There have been studies based on physical, empirical, and semi-empirical models that estimate soilmoistureoverbare soils through radar remote sensing [6–8]. Physical approaches require many input parameters such as surface roughness and slope, which are not available under practical conditions . Empirical models are only data driven, whereas semi-empirical models, while being data driven, also support theoretical considerations. In soil studies, they are site-specific and generally valid for specific soil characteristics . Previous semi-empirical studies have considered single polarization to build a relationship between soilmoisture and a backscatter model at 10 cm depth  and estimated ϑ v with a root mean square error (RMSE) of 3–6% [10–12] using C-band data. There have also been studies that have used the SAR interferometry technique and Sentinel-1data to estimate soilmoisture and compare them with in situ measurements . Even though SAR interferometry is less frequently used in the remote sensing community to estimate soilmoisture, its advantage lies in its ability to disentangle moisture and terrain roughness contributions. Most SAR-based soilmoisture estimation studies have covered small areas limited to a few hundred square kilometers [11–17]. Estimating soilmoistureover a wider area and at a higher resolution usingSAR imagery will provide information on managing water resources and irrigation scheduling that can benefit a large number of farmers .
The objective of this study is to improve the performance of semi-empirical radar backscatter models, which are mainly used in microwave remote sensing (Oh 1992, Oh 2004 and Dubois). The study is based on satellite and ground data collected on baresoil surfaces during the Multispectral Crop Monitoring experimental campaign of the CESBIO laboratory in 2010 over an agricultural region in southwestern France. The dataset covers a wide range of soil (viewing top soilmoisture, surface roughness and texture) and satellite (at different frequencies: X-, C- and L-bands, and different incidence angles: 24.3˚ to 53.3˚) configurations. The proposed methodology consists in identifying and correcting the residues of the models, depending on the surface properties (roughness, moisture, texture) and/or sensor characteristics (frequency, incidence angle). Finally, one model has been retained for each frequency domain. Results show that the enhancements of the models significantly increase the simula- tion performances. The coefficient of correlation increases of 23% in mean and the simulation errors (RMSE) are reduced to below 2 dB (at the X and C-bands) and to 1 dB at the L-band, compared to the initial models. At the X- and C-bands, the best performances of the modified models are provided by Dubois, whereas Oh 2004 is more suitable for the L-band (r is equal to 0.69, 0.65 and 0.85). Moreover, the mod- ified models of Oh 1992 and 2004 and Dubois, developed in this study, offer a wider domain of validity than the initial formalism and increase the capabilities of retriev- ing the backscattering signal in view of applications of such approaches to strongly- contrasted agricultural surface states.
signal and soilmoisture is complex as it depends on radar frequency, incidence angle, polarization, surface roughness and vegetation cover. For C band data, many studies showed that it is possible to estimate soilmoisture with reasonably high accuracy [25–28,30,31]. Many of them concerned bare soils and agricultural areas [25,27,31]. Baghdadi et al  studied the application of RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of Sentinel-1 and LANDSAT-8 and Sentinel-2 data for soilmoisture and Leaf Area Index (LAI) retrieval over irrigated grasslands. The results showed that HH (Horizontal Transmit/Horizontal Receive—like polarization) polarization is the most relevant to soilmoisture estimates. Ulaby et al.  stated, that the sensitivity of microwave signals to soilmoisture increases with a smaller incidence angle which allows better penetration of the wave. Also, the polarization of VV (Vertical Transmit/Vertical Receive—like polarization) has a higher penetration depth than HH. Zribi et al.  proposed the conventional empirical linear relationship approach between backscattering coefficient (σ°) calculated from ENVISAT-ASAR (ESA’s Environmental Satellite - Advanced Synthetic Aperture Radar) data registered in HH polarization with a dual-angle configuration and surface soilmoisture of a small watershed. However, it can also be applied to vertical (VV) polarisation. Paloscia et al. [35–37] presented statistical algorithms for the retrieval of soilmoistureoveragricultural areas applying σ° in HH and HV (Horizontal Transmit/Vertical Receive—cross polarisation) polarizations from ENVISAT-ASAR data based on the Integral Equation Model (IEM) and the artificial neural network (ANN). Balenzano et al.  studied the potential of multi-temporal C- and L-band SARdata to map temporal changes of surface soilmoisture. They found that low incidence angles (e.g., 20°–35°) and HH polarization are generally better suited to soilmoisture retrieval underneath agricultural crops than VV polarization and higher incidence angles. Mattia et al.  investigated the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages the experimental results, allowed to retrieve wheat biomass and soilmoistureusing Advanced Synthetic Aperture Radar data.
