Soil Moisture Estimation Using Surface Backscattering Coefficients Observed by the Tropical Rain Measurement Mission Precipitation Radar

Transcription

1 Soil Moisture Estimation Using Surface Backscattering Coefficients Observed by the Tropical Rain Measurement Mission Precipitation Radar Shinta Seto Communications Research Laboratory, Tokyo, Japan Alan Robock Department of Environmental Sciences, Rutgers University, New Brunswick, New Jersey Lifeng Luo Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey Taikan Oki Research Institute for Humanity and Nature, Kyoto, Japan Toshio Iguchi Communications Research Laboratory, Tokyo, Japan Katumi Musiake Fukushima University, Fukushima, Japan Submitted to Journal of Geophysical Research Atmospheres August, 23 Corresponding Author: Prof. Alan Robock Department of Environmental Sciences Rutgers University 14 College Farm Road New Brunswick, NJ 891 Phone: (732) Fax: (732)

2 - 1 - Abstract Soil moisture affects many important hydrological and meteorological processes on various scales and it is important to know the global distribution of soil moisture. Microwave remote sensing is an indispensable method of obtaining this information. We used the first space-borne precipitation radar, on the Tropical Rainfall Measuring Mission satellite, for this purpose by examining backscattering not from rainfall but from the land surface under no precipitation conditions. The spatial pattern of the land surface backscattering coefficient ( σ ) is determined mainly by the incident angle and vegetation. The seasonal pattern of σ in general does not depend on different incident angles, except in the Sahel region, where there is a large impact of from the temporal change of vegetation. We propose a soil moisture estimation algorithm that considers a mosaic of different vegetation types in each scene. The vegetation fraction is determined by the σ observed at an incident angle of 3 and then the temporal change of σ for bare soil is calculated with observation at an angle of 12. Because σ observed at 12 is not strongly affected by change of vegetation, the algorithm can simulate the seasonal pattern well even in the Sahel where vegetation changes drastically. This algorithm generally works well in regions without heavy vegetations. The algorithm works well when tested for estimating daily soil moisture in Oklahoma at a latitude of about 35 N.

3 Introduction 1.1. Soil Moisture in Hydrology and Meteorology Soil moisture is an important component of the climate system, and directly affects some important hydrological and meteorological processes such as evaporation, transpiration, and runoff [Walker and Rowntree, 1977; Shukla and Mintz, 1982, and Rowntree and Bolton, 1983]. As a result of these direct effects, near-surface temperature and humidity depend on soil moisture. Huang et al. [1996] showed that monthly temperature was well predicted by soil moisture of the previous month. Initialization of soil moisture is important for weather prediction and from an agricultural point of view, soil moisture is an index of water resources. Brumbelow and Georgakakos [21], for example, show the necessity of soil moisture information for the estimation of irrigation requirements. Soil moisture is highly variable both in space and time. Delworth and Manabe [1988] proposed a theory that the temporal scale of soil moisture is given by the ratio of field capacity to potential evaporation. Vinnikov and Yeserkepova [1991] validated this theory using in-situ soil moisture observation from Russia. In the extratropics, the spatial and temporal scales are about 5 km and 2 months, respectively [Entin et al., 2]. In field experiments, however, soil moisture variation on a smaller scale is emphasized [e.g., Bell et al., 198; Famiglietti et al., 1999]. Vinnikov et al. [1999a] named the former as the meteorological scale and the latter as the hydrological scale Remote Sensing for Soil Moisture Observation Because of the sparse network of global in situ soil moisture observations [Robock et al., 2], production of a global soil moisture data set will involve data assimilation of remotely sensed meteorological and soil moisture observations [e.g., Lakshmi et al., 1997b; Galantowicz et al., 1999; Calvet et al., 1998]. Such a system will work best if it is accomplished with a good

