Land surface soil moisture (SM), which is the water stored in the upper soil layer, is a key variable to improve our understanding of the energy and water cycles in the Earth system; thus, it is an important parameter in climate, hydrology, and environment [1
]. SM plays a crucial role in a large number of applications, including numerical weather prediction, disaster monitoring, crop yield prediction, flood and drought damage estimation, water resources management, greenhouse gas accounting, civil protection, and epidemiological modeling of water borne diseases [5
]. SM, as a key parameter in the water cycle process, has been endorsed by the Global Climate Observing System (GCOS) as one of 50 Essential Climate Variables (ECVs), which are required to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC). As many applications mentioned above require a soil moisture record that spans a longer period than the lifetime of a single sensor, SM is required for both current and historical observations. There is a need to build a long time-series SM product. Lack of spatial-temporal-consistent, long time series products, existing SM data with various resolutions, and accuracy cannot provide effective support for the study of the water cycle response mechanism to global climate change, which has been identified as one of scientific objectives of the new Chinese satellite mission of WCOM [9
]. It is necessary to build space-temporal-consistent, long time series products of SM, to answer the scientific problems in the study of the water cycle and climate change.
Both the active and passive microwave remote sensing systems can estimate SM through the observation of backscatter signals and brightness temperatures (TBs), especially passive microwaves at low frequencies. The theoretical basis of microwave remote sensing of soil moisture is that very large differences between the dielectric constants of dry soil and liquid water lead to a very large contrast between wet soil and dry soil [11
]. Several microwave radiometers onboard satellites can be used to estimate global soil moisture, such as radiometers operating at the C band and at higher frequencies [13
], including the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat, AMSR-E/AMSR2, and FY-3 (FengYun 3) from China, etc. However, these radiometers are easily affected by vegetation attenuation, are insensitive to soil moisture under conditions of moderate vegetation (water content greater than ~3 kg/m2
), and measurements can only represent information about the top of 1 cm of soil [11
]. Since the launch of SMOS of the European Space Agency (ESA), which operates at a low frequency (L-band), several similar satellites have been successively launched, including the Aquarius- and the Soil Moisture Active Passive (SMAP) mission [1
]. All of them can provide global observations in the L-band, in which SMOS provides TB observations with multiple incidence angles, and the SMAP mission and Aquarius/SAC-D are supposed to provide global measurements at both the L-band TB and backscatter. However, the SMAP radar stopped working in July 2015. L-band has more advantages in the retrieval of soil moisture, because it can penetrate the atmosphere and vegetation coverage (up to ~5 kg/m2
water content), and TB represents information on the upper 5 cm of soil [1
In order to make full use of the observations accumulated by different sensors, and to build a long time series dataset, multisource soil moisture data from historical and existing data should be merged for a long period of time. Liu et al. [18
] presented an approach for combining passive and active soil moisture. This research spanned the soil moisture observations period starting from 1979, and is of great significance to enhance our basic understanding of soil moisture in the water, energy and carbon cycles. In view of the similar and different payload configurations, the solutions for products rebuilding can be divided into two types: (1) Cross calibration method. For similar payload configurations of different sensors, we can use the method of cross calibration. Different sensors with similar payload configurations include SMMR, SSM/I, TMI, WindSat, AMSR-E/AMSR2, with multi-frequency bands ranging from C, X, Ku, Ka-bands to higher frequencies; different sensors with similar payload configurations also include sensors at low-frequencies, i.e., L band, such as SMOS, Aquarius, SMAP/Radiometer, etc. The reconstruction solution of long time series products in this case is takes cross calibrations between different sensors, unifies the algorithm, and applies the same algorithm to the observations of different sensors; (2) Taking the most credible retrieval products (such as SMOS products, etc.) as the standard reference, to train other data. For different sensors with low and high frequencies, such as AMSR-E, with the C, X, Ku, and Ka-band, and SMOS, with the L band, the reconstruction solution of long time series products may be the option in this case.
