Journal of Integrative Agriculture
April 2013
2013, 12(4): 723-731
RESEARCH ARTICLE
Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in Coastal Areas GUO Yan, SHI Zhou, ZHOU Lian-qing, JIN Xi, TIAN Yan-feng and TENG Hong-fen Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, P.R.China
Abstract Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity (ECa). Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover. Key words: remote sensing, proximal sensor, soil moisture, salinity, backscattering coefficient, soil apparent electrical conductivity (ECa)
INTRODUCTION There is a long history of coastal area reclamation in Zhejiang Province, China. During the past 40 years, more than 400 000 ha tidelands (equivalent to 4.1% of the total area of Zhejiang Province) have been enclosed for agricultural land uses and urbanization buffer zones in order to reduce the high pressure with limited land Received 16 June, 2012
resources and rapidly increasing population (Shi et al. 2002). However, during formation of coastal soil with soil salinization, desalination and decalcification, soil fluvo-aquic, hydromorphic, rich ferrallitization and resalinization process, the strong interaction among soil moisture and salinity seriously resulted differences of soil properties, and affected the soil quality and agricultural production. Consequently, it is critical to accurately quantify the variations of soil moisture and salin-
Accepted 2 November, 2012
Correspondence SHI Zhou, Tel/Fax: +86-571-88982831, E-mail:
[email protected]
© 2013, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S2095-3119(13)60290-7
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ity for the rational development and utilization of tideland resources (Mortl et al. 2011). Recently, the rapid techniques of remote sensing and proximal sensory have attracted more interests, providing favorable facilities for detecting soil moisture and salinity in the coastal area. Among the techniques, microwave remote sensing has unique advantages, including the capability to work under almost all weather conditions allowing data to be collected day or night and the penetration of microwave energy into some ground features (Dobson et al. 1992; Njokuand Entekhabi 1996). It can detect some specific properties that infrared and optical sensors cannot provide; for example, L-band microwave energy can penetrate vegetation and soil to some extent (Kobayashi et al. 2012; McColl et al. 2012). To date, several L-band microwave radiometer and radar missions have been operated, including the soil moisture active passive (SMAP) mission (Entekhabi et al. 2010) and Japanese phased array type L-band synthetic aperture radar (PALSAR) (Isoguchi and Shimada 2009) among others. ALOS/PALSAR carried an L-band radiometer with various polarization modes, since it was launched in 2006, some researchers have set about to explore the potential for monitoring parameters such as woody biomass and soil moisture. For example, Paloscia et al. (2012) retrieved and mapped agricultural biomass and soil moisture content by combining ALOS/PALSAR data at L and COSMO/SkyMed at X bands. In recent years, the development of proximal sensors makes it possible to acquire soil properties quickly, effectively and in a less-intensive manner; furthermore, the data measured can be linked with GPS to allow meaningful (geo) statistical analyses for ascertaining spatial soil patterns (Viscarra Rossel et al. 2010). Such as EM38 (Geonics Inc.,Canada) which has the potential to detect soil profile electrical conductivity with an effective depth of 1.5 m, which has a remarkable advantage on reflecting soil properties especially in the coastal saline land. The region of the present study was reclaimed in 1996, and the salinity has leached to deeper soil layers. Thus, the potential of ECa should be explored further. In regard to the relationship between ECa and soil physicals and chemical properties, some researchers (McNeill 1992; Corwin and Lesch 2005) have made a detailed discussion, and also, Yao et al. (2007) and Ye et al. (2008) introduced the superiority in coastal land and explored the soil spatial variation in
GUO Yan et al.
