The IROE algorithm
Simonetta Paloscia
Consiglio Nazionale delle Ricerche,Istituto di Ricerca sulle Onde Elettromagnetiche
On the basis of experimental results, obtained in past years by IROE Microwave Remote Sensing Group, it has beenstated that the brightness temperature (Tb) of microwave emission measured at C-band (6.8 GHz) is able to estimate
soil moisture content (SMC) in different conditions of roughness and vegetation biomass, provided that a correction
for the presence of vegetation is introduced. An algorithm has been proposed for computing SMC and correcting theeffects of vegetation by using the sensitivity of Polarization Index (PI=TbV-TbH /TbV+TbH ) at X-band (10 GHz) tobiomass.
The relation between the brightness temperature (Tb) at C-band and the soil moisture of bare soils has the following
generic form:
SMC = M + N ∗ TbC
When the soil is covered by vegetation, the slope (N) of the regression line decreases and the intercept (M) may alsochange. On the other hand, the (PI) at higher frequencies and in particular at X-band is more sensitive to thevegetation biomass, and can discriminate between several levels of Leaf Area Index (LAI) [Paloscia and Pampaloni
1988]. We can therefore assume that PI at X-band could be related to the slope (N) and intercept (M) of theregression line between PI at C-band and SMC, according to equations of the following type:
M = a + b ∗ PIXand

N = a’ + b’ ∗ PIX

Substituting these relationships in equation (3), we can choose more adequate coefficients for the regression line ofSMC retrieval, which becomes:
SMC= [a + b ∗ PIX] – [a’+ b’ ∗ PIX] ∗ TbC
This procedure can be summarized in the flow-chart of Fig.1.
This algorithm was first tested on microwave data sets collected on agricultural area using IROE airborne radiometers
at C and X band. A comparison of SMC retrieved from radiometric measurements with SMC measured on theground, is shown in Fig. 2, which refers to experimental data collected in the agricultural area of “Les Alpilles” inSouthern France [Macelloni et al, 2000]. The resulting correlation coefficient is R=0.78 and the standard error ofestimate is SE=4.31.
The algorithm was subsequently validated to a larger scale by using satellite data from SMMR. NASDA kindly
provided us with a data set of SMMR collected over 79 Russian agro-meteorological stations during a period of about
3 years (from October 1978 to December 1981), together with the corresponding ground-measured values ofgravimetric SMC. The latter data, archived by Dr. K. Masuda and Dr. V. Savelin, represented the average value ofthe first 10-cm layer: they were obtained, with a time interval of 10 days, from April to October. Five SMMR data
were picked up within an area around the target station.
From the analysis of these data sets, the slope and the intercept of the regression line SMC=M+ N*TbC were related tothe corresponding values of PI at X-band (PIX). In this case, we found that:
M= 60.5+7∗ (PIX)And
N= 0.0008 – 0.2156∗Ln(PIX)

The final equation for the retrieval of SMC therefore becomes:
SMC = [60.5+7∗ (PIX)] + [0.0008 – 0.2156∗Ln(PIX)] ∗TbC
The results of this algorithm is shown in the diagram of Fig. 3, where SMC measured on the ground was comparedwith SMC computed from SMMR data with the algorithm. Although the dispersion of experimental data is ratherhigh, we see that, at least for some ground stations, the algorithm is able to retrieve reasonable data of SMC with R on
the order of 0.70 and SE ranging between 3 and 9 [Paloscia et al. 2001]. The main problem of this validation lies on
the considerable coarse ground resolution of the SMMR at C-band. The retrieval of SMC can work only in the caseof highly homogeneous areas, for which the SMC values measured at the agro-meteorological station can be assumed
to be representative of the entire surrounding zone. Other problems were due to the poor calibration accuracy
between the two polarization channels.

References
G. Macelloni, S. Paloscia, P. Pampaloni, R. Ruisi, C. Susini, 2000, “Airborne Microwave Radiometer
Measurements on Agricultural Fields”, in ‘Microwave Radiometry of Earth’s Surface and Atmosphere’,(Pampaloni & Paloscia Eds), pp. 59-69, VSP press, Utrecht, The Netherlands
Paloscia S. and P. Pampaloni, , "Microwave Polarization Index for Monitoring Vegetation Growth", IEEE Trans. onGeoscience and Remote Sensing, GE-26, n. 5, pp. 617-621, 1988
S. Paloscia, G. Macelloni, E. Santi, and Toshio Koike, 2001, “A Multifrequency Algorithm for the Retrieval of SoilMoisture on a Large Scale using Microwave Data from SMMR and SSM/I Satellites, IEEE Trans.on Geosci. andRemote Sensing, Special Issue on Soil Moisture, vol.39, 8, pp. 1655-1661

MW DATAPI
X -b a n dTb
C -b a n d

PWC/LAIM = a + b PI X
N =a’ + b’ PI XS M C = M + N ∗T b CSMC= (a + b ∗ PIX) + [(a’ + b’ ∗ PIX) ∗ TbC]

SMCFig. 1 – The proposed algorithm

4540
SMC comp (%)

353025201510500510 15 20 25 30 35 40 45
SMC meas (%)

Fig. 2 - SMC % estimated with the algorithm as a function of SMC % of the first 5 cm layer measured
on ground. The line represents the 1:1 line. Symbols refer to different crop types: = alfalfa, •= baresoil, x = sunflower, ♦ = wheat.

40SMCcomp

3020100010203040

SMCmeasFig. 3 - SMC% estimated from SMMR data of Tb at C-band vs. SMC% measured on ground. Different
symbols refer to different Russian stations.