The NNs method is an effective nonlinear method to establish a model. It has been widely applied in remote sensing fields, including soil moisture retrieval. The general idea is to build relationships between input data (TB/backscatter, SM) and target SMs (in situ SM, model SM, or satellite SM) through NNs training, and then retrieve SM using the trained NNs. At a small scale, such as the watershed scale, NNs have been used to retrieve soil moisture, with in situ measurements as reference and airborne/satellite observations as input, including synthetic aperture radar (SAR) [19
] and radiometer observations [26
], or both active/passive data [31
]. Another approach is to use a model SM as a reference to train NNs (models such as National Centers For Environmental Prediction (NCEP), European Centre For Medium Range Weather Forecasts (ECMWF), Global Land Data Assimilation System (GLDAS)), and add some auxiliary data to the input layer (Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), Precipitation (PRC)) to obtain more ideal retrieval results [33
]. These studies built the foundations of NN application in SM retrieval. In recent years, some researchers have used NNs method to rebuild long time series global SM products. Lu et al. [36
] reconstructed a time series soil moisture dataset using SMOS and AMSR2 soil moisture products to train the NN, with daily TB, NDVI, LST, PRC, and DEM information as input data. However, this study concentrated on the Heihe River Basin, and its time-series products only had a year of products in 2012 because the limitations of AMSR2 TBs. In order to develop longer time series products, based on the ESA-funded SM fusion study program, de Jeu et al. [37
] designed three approaches to retrieve global time series soil moisture datasets during the 2003–2013 period, with SMOS and AMSR-E datasets over the June 2010–September 2011 period. Their goal was to carry out an integration of SMOS in a consistent soil moisture climate record. The first approach was based on statistical regression, which was adopted by Al-Yaari et al. [39
]. The authors retrieved global soil moisture during the 2003–2011 period using SMOSL3sm and AMSR-E TB observations as the training dataset. The second approach is a NN method developed by Rodriguez-Fernandez et al. [40
], using ECMWF SM predictions and SMOSL3sm products as reference to train the NNs, with SMOS TB, ASCAT backscatter (
), NDVI, and soil texture information as input data. Rodriguez-Fernandez et al. [41
] compared different combinations of input data (TB, NDVI, texture) and obtained the best configuration of TB to train NN. Additionally, they analyzed the contribution of auxiliary data to the accuracy of SM retrieval. The third method, called Land Parameter Retrieval Model (LPRM) fusion, was derived by van der Schalie et al. [44
]. Their research updated roughness parameterization, and optimized AMSR-E LPRM parameters for the C- and X-bands to match SMOS retrievals.
In our study, for comprehensive consideration, we adopted the second solution to rebuild long time series SM products, using a BPNN method with SMOSL3sm products as a reference. The main target of this study was to develop a reconstruction approach to obtain long time series global soil moisture datasets, on the basis of previous studies. The initial purpose of this study is to find a way to refine the former observations from the future WCOM soil moisture products, which mainly rely on L-S-C band observations. Therefore, the best option is to use SMOS observations to train the former AMSR-E/AMSR2 data. The data fusion of multi-source satellite observations lies outside of the aim of this study. On the choice of reference, from Aquarius, SMAP, and SMOS, we selected SMOS SM as the standard reference of SM products. SMOS provides multi-angular global microwave TB observations, from 2010 until the present, and its SM products have the high accuracy of 0.04 m3
. A great deal of research has evaluated SMOS SM products and demonstrated its high accuracy [45
]. For the consideration of long time series, we choose AMSR-E/AMSR2 data as the training dataset because it has the same configuration and constitutes a continuous time series, for AMSR-E, lasting from 2003 to 2011, and AMSR2, lasting from 2012 to the present. In contrast to previous studies, we implemented the Microwave Vegetation Indices (MVI) as inputs in order to provide information on the effects of vegetation. Additionally, this study serves as part of the pre-feasibility studies of the scientific objectives of WCOM. We will join the modeled and WCOM observed soil moisture to produce an even better product, and then use this to refine the former observations (including SMOS, AMSR-E/AMSR2, etc.).
