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The occurrence and development of Benggang is closely related to the decreased shear strength of collapsing walls. Plant roots can improve the soil shear resistance, and their soil reinforcing effect is restricted by soil moisture content (SMC). However, the effect and mechanism of SMC on the shear properties of rooted soil with different soil properties remain unknown. Therefore, the dominant soil erosion-resistant plant Dicranopteris linearis was selected as the research object, and shear tests were conducted to determine the shear strength response of rooted soil to SMC in the lateritic layer (LL), sandy layer (SL) and detritus layer (DL) with SMCs from 15% to 30%. The results showed that, compared with 15% SMC, the average decrease in shear strength of 30% SMC in the LL, SL and DL rooted soil were 17.37%, 21.96% and 23.36%, respectively. The rooted soil cohesion changed with increasing SMC in a binomial function, and the optimal SMC in the LL was 22.78%, which was higher than that of the SL (19.67%) and DL (18.39%). The cohesion increment of rooted soil weakened with increasing SMC, and the decrease was greatest in the SL. When the SMC increased from 15% to 30%, the internal friction angle of the rooted soil decreased by 34%, 11% and 12% in the LL, SL and DL, respectively. The Wu and Waldron’s model (WWM) correction parameters k′¯ of the LL, SL and DL were 0.59, 0.14 and 0.05, respectively. With the modified WWM, a new prediction model for the shear strength of rooted soil based on SMC was established. In short, a high SMC weakened the mechanical effect of Dicranopteris linearis-rooted soil, especially in the SL and DL of the collapsing wall, and attention should be given to drainage facilities when treating Benggang erosion.

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation. [ABSTRACT FROM AUTHOR]

Accurate high-spatial-resolution soil moisture content (SMC) datasets are crucial for applications, such as erosion modelling, flood forecasting, and agricultural production. Downscaling is an effective way to convert coarse satellite observations to a finer spatial resolution. Two downscaling approaches were proposed to improve the spatial resolution of the soil moisture active passive (SMAP) radiometer SMC. A downscaling method (method 1) based on the triangular feature space concept was developed to express SMAP L3 SMC as a polynomial function of the Global Land Surface Satellite leaf area index, preprocessed synthetic land surface temperature, and microwave polarization difference index. A second downscaling method (method 2) based on the simulated datasets was developed to express high-resolution SMC as a function of coarse-resolution SMC and sentinel-1 synthetic aperture radar observations. Downscaled SMC (1 km) was evaluated by the in situ measurements and compared by the SMAP L2 active and passive SMC product in agroforestry areas, China. The results showed that the two downscaling methods could effectively capture the spatial variability of soil moisture at 1-km spatial scales. The root-mean-square error (RMSE) of downscaled SMC for grass, shrub, and forestland is 0.052–0.055 cm3 cm−3, 0.063–0.069 cm3 cm−3, and 0.067–0.073 cm3 cm−3, respectively. The accuracies of method 1, method 2, and SMAP L2 SMC in the grassland were higher than those in the shrubland and forestland. Overall, the R and RMSE between the downscaled soil moisture from method 1, method 2, and SMAP L2 SMC were 0.613, 0.626, and 0.619 and 0.051 cm3 cm−3, 0.041 cm3 cm−3, and 0.45 cm3 cm−3, respectively. The active and SMAP passive microwave combination method has great potential for soil moisture downscaling in agroforestry areas in China.