4 - 3 - land surface model [e.g., Mitchell et al., 23]. In this paper we describe a new method for obtaining remotely-sensed soil moisture. Both infrared remote sensing and microwave remote sensing can be applied to soil moisture observation. Infrared remote sensing is an indirect method for estimating soil moisture. Thermal infrared sensors can measure the physical temperature of land surfaces under cloud free conditions. Lakshmi [2] assimilated land surface temperature in a water and energy balance model of the land surface to update the soil moisture information. This method does not work without any hydrological and meteorological knowledge, as expressed in a land surface model, and we therefore call this an indirect method. On the other hand, microwave remote sensing can estimate the soil moisture in a more direct manner. This method is based on the fact that soil moisture affects the soil dielectric constant; an increase of soil moisture increases the soil dielectric constant. Both passive and active microwave remote sensing have been applied for soil moisture observation. Passive microwave sensors measure the brightness temperature (the radiation strength of microwaves coming from the direction of the land surface). Under ideal conditions, with no vegetation or atmospheric effects, brightness temperature significantly decreases when soil moisture increases. For more than 2 years, passive microwave sensors have been in space. The Scanning Multichannel Microwave Radiometer (SMMR), which was operated from 1979 to 1987, was used to estimate soil moisture over Illinois, USA [Vinnikov et al., 1999b]. SMMR was also applied to soil moisture information over Botswana considering the effects of the vegetation layer [Van de Griend and Owe, 1993]. The next mission, the Special Sensor Microwave Imager (SSM/I) aboard Defense Meteorological Satellite Program (DMSP) satellites, operated from 1987 to the present, was employed to estimate soil moisture over the southwestern part of the USA [Lakshmi et al., 1997a].

5 - 4 - Active microwave sensors observe the backscattering coefficient of the land surface (hereafter, the backscattering coefficient) in response to an active microwave signal emitted by the instrument. As soil moisture increases, the backscattering coefficient becomes low. The history of active microwave sensors in space is shorter than that of passive microwave sensors, but some research has been done on the application of space-borne active microwave sensors for soil moisture monitoring [Engman and Chauhan, 1995]. Altese et al. [1996] developed an inverse technique to estimate soil moisture and validated it using the SAR (Synthetic Aperture Radar) on the ERS (European Remote-sensing Satellite). The Windscatterometer (WSC) aboard ERS has been investigated as a land surface sensor by some studies [e.g. Wagner and Scipal, 2; Frison et al., 1998]. Soil moisture observation by microwave remote sensing is promising for bare soil, but there are several difficulties when applied to vegetated land surface. Vegetation limits the soil moisture observation, as microwaves are attenuated by vegetation. Another limitation is that the microwave can sense only the first few centimeters of the surface. L-band observations (λ = 2-3 cm) can overcome some of the limitations with respect to vegetation and sampling depth, but L-band sensors are not in space yet. Even if L-band observations from space are available, it might be still difficult to estimate deep layer soil moisture directly Purpose of This Study We propose an algorithm to estimate surface soil moisture using observations of the Precipitation Radar onboard the Tropical Rainfall Measuring Mission (TRMM/PR). TRMM/PR, the first precipitation radar in space, started its observations in December The mission of TRMM/PR is to estimate the rain rate using the backscattering from rainfall. Simultaneously, backscattering from the land surface is observed for the correction of rainfall attenuation. Since 96% of the time there is no precipitation in the satellite footprint, most

6 - 5 - TRMM/PR observations are of the land surface. Compared with SAR, the horizontal spatial resolution of TRMM/PR is coarse (4.3 km), but the temporal resolution is relatively high. TRMM/PR covers most of the planet within 37 N and 37 S in two days. The spatial scale of TRMM/PR is suitable for the application on a global scale. The ERS/WSC, which has similar spatial and temporal scales to TRMM/PR, is also employed in this study. ERS/WSC has been developed for monitoring sea surface wind velocity, but the data over land have been recorded. The features of two sensors are shown in Table 1. The incident angle of TRMM/PR is smaller than ERS/WSC. Frequency and polarization are also different between TRMM/PR and ERS/WSC. TRMM/PR and ERS/WSC data are explained in the next section, and the backscattering coefficient is analyzed with a land surface dataset and a simple model to simulate the observed backscattering coefficient is proposed in section 3. In section 4, the soil moisture estimation algorithm is introduced. In section 5, the algorithm is applied with two different time scales. The final section is for summary and conclusions. 2. Satellite Observation Data 2.1. TRMM/PR Backscattering coefficient data are available from the TRMM standard product 2A21, which is processed and is published by NASA and NASDA (National Space Development Agency of Japan). 2A21 contains the following parameters for each scan observation: surface backscattering coefficient ( σ ), location (latitude, longitude), time, incident angle, and rain flag ( no rain or raining ). The frequency of microwave used by TRMM/PR is 13.8 GHz (Ku-band); therefore it is significantly attenuated by strong rainfall. In this case, the backscattering coefficient does not

