1. Introduction
Soil moisture (SM) plays a key role in evapotranspiration as well as the water dynamics and energy transfer between land and the atmosphere [
1,
2,
3,
4,
5,
6,
7]. SM has been assimilated in various land surface models (LSM) and atmospheric models to predict weather, drought, flood, and climate trends [
6,
8,
9]. Understanding the spatial and temporal distribution of SM can therefore help us better analyze the regional and global water dynamics and improve the hydrology related models. Root zone SM (RZSM), defined as the SM at a depth from 0.5 to 1 m, is crucial for crop production and serves as an indicator for crop yield estimation [
10,
11]. However, RZSM cannot be monitored through satellite techniques currently. In contrast, surface soil moisture (SSM) is defined as the SM up to a depth of 5 cm and represents the interface between land and the atmosphere [
5,
6,
7,
8], various methods have been proposed to estimate SSM using satellite observations. We applied satellite measurements in this study to monitor SSM over the central of Tibetan Plateau (TP), since SSM plays a key role in regulating the energy and water transfer between land and atmosphere and has a significant impact on the regional climate of TP.
The cosmic-ray soil moisture observing system (COSMOS) allows to directly measure SM at various depths [
12]. However, such point-based measurements do not fully reflect the overall situation, since SM shows a high spatial and temporal heterogeneity [
6,
9,
12]. To overcome this limitation, remote sensing techniques have been used to provide spatial and temporal continuous land surface observation and are widely applied in SSM monitoring [
6,
9]. Currently, optical, thermal, and microwave methods are available [
6,
9].
When SM increases, the reflectance decreases inversely for solar band with wavelength from 0.4 to 2.5 μm. That is to say, surface soil with high water content is darker. This is the physical principle for estimating SSM with optical methods [
6,
9]. Various experiments and approaches have been conducted to describe the relationship between surface soil reflectance and its water content [
13,
14,
15]. The thermal methods depend on the change of soil surface temperature with different SSM. Thermal inertia method only considers thermal bands, temperature/ vegetation index method analyzes solar reflective bands and thermal emissive bands together [
9,
16,
17]. However, both optical and thermal bands are affected by weather conditions, cannot be used when there is a cloud cover. For instance, the 16-day revisit cycle of Landsat 8 makes it difficult to collect enough valuable observations within a specific study period. And, the optical/ thermal bands have limited penetration, they are therefore only feasible within bare soil under clear sky. However, monitoring SSM in a vegetated area is of great importance, since SSM is an important parameter for agricultural drought monitoring and has been widely applied in many LSM over vegetated areas.
In contrast, microwave techniques can be used under any weather condition using the electromagnetic radiation in the microwave region (0.5–100 cm) [
6,
9,
18]. Both passive and active microwave sensors have been widely applied to monitor SSM [
6,
9,
18]. Passive microwave sensors collect signals emitted by soil, while active microwave sensors send a pulse and analyze the soil reflected signal. Previous research works have shown that passive microwave sensors can be used to monitor global SSM through measuring the intensity of microwave emissivity from the soil [
19,
20,
21,
22]. Currently used passive microwave sensors for SSM measurements include the scanning multi-channel microwave radiometer (SMMR), the special sensor microwave imager (SSM/I), the advanced microwave scanning radiometer for EOS (AMSR-E), soil moisture and ocean salinity (SMOS), and soil moisture active passive (SMAP) [
9,
19,
20,
21,
22]. Although showing a good temporal resolution (1–2 day), the main limitation of passive microwave methods is the coarse spatial resolution (10–35 km), making them unsuitable for applications at the fine scale.