A recent study  utilized the watershed networks with AMSR-E data to validate four alternative soilmoisture algorithms over a 7-year period. Two results from that study are relevant to SMOS validation. First, the comparison of four algorithms with the in situ data revealed quite different behavior (bias, range, and accuracy). Most algorithms could meet accuracy requirements with some modification (The target accuracy for NASA ( http://nsidc.org/data/amsr_validation/pdfs/Version_3_SDV_Plan.pdf ) and the Japanese Aerospace Exploration Agency  was 0.06 m 3 /m 3 ). Based upon the statistics and sites studied, the Single Channel Algorithm (SCA)  was found to perform as well as or better than the alternatives tested and will be used here for comparisons with SMOS. Table II summarizes the SCA statistics over the 7-year period, which will be referred to as the long term analysis.
For the preprocess procedure, original 1-km spatial resolution MODIS are projected to WGS 1984 (17N) in ENVI. The 30-m resolution DEM images are also projected to the same projection system. Resampling procedure is conducted by calculating the mean of all the vegetation index value, LST values and elevation within the larger pixel (40 km ×40 km), the MODIS 1-km resolution data is aggregated to 40-km scale. For regression model, three models are utilized by different variables for comparison. The first model takes LST and NDVI image as high-resolution input, the second model prefers LST, NDVI and DEM while the third model process LST, NDVI and TWI data instead. High-resolution soilmoisture level is computed based on the regression formula. After the preprocess and model building, SMAP data is downscaled using the regression models stated above. The final step is to validate the downscaling output with the SMAPVEX15 field observation. Results from three regression models are compared with the field data of two different dates. To evaluate the regression model, not only the
When spatially averaged, t does not seem to add sig- niﬁcant skill to NDVI prediction. However, for certain land cover types such as CBS and OS, the combination of u LPRM and t contributes to an increase in R(21) from the soilmoisture–only case because of the high R(21) of t ( Fig. 7 ). This result might be explained by the differ- ence in what t and NDVI actually measure; NDVI is sensitive to the chlorophyll concentration in the canopy while t is sensitive to the water content both in foliage and woody biomass. Even though total above-ground biomass represented in t decreases with a lack of pre- cipitation, the NDVI may show lagged response because green canopy cover is maintained in grassland or shrubland land covers during drought ( Liu et al. 2011 ). Therefore, the t information may be more useful for open and closed shrubland vegetation types. This is also conﬁrmed by the Z-score map in Fig. 8b . When u LPRM and t are combined into LR(u LPRM , t) via Eq. (15) , certain relatively arid regions (e.g., the western United States and northern Africa) are converted from negative to positive Z scores, indicating an improvement in per- formance relative to the benchmark (cf. Figs. 8a,b ). c. Case 3: Efﬁciency of hydrologic models
This study evaluates the capability of soil water content predicted from remote sensing to indicate the soil/canopy water content at short time and space scale, through comparisons with daily soilmoisturedata determined in situ, using dielectric devices. Daily aqua moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and the diurnal (daytime and night time) land surface temperature difference (DLST) are employed to retrieving daily volumetric soilmoisture content (ș) at Sparta experimental station, during the period June-August, of the years 2010, 2011, 2012 and 2014. Using the concept of apparent thermal inertia (ATI) in the remotely sensed topsoil moisture saturation index, daily ș is obtained from DLST and the volumetric saturated and residual soilmoisture content and is compared with the experimental values of volumetric soilmoisture content (SM) measured at various depths (10, 20, 30, 40, 60, 80 and 100 cm). Simple relationships are also calibrated between SM and ATI or DLST or NDVI during the years 2010, 2011 and 2014 and are tested for predicting ș, during the year 2012. Especially the three first models predict ș satisfactorily as compared with the measured SM and hence they can offer a considerable guidance in irrigated agriculture and other related fields.
