Photonirvachak Journal of the Indian Society of Remote Sensing, Vol. 34, No. 4, 2006
LARGE AREA SOIL MOISTURE ESTIMATION AND MAPPING USING SPACE-BORNE MULTI-FREQUENCY PASSIVE MICROWAVE DATA S.R. OZA@, R.R S1NGH, V.K. DADHWAL* A N D P.S. DESAI Space Applications Centre (ISRO), Ahmedabad - 380 015, India "Indian Institute o f Remote Sensing, Dehradun - 248 001, India @,Corresponding author :
[email protected]
ABSTRACT The paper reports the estimation of surface soil moisture (SM) using surface wetness Index (SWI) retrieved from multi-frequency passive microwave radiometer. A change detection algorithm was followed which transforms SWI variations in to SM variations using per pixel soil property of field capacity and air-dry status. Estimated soil moisture was compared with the point measurements made at the Monmouth and De Kalb sites of lllinois (USA) for the validation. Sensitivity of the SWI to the variations of rainfall at various vegetation fractions is analyzed. RMS error of volumetric soil moisture is found to be in the range of 6.35 to 8.85 %. The method works well up to the vegetation fraction of 40 %. Applications of the technique are demonstrated by the spatio-temporal analysis of estimated soil moisture maps for India. Characteristic increase in soil moisture was observed with the progress of monsoon from 25 to 32 week in northern India and 46 to 52 week in the costal parts ofTamil Nadu in south.
Introduction Soil moisture play a significant role in the water and e n e r g y e x c h a n g e s at the l a n d - a t m o s p h e r e interface (Shukla and Mintz, 1982) and it is an important parameter in climate research as well as in hydrological and agricultural applications. In
Received 10 December. 2005; in final form 19 June, 2006
microwave region, response o f the land is mainly governed by surface moisture condition. Passive microwave sensing is preferred for large area soil moisture (SM) estimation, as previous research work reported that the signal to noise ratio is significantly higher for passive sensors as compared to active sensors (Berger et al., 2003). Wigneron et al. (2003)
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have discussed the methods, including statistical analysis and inversion of forward models, for the retrieval o f SM using passive microwave measurements. Studies were conducted to estimate the SM by developing empirical relationship by c o m p a r i n g observed values with satellite measurements (Lakshmi et al.. 1997; Jackson and Vine, 1996; Thapliyal et al., 2003). Gohil (1999) has developed empirical model for the retrieval of SM using 6.6GHz frequency o f Oceansat-1 MSMR sensor. Singh et al. (2005a) have used this model to assess the spatial and temporal variability of soil moisture over India. They also found a close relation of SM derived from MSMR with the SM generated by National Centers for Environmental Prediction (NCEP). Microwave emission is mainly influenced by the dielectric and roughness properties of the surface. The relationship between the soil dielectric constant and the moisture content is almost linear, except at low moisture contents (Engman, 1991 ). As the fractional amount o f water increases, the emissivity decreases and the slope of the emissivity between two or more frequencies can be used to determine the surface wetness condition. Basist et al. (1998) have utilized the SSM/I frequencies viz. 19, 37 and 85 GHz for the derivation of Surface Wetness Index (SWI). They found the usefidness of SW! in monitoring surface wetness condition. SM estimation using time series processing of polarization difference temperature (PDT) was demonstrated by De Ridder (2003) and that can be classified as a change detection algorithm. This paper reports the use of SWI to estimate the surface soil moisture (SM) employing the change detection approach. Singh et al. (2005b) discussed the initial findings of the method using SWI data of four years. In this paper, further scientific detail is presented including use of SWI data from 1988 to 2003 and the issues of the effect of vegetation on estimation process. Spatio-temporal variations of surface moisture obtained by the proposed
