Retrieval of surface roughness using multi- polarized ...

Geocarto International

ISSN: 1010-6049 (Print) 1752-0762 (Online) Journal homepage: http://www.tandfonline.com/loi/tgei20

Retrieval of surface roughness using multipolarized Envisat-1 ASAR data H. S. Srivastava , P. Patel , R. R. Navalgund & Y. Sharma To cite this article: H. S. Srivastava , P. Patel , R. R. Navalgund & Y. Sharma (2008) Retrieval of surface roughness using multi-polarized Envisat-1 ASAR data, Geocarto International, 23:1, 67-77, DOI: 10.1080/10106040701538157 To link to this article: http://dx.doi.org/10.1080/10106040701538157

Published online: 06 Dec 2007.

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Geocarto International Vol. 23, No. 1, February 2008, 67–77

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Retrieval of surface roughness using multi-polarized Envisat-1 ASAR data H. S. SRIVASTAVA*{, P. PATEL{, Y. SHARMAx and R. R. NAVALGUND{ {Regional Remote Sensing Service Centre (RRSSC), Indian Space Research Organization (ISRO), 4 Kalidas Road, Dehradun 248 001, India {Space Applications Centre (SAC), Indian Space Research Organization (ISRO), Satellite Road, Ahmedabad 380 015, India xFeroz Gandhi Post Graduate College (affiliated to CSJM University, Kanpur), Rae Bareli 229 001, India (Received 28 September 2005; in final form 27 June 2007) Spatial distribution of surface roughness is very critical information for many application areas. Surface roughness is often characterized using statistical distribution. However, due to the huge complexity associated with spatial soil surfaces it is difficult to accurately characterize surface roughness over large areas using statistical distribution. Surface roughness influences SAR backscatter significantly and therefore for bare soil surfaces, surface roughness plays a critical role in determining the degree of depolarization of the SAR signal. In this paper surface roughness is retrieved using multi-polarized Envisat-1 ASAR data. The depolarization ratio [s8VH 7 s8VV] has been found to be very sensitive to surface roughness. This study demonstrates an approach that can be used to retrieve quantitative surface roughness values from a space platform without making any assumptions regarding distribution of surface roughness on the ground. Keywords: Surface roughness; Multi-polarized SAR; SAR backscatter; Envisat-1 ASAR

1.

Introduction

Surface roughness and soil moisture are two important factors that affect SAR backscatter from bare agricultural fields (Ulaby et al. 1978, Patel et al. 2001, Srivastava et al. 2005). The average surface roughness within a ground resolution cell strongly influences the strength of radar return and at times the effect of surface roughness becomes comparable to or even more than the effect of soil moisture (Borgeaud et al. 1995, Srivastava et al. 2006). Hence, an accurate measurement of surface roughness is needed to estimate soil moisture from bare agricultural fields with high accuracy. Although the importance of surface roughness and its stability for soil erosion processes has been acknowledged in many studies, this factor has rarely been incorporated in soil erosion models mainly due to the lack of consistent information on surface roughness. Quantitative surface roughness measurements are

*Corresponding author. Email: [email protected]; [email protected] Geocarto International ISSN 1010-6049 print/ISSN 1752-0762 online ª 2008 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/10106040701538157