agriculture and forest were transformed into urban class(Geymen 2008). The previous studies reported that Visakhapatnam suffers from natural disaster and other phenomena due to its geological structure and human activities. In fact, high accuracy is preferred by the planning and development of any urban area to obtain the land cover mapping. In order to support the mapping of land- cover/land-use, up-to-date information is crucial. The main aim of this paper is to analyze the Sentinel-1A SARdata sets for the land cover classification and mapping using amplitude information of dual polarimetric SAR imagery(Abdikan 2015).
All quite the slope failure types require the soilmoisture parameter knowledge. This parameter is pivotal because a great presence of water in soil increases the soil stress and reduces the soil strength (Ray R.L., 2004). The assessment of a regional or global soilmoisture profiles is a difficult task to achieve. To overcome this problem the measurement of soilmoisture by means of Synthetic Aperture Radar data was very useful, even if the microwave radiation can penetrate only a thin upper layer (Njoku E.G. et al., 2003) of the soil surface (typical value are between 1 and 5 centimetre, for landslide studies is critical to obtain soilmoisture profile of the whole soil layer). We refer to the study of Jackson T.J. (1980), Arya L.M. et al. (1983) and Liang X. et al. (1994) to a detailed discussion on the link between the surface and sub-surface soilmoisture.
interferograms have been excluded from the displacement maps. The displacement values are superimposed on the mean intensity image of both acquisitions used to generate the interferometric pair and the colour bar indicates displacement within the scene. The spatial scale of the maps is given in figure 3(l). The field boundary overlays (white polygons) identify the measured displacements within each of the study fields. It is evident in some of the plots that phase variations (which translate into the displayed surface displacements) are present within the field boundaries and may be linked to the fine-scale heterogeneities in soilmoisture content. The hypothesis tested in this paper is that increases/decreases in surface soilmoisture coexist with decreases/increases in signal path length (and thus, increases/decreases in the surface elevation). Therefore, positive surface displacement values correspond to less signal penetration (and possible clay swelling) as a potential result of an increase in soilmoisture. Similarly, negative surface displacement values identify decreases in soilmoisture.
Abstract- The objective of this study was to develop an approach for estimating soilmoisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric Synthetic Aperture Radar (SAR) and optical data. A huge dataset of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new datafrom the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual- and full-polarizations configurations) and the Normalized Difference Vegetation Index (NDVI) calculated from optical data for the estimation error of soilmoisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters (Shannon entropy and Pauli components) on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested.
Soilmoisture and vegetation biomass are primary parameters needed for understanding the environmental dynamics of Mediterranean regions. Desertification processes which take place at the margins of these regions are characterized by changes in the physical, chemical and biological properties of soils (Lavee et. al., 1993) and in the structure of natural vegetation . Remote sensing have shown its potential for studying relationships between vegetation and soil properties across transition zones (Shoshany et. al., 1995), However, most of the existing remote sensing applications in this field are based on datafrom sensors in the visible and infrared spectral bands which are limited in their sensitivity to volumetric information. Understanding the relationships between the volumetric properties of soilmoisture content and the
RMSE 0.037 ± 0.003 0.040 ± 0.003 0.036 ± 0.002 0.038 ± 0.003 0.038 ± 0.003 Correlation 0.672 ± 0.040 0.685 ± 0.043 0.680 ± 0.043 0.667 ± 0.042 0.677 ± 0.045
Experimental error statistics with 95% confidence intervals for 0-10 cm layer soilmoisture, verified against Texas A&M North American SoilMoisture Database in situ observations from1 April to 1 October 2011. OPL: Open Loop; NOBC: Data Assimilation Only; BC1: single bias correction; BCS: soil-based bias correction; BCV: vegetation-based correction. The best statistics in each category are in bold font.