• A consolidated DSL and a loose DSL occur in sandy loess and fine sand with a thickness of 3.0–3.5 cm and 1.5–2.0 cm, respectively. • The hydrochemical facies in sand media evolved from HCO 3 -Ca type to SO 4 ·Cl-Na type along the migration pathway of liquid water. • The minerals dissolved at a depth of 5–55 cm along the pathway of liquid water and deposited at surface soil. • The accumulation of dissolved solutes promotes DSL development and increases DSL thickness. • In sandy loess there is a difference between the experimental and predicted evaporation rates by Fick's law due to salt deposition. Knowledge of the parameterization of the dry surface layer (DSL) is essential for evaluating near-surface water flow and water balance in arid and semi-arid areas. Existing studies have parameterized DSL thickness and vapor flow as functions of the soil moisture content (SMC) in the surface layer to predict soil evaporation. However, hydrochemical processes related to DSL development have been ignored, including changes in hydrochemistry, the underlying hydrochemical mechanism, and the role of dissolved substances in the DSL development. Herein, we performed a series of soil evaporation experiments for 260 days and explored the factors influencing DSL development (e.g., soil texture, atmospheric temperature, SMC, solutes). Evaporation experiments were performed using silty loess, sandy loess, and fine sand with a 60-cm water table. Results showed that the cumulative evaporation of silty loess, sandy loess, and fine sand over the experimental period were 1,391.52, 460.10, and 185.53 mm, respectively, which determined by the maximum height of liquid flow continuity. The content of total dissolved solids (TDS) and major ions at the surface soil were significantly higher than the values at deep depths of 5‒55 cm, which largely depend on evaporative water loss. Evolutionary trends of chemical facies in sand media along the liquid water migration were from HCO 3 -Ca type to SO 4 ·Cl-Na type. This was attributed to mineral dissolution at a depth of 5–55 cm and their transport with liquid water, resulting in the precipitation of salt crystals at the surface soil. Furthermore, a consolidated DSL with a thickness of 3.0–3.5 cm in the sandy loess and a loose DSL with a thickness of 1.5–2.0 cm in fine sand were observed at the end of the experiments. The accumulation of solutes at the surface leads to a reduction in effective porosity and the aggregation of soil particles during continuous drying, which facilitates the consolidation of DSL in sandy loess. This overestimated the DSL thickness, resulting in a difference between the experimental and predicted evaporation rates by Fick's law. Overall, these results highlight the limitations of considering DSL thickness as a function of SMC only, providing new insights into hydrochemical processes and dissolved solutes involving DSL parameterization during continuous soil drying. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation.

Soil moisture content (SMC) acquisition is vital for crop stress diagnosis and precision irrigation. However, UAV remote sensing-based SMC monitoring usually suffers from low accuracy and spatio-temporal applicability. To address these issues, this study integrated crop physiological spectral response features into an ensemble learning model. A two-year field experiment (2022–2023) was conducted. First, fractional-order differentiation (FOD) and continuous wavelet transform (CWT) were used to enhance the responsiveness of summer maize canopy spectra to SMC and leaf physiological parameters (LPPs), including leaf area index (LAI), leaf chlorophyll content (LCC) and leaf water content (LWC). Afterwards, variable importance in projection (VIP) was adopted to characterize the spectral response properties of each parameter. Finally, a stacked ensemble learning model (SELM) based on Bayesian optimization (BO) was used to construct a SMC monitoring model, and the feasibility of fusing LPP spectral response features for SMC monitoring was evaluated. The results indicated that: (1) Changes in SMC had significant effect on LCC and LAI of summer maize, which in T4 (with field water-holding capacity of 80 %-95 %) were 14.07 % and 34.41 % higher than that in T1 (40 %-50 %), respectively. (2) Spectral transformation could significantly enhance the correlation between SMC and LPPs with canopy spectra (the average increase of R reached 0.23). (3) Consideration of the crop physiology spectral response could improve the SMC monitoring accuracy, the LCC-FOD-BO-SELM had excellent monitoring performance (R²= 0.78; RMSE = 0.019). The monitoring model fusing LPPs spectral response features had the highest accuracy (R²= 0.81; RMSE = 0.016). (4) BO could significantly reduce the overfitting problem of the monitoring model, with the maximum difference between the R2 of the training and test set after BO being only 0.01), thus improving the model's generalizability. This new approach to SMC monitoring using UAV hyperspectral data provide scientific support for precision agriculture and irrigation. [Display omitted] • FOD and CWT methods significantly enhance spectral characteristics. • LPPs spectral response features significantly improve SMC monitoring performance. • Bayesian optimization greatly improves the robustness of SELM [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) sensors, such as Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1, provide significant opportunities for soil moisture content (SMC) retrieval with relatively high spatial resolutions (10~30 m). In this work, an artificial neural network (ANN) SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model database was proposed. The SAR copolarization backscatter, the local incidence angle (LIA), and the normalized difference vegetation index were used in input vectors for the ANN algorithm for the retrieval and mapping of the ALOS-2 and Sentinel-1 SMC at a 30-m resolution. The results of the comparison between the SMC retrievals and the measured SMC show that Sentinel-1 and ALOS-2 SMC retrievals with high accuracy correspond to low-vegetation areas (crop, grass, and shrub), with a root mean square error (RMSE) of 0.021 and 0.033 cm3/cm3, respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm3/cm3) than Sentinel-1 SMC retrievals at high vegetation (e.g., forest). However, it remains challenging for soil moisture retrieval in forest land. The C-band and L-band SMC retrievals have higher RMSE (up to 0.047 cm3/cm3) at low incidence angle (<20°) and high incidence angle (>50°). In addition, by considering the impact of rainfall on the SMC, it appears that the Sentinel-1 and ALOS-2 SMC have a good response to the rainfall events. Finally, the results of the comparison between the SMC retrievals and the Soil Moisture Active Passive (SMAP) L2 SMC product show that the correlation coefficients between Sentinel-1, ALOS-2, and SMAP are higher in September when the vegetation is drying than in July when the vegetation is growing. [ABSTRACT FROM AUTHOR]