Great progress has been made in mapping regional soil moisture with active microwave sensors, a microwave pulse that is sensitive to SM is sent and received [
6,
11]. SSM is estimated based on the difference between sent and received signals [
23]. The active microwave remote sensing approaches such as synthetic aperture radar (SAR) with fine spatial resolutions (5–30 m), enable SSM monitoring at finer spatial scales. The SAR monitors SSM with three microwave bands: X-band (~3 cm), C-band (~6 cm), and L-band (~24 cm). Of these, the L-band has the strongest penetration, while X-band has the weakest penetration [
4,
24,
25]. Microwave signals with longer wavelengths as L-band can penetrate the canopy and are reflected by the soil surface [
4]. Currently, there are some SAR systems which are suitable for SM retrieval: the European remote-sensing satellite (ERS)-1/2/3 C-band, RADARSAR-1/2 C-band, advanced land observation satellite (ALOS), the phased array type L-band synthetic aperture radar (PALSAR), TerraSAT X-band (TSX), Sentinel-1/2/3 C-band (5.404 GHz) [
26,
27,
28,
29,
30]. In this study, we used the Sentinel-1A SAR with moderate penetration of the C-band signal for the grassland area.
In the past few decades, SAR has been widely used to monitor SSM [
28,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49]. There is a strong relationship between the C-band backscattering coefficient and SSM, making the C-band backscattering coefficient and effective tool to estimate SSM. However, the relationship between backscattering coefficients and SSM varies significantly under different surface types and vegetation features [
31,
32,
33,
34,
35,
36,
38,
39,
43,
44,
45,
46,
47,
48,
49]. Artificial neural network (ANN) and support vector machine (SVM) have been combined with regression algorithms to retrieve vegetated SSM [
31,
32,
33,
34,
35]. Because of the concerns about vegetation characteristics, some vegetation backscattering models have been established to describe the contribution of vegetation, with the most widely used on being the water-cloud model (WCM). Based on the radiative transfer theory, the contribution of vegetation was modeled by considering both the volume scattering produced by vegetation and the attenuation effect of soil under vegetation on the backscattering. The total backscattering of the of the vegetated surface was derived from the backscattering of bare surface using the WCM [
31,
32,
33,
34,
35,
36,
38,
39,
43,
44,
45,
46,
47,
48,
49]. The model simulated the backscattering of vegetated surfaces as a function of soil backscattering and the vegetation water content (VWC) [
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49].
Wang et al. integrated the advanced integral equation model (AIEM) with WCM to estimate SSM over vegetated area. However, it is still under debate which vegetation parameter is the most suitable one to estimate vegetation canopy backscattering in the WCM model. One of the major factors that affect the canopy backscattering is the VWC [
40,
41,
42,
44,
45,
48]. Several vegetation indices (VI) have been successfully applied to retrieve canopy VWC for grassland and agricultural areas [
49,
50,
51,
52,
53,
54]. The normalized difference vegetation index (NDVI) has been widely applied to monitor vegetation conditions and has been assimilated into the WCM to take the effects of the canopy on soil surface backscattering into consideration [
48,
49]. However, the NDVI is not the most suitable VI to be assimilated in the WCM, mainly because it is calculated based on the reflectance of the red band and the near-infrared band. The variation of the canopy’s influence on surface backscattering is mainly based on the VWC, while the red band in not that sensitive to VWC compared with shortwave infrared bands, as shown in
Figure 1 [
55]. Previous studies pointed out that two VIs, the normalized difference water index (NDWI), and the normalized difference infrared index (NDII) can be applied to monitor canopy VWC for various vegetation types with good results through linear or nonlinear models since they target the strong water absorption features of shortwave-infrared bands [
53,
54,
55,
56,
57,
58]. In this study, we applied both NDWI and NDII in the WCM to estimate SSM by considering the influence of vegetation canopy backscattering. Bao et al. tried to use Landsat retrieved VI to estimate VWC [
49]. However, with Landsat 8 has the 16-day revisit cycle, and Sentinel-1A with the 12-day revisit cycle, there is always a 2–6 day time interval between Landsat 8 and Sentinel overpassing day, which makes it a big issue to fill the time gap. Landsat 8 is very sensitive to cloud cover, only limited valid satellite observations can be collected. In this study, we applied the daily MODIS reflectance products instead of combining the Sentinel-1 products on the same observing.