Droughts usually occur after a long-term period with low precipitation or high temperature, which causes high evapotranspiration. Lasting conditions of soilmoisture deficits can have severe impacts on agricultural production, economics and society (Clark et al., 2002; Marsh, 2007). Both observations and models have indicated an increasing number of drought events that probably connect with the global climate change (Dai, 2011). In the United States, the loss due to drought is about $6-$8 billion per year (Federal Emergency Management Agency, 1995). However, the recent drought that afflicted nearly the entire North America continent and lasted almost two years from 2010 to 2012 (Freedman, 2012) had affected the agricultural production severely. For example, just the 2011 agricultural loss in Texas alone already exceeded 7 billion (Walsh, 2011). One of the possible reasons for the high cost is lack of recognition of drought events, because drought develops more slowly than other disasters such as floods and hurricanes and it is hard to recognize drought until it becomes severe (Luo and Wood, 2007).
rithm described in Dorigo et al. (2017) to give an estimate of surface soilmoisture together with its associated uncertainty. These estimates are assumed to represent the top 2–5 cm of soil. However, observations based on different microwave frequencies and soilmoisture conditions may be represen- tative of deeper layers (Ulaby et al., 1982). It has been pre- viously shown that it is best to use both active and passive retrievals together (Draper et al., 2012) and that the ESA CCI merged product performs better than either the active or pas- sive product alone (Dorigo et al., 2015). Dorigo et al. (2015) also show that the ESA CCI product performs well over west- ern Africa when judged against in situ soilmoisture obser- vations from the African Monsoon Multidisciplinary Analy- ses (AMMA) network (Cappelaere et al., 2009), with stations in Benin, Mali and Niger. When judged against the AMMA network, CCI soilmoisture was shown to have a high cor- relation (∼ 0.7) and one of the lowest unbiased root-mean- squared differences (∼ 0.04) of the 28 worldwide networks used in the study. This bodes well for our comparison over Ghana, which has a similar climate regime in the north to the sites in the AMMA network. Figure 3 shows the number of available daily soilmoisture observations in the experiment period (2009–2014) over Ghana, with the maximum number of possible observations being 2190. We can see that there is higher data availability in the north of Ghana than in the south. There are some pixels in the south for which we have no data; this is due to high vegetation cover.
Figure 9 compares the prior soil map used as the initial guess in the DA (i.e. from the Harmonised World SoilData Base) with the posterior soil map retrieved by DA. The pos- terior soil map shown is the soil map retrieved when forcing JULES with TAMSAT v3.0 rainfall. It can be seen that af- ter DA, the percentage clay is greatly reduced with increased percentages in silt and sand for the majority of grid cells. This change is reasonable for some grid cells, particularly in northern Ghana where soils are often much more sandy/silty in texture (Braimoh and Vlek, 2004). Comparing estimates of soil texture derived from CCI soilmoisture to in situ ob- servations is inevitably problematic due to issues of represen- tativity in the spatial domain. However, independent sources of verification are difficult to find over Ghana. We therefore compare our soil maps to in situ observations from the Africa Soil Profiles Database (Leenaars et al., 2014). This database is compiled by the International Soil Reference and Informa- tion Centre (ISRIC), with the quality of the data being rated from1 (highest quality) to 4 (lowest quality); here we only compare our maps to observations with a quality flag of 1 or 2. In table 1 we show the root-mean-squared error (RMSE) for our soil maps when compared to 21 in situ observations of soil texture in the north of Ghana and 36 in situ observations in the south (locations shown as red dots in Fig. 9). For the north of Ghana where we have most confidence in our results we find a reduction in RMSE for both sand and clay (almost halving the RMSE in clay). However, the RMSE for silt is increased. In the south of Ghana we do not manage to re- cover a better estimate of soil texture after data assimilation, with an increase in RMSE for silt and clay but a decrease in RMSE for sand. The inability of the data assimilation to improve soil texture estimates at certain points is most likely due to issues of spatial representativity between the modelled soil map and the in situ data. It is also possibly impacted by errors in our pedo-transfer functions, which may perform better if they were specifically calibrated for Ghanaian soils (Patil and Singh, 2016).