This study aims to retrieve soil moisture from Global Positioning System (GPS) Signal-to-Noise Ratio (SNR) data with varying analysis to compute the best-fitting analyzing methodology. Phase, amplitude, and reflector height, which are SNR-derived interferogram metrics are examined and results are proofed with respect to correlation coefficients compared with in-situ measurements. Here, Soil Moisture Content (SMC) is estimated from SNR data with four data analyzing strategies using Lomb Scargle Periodogram to retrieve dominant frequency as; (1) considering it is four times greater than background noise assuming the reflector height is inconstant in each day, (2) considering it is three times greater than background noise assuming the reflector height is inconstant in each day, (3) assuming the reflector height is constant and median values are used for overall estimations in each day, (4) assuming the reflector height is constant and median values are used in each day. To do that, the GPS Interferometric Reflectometry (GPS-IR) method was implemented to the data of OSOR station, installed in Chile (within the scope of the CAP Andes GPS Network Project carried out by UNAVCO), during 213 days from 01 January 2015 to 31 July 2015. Validation of the estimates is done by the recorded soil moistures from the Oromo Calibration Site in the LAB-net network. Results show that SMC estimated from SNR-derived metrics shows well agreement with in-situ measurements i.e. as the highest correlation of 95%; whilst the second strategy was followed. [ABSTRACT FROM AUTHOR]

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. With lack of ground observations in Pakistan, microwave sensors are viable technologies for retrieving soil moisture from space. Thus, this study compares soil moisture data from Soil Moisture and Ocean Salinity (SMOS) with Advanced Microwave Scanning Radiometer (AMSR-E) for 2010 and 2011, which were years where massive flooding occurred. We find suitable correlations (r = 0.47) for Level-2 soil moisture values for both satellites and report temporal variability during the pre-monsoon, monsoon, post-monsoon and winter seasons. This study motivates using satellite retrievals as a start towards more comprehensive studies in Pakistan.

Highlights • Spatially varying Statistical Soil Moisture Profile (SSMP) model is developed. • SSMP considers Hydrologic Soil Groups (HSGs) to impart spatial transferability. • SSMP model is able to estimate soil moisture profile at ungauged locations. • Promising to assess vertical soil moisture profile over a large area. Abstract Information on vertical Soil Moisture Content (SMC) profile is important for several hydro-meteorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses only surface soil moisture (0–5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation is obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5–10, 10–20, 20–51 and 51–102 cm) of all four HSGs during both model development and spatial validation given the model complexity. Hence, the spatially-varying SSMP model is suitable at the ungauged locations by incorporating the HSG information. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the model to develop a vertical soil moisture profile map over a large area. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the correlation between CyGNSS-derived global navigation satellite systems reflectometry (GNSS-R) bistatic reflectivity, ${\Gamma _{rl}}$ , and soil moisture active passive (SMAP) derived brightness temperature, $T{}_I/2$ , over land surfaces. This parametric study is performed as a function of soil moisture content (SMC), vegetation opacity $\tau $ , and albedo $\omega $. Several target areas, classified according to the International Geosphere-Biosphere Program (IGBP) land cover types, are selected to evaluate potential differentiated geophysical effects on “active” (as many transmitters as navigation satellites are in view) and passive approaches. Although microwave radiometry has potentially a better sensitivity to SMC, the spatial resolution achievable from a spaceborne platform is poor, ∼40 km. On the other hand, GNSS-R bistatic coherent radar pixel-size is limited by half of the first Fresnel zone, which provides about ∼150 m of spatial resolution (depending on the geometry). The main objective of this “active”/passive combination is twofold: a) downscaling the SMC, b) complement the information of microwave radiometry with GNSS-R data to improve the accuracy in SMC determination. The Pearson linear correlation coefficient of ${\Gamma _{rl}}$ and $T{}_I/2$ obtained over Thailand, Argentinian Pampas, and Amazon is ∼−0.87, ∼−0.7, and ∼−0.26, respectively, while the so-called tau–omega model is used to fit the data. Results over croplands are quite promising and deserve special attention since the use of GNSS-R could benefit agricultural and hydrological applications because of: a) the high spatio-temporal sampling properties, b) the high spatial resolution, and c) the potential combination with microwave radiometry to improve the accuracy of the measurements. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.

