A method for retrieving soil moisture from GNSS-Rby using experiment data①

Mao Kebiao (毛克彪)

(*National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China)(**International Agricultural Big Data and Nutrition Research Institute in China, Pokfulam, Hong Kong, P.R.China) (***State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences and Beijing Normal University, Beijing 100875, P.R.China)(**** Hydrometeorology and Remote Sensing Laboratory, The University of Oklahoma, Norman 73072, USA)



A method for retrieving soil moisture from GNSS-Rby using experiment data①

Mao Kebiao (毛克彪)②

(*National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China)(**International Agricultural Big Data and Nutrition Research Institute in China, Pokfulam, Hong Kong, P.R.China) (***State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences and Beijing Normal University, Beijing 100875, P.R.China)(****Hydrometeorology and Remote Sensing Laboratory, The University of Oklahoma, Norman 73072, USA)

Soil moisture is a key parameter in agricultural irrigation. The L band(1.58GHz)on board global position system (GPS) satellite is well suited for monitoring the change of soil moisture. In order to investigate the potential of retrieving soil moisture using the L-band GPS bistatic radar, this paper analyzed a retrieval method by using field experiment data. In order to investigate the relationship between the soil moisture (corresponding roughly to the 0～5cm top soil layer) and the signal-to-noise ratio (Pr-SNR) to the direct GPS signal-to-noise ratio (Pd-SNR), an experiment was conducted in Hulunber grassland of China in 2009 and 2011. Six field sites in the soil moisture experiment were utilized to analyze the relationship between soil moisture and the ratio of Pr-SNRto Pd-SNRand the square of correlation coefficient was about 0.9 when the surface type was known and the elevation angle of the satellite ranged from 65 to 85 degrees approximately. The analysis shows that ratio of Pr-SNRto Pd-SNRcan be used to monitor the soil moisture, because the ratio of Pr-SNRto Pd-SNRmaximized the elimination of the influence of different signals from different GPS satellites. The estimation accuracy could be improved if we make full use of the empirical knowledge on elevation angles of GPS satellites and ground roughness of different surface types.

soil moisture, global position system (GPS), global navigation satellite system-reflection (GNSS-R)

0 Introduction

Soil moisture is a key parameter in agricultural irrigation. In the development of microwave remote sensing, the radar has been proven as one of the best methods to retrieve soil moisture in the last 25 years. The global positioning system (GPS), which was developed by the United States Department of Defense in 1980, has grown from a military positioning system to an indispensable, multi-disciplinary, civilian-use commodity[1]. GPS satellite navigation system matured in the 1990s. The GPS constellation broadcasts a civilian-use carrier signal at 1.58GHz, which is an optimal frequency for soil moisture remote sensing. People first discovered the potential application of the GPS reflection signal from the ocean and the European Space Agency (ESA) thought that the L-band signal of GPS can be used as a marine scatterometer[2]. Martin-Neira[3]used the reflection of GPS signals to measure sea surface height. Many other scientists also did a lot of work using the GPS reflection signal to obtain information of the ocean surface (roughness, wind speed and sea surface height), and they obtained a series of useful results[4-16]. The technology based on the GPS reflection signal is called as the global navigation satellite system-reflection (GNSS-R) remote sensing. In many studies, the scattering of the GPS signal from the land surface has been observed, but the study about soil moisture retrieved by using reflected GPS scattering signal has not been conducted as much because the land surface is very complex[17]. Masters made some analysis of the reflected GPS reflection signal obtained from SMEX02, and initial results shows that the GPS signal reflection can be used to monitor the changes in soil moisture[17-19].

In recent years, some initial results have also been presented in some conferences[20-22]. Not much research is done using GPS signals to retrieve soil moisture, and no one general physical algorithm has been published. The main reason is that the inversion mechanism of GPS reflection signal is still not very ripe. In this study, the theory of soil moisture retrieved from GNSS-R is introduced in Section 1 and some analysis is performed using the experiment data in Section 2. Finally, an evaluation is made on the methods of retrieving soil moisture from GNSS-R in Section 3.

