Oases are important parts of the ecosystem that constitute arid and semi-arid regions; they are the “source of life” throughout the arid and semi-arid regions. China’s oases are mainly distributed in the five northwestern provinces, especially in the Xinjiang Uygur Autonomous Region (hereinafter referred to as “Xinjiang”). The Xinjiang oases covers 8% of the total area, but bears more than 90% of the cultivated land, population, and Gross Domestic Product (GDP) of Xinjiang. Meanwhile, the inner ecological environments of oases are fragile; once they are affected or destroyed by human or natural causes, it is difficult for them to restore to normal.
Soil moisture is an important parameter in meteorological, agro-environmental, and hydrological studies [1
]. In meteorological research, accurate detection of surface soil moisture is important and can be applied in a variety of hydrological and climate models (e.g., rainfall mapping, drought monitoring, vegetation water requirement analysis, etc.). In agricultural research, the most important thing is to estimate the crop yields; in addition, the surface soil moisture can be introduced into the basin hydrological model as a priori knowledge (which can predict the deep soil moisture content in order to study vegetation water stress, etc.). Finally, soil moisture can help predict natural disasters, such as debris flows, landslides, etc.; meanwhile, surface soil moisture mapping plays a crucial role in improving local and even global climate predictions [2
]. Moreover, soil moisture is an important indicator for monitoring land degradation and drought [3
], an indispensable key factor in the land water cycle [4
], and the basic guarantee for vegetation growth and development. It has guiding significance in estimating crop yield and drought monitoring [7
In arid and semi-arid regions, the spatial and temporal soil moisture retrieval can be used to better understand the changes of soil moisture migration, to further understand the ecological effects of vegetation and, simultaneously, improve soil moisture holding capacity and vegetation water use efficiency (and, accordingly, play an important role in the sustainable development of an oasis ecology). Therefore, the importance of soil moisture for the sustainable development of an oasis is self-evident, and real-time monitoring of large-scale soil moisture is of great significance [9
At present, high-precision retrieval of spatial and temporal distribution of soil moisture is an urgent problem to be solved [10
], to which the hotspots and difficulties lies in the soil moisture retrieval under vegetation coverage. At present, the soil moisture monitoring method is based on the soil moisture monitoring site, which is time-consuming and labor-intensive, and the observation site is limited and unevenly distributed. On the other hand, with the rapid development of remote sensing technology, more and more people use remote sensing methods to monitor soil moisture. After several decades development of remote sensing technology, the data sources of soil moisture retrieval mainly include optical, microwave and thermal infrared [12
]. Optical remote sensing has developed rapidly and has become a high time resolution, high spectral resolution, and high spatial resolution data [13
], but it is susceptible to the external environment, such as atmosphere, clouds, fog, etc. It is susceptible to leaves and stems under vegetation-covered areas; with its strong penetrating ability, microwave remote sensing can realize all-day observation under various meteorological conditions, and most importantly, it is sensitive to soil moisture changes, making microwave remote sensing widely used in large-scale mapping of soil moisture in arid and semi-arid regions [15
The microwave remote sensing has two kinds of observing systems, active and passive missions. The passive mission provides global soil moisture products with a high temporal resolution (2–3 days) and high retrieval accuracy [16
], but with a poor spatial resolution. The active mission provide a high spatial resolution data, but can be influenced by the soil and radar’s system. Taking the study area into account, it was not fit for the passive remote sensing soil moisture derive, so the active mission was selected as the data source. Over the past few decades, active microwave remote sensing data have been successfully used for estimating soil moisture due to a finer spatial resolution [17
]. Several soil moisture retrieval algorithms have been developed and tested for multiple Synthetic Aperture Radar (SAR) satellites operated at the L/C/X-bands [18
], such as Advanced Land Observing Satellite-2 (ALOS-2) [16
], Radarsat-2 [20
], Advanced Synthetic Aperture Radar (ASAR) [22
], Sentinel-1 [23
], and Multi-temporal X-band SAR (TerraSAR-X) [28
]; studies that have used these data to estimate bare soil moisture have achieved promising results. Based on this, several soil moisture models have been proposed to the bare land, including the statistical models (Oh model [30
] and Dubois model [31
]) and physical models (the integral equation model (IEM) [32
] and advanced IEM (AIEM) [33
]). Combined with the vegetation scattering model [38
], which can be used to quantify vegetation attenuation of radar signals in radiative transfer function models [40
], the bare soil scattering model can adapt to the vegetation areas. To eliminate the effect of vegetation, some studies [39
] have attempted to use additional vegetation information provided by optical remote sensing, which has been widely used to derive information of vegetation properties. Furthermore, other studies [43
] have suggested that the accuracy of soil moisture estimates was significantly improved when considering synergy between radar and optical data as compared to estimates from SAR data only.
The aim of this paper is to verify the feasibility of the soil moisture retrieval model based on bare surface scattering models and vegetation scattering models in an oasis region, in order to accurately derive soil moisture in the oasis of the arid region, which can better understand the response of the surface parameters and the canopy to backscatter. Based on this purpose, the Ugan-Kuqa River Delta Oasis in the Aksu region of Xinjiang was selected as our target area, with microwave data as the main and supplemented by optical data. We analyzed the relationship between backscattering coefficient and soil moisture based on microwave and optical data; therefore, a surface soil moisture retrieval model was conducted. The basic of this work is, coupled, the Dubois model and ratio model, using the surface roughness, soil moisture, and radar system parameters to simulate the backscattering coefficient of the bare soil to remove the backscatter contribution of vegetation and conduct a surface soil moisture retrieval model. This method can provide a theoretical support for the sustainable development of oases.
According to the analyses of the soil moisture retrieval accuracy, which is not high, there are uncertain factors in the retrieval process. The main aspects are as follows: (1) it is difficult to maintain the same time for field survey data, microwave data, and the optical data. During this period, it is assumed that the vegetation index remain unchanged, but July is a period of vigorous vegetation growth, which will lead to a miscalculation of vegetation scattering. (2) The parameter error of soil moisture is caused by the nonstandard sampling and the uncertainty of data processing in the laboratory. (3) There are systematic errors in the model itself, such as the backscattering coefficients simulated by the Dubois model and the ratio model, which will affect the accuracy of the soil moisture retrieval. Nevertheless, this method is still feasible if the purpose is to obtain the soil moisture in an oasis region.
Note that two models have equal importance to the soil moisture derived. The Dubois model can establish an empirical relationship and the ratio model can eliminate the vegetation scattering contribution. Among them, the ratio model is suitable for low shrubs, crops, and is not fit for arbor. However, when it comes to the soil moisture derived under forest coverage, the continuous Michigan Microwave Canopy Scattering (MIMICS) model is commonly used to simulate the scattering characteristics of forests, which is suitable for arbor. In this situation, different vegetation coverage could influence the detection results. Therefore, this method may not be suitable for temperate forests or tropical forests.