Remote sensing has been widely applied to map soil salinity in the last few decades. However, a notable decrease in the accuracy of soil salt content (SSC) predictions occurred when the soil surfaces were partially vegetated. To minimize the influence of partial vegetation cover on spectral reflectance, we applied a spectral separation method, non-negative matrix factorization (NMF), to extract soil spectral information from a controlled field experiment with three varying factors [vegetation coverage, soil moisture content (SMC), and SSC]. The method was applied without prior knowledge of, or restrictions on the mixed and source spectra. Soil samples and spectral reflectance collected on three periods were used to determine the effectiveness of NMF-extracted soil spectra with partial least squares regression (PLSR). The results indicated that SSC can be predicted by bare soil spectra. NMF effectively separated soil spectra from the observed spectra, and the SSC was successfully predicted from the extracted soil spectra within a wide range of vegetation cover (0%-64.7%) within defined moisture levels (-1 by weight). The approach proposed in this study will improve the prediction accuracy of SSC for partially vegetated surfaces and will expand the application of remote sensing.

In this study, an integration of microwave data obtained from the SMAP and AMSR2 satellite radiometers has been attempted, to achieve an accurate estimation of the Soil Moisture Content (SMC). This research aimed to overcome the failure of radar sensor in SMAP satellite as well as the failure to generate the radar/radiometer combined SMC product at a spatial resolution of 9 km × 9 km. A disaggregation technique, based on the Smoothing Filter based Intensity Modulation (SFIM), enabled us to obtain co-located SMAP and AMSR2 brightness measurements at L, C, X, Ku and Ka bands at approximately 10 km × 10 km on the selected test area, which corresponds to the entire Italian territory. These disaggregated microwave data were used as inputs of the “HydroAlgo” retrieval algorithm based on Artificial Neural Networks (ANN), which were able to exploit the synergy between radiometric acquisitions from these two sensors. The algorithm was defined, implemented and tested using all the overlapping orbits of SMAP and AMSR2 over Italy throughout the 9_month period between April and December 2015. Distributed SMC reference values for implementing and validating the algorithm were obtained from the Soil Water Balance hydrological model, SWBM. Through HydroAlgo, an SMC product at a resolution of approximately 10 km × 10 km was obtained. This result is close to the original Radar/Radiometer SMC product from SMAP, with an average correlation coefficient R > 0.75 and RMSE ≅ 0.03 m 3 /m 3 , in both ascending and descending orbits. [ABSTRACT FROM AUTHOR]

The Earth’s surface bistatic reflectivity Γ L H C P , C y G N S S is experimentally characterized using the novel Global Navigation Satellite Systems Reflectometry (GNSS-R) L-band passive multistatic radar technique from the Cyclone Global Navigation Satellite Systems (CyGNSS) eight-microsatellite constellation. The focus of this study is to evaluate the influence of the GNSS satellites’ elevation angle θ e on Γ L H C P , C y G N S S , as a function of soil moisture content (SMC) and effective surface roughness parameter h . As the average response, the change of the scattering regime at a global scale and considering also vegetated surfaces appears at θ e ≈ 55°. This empirical observation is understood as a change on the dominant scattering term, from incoherent to coherent. Then, the correlation of Γ L H C P , C y G N S S and SMC is evaluated as a function of θ e over specific sparsely vegetated target areas. The smoother the surface, the higher the angular variability of the Pearson correlation coefficients. Over croplands (e.g., Argentinian Pampas), an improved correlation coefficient is achieved over angular ranges where the coherent scattering regime becomes the dominant one. As such, this function depends on the surface roughness. The maximum correlation coefficients are found at different θ e for increasing mean roughness levels: r P a m p a s ≈ 0.78 at θ e ≈ [60,70]°, r I n d i a ≈ 0.72 at θ e ≈ [50,60]°, and r S u d a n ≈ 0.74 at θ e ≈ [30,40]°. SMC retrieval algorithms based on GNSS-R multi-angular information could benefit from these findings, so as to improve the accuracy using single-polarized signals.