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required in order to characterize the temporal evolution of any given soil structure. However, due to the huge complexity of 3-dimensional soil surface patterns, soil surfaces are considered as random surfaces and thus characterized by means of statistical distribution and statistical parameters. Unfortunately, in the real world situation, one scarcely knows the spatial surface roughness distribution over a large agricultural area and this leads to inadequate representation of the surface roughness. A methodology is required that is able to describe surface roughness in such a way that it represents the real ground situation. Efforts have been made by many researchers to arrive at optimal combinations of sensor parameters, which are required for resolving the effects of soil moisture and surface roughness on SAR backscatter. Srivastava et al. (2003) have evolved a methodology to incorporate the effect due to surface roughness in soil moisture retrieval models using multi-incidence angle Radarsat-1 SAR data. With the advent of a multi-polarized advanced SAR system onboard Envisat-1, it is feasible to attempt the retrieval of quantitative surface roughness information using multi-polarized SAR data. This paper deals with one such attempt where multi-polarized Envisat-1 ASAR data have been studied for the quantitative measurement of surface roughness. 2. Description of surface roughness and its parameters Surface roughness is entirely different from topographic relief. Surface roughness is measured in centimetres and is determined by textural features comparable in size to the radar wavelength such as sand, gravel, cobble particles and roughness introduced due to agricultural activities e.g. ploughing. In contrast to this, topographic relief is measured in metres (up to hundreds or thousands of metres) and includes features such as hills, mountains and valleys that are expressed on images as highlights and shadows (Sabins 1996). In general, a surface roughness profile is defined by two parameters, a random height variations component with certain statistical properties superimposed on a periodic or a flat surface. The statistical variation of the random component is characterized by its standard deviation of surface height (s) and surface correlation length (l) as described below (Ulaby et al. 1990). 2.1 Standard deviation of surface height (s) The standard deviation of surface height (s) can be written as: ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ffi 2 s¼ Z2  Z

ð1Þ

2

Where Z is the mean height of the surface and Z is the second moment. For a surface in the x-y plane whose height at a point (x,y) is z(x,y) above the x7y plane, a statistically representative segment of such surface of dimensions Lx and Ly centred at the origin, the mean height of the surface and its second moment can be written as: 1 Z¼ Lx Ly

ZLx=2

ZLy=2 zðx; yÞdxdy

Lx=2 Ly=2

ð2Þ

Surface roughness retrieval

Z2

1 ¼ Lx Ly

ZLx=2

ZLy=2

z2 ðx; yÞdxdy

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ð3Þ

Lx=2 Ly=2

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2.2

Surface correlation length

The correlation length of a surface provides a reference for estimating the statistical independence of two points on the surface. The heights of two points on a surface profile may be considered to be (approximately) statistically independent of one another, if they are separated by a horizontal distance greater than the correlation length of that surface profile. The normalized auto correlation function (which is a measure of the similarity between the height Z at a point x and at a point x0 distant from x) for a one-dimensional surface profile Z(x) is defined as: R Lx=2 rðx0 Þ ¼

ZðxÞZðx þ x0 Þdx R Lx=2 2 Lx=2 z ðxÞdx

Lx=2

ð4Þ

The correlation length of a surface profile is defined by the value of displacement for which the value of auto correlation is 1/e. If l is the correlation length of a surface profile then rðlÞ ¼ 1=e

2.3

Surface slope

The standard deviation of surface height (s) measures the vertical scale roughness of a surface whereas l measures the horizontal scale roughness of a surface. For this reason a surface with a rapidly varying height profile will have short value of l. In the extreme case of a perfectly smooth (specular) surface, every point on the surface is correlated with every other point with a correlation coefficient of unity. Hence for this case l will be ?. In order to arrive at a measure that represents the variations in surface roughness in both directions, i.e. the vertical direction as well as the horizontal direction, one needs to combine rms height and correlation length into a single parameter. One such parameter is surface slope, which describes the roughness variation in the horizontal as well as in the vertical directions. The parameter surface slope depends on the distribution of the surface roughness. Commonly used surface roughness statistical distributions are Gaussian, exponential and Weibull. The surface slope depends upon the surface pffiffiffi distribution, for example, if the surface is Gaussian, the surface slope is 2s=l and if it is pffiffiffi exponential then it is 2s=l2 .

3.

Conceptualization

In reality, one scarcely knows the spatial surface roughness distribution and one tends to assume either normal or exponential distribution for the surface roughness.