The Tibetan Plateau, the highest plateau on earth, contains a large amount of permafrost soil, widely distributed. The energy and water dynamics on this plateau is very sensitive to the climate change on the Asian continent and, subsequently, on a global scale. TP makes the monsoon on its southwestern side weaken in the summer, while the drought in central Asia consequently intensifies. The heat rising by TP in summer strengthens atmospheric circulation, and the SM condition in TP acts as an amplifier for the drought in central Asia and the monsoon in East Asia. Against the background of a changing climate, the thawing of the permafrost layer with grassland as the main vegetation type will considerably impact the water dynamics on the TP. Based on previous studies, the soil energy–water distribution and freezing–thawing processes vary spatially, and estimating SM at high spatial and temporal resolution is of great importance to develop protection measures for the plateau [
59,
60]. In this context, we estimated volumetric SSM (m
3/m
3) with vegetation cover (grassland) in the central of the TP, based on a simplified WCM (sWCM) through the soil backscattering coefficient and signal incident angle, which can be extracted from Sentinel-1 products. Since in the winter (from December to February) the soil was completely frozen, the study was conducted in spring, summer, and winter (from March to November). To eliminate the impact of backscattering coefficients from vegetation canopy, we integrate VI (NDII and NDWI) in the WCM. Both NDII and NDWI can be derived through MODIS shortwave infrared and near infrared bands. To better understand soil properties on the TP, including ST and SM at various depths, an in situ measurement network has been built in Naqu, the center of the TP, providing temporal continuous ST and SM data at four depths. We validated the retrieval method with ground measurements to demonstrate the feasibility and performance.
4. Results and Discussion
We estimated vegetated SSM within the central TP through sWCM combining multi-source satellite observations (MOD09 and Sentinel-1A SAR) and in situ measurements from CTP-SMTMN. In situ measurements were also applied to validate the monitoring results. Based on the statistical results obtained for 2015 (
Table 3), the sWCM integrating MODIS and SAR observations can be applied to effectively retrieve SSM in the study area. Both NDII and NDWI were suitable (
R > 0.6) and can be integrated into the sWCM for SSM monitoring. The NDII worked relatively better, with a higher
R2, a lower RMSE, and a lower ubRMSE. Both NDII-sWCM and NDWI-sWCM slightly overestimated SSM with positive bias values over the study.
Table 4 and
Table 5 are the statistical results of NDII-sWCM and NDWI-sWCM, respectively.
Figure 4a illustrates the scatterplots between observed and predicted SSM values, considering VI as NDII or NDWI separately (with 1:1 red line shown). Both NDII and NDWI overestimated SSM under low SSM conditions (in situ SSM < 0.15 m
3/m
3), and both VIs underestimated SSM under high SSM conditions (in situ SSM > 0.2 m
3/m
3).
Figure 4b represents the histogram of the differences between observed and predicted SSM values, the value accumulated (over 70%) between −0.05 to 0.05 m
3/m
3 for NDII, between −0.06 to 0.07 m
3/m
3 for NDWI.
Figure 5a shows the time series of observed and predicted SSM values for eight validation sites separately. The red line represents the observed SSM values, green line represents the predicted SSM values with NDII integrated in sWCM, blue line represents the predicted SSM values with NDWI integrated in sWCM. For sites MS3552 and MS3603, the trend of predicted SSM (NDII-sWCM and NDWI-sWCM) matched the observed SSM quite well. For all sites, summer (May to August) has the highest SSM values which is consistent with the information provided above, that summer has the most precipitation within the year. Observed SSM values drop rapidly at the beginning of August for all sites, predicted SSM values of some sites drop rapidly at the same time. Such situation is not understood yet.