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. With lack of ground observations in Pakistan, microwave sensors are viable technologies for retrieving soil moisture from space. Thus, this study compares soil moisture data from Soil Moisture and Ocean Salinity (SMOS) with Advanced Microwave Scanning Radiometer (AMSR-E) for 2010 and 2011, which were years where massive flooding occurred. We find suitable correlations (r = 0.47) for Level-2 soil moisture values for both satellites and report temporal variability during the pre-monsoon, monsoon, post-monsoon and winter seasons. This study motivates using satellite retrievals as a start towards more comprehensive studies in Pakistan. [ABSTRACT FROM PUBLISHER]

• The potential of low-cost UAV-based multi-sensor data for maize SMC prediction was evaluated. • Three machine learning algorithms were tested to choose the best method for SMC estimation. • Mild to modest deficit irrigation treatments are more conducive to soil moisture monitoring. • RFR provide promising results across growing seasons, stages, and water status. Timely and accurate estimation of soil moisture content (SMC) is essential for precise irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing with a high spatiotemporal resolution has become a promising method for SMC monitoring. Many existing SMC models have only been tested at a specific crop growth stage using a single type of sensor, and the effects of growth stage and irrigation variation on SMC estimation accuracy remain unclear. To address these limitations, this study used UAV-based multimodal data to quantify SMC in a maize field under various levels of irrigation over two years using three machine learning algorithms (MLA): partial least squares regression (PLSR), K nearest neighbor (KNN), and random forest regression (RFR). The results demonstrated that multimodal data fusion improves the SMC estimation accuracy regardless of the MLA, especially the joint use of thermal and multispectral data. Among three SMC regression models, the RFR model produced the most accurate SMC estimation for the two growing seasons regardless of sensor combinations. The RFR model using all three types of data generated the most accurate and robust SMC estimation at the vegetative stage with R2 of 0.68 and 0.78, and rRMSE of 20.82% and 19.36% for 10- and 20-cm soil depths, respectively; it also produced the best SMC estimation accuracy under well-watered and mild to modest deficit irrigation treatments for both soil depths. The study shows that the high spatial–temporal maps of SMC using UAV-based multimodal data has promising potential for supporting decision-making in irrigation scheduling at the farmland scale. [ABSTRACT FROM AUTHOR]

The characteristics of soil moisture content (SMC) distribution in an area are necessarily analyzed for the design and construction of sponge cities. Combining remote sensing data with experimental data, this paper establishes a machine learning model to reveal the characteristics of SMC. Taking Beijing as an example, the SMC distribution was obtained and the characteristics were analyzed after training and validating. When comparing different machine learning methods, it can be concluded that the support vector classifier (SVC) method trained with remote sensing and grayscale data can achieve the highest accuracy (76.69%). The calculation results show that the districts with the highest and lowest SMC value are Xicheng District (19.94%) and Daxing District (11.04%), respectively, in Beijing. The mean SMC value of Beijing is 15.65%. The SMC distribution characteristic in Beijing shows that the soil in the west and north are relatively wet, while the soil in the east and south are relatively dry. Therefore, it is suggested that the timely monitoring of the SMC of vegetation covered areas at the north and west should be carried out. Water conservation facilities also need to be established with the development of city constructions in the south and east areas.

Remote sensing has been widely applied to map soil salinity in the last few decades. However, a notable decrease in the accuracy of soil salt content (SSC) predictions occurred when the soil surfaces were partially vegetated. To minimize the influence of partial vegetation cover on spectral reflectance, we applied a spectral separation method, non-negative matrix factorization (NMF), to extract soil spectral information from a controlled field experiment with three varying factors [vegetation coverage, soil moisture content (SMC), and SSC]. The method was applied without prior knowledge of, or restrictions on the mixed and source spectra. Soil samples and spectral reflectance collected on three periods were used to determine the effectiveness of NMF-extracted soil spectra with partial least squares regression (PLSR). The results indicated that SSC can be predicted by bare soil spectra. NMF effectively separated soil spectra from the observed spectra, and the SSC was successfully predicted from the extracted soil spectra within a wide range of vegetation cover (0%–64.7%) within defined moisture levels (<0.15\;\g g^-1 by weight). The approach proposed in this study will improve the prediction accuracy of SSC for partially vegetated surfaces and will expand the application of remote sensing. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Growth and physiological responses of two sweet potato cultivars were quantified against five levels of soil moisture. [•] Shoot growth of sweetpotato cultivar Evangeline decreased more rapidly than Beauregard with shrinking soil moisture. [•] With decreasing soil moisture, shoot biomass declined more rapidly than root biomass in both cultivars. [•] Maintaining soil moisture closure to field capacity during early season is beneficial for early crop development. [Copyright &y& Elsevier]

