system and identifying the training sets where

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October 2001 1169

Types of salt- affected soil PH ECe (dS1m) ESP Predominant anionslcations

Saline soils c8.5 >4.0 <15 Chlorides, sulphates, some times nitrites of sodiumlsmall amount of bicarbonates Saline-alkali >8.5 >4.0 >15 Carbonates and bicarbonates of sodium Alkali soils X . 5 <4.0 >15 Carbonates and bicarbonates of sodium

Source: U.S. Salinity Laboratory Staff, 1969

field observations were made, the spectral response patterns of different types of salt-affected soils were generated. A similar procedure was adopted for classifying LISS-111 and PAN-merged data, IHS-transformed data, and LISS-II data. In addition, class separability analysis was carried out by computing the trans- formed divergence (TD) and Bhattacharya distance (BD) values. All the data sets were classified using a per-pixel Gaussian max- imum-likelihood classifier, and the color-coded digital out- puts were generated on a Tectronics Phaser 560 ink jet printer.

Accuracy Estimation For quantitative estimates of the classification accuracy of color-coded maps generated from LIss-u, LIS-m, PAN data, and combinations thereof, sample areas representing different types of salt-affected soils were selected randomly (Congalton et aL, 1983). An adequate number of sample points represent- ing different types of salt-affected soils were identified on the color-coded maps for accuracy estimation. Because all four data sets were registered digitally to each other with sub-pixel accu- racy and resampled to a 6-m pixel dimension, the sample areas used for accuracy estimation of one data set were also valid for the other data sets. A one-to-one comparison of the categories mapped from all the data sets and ground-truth data and the information available in the published report (National Remote Sensing Agency, 1995) was made. An accuracy estimation in terms of overall accuracy, errors of omission and errors of com- mission, and Kappa coefficient (2 ) was subsequently made after generating confusion matrix. The Kappa coefficient ( K )

was computed as follows (Bishop et al., 1975):

Staff, 1969). These soils have been further categorized based on the severity or magnitude of the problem (Table 4) into slight (SINI), moderate (S2~2), and strong ( ~ 3 ~ 3 ) . The slightly saline- alkali soils (SINI) qualify as v p i c Ustochrepts whereas mod- erately and strongly saline-alkali soils as Typic Halaquepts (U.S. Department of Agriculture, 1998). The morphological, physical, and chemical properties of these soils are given in Tables 5a, 5b, and 5c, and the range of a few critical chemical characteristics such as pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP) is presented in Table 6. A close look at Table 6 indicates that the pH values for slightly saline-alkali soils ( ~ 1 ~ 1 ) range from 8.6 to 8.9, Ecvalues from 1.2 to 1.6, and ESP values from 9.2 to 14.2. In the case of moderately saline-alkali soils ( S Z N ~ ) , the pH, Ec, and ESP values have been found to be in the range of 9.2 to 9.6,9.3 to 15.6, and 31.4 to 39.9, whereas the ranges of the value for these parameters for strongly saline-alkali soils ( ~ 3 ~ 3 ) are 9.8 to 10.0,30.4 to 42.4, and 45.6 to 50.8, respectively. While strongly saline-alkali soils ( ~ 3 ~ 3 ) invariably do not support any crop and are lying barren, the moderately and slightly saline-alkali soils have been found to support crops to a varying degree depending upon the magnitude of the problem and prevailing management prac- tices. The pH values of these soils have been found to increase with depth whereas Ec and ESP values, in general, show a mixed trend.

Spectral Separability Seperability helps refining the digital classification. It has long been used as an index of the efficiencv of sensor data to distin-

where r is the number of rows in the matrix, xii is the number of observations in row i and column i (the ith diagonal elements), xi+ and x+! are the marginal totals of row r and column i, respec- tively, and Nis the number of observations.

Results and Discussion The discussion focuses on the nature and magnitude of soil salinity and/or alkalinity, spectral separability of salt-affected soils, and its thematic accuracy for evaluating the performance of various sensors data and combinations thereof.

Salt-AMed Soils Based on chemical characteristics, salt-affected soils of the area could be categorized as saline-alkali (US. Salinity Laboratory

guish various terrain featurestphenomena. For an objective evaluation of the performance of LIs-nI, PAN, and Llss-n data with respect to categorization of salt-affected soils, two class separability indices-Transformed Divergence (TD) and Bhat- tacharya Distance (ED) (Kailath, 1967)-have been used. A close look at Table 7 indicates a fairly good spectral separabil- ity of slightly saline-alkali soils (SINI) and strongly saline- alkali soils ( ~ 3 ~ 3 ) , and moderately saline-alkali soils ( ~ 2 ~ 2 ) and strongly saline-alkali soils ( ~ 3 ~ 3 ) in the LIS-m, LISS III and PAN hybrid, and LIss-11 data. The TD values have been found to be maximum (2.00) for LISS-n, LIS-m, and LIS-m and PAN data with the IHS transformation (Table 7), indicating thereby a very good spectral separability of these soils. Incidentally, with a TD value of 1.99, LIS-UI, and PAN data without the IHS transforma- tion also exhibit almost similar spectral separability. The spec- tral separability of slightly saline-alkali soils (SINI) and moderately saline-alkali soils (SZN2) presents a slightly differ- ent picture. Overall separability of these two categories of salt- affected soils has been poor in all the data sets. The order of sep- arability of these categories has been LIS-m > LISS-II > LIS-m and PAN (IHS transformed) and LIS-111 and PAN data. The Bhatta- charya Distance (ED) values for all the three categories of salt- affected soils also portray the similar trend.