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This leads to inadequate representation of the surface roughness even though the actual measurement is carried out using a roughness (gridded) plate at a few locations (Ulaby et al. 1990, Srivastava 2000). In order to avoid the assumptions on the distribution of surface roughness, many researchers have compared the different indices that are used to characterize surface roughness measurements. Their studies concluded that standard deviation based roughness indices were closely related to surface area based indices. For this reason, in this paper it has been attempted to retrieve the surface standard deviation or surface rms height from multi-polarized Envisat-1 ASAR data. It is well known that whenever a SAR signal undergoes multiple reflections there are high chances that it may change its state of polarization. This phenomenon is called depolarization of the SAR signal. Depolarization takes place whenever the SAR signal interacts with vegetation volume or rough surfaces like rough soil surface. For vegetation covered soil, density, structure and volume of the vegetation determine the degree of depolarization (Patel et al. 2006) whereas for bare agricultural surfaces, surface roughness affects depolarization. As the scope of multiple scattering is significantly lower for smooth surfaces compared to rougher surfaces, the percentage of total incident electro magnetic radiation (EMR) that changes its state of polarization is significantly less for smooth surfaces. This leads to lower values of cross-polarized SAR backscatter from smooth surfaces compared to rougher surfaces. Therefore the dependence of cross-polarized SAR backscatter (s8VH or s8HV) on surface roughness can be exploited to retrieve quantitative values of surface roughness prevailing in the field at the time of the satellite pass. 4. Dataset and study area In order to explore the potential of multi-polarized SAR data towards the retrieval of surface roughness from a space platform without making any assumptions on the distribution of surface roughness conditions on the ground, two pairs of like (VV) and cross (VH) polarized swath-4 beam mode Envisat-1 ASAR datasets (20 May 2005 and 9 December 2006) have been acquired over Saharanpur and Haridwar districts in the states of Uttar Pradesh and Uttarakhand, India. Envisat-1 ASAR is a multi-polarized C-band (5.3 GHz) SAR sensor capable of acquiring data at varying incidence angles. One scene from the Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning-III (LISS-III), dated 24 May 2005 has also been acquired to assist in ground truth processing. Spectral bands for IRS LISS-III are green (0.52–0.59 mm), red (0.62–0.68 mm), infrared (0.77–0.86 mm) and shortwave infrared (1.55–1.70 mm) with a spatial resolution of 23.5 m. The study area is mostly a flat level terrain and is dominated by agricultural land. This area includes irrigated as well as un-irrigated agricultural land and therefore provides a full range of soil moisture. However, during data acquisition in the third week of May and the beginning of December most of the agricultural fields were fallow, except for sugarcane. Geographical coordinates for the four corners of the study area are 778250 5500 E and 308010 3300 N, 778520 5600 E and 308070 0500 N, 778580 5000 E and 298390 0800 N and 778320 2500 E and 298320 1600 N. A global positioning system (GPS) based mobile mapping unit and 1:50 000 scale SOI (Survey of India) toposheets were also used for fieldwork and for identification of sampling locations on the image. A total of 57 sampling locations were selected for ground truth data collection. All the fields selected for ground truth data collection

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were bare with very low values of soil moisture. Low values of soil moisture enhance the effect of surface roughness on to SAR backscatter. Field photographs of soil roughness profiles on a gridded steel plate have been taken at five places within a field. These photographs were used to calculate the value of rms height and to arrive at the average value of rms height for all of the 57 sampling locations. From of total of 57 sampling locations (31 for 20 May 2005 pass and 26 for 9 December 2006 pass), data collected from 42 sampling locations (23 for 20 May 2005 pass and 19 for 9 December 2006 pass) were used for model development whereas data collected from 15 sampling locations (8 for 20 May 2005 pass and 7 for 9 December 2006 pass) have been selected randomly for model validation.

5. 5.1

Methodology Data pre-processing

For the Envisat-1 ASAR data supplied by the European Space Agency (ESA), output scaling in terms of gain and offset is applied to the data to ensure optimum utilization of the available dynamic range. A digital number (DN) image was converted to a radar backscatter (s8) image with the help of header information provided with the data. Local incidence angle information is also used to normalize the effect of the local incidence angle on SAR backscatter. After conversion of DN to s8, speckle suppression was carried out using the Enhanced Lee-filtering algorithm (Lee 1986). 5.2

Image processing

An IRS L-III optical image dated 24 May 2005 over parts of Saharanpur and Haridwar districts was georeferenced using the Ground Control Points (GCPs) from 1:50 000 scale Survey of India (SOI) topographic maps. The registration accuracy for map to image was within a pixel. Once optical data have been georeferenced the Envisat-1, swath-4 beam mode ASAR datasets, dated 20 May 2005 and 9 December 2006, were registered with respect to optical data using the nearest neighbourhood method of resampling (Duggin and Robinore 1990). After georeferencing all the datasets, rail/road/canal networks and ground truth locations were digitized and transferred on to the satellite images. All the sampling locations were identified on the SAR image and their respective backscattering coefficient values were extracted from Envisat-1 ASAR images (VV and VH polarized images) to obtain the values of (s8VV) and (s8VH) at all the 57 sampling locations. Once the backscattering coefficient values were obtained, models for surface roughness (rms height) retrieval were developed.

