Original papers
Online soil moisture retrieval and sharing using geospatial web-enabled BDS-R service

https://doi.org/10.1016/j.compag.2016.01.005Get rights and content

Highlights

  • Online soil moisture retrieval and sharing.

  • Can be extended for any GNSS-R signal and any soil moisture retrieval algorithm.

  • Make continuous spatial–temporal observation of soil moisture possible.

Abstract

Global navigation satellite system-reflectometry (GNSS-R) signals have recently being used in the monitoring of soil moisture (SM) due to their high availability and widespread existence. GNSS-R signal-based online SM retrieval and real-time product sharing has advantages over other methods because it features the instant and automatic retrieval of SM, and low-latency SM product publishing and sharing, enabling a notable decrease in human and material resource consumption and a timely SM product delivery; however, prior to this manuscript, there has not been a study describing how to do it. This paper uses one type of GNSS-R signals, the Beidou navigation satellite system-reflectometry (BDS-R) signal, as the data source and proposes a geospatial web-enabled BDS-R (GWEB) service. This method tackles the problems of SM retrieval and sharing by implementing the original BDS-R signal publishing, the online SM retrieval, and the SM product sharing. In the GWEB service, a web processing service is adopted to encapsulate the SM retrieval algorithm, and a sensor observation service is applied to publish the original BDS-R observations and the SM products. Signals of the Baoxie BDS-R SM monitoring station in Wuhan, China for the period of November 17, 2014 to November 24, 2014 are used as input in the validation experiment, and the SM products are demonstrated in the sharing platform. Meanwhile, the BDS-R derived SM products are compared with the corresponding SM values measured by ground soil hygrometers, indicating the feasibility of the service for online SM retrieval and sharing.

Introduction

Soil moisture (SM) is a critical factor in various environmental studies, and is also a key variable in many applications, such as the estimation of drought severity and duration, irrigation scheduling, the study of soil erosion and evapotranspiration, the control of forest fire hazards, and forest management (Rahimzadeh-Bajgiran et al., 2013). The global navigation satellite system reflectometry (GNSS-R), including reflectometry of the global positioning system (GPS) in the United States, the GLONASS of Russia, the Galileo of Europe, and the COMPASS (Beidou) of China, has some advantages compared with other remote sensing data sources (i.e. data from satellite radiometers or scatterometers) for SM retrieval, including: (1) signals lie in the most sensitive frequency band for SM microwave remote sensing (Schmugge and Jackson, 1993, Shutko, 1982, Ulaby, 1974); (2) signals are continuous, have global coverage, and can be obtained in all weather conditions (Jin and Komjathy, 2010); (3) signal acquisition is inexpensive and is readily available in real time; (4) reflected signals are not dramatically contaminated by thermal background variations in comparison to microwave radiometry signals; and (5) scatterometry from space has the potential for better spatial resolution than microwave radiometry.

With the five characteristics mentioned above, GNSS-R signals begin to be used as a new data source for SM monitoring (Jin and Komjathy, 2010, Rodriguez-Alvarez et al., 2011a, Jin et al., 2011). From 2002 to 2005, the National Aeronautics and Space Administration and Colorado University (both in the USA) conducted a series of experiments on GNSS-R measurement of SM (Masters et al., 2004). Larson et al. (2010) applied the GPS signal-to-noise ratio (SNR) data to invert the near surface SM. Saleh et al. (2009) used a two-step inversion approach to retrieve SM from the L-band data. Rodriguez-Alvarez et al. (2011b) performed SM and vegetation height retrievals by applying the Interference Pattern Technique (IPT). Egido et al., 2012, Egido et al., 2014 studied the effects of different land bio-geophysical parameters on GNSS scattered signals and used experiments to assess the sensitivity of the parameters. Arroyo et al. (2014) reformulated the IPT equations for the dual polarization case and extended its use. Camps et al. (2014) proposed a method to optimize the configuration of a generic interferometric GNSS-R altimeter and evaluated its performance.