Alkalinity

Salinity ECe(dS1m) PH ESP ClasslRcatlon Accuracy

Slight Salt-affected soil maps derived kom IRS-IC LIS-III and LIS-m and

4-8 8.5-9.0 Moderate 8-30 9.0-9.8

<I5 PAN-merged data are included in Figures 2 and 3. As can be seen 15-40 Strong >30 >9.8 from these maps, salt-affected soils are confined to the areas '40

which are commanded by the canal. The case is similar with

1170 October ZOO1 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

I I

Figure 2. ~lsslll image of the part of the study area (a) and corresponding salt-affected soil map (b).

Figure 3. Hybrid image of ~lsslll and PAN of the part of the study area (a) and corresponding salt-affected soil map (b).

PWTOQRAMhlKIPHC INQINEEWNG & REMOTE SENSING October 2001 1171

- -

Effervescence Horizon Depth (cm) Color Texture Structure Cutans with HC1

A1 0-12 10YR412 Sil Moderate, medium, Sub-angular blocky Nil - A2 12-22 10YR312 Sil Moderate, medium, Sub-angular blocky Nil - Bwl 22-39 10YR3/2 S ic Moderate, medium, Sub-angular blocky Nil - Bw2 39-69 2.5Y4/2 Sic1 Moderate, medium, Sub-angular blocky Nil - BC 69-90+ 2.5Y512 Sic Moderate, medium, Sub-angular blocky Nil Strong

Horizon Sand [%) Silt (%) Clay [%) PH EC Na' CEC ESP (%)

A1 A2 Bwl Bw2 BC

Effervescence with Horizon Depth (cm) Color Texture Structure Cutans HCl

AP 0-15 2.5Y 614 Sil Fine, weak, Sub-angular blocky Nil Strong Bwl 15-38 2.5Y 514 Sil Medium, Moderate, Sub-angular blocky Nil Slight Bw2 38-84+ 2.5Y 514 Si C1 Medium, Moderate, Sub-angular blocky Nil Slight

Horizon Sand (%) Silt (%) Clay (% 1 PH EC Na+ CEC ESP (%)

AP Bwl Bw2

Effervescence with Horizons Depth (cm) Color Texture Structure Cutans HC1

A1 0-15 5Y 513 LS Fine, weak, and platy Nil Strong Bwl 15-28 5Y 513 LS Fine, moderate, and platy Nil Strong Bw2 28-48 5Y 513 LS Medium, moderate, and platy Nil Slight C1 48-60+ 2.5Y 512 LS Medium, moderate, and platy Nil Violent

Horizon Sand (%) Silt (%) Clay (%) PH EC Na+ CEC ESP (%)

A1 Bwl Bw2 C1

Property S l N l S2N2 S3N3

PH 8.6-8.9 9.2-9.6 9.8-10.0 EC 1.2-1.6 9.3-15.6 30.4-42.4 ESP 9.0-14.2 31.4-39.9 45.6-50.8

Note: SlNl: Slightly saline-alkali soils S2N2: Moderately saline-alkali soils S3N3: Strongly saline-alkali soils

waterlogged areas. However, waterlogged areas are mostly con- fined along the canal course. The seepage of water fiom the canal leads to a waterlogging condition. As mentioned earlier, three categories of salt-affected soils, namely, slightly saline- alkali, moderately saline-alkali, and strongly saline-alkali hav- ing direct bearing on their reclamation, could be mapped.

The accuracy estimate of a thematic map serves as a valu- able and objective tool for evaluation of its reliability. A com- parison of the accuracy estimates for LISS-n- and LIS-a-derived

1172 October 2001

Data Type

LISS-111 + LISS-111 + LISS-111 PAN [IHS) PAN LISS-I1

Category 1 2 1 2 1 2 1 2

Note: 1: Bhattacharya Distance 2: aansformed Divergence

SlNl: Slightly saline-alkali S2N2: Moderately saline-alkali S3N3: Strongly saline-alkali

salt-affected soil map (Table 8) reveals overall accuracy figures of 89.6 percent and 85.9 percent, respectively. Because the IRS- IB LISS-11 data were collected one day before the IRS-IC overpass

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

TABLE 8. ACCURACY ESTIMATES OF SALT-AFFECTED SOILS DERIVED FROM VARIOUS SENSORS

Error of Commission Error of Omission Overall

Sensor Accuracy S l N l S2N2 S3N3 S l N l S2N2 S3N3 Coefficient Kappa

LISS-I11 85.89 3.9 8.3 1.4 7.3 3.7 1.3 0.835 LISS-IJ.I + PAN(IHS] 81.51 17.4 11.8 0.6 11.5 17.4 1.3 0.812 LISS-I11 + PAN 76.53 31.2 21.9 0.4 15.9 38.2 1.6 0.811 LISS-TI 89.62 13.1 19.6 8.1 9.8 3.1 8.1 0.871