6.

Results and discussion

6.1

Model development

In order to explore and compare the potential of like (s8VV) and cross (s8VH) polarized SAR backscatter towards the retrieval of surface roughness, two separate models have been developed relating soil surface rms height to SAR backscatter values extracted from like (VV) and cross (VH) polarized Envisat-1 ASAR images.

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These models are developed using linear regression analysis keeping rms height as a dependent variable and (s8VV) and (s8VH) as an independent variable. These models are given by equations (5) and (6) and the variation of like and cross polarized SAR backscatter with soil surface rms height is shown in the form of scatterplots in figures 1 and 2. rms height ¼ A þ B ðs VV Þ

ð5Þ

rms height ¼ A þ B ðs VH Þ

ð6Þ

A total of 42 data samples have been used to derive the coefficients of these models with the help of linear regression analysis. Results of the regression analysis are given in table 1 and the derived coefficients are obtained as: rms height ¼ 3:02 þ 0:14 ðs VV Þ

ð7Þ

rms height ¼ 4:71 þ 0:14 ðs VH Þ

ð8Þ

The coefficient of determination (R2) was found to be 0.16 for the model given in equation (5) and 0.63 for the model given in equation (6) whereas values of F statistics for models given in equations (5) and (6) are found to be 7.81858 and 67.24379, respectively with the significance of F statistics being 0.00791 and 4.239E-10. These results support the concept behind the study that due to the close association between degree of depolarization and surface

Figure 1.

Variation of s8VV with surface roughness (rms height).

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Surface roughness retrieval

Figure 2.

Variation of s8VH with surface roughness (rms height).

Table 1. Results of regression analysis performed to retrieve surface roughness using different models.

Model used rms height ¼ A þ B * 8VV rms height ¼ A þ B * 8VH rms height ¼ A þ B * [8VH 7 8VV]

A

B

R2

F statistics

Significance of F statistics

3.02 4.71 4.27

0.14 0.14 0.22

0.16 0.63 0.74

07.81858 67.24379 111.88939

0.00790 4.2399E-10 3.72899E-13

No. of data points 42 42 42

roughness, rougher fields cause more depolarization, resulting in higher SAR backscatter for cross polarized data. In contrast to this, like polarized SAR backscatter shows poor variation with surface roughness, resulting in an R2 value of only 0.16. Here it is required to emphasize that although the value of R2 for the model given by equation (5) is only 0.16, the (s8VV) can still be exploited to estimate that component of a signal that is depolarized due to surface roughness. This has been achieved by taking the difference between cross and like polarized SAR backscatter in such a way that it represents the amount of depolarization from like polarized SAR backscatter caused due to surface roughness. This term has been defined as the depolarization ratio [(s8VH) 7 (s8VV)] (Oh et al. 1992). The variation of the depolarization ratio [(s8VH) 7 (s8VV)], with rms height is shown in figure 3.

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Figure 3. height).

H. S. Srivastava et al.

Variation of depolarization ratio [s8VH 7 s8VV] with surface roughness (rms

The model developed for roughness retrieval using the difference between cross and co-polarized SAR backscatter values, represented by the depolarization or crosspolarization ratio is given by rms height ¼ A þ B ½ðs VH Þ  ðs VV Þ

ð9Þ

A linear regression analysis between rms height and [(s8VH) 7 (s8VV)] has been performed to derive the model parameters. Results of the regression analysis are given in table 1. The derived coefficients are: rms height ¼ 4:27 þ 0:22 ½ðs VH Þ  ðs VV Þ

ð10Þ

The coefficient of determination for the model given by equation (9) was observed to be 0.74 and the F statistic for the same was observed to be 111.8894 with the significance of the F statistics being 3.728E-13. Such high values of R2 and F statistics in the case of combined use of cross and like polarized SAR backscatters, represented in terms of the depolarization ratio, explained the fact that the depolarization ratio is not only representing the depolarized return signal caused by the soil surface roughness but at the same time it also takes care of the relative proportion of the signal that is depolarized from the total signal strength.