7 - 6 - represent the land surface status. However, the ratio of rainfall occurrence to total observations is less than 1% in the most of regions, as is shown in Figure 1. The average for the entire region is less than 4%. While data observed during rainfall have to be excluded, still 96% of total observation is available. We prepared a monthly 1 x 1 grid dataset of the surface backscattering coefficient. All the data falling into each grid box, except those observed during the rainfall were averaged in units of db. The dataset was prepared for each incident angle, with increments of.75. These gridded data are used in sections 3.1, 3.2 and 5.1. In section 5.2, we use daily data, explained later ERS/WSC Surface backscattering data from ERS/WSC were obtained from the Wismann [1999] CD-ROM. It contains monthly and 3-monthly averages of the backscattering coefficient and the number of observation for each 5 km grid. As 3-monthly data, the average backscattering coefficient is available for each incident angle (with 5 steps from 17 to 57 ). For monthly data, only a linear regression line between the backscattering coefficient and the incident angle are published. The monthly average for each incident angle was calculated using the monthly regression lines. The horizontal resolution was converted into 1 x 1 or 2 x 2 for this study. 3. Analysis for Modeling of the Backscattering Coefficient The purpose of this section is to propose a simple model to explain the backscattering coefficient observed by TRMM/PR. For that purpose, the relationship between the backscattering coefficient and land surface dataset is analyzed. In the analysis, both TRMM/PR and ERS/WSC data are used in order to examine the behavior of the backscattering coefficient over a wide range of incident angle ( to 57 ).

8 Spatial Variation In the first place, the spatial variation of annual average of the backscattering coefficient is examined with land cover and vegetation index data. We used the Global Land Cover Characterization (GLCC) produced by the U.S. Geological Survey [Loveland et al., 2] for land cover data. The original map has an Interrupted Goode Homolosine Projection which we converted into a Cartesian grid with a resolution of.1. After that, their 24 land cover types were united into large nine land cover types: cropland, irrigated cropland, grassland, shrubland, savanna, forest, water, barren, and ice. By taking the predominant land cover type, the data were converted into 1 resolution. The incident angle dependence of the backscattering coefficient for seven land cover types (water and ice excluded) is shown in Figure 2. This figure is drawn for the annual average from March 1998 to February 1999 using observations both from TRMM/PR and ERS/WSC, using data limited to 37 S-37 N. A small discontinuity of the backscattering coefficient can be found around an incident angle of 11, which is a result of the dependence of the surface detection algorithm on different incident angles [Kozu et al., 2]. It is surprising that the backscattering coefficient observed at 18 from TRMM/PR and that observed at 2 from ERS/WSC match so well for each land cover type, even though the system parameters, such as the frequency and polarization, are different. The two curves for the different sensors are almost continuous, especially for forest, and have only a small difference for desert. From this figure we can conclude that the incident angle has the strongest impact on the backscattering coefficient rather than other system parameters. With a relatively small incident angle, bare soil produces the highest backscattering coefficient among the seven land cover types, and forest the lowest value. On the contrary, with a larger incident angle, the reverse is true. The difference among the land cover types is the smallest at an incident angle around 12. From another point

9 - 8 - of view, one could say that the incident angle dependence (defined as the slope of the backscattering coefficient against the incident angle) is strong for bare soil, but weak for forest. The qualitative relationship between the backscattering coefficient and vegetation is supported by an analysis using NDVI (Normalized Differential Vegetation Index) more quantitatively. NDVI corresponds to the activity, quantity, and fraction of vegetation. The NDVI data used here were obtained from the Pathfinder NDVI Land (PAL) data archived in the NASA Goddard Space Flight Center. Spatial and temporal resolutions of the data were 1 x 1 and monthly. Scattergrams between the annual average of the backscattering coefficient observed at 3, 12, and 18 from TRMM/PR and 2 and 55 from ERS/WSC and NDVI are shown in Figure 3. Here, the data for the entire year 1998 are averaged both for the backscattering coefficient and NDVI. The solid line in the scattergram of Figure 3 is the linear regression line. The correlation is negative when the backscattering coefficient is observed at 3, essentially zero when it is observed at 12, and positive when it is observed at > 18. These two analyses give the same conclusion about the spatial variation of the backscattering coefficient. It is determined mainly by the incident angle as a system parameter and the land cover type (especially vegetation) as a land surface parameter Temporal Variation Monthly Time Series Monthly time series of the backscattering coefficient observed at 3-18 (.75 step) with TRMM/PR and at (5 step) with ERS/WSC are shown for our four regions (Table 2) in Figure 4. Oklahoma has much rainfall in winter. The backscattering coefficient observed by TRMM/PR is low April-September and high October-March. This pattern is common to all the incident angles between 3 and 18. The backscattering coefficient observed by ERS/WSC shows relatively small seasonal variation. Still it shows lower values in summer and higher