China. Soil properties often correlate to other properties. Precisely, cokriging interpolation method can be used to study relationships and to spatially interpolate a soil property which is difficult to measure based on correlated soil properties that are more easily detected (McBratney and Webster 1983). Until now, cokriging methods have been used for many soil properties, such as water content, bare soil surface temperature, percent sand content and so on (Yates and Warrick 1987; Danielsson 1998; Park 2011; Zhang et al. 2012). At present, with the developing of many indirectly, quickly and in situ proximal sensors, more and more easily measured data were used as auxiliary variable for cokriging approach. However, from our literature search, all auxiliary data used for cokriging in previous studies were measured data in laboratory or in situ, and few from remotely sensed imagery because the vegetation cover and soil surface layer hamper soil information retrievals (Wang and Qu 2009; Wu et al. 2009). The integrative use of the new proximal soil sensing technologies proposed by soil scientists, remote sensing and geostatistical analysis has the potential to successfully monitor soil properties and their spatial variations. In the present study, soil moisture and salinity variability can be rapidly detected in large scale because of the sensitive of microwave remote sensing to the topsoil moisture and precision in situ of collecting soil salinity with an effective depth of 1.5 m, quickly followed by the maximum frequency of data output of 10 readings per second. The objectives of this paper are to (1) build a simplified soil moisture inversion model over the bare saline soil via backscattering coefficient extracted from ALOS/PALSAR; (2) analyse the interaction and correlation between soil moisture and salinity; and (3) detect and map variations of ECa with auxiliary variable of backscattering coefficient by cokriging interpolation method.
RESULTS Soil moisture inversion with backscattering coefficient Soil moisture is reflected by the backscattering coefficient extracted from ALOS/PALSAR radar remote sensing imagery. In this paper, data collection from field B © 2013, CAAS. All rights reserved. Published by Elsevier Ltd.
Integrating Remote Sensing and Proximal Sensors for the Detection of Soil Moisture and Salinity Variability in
was used for modeling and data collected on field A was used for validation. The measured soil moisture (y) and extracted multi-polarization backscattering coefficient (x) data collected for field B were plotted and linear, conic, logarithmic, natural logarithmic, exponential, and power curves were fit to the data indicating the optimized models of y= and y=
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in Fig. 2. The water content ranged from 13.62 to 29.39% with a variation coefficient of 14.47%.
(R2=0.9589)
(R2=0.9484) for horizontal transmit and
horizontal receive (HH) and horizontal transmit and vertical receive (HV) polarization mode respectively. Applying the two fitted models to inverse soil moisture in field A at same time for ALOS/PALSAR radar images, the relationship between soil moisture inversed and measured was shown in Fig. 1. The results demonstrated that the two trend lines were almost parallel with the 1:1 line with better proximity of HH polarization mode (R2=0.761) than HV mode (R2=0.722). That is to say, this empirical model can be applied to inverse soil moisture with better accuracy under the condition of the same sensor parameters or surface roughness. Afterwards, spatial variability via HH polarization mode data in field A was detected from retrieved soil moisture. Spatial variability of soil moisture was shown
Fig. 1 Comparison between soil moisture inversion and measurements.
Fig. 2 Spatial variability of soil moisture.
Spatial variability of backscattering coefficient and ECa Good soil moisture inversions make it acceptable to apply backscattering coefficient to radar image to retrieve soil moisture to some extent, so the spatial distribution of backscattering coefficient implies the soil moisture characteristics. The backscattering coefficient ranged from 0.75 to 10.80 dB and -24.32 to -18.17 dB for HH polarization and HV polarization mode respectively. Afterwards, semivariance simulations were conducted for analyzing the spatial variability of backscattering coefficient, implying that the exponential model and Gaussian models were optimal for HH and HV mode (Fig. 3). According to Shi et al. (2005), the ratio of nugget to sill can be used to asses spatial distribution magnitude of soil attributes (if this ratio is 25% or less, the spatial dependence is considered strong, when it is between 25 and 75% the spatial dependence is normal, and, finally, when it is more than 75% the spatial dependence is characterized as weak). In this study, ratios of nugget to sill value were 0.09 and 0.10 signifying strong spatial variability. On the other hand, strong correlations were found between ECa and the backscattering coefficient (r=0.527, P