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Surface roughness retrieval Table 2.

Model used

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rms height ¼ A þ B * 8VV rms height ¼ A þ B * 8VH rms height ¼ A þ B * [8VH 7 8VV]

6.2

Results of validation analysis. Root mean square (rms) error between observed and estimated values of surface heights

No. of data points

1.55 1.29 0.82

15 15 15

Model validation

Validation is an essential component of a statistical approach based analysis. It requires a validation dataset consisting of independent observations of the parameter to be estimated and the observed values of the parameters should not be used to arrive at an estimate of the parameter. Keeping these views in mind, an independent set of randomly selected data points are used for model validation. Moreover the size of the validation points has also been taken as 15, which is higher than the minimum data points required for model validation determined using the precision power approach as suggested by Brooks and Barcikowski (1996), keeping the criteria that the sample correlation coefficient is not to decrease by more than 0.05, no matter what the expected value of the correlation coefficient. Soil surface roughness (rms height) of 15 validation data points have been estimated by substituting the value of SAR backscatter values extracted from like and cross polarized Envisat-1 ASAR images from their respective sampling locations in models represented by equations (7), (8) and (10). These estimated values of rms height, and corresponding measured (actual from field) rms height from sampling locations during ground truth data collection, were compared to calculate the root mean square (rms) error. The results obtained are shown in table 2. The rms error was observed to be maximum (1.55), for models developed using s8VV (equation (5)) and minimum (0.82), for models developed using (s8VH 7 s8VV) (equation (9)). The root mean square error for models developed using s8VH (equation (6)) was found to be 1.29. Lower values of rms error between observed and estimated values of soil surface roughness for those models where s8VH is used support the strong basis behind conceptualization of the study that depolarization of the SAR signal can be related to surface roughness conditions prevailing in the fields at the time of satellite pass. 7.

Conclusion

This paper presents the outcome of the study to explore the feasibility of the use of multi-polarized Envisat-1 ASAR data to retrieve surface roughness conditions. Statistical analysis carried out to relate surface roughness to s8VV, s8VH and (s8VH 7 s8VV), resulted in the highest value of R2 to be 0.74 along with F statistics ¼ 111.88939 and lowest value of rms error ¼ 0.82 along with significance of F statistics ¼ 3.7289E-13, for the case of (s8VH 7 s8VV), which strongly suggest that the depolarization ratio defined by [(s8VH) 7 (s8VV)] is a good indicator of surface roughness derived from multi-polarized SAR data. The significant outcome of the

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study is that it offers an approach to estimate the quantitative values of surface roughness from a space platform, without making any assumptions on the distribution of surface roughness conditions on the ground.

Acknowledgements Mr. Hari Shanker Srivastava is extremely thankful to Dr. V. Jayraman, Director, RRSSC/NNRMS & EOS, ISRO, Head-quarters, Bangalore, for his keen interest in the study and for his encouragement and support during the course of study. Mr. Hari Shanker Srivastava is also thankful to Prof. V. K. Jha, former Head RRSSC-D and Dr. K. P. Sharma, Head-in-charge, RRSSC-D for encouragement and support. Ms. Parul Patel extends her sincere thanks to Mr. J. S. Parihar, Group Director, AFEG/SAC/ISRO and Mission Director, EOAM, and Dr. Manab Chakraborty, Group Director, GTDG/SAC/ISRO Ahmedabad for their encouragement. Ms. Parul Patel is also thankful to Dr. S. Panigrahy, Head, AMD/SAC/ISRO and Dr. S. Mohan, Head, ATDD/SAC/ISRO, Ahmedabad for their encouragement and support. Dr. Yamini Sharma extends her sincere thanks to Dr. K. N. Bhargava, Principal, Feroz Gandhi Post Graduate College (CSJM University), Rae-Bareli for providing necessary support during the course of study.

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