10 - 9 - value in winter. The Sahel is a semi-arid area. The backscattering coefficient at 3 reaches a maximum in May and a minimum in November, and the dynamic range is about 4 db. The backscattering coefficient at 18, however, reaches a maximum in August and a minimum in February, and the dynamic range is about 2 db. The month when the backscattering coefficient reaches a peak occurs later for larger incident angles. The backscattering coefficient observed with ERS/WSC shows similar seasonal variation to that at 18 with TRMM/PR, and the seasonal variation difference is relatively high. The Sahel is different from Oklahoma in terms of the seasonal patterns at different incident angles. In the Amazon and the Sahara, the dynamic range is quite small. While the annual average of the backscattering coefficient is quite different in the Amazon and the Sahara, the seasonal variation has similar features. The jungles of the Amazon and desert of the Sahara have different backscatter, but little seasonal variation. In the case of the Amazon, we are seeing the vegetation canopy and do not expect to retrieve soil moisture. In the case of the Sahara, there is little soil moisture to observe. The incident angle dependence of the backscattering coefficient for February, May, August, and November, 1998 are shown in Figure 5. In Oklahoma, the Amazon, and the Sahara, the incident angle dependence is almost constant. In the Sahel, however, a stronger incident angle dependence is seen in February and May compared with that in August and November. This may indicate a vegetation signal as well as a soil moisture signal in the Sahel Principal Component Analysis We further examine the seasonal pattern of the backscattering coefficient at different incident angles with principal component analysis, using the monthly time series of backscattering coefficient observed at 3 and 18 (denoted as σ m (3 ) and σ m (18 ) for month m). The deviations of σ m (3 ) and σ m (18 ) from the annual average are denoted as σ m (3 )

11 - 1 - and σ m (18 ). The scattergrams between σ m (3 ) and σ m (18 ) for the four regions are shown in Figure 6. For the Sahel, the scatter points are widely spread, showing the different seasonal variation between σ m (3 ) and σ m (18 ). We calculated the first and second component vector (p 1, p 2 ) and the corresponding scores (x 1,m and x 2,m ), producing the following relationship, σ = = p + p. (1) o o m ( σm(3 ), σm(18 )) x1, m 1 x2, m 2 The variance of the {x 1,m } (m = 1, 2,...12) is defined to be c 1 for each grid. In the same way, the variance of the {x 2,m } (m = 1, 2,...12) is defined to be c 2. The values c 1 and c 2 correspond to the long radius and the short radius of the oval enveloping the scatter points, respectively. The variance c 1 is large for Oklahoma and the Sahel (2.3 and 1.975, respectively) as the dynamic range of the seasonal variation is generally large, but small for the Amazon and Sahara (.164 and.17, respectively). The variance c 2 is small for Oklahoma, the Amazon, and the Sahara (.12,.5, and.1, respectively) as the seasonal pattern is similar for 3 and 18, but large for the Sahel (.462) as the seasonal pattern is different for different incident angles. Therefore, c 2 can be an index for the difference of the seasonal pattern at different incident angles. The above method is applied to each 1 x 1 grid box between 37 S and 37 N. Global maps of variances c 1 and c 2 are shown in Figure 7. The variance c 1 is large in India, Thailand, China, the Sahel, the Great Plains, the southeastern part of Africa, Mozambique, and the southern part of Brazil (upper panel), while the variance c 2 is large only around the Sahel (lower figure). This indicates that the different seasonal pattern at different incident angles can be seen only in the Sahel.