1 The principle of Soil Moisture Retrieved from GNSS-R

Measurement of GPS signals reflected from the land surface is analogous to a bistatic radar system, with transmitters located at each GPS satellite and a separate receiver located above the surface of the Earth. In bistatic systems, the scatterings are mainly forward[18，19]. The GPS receiver can receive signals from at least 4 satellites. The theory basis for soil moisture retrieved from GPS signals reflected from ground is that the L band has a high sensitivity for soil moisture, which could be shown in Fig.1.

Fig.1 The Illustration of GPS reflection signal from Satellite to Receiver

The direct signal (Pd) received by GPS receiver is depicted in

(1)

where Ptis the transmitted power, Gtis the transmitter antennas gain, λ is the wavelength, Rdis the distance of a direct signal from the GPS signal receivers, and Gdis the receiver antenna gain. Rsis assumed to be the distance from signals to the ground, Rris a reflection signal from the ground to the receiver, σ0is the scattering coefficient per unit area , Gris the receiver antenna gain, and Pris the received power after being reflected by the ground:

(2)

For smooth surface, Γ is assumed as reflectivity, so Eq.(2) can be simplified as

(3)

Microwave observations are sensitive to soil moisture by the effects of the moisture on the dielectric constant, and hence the emissivity of the soil. The relationship between reflectivity and emissivity is displayed in

Γ=1-E

(4)

The scattering coefficient (σ0) in Eq.(2) and the reflectivity in Eq.(3) are mainly affected by soil moisture and surface roughness. Therefore the strength of the GPS reflection signals can be used to monitor the change of soil moisture. On the other hand, the GPS signal receiver is influenced by noise, and the output signal contains noise. The output noise can be expressed as Nr[1].

Nr=kTrBr

(5)

k is Boltzmann’s constant, Tris the equivalent noise temperature, Bris the bandwidth of the receiver. The ratio of signal to noise is

(6a)

(6b)

where Pd-SNRis the ratio of the direct signal to the noise. Pr-SNRis the ratio of the reflected signal to the noise. Since signals and noises are mixed together, it is difficult to separate. Thus the matter of reducing noise and improving the signal to noise ratio is very important. Shown from Eq.(6), the signal is also influenced by the distance. The nominal orbit periods of GPS satellites around the Earth are about 11 hours and 58 minutes, so the distance from the satellite to the same position of the ground is changed. In order to eliminate the influence of distance and the different transmission powers of different GPS satellites, the Pr-SNRis normalized by Pd-SNR.

(7)

If the GPS receiver is on the ground, and Rs+Rs≈Rd, Gr≈Gdin Eq.(7), then Eq.(7) can be simplified as

(8)

Eq.(8) is not influenced by distance, the ratio of Pr-SNRto Pd-SNRis approximately equal to the reflectivity Γ.

2 The analysis based on experiment data

In May of 2009 and July of 2011, two experiments were conducted to collect ground reflected GPS bistatic radar data using the GPS receiver, and soil moisture measured by using TZS-I (soil moisture meter/tester). The range of measuring soil moisture was 1%～100%, the time for test was less than 2 second, the absolute error is less than 2%, and the relative error was less than 3% (http://www.94117.net/proview.asp?id=9853). The purpose was to characterize the relationship between soil moisture and ratio of GPS Pr-SNRto Pd-SNR. A cross of about 1.5 meters in height was designed to mount a low-gain, zenith RCP (right-hand circularly polarized) patch antenna viewing the sky and a replica nadir LCP (left-hand circularly polarized) patch antenna viewing the ground, which was shown in Fig.2. A processing system for the extensive GPS bistatic radar data sets collected with the DMR had been developed[23]. The processing included accurate georeferencing and time stamping, estimation of higher level parameters, and coordination with supporting ancillary data sets. The signals captured by GPS satellites were displayed on a computer, which was shown in Fig.2. Since it seldom rained in the Hulunber grassland in May of 2009 and July of 2011, the soil was very dry. In order to change soil moisture, we watered four bare soil field sites and two grass field sites and made different observations (Fig.2). A total of 168 data sets were selected when the elevation angles of GPS satellites ranged from approximately 65 to 85 degrees. Of the original set, 98 data sets were used as training data sets to build the statistical regression method (3), which was shown in Fig.3.