Note: S l N l : Slightly saline alkali S2N2: Moderately saline alkali S3N3: Strongly saline alkali

under almost identical illumination condition, and both the sensors, i.e., LISS-n and LIS-ILI, are also similar with regard to spectral and radiometric characteristics, the difference in the classification performance of these two sensors data could, therefore, be attributed only to the difference in their spatial res-olution. This observation is in conformity with the one made by Acevedo et al. (1984) and Irons et al. (1985) while ana- lyzing simulated Landsat Mss and TM data. However, a contrary situation has also been noticed wherein a marginal improve- ment in the classification accuracy of soils of the Great Hungar- ian plain was observed while working with Landsat TM bands 2,3, and 4, and SPOT-MLA data (Buttner and Csillag, 1989).

Merging LIS-III digital data with the PAN data has resulted in a marginal decrease in the overall classification accuracy (Table 8), owing probably to the poor spectral definition of salt- affected soils in the latter sensor data resulting from replace- ment of the green (0.52 to 0.59 pm) and red (0.62 to 0.68 pm) bands of LIS-~n data by the PAN (0.55 to 0.75 pm), and the poor radiometry (6-bit) of the PAN raw data. Furthermore, only a nominal improvement in the overall classification accuracy was observed when the intensity, hue, and saturation (MS) transformation was applied to the LIS-111 data followed by replacing the intensity (I) component by the PAN data, restoring subsequently to the RGB components, and linearly stretching the resultant digital data. Because only one of the three compo- nents of the MS transforms of the LIS-IU data were replaced by the PAN data while keeping the other two components-hue and saturation-undisturbed, the spectral information of the LIS-ILI data could be better utilized. Kappa coefficient (2) values also manifest a similar trend (Table 8). The errors of omission and commission for LIS-III and PAN-merged data are abnormally

I high (as high as 38.2 percent). Furthermore, in the case of -. strongly saline-alkali soils, the errors of omission as well as the errors of commission are relatively very small (less than 10 per- cent) when compared to the other two categories, i.e., slightly and moderately saline-alkali soils. Interestingly, the values of errors of omission and commission are relatively higher for the LISS-II-derived salt-affected soil map as compared to the LIS-m- derived map (Table 8).

As with overall accuracy, both errors of commission as well as errors of omission tended to increase except for the strongly saline-alkali soils category when LIS-III data were merged with the PAN either by simple replacement of the green band of LIS- III (0.52 to 0.59 ,an) by PAN or through the MS transformation. However, the errors of omission and commission do exhibit a substantial decrease when the MS transform is applied to the LIS-III data and the intensity component is replaced by PAN data (Table 8).

Conclusions The study has vividly demonstrated the utility of high spatial resolution satellite data for mapping salt-affected soils at larger scales, which enables delineating small pockets of such soils. However, improvement in the spatial resolution does not result in the improvement in the classification accuracy, owing

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

probably to increased intra-class spectral variability. In fact, a comparison of the salt-affected soil map derived from the IRS-iB LISS-I1 data with a 36.25-m spatial resolution with that of the ~ S - I C ~1S-m data (23.5 m) based map, reveals that the former has an edge over the latter with respect to overall classification accuracy. Such an observation is in conformity with those expe- rienced by other researchers (Acevedo et al., 1984; Irons et al., 1985). Merging the LIS-III data with the 5.8-m resolution PAN data, either by simple replacement or through the IHS transfor- mation, has been found to deteriorate further the overall the- matic accuracy. It could probably be due to the broad spectral band (500 to 750 nm) of the PAN sensor, which contributes to a relatively poor spectral definition of salt-affected soils. In order to exploit the full potential of such data, additional ground truth and improved classification algorithms capable of taking into account contextual information from multispec- tral data need to be used. Overall accuracy figures achieved from the various sensors data, i.e., LISS-11, LIS-III, and LIS-III and PAN hybrid are 89.6 percent, 85.9 percent, and 81.5 percent, respectively. The wealth of information on salt-affected soils that could be derived from spaceborne multispectral data will be further enhanced with the availability of higher spatial reso- lution data from the recently launched ~KoNos-II and future Earth observation missions such as Cartosat-1, IRS-P6 (Resourcesat), Cartosat-2, Quickbird, etc.

Acknowledgments The authors are indebted to Dr. D.P. Rao, Director, NRSA, Hyderabad for providing financial assistance and facilities during the course of the investigation. Thanks are due to Shri. S.K. Bhan, Deputy Director (Applications). Secretarial support provided by Mr. Vijaya Chandra and Mrs. B. Padmavathi is also gratefully acknowledged.

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(Received 24 April 2000; accepted 08 October 2000; revised 02 March 2001)

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