12 Statistical Analysis of ERS/WSC Here we analyze the backscattering coefficient observed by ERS/WSC with global datasets of soil moisture and NDVI to examine the dependence of the seasonal variation of the backscattering coefficient on land surface parameters. The soil moisture dataset used here was produced by Nijssen et al. [21] the using Variable Infiltration Capacity (VIC) macroscale hydrological model forced by surface meteorology data. The NDVI data are the same as in section 3.1. All the data are monthly for 1992 and 1993 with 2 resolution covering almost the whole globe. The correlation coefficient between the backscattering coefficient and soil moisture (denoted as CC-S) and the correlation coefficient between the backscattering coefficient and NDVI (denoted as CC-N) are calculated for each incident angle. Each 2 x 2 gridbox is classified into 5 types, as functions of CC-S and CC-N (Table 3). SS means that only CC-S is significantly positive and SN means that only CC-N is significantly positive. WS and WN mean that both CC-S and CC-N are significantly positive. If CC-S is larger than CC-N, it is classified as WS. Otherwise, it is classified as WN. Because it is possible that soil moisture and NDVI have a direct relationship, WS does not always mean that soil moisture and the backscattering coefficient have a direct relationship. In the case of SS, such a possibility is smaller, and we conclude that soil moisture affects the backscattering coefficient. In those SS regions with a small seasonal cycle of vegetation, it is still possible that soil moisture is influencing the backscatter by affecting the vegetation. Global maps of classification for 2 and 55 are shown in Figure 8. In the Asian monsoon region, many grids are classified as SS or WS. On the other hand, in the Sahel of Africa and in high latitudes, many grids are classified as WN or SN. Figure 9 shows the percentage of each classification for different incident angles. It is clearly seen that SS and WS

13 are dominant for smaller incident angles and SN and WN are dominant for larger incident angles. This implies that the backscattering coefficient observed at smaller incident angle tends to be more affected by soil moisture Modeling of the Backscattering Coefficient The above analysis reveals some basic relationships between land surface status and the observed backscattering coefficient, summarized as follows: A) The annual average of the backscattering coefficient is well related to the vegetation index. B) According to the analysis of TRMM/PR, the temporal change of the backscattering coefficient is not caused by the changes of vegetation except for the Sahel. C) According to the analysis of ERS/WSC, the temporal change of the backscattering coefficient tends to be affected by the soil moisture if the incident angle is small. In this section, a simple model is selected to explain these basic relationships. We have two candidates, a layer model and a mosaic model. Layer model assumes that the vegetation layer completely covers the bare soil. The total backscattering coefficient σ is written as σ = e σ + (1 e ) σ, (2) 2τcosθ 2τcosθ s v where τ is the optical thickness of vegetation layer, θ is the incident angle, σ s is the backscattering coefficient from the soil surface, and σ v is the backscattering coefficient from vegetation. Only σ s is related to soil moisture, and σ v is not. Penetration through vegetation layer is assumed (the first term) to explain the relationship between soil moisture and observed backscattering coefficient. Another model is a mosaic model, which assumes that the land surface is composed of a vegetated area and a bare soil area. It is written as

14 σ = (1 f ) σs + f σv, (3) where f is the fraction of vegetated area ( f 1). Penetration through vegetation is not assumed in this model, but the total backscattering coefficient can be related with soil moisture, unless f = 1. In (2) and (3), the backscattering coefficients are expressed in a units of [m 2 /m 2 ], and not [db]. The conversion between [m 2 /m ] and [db] isσ [db] = 1log σ [m /m ]. These two models have a similar structure, while the idea of the modeling is quite 1 different. The weighting coefficient for σ v is called the vegetation coefficient. The vegetation coefficient is 2 cos 1 e τ θ for the layer model and f for the mosaic model. We have two reasons to avoid using the layer model. One of the reasons is that the penetration is difficult for relatively high frequencies such as the 13.8 GHz of TRMM/PR. It is believed that L band radar has the ability to get information under a vegetation layer, while C band and X band does not. TRMM/PR uses Ku band, much higher than usual surface observation sensors, so it is unrealistic to assume penetration through the vegetation layer. Another reason follows from basic relationship B) above. The vegetation coefficient of the layer model is determined by the optical thickness of vegetation, which is mainly determined by LAI and vegetation water content. If the layer model is valid, in many places in the tropical zone, the temporal change of LAI should affect the temporal change of the backscattering coefficient. However, temporal change of the backscattering coefficient caused by the vegetation change is found only in the Sahel. Thus, the layer model does not explain the observations. The vegetation coefficient of the mosaic model is related with the vegetated area fraction. In the Sahel, the vegetated fraction changes seasonally when the plants grown in rainy season. In other regions, such change from bare soil to vegetation is rare. Therefore, the mosaic model