approach for the India is also explained by demonstrating some applications. Methodology Moisture plays an important role in determining the soil emissivity, which is reflected in SWI. Basist et al. (1998) defined the SWI as SWI = At T, where, /xe = 13o [e (fg- e (fi)] + 131 [e (f3)- ~ (f_,)] Here, fl, f2, and f3 represent the 19, 37 and 85 GHz vertically polarized channels, respectively, and 130, 131 are proportionality constants. The moisture holding capacity of the soil is dependent on the composition of soil. It is assumed that SWI will be minimum at air-dry moisture status, which is minimum moisture level of soil. At the maximum moisture level, which is field-capacity level, SWI will have the maximum value. A variation in moisture content in soil from its air-dry status is reflected in the variations in the SWI from its historical minimum value. Increase in moisture from air-dry status to field capacity, increases the values of SWI from its historical minima to maxima. This increase can be expressed by following relationship SM i = SMad * (WI i - Wlmin) Where,
SM i =
+
(( SMfc - S M a d )
/ (Wl .... -
Wl,nin))
Soil Moisture at pixel i
SMfc = Field capacity of soil at pixel i SMad = Air-dry moisture level of soil at at pixel i Wl .... t~ Wlmin = Maximum and minimum SWI on historical dataset at pixel i WI~ = SWI in wetness composite image at pixel i Da~Used
Images o f the weekly composite Surface Wetness Index (at 0.33 ~ resolution) were obtained from National Oceanic and Atmospheric Administration (NOAA, http://www l.ncdc.noaa.gov)
Large Area Soil Moisture Estinmtion and Mapping using... for the years from 1988 to 2002. To get the pixel level soil information of field capacity (SMfc) and air-dry (SMad) moisture levels, a 17-class soil layer image at 1 km resolution was obtained from Food and Agriculture Organization (FAO). The database of soil moisture within a 0-10 cm soil profile from April-October 1988 was obtained from the Global Soil Moisture Data Bank (GSMDB) (Robock et al., 2000) for Monmouth (40.92~ 90.73 ~ W) and De Kalb (41.85 ~ N, 88.85 ~ W) stations of Illinois (USA). Since 0-10 cm profile is the lowest level available in the dataset, it was used for the comparison. Ten-day composite NDVI products of the SPOTVEGETATION were obtained form the web site of Flemish Institute for Technological Research (Vito), Belgium for the year 2000, which was utilized to assess the effect of vegetation fraction (VF) on the SM estimation process. Weekly rainfall (RF) for the year 2000 of Amritsar station (74.88 ~ E, 31.63~ was taken from the weekly weather reports of India Meteorological Department.
Dam Ana~s~ A multi-temporal data set of weekly SWI, from 1988 to 2003, was analyzed to generate images of the pixel level historical maximum and minimum values. For the integration of soil information, soil layer was brought to the scale of the SWI image considering the maximum occurrence of the soil class by applying the majority filter. Using the pixel level information of Wl .... and Wlm~nand associated soil class information of SMrc and SMad, each pixel o f the SWI image was c o n v e r t e d to the corresponding Soil Moisture (SM) value. For the region where surface is flooded with the water during specific period of time, such as rice growing area, value of WI .... was selected as discussed by Singh et al. (2005b). Comparison of estimated and observed soil moisture was made at the two stations of Illinois, viz. Monmouth and De Kalb for the year 1988 to validate the algorithm.
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Sensitivity of SW1 to the various amounts of fractional vegetation cover and rainfall was studied for the Amritsar site, in Punjab state. Equation suggested by Gutman et al. (1998) was used to transform the NDVI values to VF values, which was developed using the coefficients associated with 0 and 100% vegetation fraction for 8 km NOAA AVHRR NDVI data products. Oza et al. (2004) have tuned these coefficients for the SPOT-VEGETATION sensor for Indian region. These coefficients were utilized to convert ten-day composite NDVI products of SPOT-VEGETATION to corresponding VF images. For the study of soil moisture maps, the data set for the region from 64-100 ~ N longitudes and 4-40 ~ E latitudes covering India and surroundings was selected. A time series of soil moisture maps, at weekly time scale, were generated using the weekly SWI images for the study years and analyzed for the weekly as well as across-the year variations. Results and Discussion
Temporal Variability o f S W l
Temporal pattern of Wl, RF and VF for Amritsar site is shown in Fig. 1 for the period of May to MidNovember 1998. These temporal patterns can be understood by dividing the entire growth period in different time frame as shown in figure. Tinte frame A
This period from mid-may to mid-June is associated with the rice transplanting operation in study region. It is observed that SWI has increasing trend, which is due to the high amount of irrigation. The slow increase in VF (VF