the future, and the reference SSM data used in this study will contribute to quantify the added value of the new versions. This is true for ASCAT, also. In a recent study, Albergel et al. (2012) have used the SMOSMANIA data to benchmark the SMOS and ASCAT SSM products. While they used the first version of the ASCAT SSM product provided by EU- METSAT, based on the ERS algorithm, an updated version was used in this study (Sect. 2.1.2). For the 12 westernmost, and for the 9 eastern SMOSMANIA stations, the mean ab- solute correlation scores obtained by Albergel et al. (2012) for ASCAT are 0.52 and 0.33, respectively. In this study, the corresponding score values are 0.71 and 0.53, respectively. Although the considered data set is not exactly the same (we used morning observations, only, while Albergel et al. (2012) used the pooled morning and evening observations), it can be concluded that much better results are obtained with the up- dated ASCAT SSM product. The good correlations found using the upgraded ASCAT product are consistent the find- ings of Brocca et al. (2010b) and Draper et al. (2011), based on the same product.
Two drought episodes in February 2006 and July 2011 were selected to investigate spatial variability in drought indices. The corresponding USDM maps were used in the analysis. According to Oklahoma Water Resources Board , the period of September 2005 to March 2006 was the driest cool growing season in Oklahoma since 1921. The departure from the normal precipitation during this season ranged from −366 mm for the Southeast division (Wister) to −105 mm for the Panhandle region (Goodwell), and the severity of drought gradually reduced from east to west. In February 2006, Wister and Pawnee were under D4 and D3 categories, respectively. The remaining sites were facing D2 category. The spatial variability of SMEI-1 was in agreement with USDM (Figure 6), ranging from −3.34 at Wister to −1.14 at Goodwell. NSM and Z-Index also showed a similar pattern. However, SWDI had a nearly opposite trend, showing an increase in drought severity from east to west. This was most probably because this index is solely based on soilmoisture availability in the root zone and does not include other parameters. In addition, it is not normalized based on the past data at each site. Despite having smaller departures of soilmoisturefrom average, the magnitude of soilmoisture was smaller in western sites compared to eastern ones due to their natural aridity. Hence, SWDI signaled a more severe drought. Keyantash and Dracup  mentioned that indices that are normalized are more appropriate for comparing across locations.
Abstract. The marked reduction in infiltration rate caused by formation of a soil surface seal due to water droplet impact on baresoil is a well known phenomenon but is rarely considered in infiltration models, especially under center pivot irrigation. The objective of this study was to develop a soil infiltration model for center pivot sprinkler irrigation that incorporates the transient reduction in soil surface seal hydraulic conductivity as affected by soil and sprinkler characteristics and investigate the effect soil sealing characteristics and sprinkler selection have on infiltration depth. A sealing soil infiltration model was developed using an explicit finite difference solution scheme with a transient soil seal formation model, which is unique from other studies in that it explicitly uses droplet specific power as the driving factor for formation of a soil surface seal. The model was calibrated to four specific soils then applied to center pivot irrigation for five common center pivot sprinklers to evaluate the effect sprinkler selection has on infiltration depth. Due to the high susceptibility of the soils to surface sealing from water drop impact, the sprinkler with the largest wetted diameter was predicted to maximize infiltration depth.
The approach proposed in this contribution could complement existing methods (radiometric or scat- terometric) for soilmoisture retrieval. However, with this work we do not claim to introduce an operational, efficient, and validated technique for soilmoisture re- trieval. We report first experiments with L-band data, which we selected considering the coherence advan- tage and the fact that closure phases are larger in L- band compared to higher frequencies. Our first results are promising; however, the only L-band spaceborne SAR sensor today is PALSAR-2 onboard ALOS-2, and for any operational soilmoisture applications its spa- tial and temporal sampling is likely insufficient. How- ever, more satellite L-band SAR’s are being launched, and we should prepare today for future opportunities. The physical modelling and understanding is also not complete: in the data set we examined, for ex- ample, the scale of the closure phase signal is rather weak over the (relatively small) areas of pasture and rice fields. Surprisingly, we were able to perform con- sistent inversions over large forested areas, where the closure signal is very strong. All this shows the need for further understanding before an operational algo- rithm can be designed and the potential of closure phases can be fully harnessed.