15 is better for this region. 4. Soil Moisture Estimation Algorithm In this section, a soil moisture estimation algorithm is proposed based on the mosaic model selected in the previous section Optimal Incident Angles Which incident angle is optimum to use for soil moisture observation? We answer this question in this section by calculating the sensitivity of the backscattering coefficient using the mosaic model. Differentiating the equation for mosaic model (3) by the vegetation fraction, we obtain the next equation, where * denotes that σ is expressed in db, and b is defined as b = σ [db] σ [db]. The sensitivity of σ to the vegetation coefficient is * * s v * b /1 σ = b /1 f f + f (1 )1 (4) Similarly, the sensitivity of σ to σ s can be calculated as follows: * b /1 σ (1 f )1 = σ (1 f )1 + * b /1 s f (5) These relationships are shown in Figure 1. The lower figure shows * σ for f =.1, and the upper figure shows * σ for = 1[dB]. Assuming that the average value of bare soil * σ s and forest in Figure 2 can represent σ s and σ v respectively, the value b is 3.3 [db] for 3, -7.9 [db] for 55, and almost zero for 12. To get information about temporal soil moisture change, a higher sensitivity to soil moisture is necessary, which favors a small incident angle. Under the condition that the vegetation coefficient changes significantly, however, 12 is optimum to get soil moisture information because it has the least vegetation signal.

16 Algorithm The soil moisture estimation algorithm is based on the results of the above analyses. The first step is to calculate f. If W is the soil moisture, and assuming that the soil is completely dry (W = %), the basic equation is written as, σ (%) = (1 f ) σ (%) + f σ (6) s v As σ s (%) and σ s are given from Figure 2, f can be calculated if σ (%) is known. If we can assume that f is constant throughout a period, W is the only variable to affect σ. Therefore, the minimum value of σ can be assigned to the lowest W. For this calculation, f is assumed to be constant through an estimation period, and the 1% percentile of the σ distribution is regarded as σ (%). To avoid extremely small values of σ (%), we use the 1% percentile rather than the minimum. The observations at 3 are used to calculate f, because the gap between σ s (%) and σ v is large enough to calculate f. If σ (%) > σ s (%), f is set to zero. On the other hand, if σ is small and the calculated f >.98, f is set to.98, to avoid divergence of the calculation in the next step. is obtained: In the second step, by subtracting equation (6) from equation (3), the following equation σ ( W%) σ (%) = (1 f){ σ ( W%) σ (%)} (7) s s As f and σ (%) are known for each target region, the value in the brace on the right hand side is calculated. Then, this value is converted into the soil moisture W using the relationship induced from the Integral Equation Model (IEM), which is a theoretical equation for

17 rough surface scattering [Fung et al., 1992]. This relationship can be applied on the assumption that soil surface roughness is not changed in time, and the same relationship can be used for different roughness. 5. Application We applied the algorithm to two cases with different time scales Monthly We applied the algorithm globally with a monthly time step. This estimation is done for each.25 gridbox between 37 S and 37 N. σ s (%) is set to be 2.5 [db] and σ v is set to be -8.5 [db] for all the boxes. The regional average estimates are compared with calculations by VIC model [Nijssen et al., 21] for the four regions in Figure 11. Judging by this, the seasonal pattern is well simulated for Oklahoma and the Sahel. In the Sahel, while the vegetation fraction is set to be constant throughout the year, the seasonal pattern is not so bad. This is an advantage of using observations at 12 in the algorithm. In the Amazon, the seasonal pattern is not so good, because the soil moisture information signal is weak due to large vegetation fraction. In the Sahara, it is natural that the estimate has almost no seasonal variation. When we estimate with a different set of ( σ (%), s σ v ), the estimate changes, especially for the vegetated area, but the seasonal pattern does not change much. Figure 12 shows global maps of soil moisture estimate in February and August. The Asian monsoon area, the Sahel, and the southeastern part of America shows the largest seasonal changes Daily One of the advantages of the TRMM/PR is the short time interval between observations as an active microwave sensor. To make the use of this advantage, the algorithm is applied with

18 a daily time step in Oklahoma, chosen because at the poleward edge of the orbit, the region is densely sampled, and long time soil moisture observations are available in this region. Oklahoma, located in the center of the US, is covered by a dense meteorological observation network called the Oklahoma Mesonet [Brock et al., 1995; Shafer et al., 1993]. The Oklahoma Mesonet has 114 automated observation stations, and each station has a 1 m 2 area and a 1 m tower. Core parameters (air temperature, humidity, wind speed, barometric pressure, rainfall, incoming solar radiation, and soil temperature) are measured at all the stations. Soil moisture is a supplemental parameter and was measured at 72 stations in 1998 and 13 stations in 1999 and 2. The sites with soil moisture observation are indicated as red points in Figure 13. The background color in Figure 13 indicates the GLCC. Cropland and grassland dominate the western part, while forest dominates the eastern part. Soil moisture is measured automatically by the heat dissipation method once every 3 minutes at four depths: 5, 25, 6, 75 cm. Our algorithm is designed to use the backscattering coefficient observed at 12, however the backscattering coefficient at 12 is obtained once every several days at each point. To give soil moisture estimates as frequently as possible (ideally, daily), we should use observations at multiple incident angles. The correlation coefficient between the backscattering coefficient at different incident angles was calculated from the monthly average time series through 1998 and If there is significant correlation between σ (X ) and σ (12 ), the daily average of σ (X ) is converted to σ (12 ). The sampling area is set to be within.5 (lat./lon.) from each reference site. As an example, the time series of Hobart is shown (Figure 14). The satellite estimates and in-situ observations at 5 cm show similar phases of temporal variation. Both values rise rapidly just after precipitation and gradually decrease during the no-rainfall period. In-situ