The aforementioned studies have mainly focused on the development of different inversion algorithms and instrument designs to estimate SM content. None of these studies addressed online SM retrieval or the real-time sharing of SM data retrieved from GNSS-R. Without online retrieval and data sharing, on one hand, the time delay problem between signal acquisition and SM product availability is difficult to solve, affecting the efficiency of SM related decision-making, and on the other hand, repeated but incomplete measurements can be caused, resulting in imbalanced availability of SM measurements and huge wastes of manpower and resources. Therefore, it is imperative to propose an online SM retrieval and real-time data sharing service to resolve these problems.

The sensor web enabling sensor and observation sharing (Nittel, 2009, Bröring et al., 2011) and the web processing service (WPS) capable of algorithm encapsulation and online processing (Schut and Whiteside, 2007) of the open geospatial consortium (OGC) can be used in SM retrieval and real-time data sharing. Sensor web has been used alone or combined with WPS or other web services in wildfire hot pixel determination (Chen et al., 2010a), multiple feature classification and extraction (Chen et al., 2010b), flood detection (Auynirundronkool et al., 2012), integrated sensing of SM (Phillips et al., 2014), and information infrastructure construction for precision agriculture (Chen et al., 2015). This paper adopts the Beidou navigation satellite system reflectometry (BDS-R) signals as the data source, proposes a geospatial web-enabled BDS-R (GWEB) service, and combines sensor model language (SensorML) (Botts and Robin, 2007), observations and measurements (O&M) (Cox, 2007a, Cox, 2007b), and sensor observation service (SOS) (Na and Priest, 2007) of the sensor web with WPS to achieve instant SM retrieval and sharing.

In the forthcoming sections, we illustrate the GWEB service and validate its feasibility for online estimation of SM and SM product publishing. The development of the GWEB method is presented in Section 2. The overall architecture is described in Section 2.1, the BDS-R sensor registration and observation insertion service is presented in Section 2.2, the online SM retrieval service is described in Section 2.3, and the real-time SM product publishing service is presented in Section 2.4. The experiment to instantiate the GWEB service is stated in Section 3. The discussion about the GWEB method for online SM estimation and sharing is provided in Section 4. Finally, Section 5 summarizes this work and describes future directions for this research.

Section snippets

Architecture and components

The GWEB service is composed of a three-tier architecture, including the BDS-R sensor layer, the geospatial web service layer, and the application layer. The BDS-R sensor layer is responsible for data acquisition from the BDS-R equipment, and provides data support for the geospatial web service layer. The geospatial web service layer is the core of the entire architecture and consists of three sub-services: the sensor registration and observation insertion (SROI) service, the online SM

Experiments and results

The experimental site is a flat, bare field without much tall buildings, trees or forests in the surroundings; it is located in Baoxie town, Wuhan, China. A long-term ground-based BDS-R SM monitoring station has been established there at the latitude and longitude coordinates of 30.46995°N, 114.52709°E. The monitoring station is mainly composed of a RHCP antenna, a LHCP antenna and their central control units. The RHCP antenna is directed upwards and is intended to receive the direct satellite

Trend comparison

The experiment takes both the rainy and sunny days into consideration to prove the validity of the BDS-R derived SM results. As shown in Fig. 10, during the experimental period from November 17, 2014 to November 24, 2014, there were rainfall events on November 23 and 24, whereas it was sunny on other days. In the variation trend of ground-truth data, the SM values gradually decline from November 17 to November 22, while the SM on November 23 and 24 both increases noticeably. The BDS-R derived

Conclusion

GNSS signals are widespread and can be accessed easily. Recently, using the ubiquitous and inexpensive GNSS signal data to perform SM retrieval has become a more widely used method for the estimation of SM. However, SM retrieval at present is performed individually and not in real time, resulting in time lag of SM products, duplication of efforts, and the waste of resources. The GWEB service proposed in this paper provides an integrated service for BDS-R sensor registration, original BDS-R

Acknowledgements

This work was supported by grants from the National High Technology Research and Development Program of China (863 Program) (no. 2013AA01A608), the National Nature Science Foundation of China (NSFC) Program (no. 41171315), the Program for New Century Excellent Talents in University under Grant NCET-11-0394, the National Natural Science Foundation of China (no. 41301441), and the China Postdoctoral Science Foundation funded project (no. 2014M562050).

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