(9)

A total of 70 data sets were used as test data to evaluate the method, which is shown in Fig.4. The squared correlation coefficient was about 0.9 and the average error was about 0.012 cm3/cm3. As shown in Figs 3 and 4, the ratio of Pr-SNRto Pd-SNRwas not very sensitive when the ground soil moisture was about 0.15cm3/cm3, and the main reason for this was due to the influence of elevation angles of GPS satellites as we did not water the soil. The sensitivity improved after watering the soil. Compared with the analysis of Pr-SNRin Refs[17-19], the ratio of Pr-SNRto Pd-SNRwas more suitable for monitoring the change of soil moisture, because Pr-SNRwas normalized by Pd-SNR, which maximized the elimination of the influence of different signals from different GPS satellites. All analyses indicate that soil moisture could be retrieved from reflected signals of GPS satellite from the ground if we utilize the empirical knowledge from the elevation angles of GPS satellites, ground roughness, and surface types.

Fig.2 Field experiment in Hulunber grassland, China, LCP (left-hand circularly polarized),RCP (right-hand circularly polarized), DMR (delaying mapping receiver)

Fig.3 The relationship between measured ground soil moisture and the ratio of Pr-SNR to Pd-SNR.

Fig.4 Validation

3 Conclusion

GPS signals are influenced slightly by the clouds and the atmosphere, which makes it advantageous for studies of global change. The GPS signals scattered by the land surface will fluctuate due to variations of soil moisture, surface roughness, permittivity, and vegetation. To investigate the potential for retrieving soil moisture by using the L-band GPS bistatic radar, this paper analyzed GPS ground reflecting signals to monitor the change of soil moisture. The analysis of field experiment data in Hulunber grassland in China showed that the square of correlation coefficient was about 0.9 between the soil moisture and the ratio of Pr-SNRto Pd-SNRwhen surface type was known and the elevation angle of satellite ranged from approximately 65 to 85 degrees. The ratio of Pr-SNRto Pd-SNRwas suitable for monitoring the change of soil moisture, because the ratio of Pr-SNRto Pd-SNRmaximized the elimination of the influence of different signals from different GPS satellites. A statistical regression method was built and the average error was about 0.012cm3/cm3after we make full use of the empirical knowledge (the elevation angles of GPS satellites, ground roughness and surface type). The method is mainly suitable for bare soil and low vegetation coverage area, and the method should be revised if the vegetation is very high.

With the development of the European Union’s Galileo and China’s Beidou navigation satellite plans, different frequencies of the L-band and more ground reflected signals could be used. Multiple L bands can aid in overcoming inadequacies of the previous method mentioned above, thus the study of soil moisture retrieval algorithm from GNSS-R SNR remains a hot issue. On the other hand, research about how to take the advantage of radar images, passive microwave, thermal, and optical data to improve retrieval accuracy may yield critical results. The establishment of a multiple scale observational system is very important and can help provide good data source for data assimilation systems.

Acknowledgment

The authors would like to thank the anonymous reviewers and editors for their valuable comments, which greatly improved the presentation of this paper. Thanks to NASA for providing MODIS data. This research was financially supported by National Natural Science Foundation of China (No. 41440047), National Nonprofit Institute Research Grant of CAAS (No. IARRP-2015-26), Supported by Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201515 ), National Basic Research Program of China (2013BAC03B02).

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Mao Kebiao, born in 1977. He received the Ph.D. degree in geographic information systems from the Chinese Academy of Sciences in 2007，the M.S. degree from Nanjing University in 2004， and the B.S. degree from Northeast Normal University in 2001.He is currently with the Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, and Chinese Agricultural Big Data and Nutrition Research Institute, Pokfulam, Hong Kong. He has published more than 90 papers in international and Chinese scientific journals and applied for ten patents for inventions. His research interests include global climate change, Agricultural Big Data, agricultural disaster, geophysical parameters retrieval (like land surface temperature and emissivity, soil moisture, water vapor content).

10.3772/j.issn.1006-6748.2015.02.015

①Supported by the National Key Basic Research Program of China (No. 2010CB951503, 2013BAC03B00).

②To whom correspondence should be addressed. E-mail: maokebiao@126.com Received on Jan. 23， 2014******, Ma Ying***, Shen Xinyi****, Xia Lang*, Tian Shiying*, Han Jiaqi*, Liu Qing*