• This paper presents a real-time fuzzy inference based automated irrigation controller implemented in LabVIEW, that utilizes the data logged from the sensor network and communicated using GSM/GPRS module. • The proposed drip irrigation scheduler receives the data from soil moisture, humidity, air temperature and water level sensor, in order to decide the percentage valve opening. • The time interval for which drip irrigation regulation is controlled by supply frequency to operate pumps at low tariff period using availability based tariff. The efficient and sustainable irrigation mechanism results in reduced water loss and improved crop productivity. This paper presents a real-time fuzzy inference based automated irrigation controller implemented in LabVIEW, that utilizes the data logged from the sensor network and communicated using GSM/GPRS module. The proposed drip irrigation scheduler receives the data from soil moisture, humidity, air temperature and water level sensor, in order to decide the percentage valve opening. Recently the open channel system have transformed into pressurized pipe systems which utilizes large amount of energy for uniform distribution of water. In order to optimize the energy consumption utilized in irrigation, availability based tariff (ABT) is implemented. The time interval for which drip irrigation is regulated, is controlled by supply frequency to operate pumps in low tariff period. This technique is applicable for wide range of crops which are grown in Kharif and Rabi season. The field experimentation results shows that the cost of irrigation is reduced by 30% as the water and electricity is efficiently utilized. Water saving is 45% more as compared to flood irrigation mechanism. Cost benefit achieved for Sorghum crop cultivation by proposed fuzzy based irrigation technique is higher by Rs.598/acre than the profit realized by manual flood irrigation. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates. [ABSTRACT FROM AUTHOR]

The Earth's surface bistatic reflectivity Γ L H C P , C y G N S S is experimentally characterized using the novel Global Navigation Satellite Systems Reflectometry (GNSS-R) L-band passive multistatic radar technique from the Cyclone Global Navigation Satellite Systems (CyGNSS) eight-microsatellite constellation. The focus of this study is to evaluate the influence of the GNSS satellites' elevation angle θ e on Γ L H C P , C y G N S S , as a function of soil moisture content (SMC) and effective surface roughness parameter h. As the average response, the change of the scattering regime at a global scale and considering also vegetated surfaces appears at θ e ≈ 55°. This empirical observation is understood as a change on the dominant scattering term, from incoherent to coherent. Then, the correlation of Γ L H C P , C y G N S S and SMC is evaluated as a function of θ e over specific sparsely vegetated target areas. The smoother the surface, the higher the angular variability of the Pearson correlation coefficients. Over croplands (e.g., Argentinian Pampas), an improved correlation coefficient is achieved over angular ranges where the coherent scattering regime becomes the dominant one. As such, this function depends on the surface roughness. The maximum correlation coefficients are found at different θ e for increasing mean roughness levels: r P a m p a s ≈ 0.78 at θ e ≈ [60,70]°, r I n d i a ≈ 0.72 at θ e ≈ [50,60]°, and r S u d a n ≈ 0.74 at θ e ≈ [30,40]°. SMC retrieval algorithms based on GNSS-R multi-angular information could benefit from these findings, so as to improve the accuracy using single-polarized signals. [ABSTRACT FROM AUTHOR]

The characteristics of soil moisture content (SMC) distribution in an area are necessarily analyzed for the design and construction of sponge cities. Combining remote sensing data with experimental data, this paper establishes a machine learning model to reveal the characteristics of SMC. Taking Beijing as an example, the SMC distribution was obtained and the characteristics were analyzed after training and validating. When comparing different machine learning methods, it can be concluded that the support vector classifier (SVC) method trained with remote sensing and grayscale data can achieve the highest accuracy (76.69%). The calculation results show that the districts with the highest and lowest SMC value are Xicheng District (19.94%) and Daxing District (11.04%), respectively, in Beijing. The mean SMC value of Beijing is 15.65%. The SMC distribution characteristic in Beijing shows that the soil in the west and north are relatively wet, while the soil in the east and south are relatively dry. Therefore, it is suggested that the timely monitoring of the SMC of vegetation covered areas at the north and west should be carried out. Water conservation facilities also need to be established with the development of city constructions in the south and east areas. [ABSTRACT FROM AUTHOR]