The rest of this paper is organized as follows. Section 2
presents the different data used in the study and the BPNN method used to retrieve soil moisture. Section 3
shows the results of BPNN and evaluates NNsm with SCAN in situ SM observations. Then, Section 4
discusses the accuracy and advantages of NNsm in comparison to satellite SM products, and the resulting datasets of other methods. Finally, Section 5
This study investigates the feasibility of a BPNN method to build a long time-term soil moisture time series using SMOSL3sm products and AMSR-E/AMSR2 TB observations. First, the BPNNs on every grid were trained using SMOSL3sm products as a training target, and we took reflectivity (R) and the MVI from AMSR-E/AMSR2 TB observations during July 2010–June 2011 and the entire year of 2013 as inputs. With these BPNNs, we built long time series of global soil moisture from 2003 to 2015, using AMSR-E TB in 2003–2011 and AMSR2 TB in 2013–2015.
We evaluated the quality of the training step over the training period (July 2010–June 2011, and 2013), and it achieved a good agreement between the NNsm and SMOSL3sm products, with a mean global value of CC = 0.67, RMSE = 0.055 m3/m3 and Bias = −0.0005 m3/m3. A specific analysis on selected SCAN sites shows that the trend of the trained NNsm is consistent with that of SMOSL3sm, with a high CC value. These results ensure the following step of building a long time series of soil moisture.
The long time series and anomaly time series of NNsm were evaluated against in situ SCANsm observations. It turns out that our result NNsm has a high consistency and accuracy with reference SMOSL3sm and the in situ SCANsm, and captured the temporal dynamics of soil moisture, with CC = 0.52, RMSE = 0.084 m3/m3 and a Bias with a magnitude of 10−3. Over most of the SCAN sites, NNsm well captures the in situ SCANsm and has strong seasonal and interannual variations. However, in some SCAN sites, although our method can capture the interannual dynamic variety well, there are some differences in the dynamic ranges between NNsm and SCANsm. This can be mainly explained by two factors, in which one is the differences between the reference SMOSL3sm and the in situ SCANsm, and the other is the heterogeneity in the satellite footprint. The soil moisture of some sites has weak interannual variations. Through our analyses we found that the land cover is mainly croplands. Thus, influenced by irrigation in these areas, soil moisture changes faster and has no obvious interannual variations. In some semi-arid sites, NNsm has overestimated or underestimated soil moisture. There are differences between the reference SMOSL3sm and the in situ SCANsm, which is the reason for overestimations or underestimations.
To further evaluate the accuracy and state the advantages of NNsm, we compared it with AMSR_LPRM products and a regression method. Comparative results show that NNsm is more consistent and continuous with SCANsm than the performances of AMSR_LPRM and Reg_sm. NNsm has significant advantages relative to the regression method, with a higher accuracy and longer time series.
For improvement and further research, we wish to continue the following work: (1) for the improvement of our BPNN method, we can test different combinations of Rs and MVI. For example, we can use low or high frequency bands of Rs and MVI, as we used all frequencies in this study, which may not be the optimal input combination for BPNN training; (2) Another direction is to train BPNNs for future WCOM. Because WCOM will have no overlapping data with AMSR-E data, we can train BPNNs using only the AMSR2 data and apply the BPNNs to cross-calibrated AMSR2 and AMSRE data.
In conclusion, through BPNNs training, this study provides a promising method to build long time series of global soil moisture products. The BPNN method can produce surface soil moisture in terms of absolute values and temporal variations. In addition, the BPNN method is independent of various ancillary data, and only relies on the reference SMOS data. This method can be applied in other satellite missions, such as SMAP and future WCOM satellite mission, so long as they have an overlap period with AMSR-E/AMSR2 observations.