19 observations in deeper layers are less sensitive to the precipitation than observations in the 5 cm layer and the satellite estimates are more sensitive to the precipitation. The vegetation cover ratio f is calculated to be.694,.695, and.751 in 1998, 1999, and 2, respectively. The correlation coefficient between the satellite estimates and in-situ observation at 5 cm is.723,.722, and.764 in 1998, 1999, and 2, respectively. We applied this algorithm i to the other stations. At many stations, the difference between the observed soil moisture and the estimates are large, but the correlation coefficient is generally positive and high. In some cases, the observed soil moisture does not respond to the rainfall, while the estimate does. It may be because that the estimated soil moisture is for a much shallower layer than 5 cm. The estimated vegetation fraction ratio f and the correlation coefficient are calculated for the sites south of f is higher in the eastern part of the region and lower in the western part, corresponding with the land cover types. The soil moisture correlation coefficients are not related to the land cover types, and generally are > Conclusions We have developed a new soil moisture estimation algorithm using active microwave sensing. The advantages of TRMM/PR as a land surface sensor against the synthetic aperture radars are its small incident angle and high temporal resolution. The backscattering coefficient has a relationship with soil moisture when it is observed at smaller incident angles. This is shown by the sensitivity analysis of the proposed model and is also inferred frpm the data analysis of ERS/WSC. The observations at incident angles < 2 are not available for other active microwave sensors in space. Monthly estimation shows that the seasonal pattern is well simulated where vegetation is not too dense. Even in the Sahel, where the vegetation fraction changes, the algorithm can produce a valid seasonal pattern. That is because the observations at 12 are used, where the backscattering coefficient is less affected by the temporal change of

20 vegetation. We think that the proposed method is the first one to apply the precipitation radar for soil moisture observation. Of course, the algorithm is too simple and can certainly be improved. We neglected the differences of vegetation and soil types, and assumed a constant soil roughness. These things will have to be considered according to data availability. Owing to the relatively high temporal resolution, the daily estimates can be obtained for Oklahoma. Still, it is difficult to do daily estimation for the whole region, because the sampling ratio decreases around the equator. To overcome this point, a combined algorithm with passive microwave sensors should be developed in the future. Acknowledgments. This study has been supported by Ministry of Education, Culture, Sports, Science, and Technology through a scientific grant and Fellowships of the Japan Society for the Promotion of Science for Japanese Junior Scientists. The work at Rutgers University was supported by NOAA OGP GAPP grants GC99-443b and NA3AR43157 (A. Robock, PI), the Cook College Center for Environmental Prediction, and the New Jersey Agricultural Experiment Station. TRMM/PR data were provided by the National Space Development Agency of Japan (NASDA). NDVI data were produced through funding from the Earth Observing System Pathfinder Program of NASA s Mission to Planet Earth in cooperation with the National Oceanic and Atmospheric Administration. We thank the NOAA Office of Global Programs and NASA Land Surface Hydrology Program for their purchase of the Oklahoma Mesonet meteorological and soil moisture and temperature data for their funded investigators.