Figure 5b,c illustrates the scatter plot between observed and predicted SSM values when NDII or NDWI were applied in the sWCM separately. In one site (BC08), sWCM overestimated SSM for the whole study period; while in two sites (BC04 and MS3501), sWCM underestimated SSM for most of the study period. In three sites (BC03, MS3523, and MS3552), SSM was overestimated when it was low and underestimated when it was high, which is consistent with the results from
Figure 4. In two sites (MS3559 and MS3603), the scatterplots were distributed along the 1:1 red line. When the same parameters were applied, different validation sites yielded different results.
Figure 6a shows the Landsat 8 false color image using the reflectance band (band1) as red, the shortwave-infrared band (band2) as green, and the near-infrared band (band3) as blue, where red indicates regions with vegetation cover and brown indicates bare soil. From left to right, the images acquired are on 25 May, 18 July, and 22 October. Based on the false color images, in May and October, the study area contained mostly bare soil, while in July, it was fully covered by grass.
Figure 6b,d, from left to right, are the generated SSM images when NDII or NDWI were integrated in the sWCM separately.
Figure 6c,e show the scatter plots between observed and predicted SSM values when NDII or NDWI were applied in the sWCM, respectively. In spring, both NDII and NDWI overestimated SSM. In summer and autumn, the distribution was along the 1:1 red line and the scatterplots were also accumulated along the 1:1 red line, suggesting that the model works better for soil with vegetation cover compared with bare soil. We observed a spatial-temporal pattern of SSM distribution, with the highest and lowest levels in summer and winter, respectively. Most likely, this is because the active layer starts to thaw in spring, resulting in a rapid increase in SSM. In contrast, in autumn and winter, the soil is mostly frozen, with decreased SSM levels. In spring, the eastern part of the study area showed relatively high SSM values when compared to the western part, while in summer, SSM was lowest in the central part. In winter, the lowest levels were observed for the central and eastern parts. Both images generated by NDII-sWCM and NDWI-sWCM illustrate the same distribution. The NDII-sWCM show lower SSM values compared to the NDWI-sWCM for spring, summer, and autumn.
Although the model proposed in this study achieved good results in the grassland area in TP, we recognize that there are some uncertainties. First, there were uncertainties from satellite measurements, as the MODIS solar bands have associated atmospheric uncertainties. Second, some uncertainties were associated with the ground observations, mainly caused by technique design and experimental design, although calibration and validation were conducted. Third, we estimated the backscattering of the vegetation canopy mainly based on the VWC, but there are other factors that also affect the surface backscattering of active microwave signals such as soil surface roughness. Fourth, the WCM only considers the surface backscattering composed of backscattering from soil and vegetation and assumes there is no reflection within the vegetation canopy. Such assumption can, however, not be made for the “real world”. Soil texture and soil roughness are important factors that affect the backscattering of C-band signals, and omitting such factors may cause uncertainties in the retrieved SSM values when there is a vegetation cover. In addition, both NDII-sWCM and NDWI-sWCM were more suitable when there was a vegetation cover, and the model may need to be revised in this regard. From the scatter plots shown in
Figure 4a, we recognized that sWCM underestimates SSM under high SSM conditions in summer and overestimates SSM under low SSM conditions in spring and autumn. The uncertainties within field experiments may induce the monitoring errors. From time series shown in
Figure 5a, we see that although the average in situ SSM value is relatively high in summer, for each sampling site the SSM observations vary a lot. There are days for some sites with extremely low SSM values, while other sites with relatively high SSM observations. Besides that, both MODIS and Sentinel-1A only measure the land surface properties, there may be a delay for surface condition change caused by SSM, and it may not be timely observed by satellite sensors. Although the soil temperature is above 0 °C in March and November within the study area, there may exist ice crystals in the soil, the heat absorption or release within the phase change of water may also affect SSM monitoring. In this study, we analyzed the study period together, while the empirical parameters in the sWCM varies under different conditions. If different seasons were studied separately, the monitoring results may be improved.