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25 Table 1. Main Features of Tropical Rainfall Measurement Mission Precipitation Radar (TRMM/PR) and European Remote-sensing Satellite Wind Scatterometer (ERS/WSC). Sensor TRMM/PR ERS/WSC Period (year) Original purpose Precipitation Ocean wind Incidence angle ( ) Frequency (GHz) Polarization HH VV Horizontal resolution (km) Swath width (km) Table 2. Location of the four study regions. Region Longitude Latitude Oklahoma 96-1 W N Sahel -4 E 1-14 N Amazon W 6-1 S Sahara 2-24 E 2-24 N Table 3. Classification scheme for the analysis in section CC-S is the correlation coefficient of backscattering with soil moisture, and CC-N is the correlation coefficient of backscattering with NDVI. The.5 criterion corresponds with a significance level of 9%. Type Conditions SS CC-S.5 and CC-N <.5 SN CC-S <.5 and CC-N.5 WN CC-S.5 and CC-N.5 and CC-S CC-N WS CC-S.5 and CC-N.5 and CC-S < CC-N none of the above CC-S <.5 and CC-N <.5

26 Figure Captions Figure 1. The ratio of rainfall occurence to total observations in August Figure 2. The annual average of the backscattering coefficient observed by TRMM/PR and ERS/WSC for different land cover types. Figure 3. Scattergrams of the annual average of NDVI and the backscattering coefficient. The backscattering coefficient is observed at (a) 3, (b) 12, (c) 18, (d) 2, and (e) 55. (a)-(c) are for TRMM/PRECIPITATION, and (d) and (e) are for ERS/WSC. Figure 4. Monthly time series of the backscattering coefficient observed by TRMM/PR and ERS/WSC for the four regions. Figure 5. The seasonal variation of the relationship between incident angle and the backscattering coefficient for the four regions Figure 6. Scattergrams of σ (3 ) and σ (18 ) and principal component analysis of the four regions. Figure 7. Global maps of the variance of the first and the second principal component scores. Figure 8. Classification based on the correlation coefficient between the backscattering coefficients and the soil moisture and that between the backscattering coefficients and NDVI. The incident angle is 2 for the top panel and 55 for the bottom panel. Figure 9. Histogram for the classification shown in Figure 8, with incident angles from 2 to 55. Figure 1. Upper panel: The sensitivity of the total backscattering coefficient to the backscattering coefficient from bare soil. Lower panel: The sensitivity of total backscattering coefficient to the vegetation coefficient.

27 Figure 11. Monthly time series of estimated soil moisture with VIC model outputs as reference for the four regions. Figure 12. Monthly maps of soil moisture estimates for February 1998 and August Figure 13. Locations of Oklahoma Mesonet sites where soil moisture observation is available. Background colors indicates land cover types. Figure 14. Time series of surface soil moisture estimates from TRMM/PR compared with in-situ observations at the Oklahoma Mesonet for 1998, 1999, and 2. Figure 15. Maps for estimated vegetation fraction (upper panel) and the correlation coefficients (lower panel) between observations (5 cm) and estimates.

28 Figure 1. The ratio of rainfall occurence to total observations in August 1998.

29 Figure 2. The annual average of the backscattering coefficient observed by TRMM/PR and ERS/WSC for different land cover types.

30 Figure 3. Scattergrams of the annual average of NDVI and the backscattering coefficient. The backscattering coefficient is observed at (a) 3, (b) 12, (c) 18, (d) 2, and (e) 55. (a)-(c) are for TRMM/PRECIPITATION, and (d) and (e) are for ERS/WSC.

31 - 3 - Figure 4. Monthly time series of the backscattering coefficient observed by TRMM/PR and ERS/WSC for the four regions.

32 Figure 5. The seasonal variation of the relationship between incident angle and the backscattering coefficient for the four regions.

33 Figure 6. Scattergrams of regions. σ (3 o ) and σ (18 o ) and principal component analysis of the four

34 Figure 7. Global maps of the variance of the first and the second principal component scores.

35 Figure 8. Classification based on the correlation coefficient between the backscattering coefficients and the soil moisture and that between the backscattering coefficients and NDVI. The incident angle is 2 for the top panel and 55 for the bottom panel.

36 Figure 9. Histogram for the classification shown in Figure 8, with incident angles from 2 to 55.

37 Figure 1. Upper panel The sensitivity of the total backscattering coefficient to the backscattering coefficient from bare soil (Lower panel: The sensitivity of total backscattering coefficient to the vegetation coefficient.

38 Figure 11. Monthly time series of estimated soil moisture with VIC model outputs as reference for the four regions.

39 Figure 12. Monthly maps of soil moisture estimates for February 1998 and August 1998.

40 Figure 13. Locations of Oklahoma Mesonet sites where soil moisture observation is available. Background colors indicates land cover types.

41 - 4 - Figure 14. Time series of surface soil moisture estimates from TRMM/PR compared with in-situ observations at the Oklahoma Mesonet for 1998, 1999, and 2.

42 Figure 15. Maps for estimated vegetation fraction (upper panel) and the correlation coefficients (lower panel) between observations (5 cm) and estimates.